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Terink, W., A.F. Lutz, W.W. Immerzeel. 2015. SPHY v2.0

1. Figure 34 Adding points to the locations Shapefile layer using the accuflux map 3 4 7 If you are finished with adding the points then you again can click the Toggle Editing button and Save your edits The next step involves converting the locations Shapefile layer to a raster layer This can be done using the v to rast attribute tool in QGIS under Processing Toolbox see Figure 35 Within this toolbox Figure 36 set the locations layer as Input vector layer make sure that the id column is selected set the GRASS region extent by specifying the clone map layer and set the GRASS region cellsize as determined before Finally choose a Rasterized layer name e g locations tif and click Run The final step again involves converting the resulting GeoTiff raster from step 5 to a PCRaster map format This can be done using the Raster gt Conversion gt Translate tool see Figure 27 and Figure 28 The only additional step required here is to click the Edit button see Figure 37 and add the following syntax ot Float32 see Figure 38 Finally click OK and again OK and again OK and Close to finish the conversion 53 Processing Toolbox E raster Y profile Outputs the raster layer values lying on user defined line s Y trandom Creates a raster layer and vector point map containing randomly located points Y trandom raster Create random rast
2. The next step involves adding points to the Shapefile where you want the SPHY model to report time series Often these points correspond with the locations of discharge measurement stations If you have an existing Shapefile of discharge measurement stations in your basin then you can easily drag this file into QGIS to identify these locations Now you can start adding points to the newly created Shapefile by following these steps 1 Make sure the locations layer is selected Then click Toggle Editing to change the layer to editing mode see Figure 32 E QGIS 2 8 1 Wien test Project Edit View Layer Settings Plugins Vector Raster DEBRRA AVES 1 000000 1270 53110 Figure 32 Toggle Editing for Shapefiles 2 Then click the Add Feature option see Figure 33 Now you can start adding points to the map where you want the SPHY model to create time series output The accuflux map can help you determining if you are adding a point to the river network Add as many as points as you like For each point you need to provide an ID number Start with ID 1 then ID 2 etc In the example of Figure 34 we added 3 points to the locations layer s2 fia IR QGIS 2 8 1 Wien test a E a 4 Project Edit View Layer Settings Plugins Vector ro A SA M ODEBRANO pa a i BX layers YEG 3 locations x basins F 92 aceufliy Figure 33 Add Feature for Shapefiles
3. cancel _ Figure 17 Adding the PCRaster bin folder to the Path system variables Edit System Variable Variable name PYTHONPATH Variable value L Program Files x86 PCRaster40 python Coox cancel _ Figure 18 Editing or creating the PYTHONPATH variable for the PCRaster package 7 ia B Command Prompt Microsoft Windows Version 6 1 76611 Copyright lt c 2009 Microsoft Corporation All rights reserved C Users wilco gt percale Percale Dec 4 2013 win32 msuc gt USAGE pcercalc options expression percalc options f scriptFile gt f percalc F options gt flags gt set seed Cinteger gt for random generator default is based on current time z overrule script bindings update timeseries files at end of each timestep set run directory debug mode check MU creation on assignment comparing against clone or areamap boolean mask z strict Case significant filename check Unix portability gt print profile information optimi with areamap MU compression use less memory but more temporary disk storage test argument substitution ERNEST S ANT Ret Figure 19 Command prompt view of testing a successful installation of PCRaster after entering the pcrcalc command 4 2 4 SPHY v2 0 source code The SPHY v2 0 source code can be obtained from the SPHY model website http www sphy nl software The source code is available as a zip file SPHY2 0 zip and needs to be extracted
4. 2010 Goward S N and Huemmrich K F Vegetation canopy PAR absorptance and the normalized difference vegetation index An assessment using the SAIL model Remote Sens Environ 39 119 140 doi 10 1016 0034 4257 92 90131 3 1992 Grantz K Rajagopalan B Clark M and Zagona E A technique for incorporating large scale climate information in basinscale ensemble streamflow forecasts Water Resour Res 41 doi 10 1029 2004WR003467 2005 Groot Zwaaftink C D Mott R and Lehning M Seasonal simulation of drifting snow sublimation in Alpine terrain Water Resour Res 49 1581 1590 doi 10 1002 wrcr 20137 2013 Hall D K Riggs G A Salomonson V V DiGirolamo N E and Bayr K J MODIS snow cover products Remote Sens Environ 83 181 194 doi 10 1016 S0034 4257 02 00095 0 2002 Hargreaves G H and Samani Z A Reference Crop Evapotranspiration from Temperature Appl Eng Agric 1 96 99 doi 10 13031 2013 26773 1985 HEC Hydrologic Engineering Center HEC computer software for hydrologic engineering and planning analysis available at http www hec usace army mil software last access 03 September 2014 2014 Hewlett J Soil moisture as a source of base flow from steep mountain watershed Tech rep US forest Service Southeastern Forest Experiment Station Asheville North Carolina 1961 Heynen M Pellicciotti F and Carenzo M Parameter sensitivity of a distributed enhanced te
5. Bracken C Rajagopalan B and Prairie J A multisite seasonal ensemble streamflow forecasting technique Water Resour Res 46 doi 10 1029 2009WR007965 2010 Bramer L M Hornbuckle B K and Caragea P C How Many Measurements of Soil Moisture within the Footprint of a Ground Based Microwave Radiometer Are Required to Account for Meter Scale Spatial Variability Vadose Zone J 12 3 doi 10 2136 vzj2012 0100 2013 Brutsaert W De Saint Venant Equations Experimentally Verified J Hydr Eng Div ASCE 97 1387 1401 1971 60 i Brutsaert W Hydrology An introduction Cambridge University Press Cambridge 2005 Carlson T N and Ripley D A On the relation between NDVI fractional vegetation cover and leaf area index Remote Sens Environ 62 241 252 doi 10 1016 S0034 4257 97 00104 1 1997 Clark M P Slater A G Rupp D E Woods R A Vrugt J A Gupta H V Wagener T and Hay L E Framework for Understanding Structural Errors FUSE A modular framework to diagnose differences between hydrological models Water Resour Res 44 W00B02 doi 10 1029 2007WR006735 2008 Clark M P Nijssen B Lundquist J D Kavetski D Rupp D E Woods R A Freer J E Gutmann E D Wood A W Brekke L D Arnold J R Gochis D J and Rasmussen R M A unified approach for process based hydrologic modeling 1 Modeling concept Water Resour Res 51 2498 2514 doi 10 1002 2015WR
6. Location NS Bias Validation calibration Dainyor bridge 0 39 58 2 Validation Besham Qila 0 66 24 7 Validation Tarbela inflow 0 63 34 6 Calibration Marala inflow 0 65 12 0 Validation Pachuwarghat 0 90 1 6 Validation Rabuwa Bazar 0 65 22 5 Validation Turkeghat 0 87 5 4 Calibration Chatara 0 87 7 9 Calibration Contribution to total flow GM 00 100 GE 10 1 20 0 EN 20 1 30 0 Average EY 30 1 40 0 Q m s P 40 1 50 0 55 E 50 1 60 0 Elevation m a s l I l E 60 1 70 0 0 2000 4000 6000 gt 8000 GE 70 1 80 0 E 80 1 90 0 O 200 400 600 800 1000 km HE 00 1 1000 gt 4000 Figure 7 The contribution of glacier melt a snowmelt b and rainfall c to the total flow for major streams in the upstream basins of the Indus Ganges Brahmaputra Salween and Mekong during 1998 2007 Lutz et al 2014a For basins with snowmelt being an important contributor to the flow besides calibration to observed flow the snow related parameters in the SPHY model can also be calibrated to observed snow cover For the Upper Indus basin the snow related parameters degree day factor for snow DDF and snow water storage capacity SSC were calibrated independently using MODIS snow cover imagery Lutz et al 2014b The same MODIS data set was used as in Immerzeel et al 2009 From the beginning of 2000 until halfway through 2008 the snow cover imagery was averaged for 46 different period
7. Models like e g MIKE SHE Refshaard and Storm 1995 Oogathoo et al 2008 Deb and Shukla 2011 and LISFLOOD Van Der Knijff Younis and De Roo 2010 have the advantage of being flexible in terms of the spatial and temporal resolutions but their disadvantages are that they do not include glacier processes and that they are not open source and therefore not available to the larger community It is clear that all these models have their pros and cons in terms of i processes integrated ii field of application iii scale of application and iv implementation Table 1 shows the pros and cons of some well known hydrological models including the Spatial Processes in HYdrology SPHY model Over the last couple of years we have developed the SPHY model and improved its usefulness by applying the model in various research projects SPHY has been developed with the explicit aim of simulating terrestrial hydrology under various physiographical and hydroclimatic conditions by integrating key components from existing and well tested models HydroS Droogers and Immerzeel 2010 SWAT Neitsch et al 2009 PCR GLOBWB Beek and Bierkens 2008 Bierkens and Beek 2009 Wada et al 2010 Sperna Weiland et al 2010 SWAP Dam et al 1997 and HimSim Immerzeel et al 2011 Based on Table 1it is clear that SPHY i integrates most hydrologic processes including glacier processes ii has the flexibility to study a wide range of applications including clim
8. When all is set press q to quit and then press y to confirm the map creation Then drag the newly created map into QGIS to check if the map has the correct extent Remember to set the CRS of the clone map after dragging the map into QGIS 5 3 DEM and Slope Before you continue with the next steps make sure that you have opened the Processing Toolbox in QGIS see Figure 21 Next make sure that you select the Advanced interface from the Processing Toolbox see Figure 22 _ eg me Processing Help EN i myi i ae z F x 483 eae ee f a aay To Cog m a oO I R History S A Z 9 a o gS Options 2 Results Viewer gt Commander Ctrl Alt m Processing Toolbox Recently used algorithms Reproject layer y v report Reports geometry statistics for vectors S Resampling S Interpolate Cubic spline S Thin plate spline tin S Thin plate spline global t GDAL OGR 45 geoalgorithms wy GRASS commands 160 geoalgorithms 2 amp Models 0 geoalgorithms Orfeo Toolbox Image analysis 83 geoalgorithms L QGIS geoalgorithms 103 geoalgorithms t amp SAGA 2 1 2 235 geoalgorithms 3 E Scripts 0 geoalgorithms Figure 21 Opening the Processing Toolbox A Processing Toolbox E x Recently used algorithms H E H E L Reproject layer w v report Reports geometry statistics for vectors S Resampling Interpolate Cubic spline Th
9. c cccecececeeeeeeeeeeeeeee scenes saeeeeaaeseeeeeeeeeesaeeseaeeeeeeeess 50 Saving the translated raster as a PCRaster Raster File Map n 50 Create new shapefile layer ccccececeeeeeeeceeeeeeeaeeeeeeeceeeeeseaeeeeaaeseeeeeseaeeesaeeseneeseneeess 51 Setting the properties of the New Shapefile Layer ccscceccssseeeesesneeesesseeeeeeaas 52 Toggle Editing for Shapeffiles ccccccccscessececseceeeessneeeeecsneeeeeseeeeeeessneaeesseeeaeesseneaees 52 Add Feature for Shapefiles ccceccceceeeceeeeeeeeeeeceeeeeeaeeesaaeeseaeeseeeeesaeeesaeeeeeeeenaees 53 Adding points to the locations Shapefile layer using the accuflux map 06 53 Selecting the v to rast attribute tool from the Processing Toolbox 0 eseeee 54 Setting the options in the v to rast attribUte tool cccecceesseeeeeseeeeessseeeeesseeeeeeaas 54 Editing the command for Translation cccceecceeeeeeeeeeeeeeeeeeeeseneeeeeseneeeeeseneeeeeeneeaees 55 Adding the ot Float32 syntax to the command for Translation 0ceeeees 55 RROCIASSITY TOO lissas a A S aE E dendbenaceun lt aeveutheeed 56 Reclassify tool dialog DOX recresnincnicnnnnenann E 57 GRASS aggregation tool ccccecccceeeeeeceeeeeecaeeeeaeeseeeeeceaeeesaaeeseneeseeeesaeessaeeseneeseaees 58 GRASS aggregation tool dialog DOX ccccceeceeesceceeeeeceneeeeaeeeeeeeseeeesaeeesaaeeeeneeeeaees 58 1 Introduction The number and dive
10. the amount of frozen meltwater on day t 1 The units for all terms are mm The capacity of the snowpack to freeze snowmelt is characterized by introducing a calibrated water storage capacity SSC mm mm which is the total water equivalent of snowmelt mm that can freeze per mm water equivalent of snow in the snow storage The maximum of meltwater that can freeze SSW mm is thus limited by the thickness of the snow storage SSWmaxt SSC SS Equation 18 Then the amount of meltwater stored in the snowpack and that can freeze in the next time step is calculated as Sai 0 if Togt lt 0 t min SSWmax t SSWr 1 Pre Aactt if Tonge 2 0 Equation 19 with SSW the amount of meltwater in the snowpack on day t SSWmaxt the maximum of meltwater that can freeze on day t SSW _ the amount of frozen meltwater on day t 1 P the Fa 17 amount of rainfall on day t and A the actual snowmelt on day t The units of all terms are in mm The total snow storage SST mm consists of the snow storage and the meltwater that can freeze within it according to SST SS SSW 1 GlacF Equation 20 with 1 GlacF the grid cell fraction not covered with glaciers In SPHY it is therefore assumed that snow accumulation and snowmelt can only occur on the grid cell fraction determined as land surface Snow falling on glaciers is incorporated in the glacier module 2 5 3 Snow runoff Runoff from sn
11. 33 133 146 doi 10 1016 0022 1694 77 90103 2 1977 Pechlivanidis l Jackson B McIntyre N andWeather H Catchment scale hydrological modelling a review of model types calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications Global NEST Journal 13 193 214 2011 Pellicciotti F Brock B Strasser U Burlando P Funk M and Corripio J An enhanced temperature index glacier melt model including the shortwave radiation balance development and testing for Haut Glacier d Arolla Switzerland J Glaciol 51 573 587 doi 10 3189 172756505781829124 2005 Fo 65 Peng D Zhang B and Liu L Comparing spatiotemporal patterns in Eurasian FPAR derived from two NDVI based methods International Journal of Digital Earth 5 283 298 doi 10 1080 17538947 201 1 598193 2012 Piechota T and Chiew F Seasonal streamflow forecasting in eastern Australia and the El Ni o Southern Oscillation Water Resour Res 34 3035 3044 1998 Pomeroy J W Gray D M Brown T Hedstrom N R Quinton W L Granger R J and Carey S K The cold regions hydrological model A platform for basing process representation and model structure on physical evidence Hydrol Process 21 2650 2667 doi 10 1002 hyp 6787 2007 Rafn E B Contor B and Ames D P Evaluation of a Method for Estimating Irrigated Crop Evapotranspiration Coefficients from R
12. are difficult to observe on the large scale that they are generally applied on Bastiaanssen et al 2007 The most important aspect of applying models is in their use in exploring different scenarios expressing for example possible effects of changes in population and climate on the water cycle Droogers and Aerts 2005 Models are also applied at the operational level to explore interventions management scenarios to be used by water managers and policy makers Examples of this are changes in reservoir operation rules water allocation between sectors investment in infrastructure such as water treatment or desalination plants and agricultural and irrigation practices In other words models enable hydrologists and water managers to change focus from a re active towards a pro active approach Over the past decades the land surface and hydrologic communities have made substantial progress in understanding the spatial presentation of fluxes of water and energy Abbott et al 1986 Wigmosta et al 1994 Van der Kwaak and Loague 2001 Rigon et al 2006 Their efforts have led to the development of well known hydrological models such as e g VIC Liang et al 1994 1996 SWAT Neitsch et al 2009 TOPKAPI ETH Finger et al 2011 Ragettli and Pellicciotti 2012 Ragettli et al 2013 Ragettli et al 2014 LISFLOOD Van Der Knijff et al 2010 SWIM Krysanova et al 2015 Krysanova et al 2000 Krysanova et al 1998 HYPE Lindstr m et al 201
13. cells to drain in into them 5 5 Preparing stations map and sub basins map To prepare a stations map it is easiest to use a vector file with the point locations for example a shapefile to a PCRaster grid map file You can create a new shapefile with points in QGIS under Layer gt New gt New Shapefile layer 7 QGIS 2 4 0 Chugiak Project Edit View Layer Settings Plugins Vector Raster Database Web Processing Help a New Shapefile Layer h Ctrl Shift N e Embed Layers and Groups New SpatiaLite Layer Ctrl Shift A Add from Layer Definition File Create new GPX layer 19 PO naea t mA o Figure 30 Create new shapefile layer Make sure that you select Point and that the CRS corresponds see Figure 31 with the EPSG that you have defined in Section 5 1 Finally click OK to create the New Shapefile Layer and save it under a useful name for example locations shp fe 51 Z New Vector Layer e es Type Point Line Polygon File encoding System X Selected CRS EPSG 4326 WGS 84 e amp Default CRS EPSG 4326 WGS 84 EPSG 32736 WGS 84 UTM zone 36S EPSG 32648 WGS 84 UTM zone 48N EPSG 32645 WGS 84 UTM zone 45N EPSG 32631 WGS 84 UTM zone 31N Add to attributes list Attributes list l p ie Width pes i g id Integer 10 t Col UL Remove attribute Figure 31 Setting the properties of the New Shapefile Layer
14. observations from an alpine site Adv Water Resour 55 131 148 doi 10 1016 j advwatres 2012 07 013 2013 FAO FAOWater CropWater Information available at http Avww fao org nr water cropinfo html last access 09 June 2014 2013 Feddes R Kowalik P and Zaradny H Simulation of field water use and crop yield Simulation Monographs Pudoc Wageningen University 1978 Finger D Pellicciotti F Konz M Rimkus S and Burlando P The value of glacier mass balance satellite snow cover images and hourly discharge for improving the performance of a physically based distributed hydrological model Water Resour Res 47 W07519 doi 10 1029 2010WR009824 2011 Foglia L Hill M C Mehl S W and Burlando P Sensitivity analysis calibration and testing of a distributed hydrological model using error based weighting and one objective function Water Resour Res 45 W06427 doi 10 1029 2008WR007255 2009 Gat J R Bowser C J and Kendall C The contribution of evaporation from the Great Lakes to the continental atmosphere estimate based on stable isotope data Geophys Res Lett 21 557 560 doi 10 1029 94GL00069 1994 Gill M A Flood routing by the Muskingum method J Hydrol 36 353 363 doi 10 1016 0022 1694 78 90153 1 1978 Gopalan K Wang N Y Ferraro R and Liu C Status of the TRMM 2A12 Land Precipitation Algorithm J Atmos Ocean Tech 27 13438 1354 doi 10 1175 2010JTECHA1 454 1
15. of some well known hydrological models including the SPHY model A categorization is made between i processes that are integrated ii field of application iii scale of application and iv implementation SPHY TOPKAPI SWAT VIC LIS SWIM HYPE mHM MIKE PCRGLOB GEO ETH FLOOD SHE WB top Processes integrated Rainfall runoff Evapotranspiration Dynamic vegetation growth Unsaturated zone Groundwater Glaciers Snow Routing Lakes incorporated into routing scheme Reservoir management I ZZ I I 1I I Z i ob Field of application Climate change impacts Land use change impacts Irrigation planning Floods Droughts Water supply and demand I I t i 2 Scale of application Catchment scale River basin scale Mesoscale river basins Global scale Farm level Country level Fully distributed Sub grid variability Flexible spatial resolution Hourly resolution Sub daily resolution Daily resolution I I Z Implementation Open source i Forcing with remote sensing GIS co
16. of your Python installation it is required to add your Python installation folder to the Path system variable This is shown in Figure 15 for our case which was the c Python27 installation folder It is important to have a semicolon between the system variables 6 Finally click OK and OK again in order to complete the installation of Python aep Computer Name Hardware Advanced System Protection Remote You must be logged on as an Administrator to make most of these changes Performance Visual effects processor scheduling memory usage and virtual memory User Profiles Desktop settings related to your logon I Startup and Recovery System startup system failure and debugging information Environment Variables ok Cance Figure 13 System properties to set Environmental Variables Pr 39 Environment Variables User variables for wilco Variable Value MRT_DATA_DIR c MRT data E MRT_HOME c MRT W Path c MRT pin C texlive 20 12 bin win32 SWAP c SWAP swap3236 executable New edit Delete System variables Variable Value gt Os Windows_NT C Program Files x86 GDAL C Python jam PATHEXT COM EXE BAT CMD VBS VBE J5 PGSHOME c HEG HEG HEG_Win TOOLKIT_MTD D New Edit Delete Edit System Variable Variable name Path Variable value SWAP swap3236 executable Slain Coa F
17. on day t Qroutr 1 m s the routed streamflow on day t 1 Fj the flow direction network and kx the flow recession coefficient kx has values ranging between 0 and 1 where values close to 0 correspond to a fast responding catchment and values approaching 1 correspond to a slow responding catchment The user can opt to route each of the four streamflow contributors separately which may be useful if one wants to evaluate for example the contribution of glacier melt or snowmelt to the total routed runoff However this increases model run time substantially because the accuflux function which is a time consuming function needs to be called multiple times depending on the number of flow contributors to be routed 2 8 2 Lake reservoir routing Lakes or reservoirs act as a natural buffer resulting in a delayed release of water from these water bodies SPHY allows the user to choose a more complex routing scheme if lakes reservoirs are located in their basin of interest The use of this more advanced routing scheme requires a known relation between lake outflow and lake level height Q h relation or lake storage To use this routing scheme SPHY requires a nominal map with the lake cells having a unique ID and the non lake cells having a value of 0 The user can supply a Boolean map with True for cells that have measured lake levels and False for lake cells that do not have measured lake levels This specific applicati
18. processes such as interception effective precipitation and potential evapotranspiration Figure 2 provides a schematic overview of the SPHY modeling concepts Gla Ctac 1 c2 Land covered with snow 1 Glac 1 Figure 1 Illustration of SPHY sub grid variability A grid cell in SPHY can be a partially covered with glaciers or b completely covered with glaciers or c1 free of snow or c2 completely covered with snow In the case of c1 the free land surface can consist of bare soil vegetation or open water 10 P a P P or P snow accumulation debris free L snow store irefreezing P P or P debris covered fraction Foc fraction Fa snow water store snow met snow runof melt from melt from 1 debris jj clean ice A infiltration covered ice f ier SW rootzone total glacier l lateral flow melt t i T capillary rise percolation glacier runoff glaciers SW subzone lateral flow seepage glacier percolation SW groundwater layer groundwater recharge Figure 2 SPHY modeling concepts The fluxes in grey are only incorporated when the groundwater module is not used Abbreviations are explained in the text The soil column structure is similar to VIC Liang et al 1994 1996 with two upper soil storages and a third groundwater storage Th
19. to a folder on your hard drive In our case we created the folder c SPHY and unzipped the contents of SPHY2 0 zip to this folder After unzipping the contents of SPHY2 0 zip to a folder of your preference installation has been completed successfully The SPHY model v2 0 source code can be downloaded directly using the link below Download SPHY v2 0 The login credentials that are required to download software and data from the SPHY model website can be obtained using the link below http www sphy nl software download sphy fa 43 5 Build your own SPHY model A SPHY model preprocessor has been developed that enables the user to automatically generate SPHY model input data for a selected area of interest This preprocessor has been developed as a plugin for QGIS and generates the input data using a database that can be selected by the user Currently only one database can be used by the preprocessor the Hindu Kush Himalaya database The name of the SPHY model preprocessor is SphyPreProcess v1 0 and is described together with the SPHY model plugin in the SPHY GUIs manual Terink et al 2015b If your area of interest is not covered by the extent of the database then you can choose to create your model input data manually as is done in the Pungwe case study Terink et al 2015a You will need the PCRaster command line functions and GIS software like the open source QGIS The steps that are required to do this are descr
20. 0 mHM Samaniego et al 2010 PCR GLOBWB Beek and Bierkens 2008 Bierkens and Beek 2009 Wada et al 2010 Sperna Weiland et al 2010 MIKE SHE Refshaard and Storm 1995 Oogathoo et al 2008 Deb and Shukla 2011 and GEOtop Rigon et al 2006 Endrizzi et al 2013 Endrizzi et al 2011 amongst others The number of existing hydrological models is probably in the tens of thousands Droogers and Bouma 2014 Some existing model reviews cover a substantial number of models IRRISOFT Irrisoft 2014 114 USGS 2014 110 EPA 2014 211 USACE HEC 2014 18 All these hydrological models are different with respect to i the number and detail of hydrological processes that are integrated ii their field and iii scale of application and iv the way they are implemented Whereas for example the SWIM Krysanova et al 2015 Wa 7 Krysanova et al 2000 Krysanova Miller Wohlfeil and Becker 1998 and HYPE Lindstr m et al 2010 models both include all major hydrological processes the SWIM model is typically developed for large scale large river basins to continental applications and the HYPE model operates on the sub basin scale Therefore these models contain less detail in contrast to fully distributed models operating at grid level such as e g GEOtop Rigon et al 2006 Endrizzi et al 2013 Endrizzi et al 2011 and TOPKAPI ETH Finger et al 2011 Ragettli and Pellicciotti 2012 Ragettli et al 2013 Ragettli et al 2014
21. 00 2007 and b MODIS observed snow cover 2000 2007 F 33 3 3 Flow forecasting In data scarce environments and inaccessible mountainous terrain like in the Chilean Andes it is often difficult to install instrumentation and retrieve real time physical data from these instruments These real time data can be useful to capture the hydroclimatic variability in this region and improve the forecasting capability of hydrological models Although statistical models can provide skillful seasonal forecasts using large scale climate variables and in situ data Piechota and Chiew 1998 Grantz et al 2005 Regonda et al 2006 Bracken et al 2010 a particular hydropower company in Chile was mainly interested in the potential use of an integrated system using measurements derived from both Earth observation EO satellites and in situ sensors to force a hydrological model to forecast seasonal streamflow during the snow melting season The objective of the INTOGENER INTegration of EO data and GNSS R signals for ENERgy applications project was therefore to demonstrate the operational forecasting capability of the SPHY model in data scarce environments with large hydroclimatic variability During INTOGENER data retrieved from EO satellites consisted of a DEM and a time series of snow cover maps Snow cover images were retrieved on a weekly basis using RADARSAT and MODIS Parajka and Bl schl 2008 Hall et al 2002 imagery These images were used to up
22. 007 CMIP5 Taylor et al 2012 which were used as a basis for the Assessment Reports prepared by the Intergovernmental Panel on Climate Change IPCC In central Asia SPHY was applied in a study ADB 2012 Immerzeel et al 2012 Lutz et al 2012 that focused on the impacts of climate change on water resources in the Amu Darya and Syr Darya river basins SPHY was used to quantify the hydrological regimes in both basins and subsequently to project the outflow from the upstream basins to the downstream areas by forcing the model with an ensemble of five CMIP3 GCMs The SPHY model output fed into a water allocation model that was set up for the downstream parts of the Amu Darya and Syr Darya river basins In the Himalayan Climate Change Adaptation Programme HICAP led by the International Centre for Integrated Mountain Development ICIMOD SPHY has been successfully applied in the upstream basins of the Indus Ganges Brahmaputra Salween and Mekong rivers Lutz et al 2013 Lutz et al 2014a In this study the hydrological regimes of these five basins have been quantified and the calibrated and validated model Figure 6 was forced with an ensemble of eight GCMs to create water availability scenarios until 2050 Table 3 lists the calibration and validation results Based on the validation results we concluded that the model performs satisfactorily given the large scale complexity and heterogeneity of the modeled region and data scarcity L
23. 017198 2015a Clark M P Nijssen B Lundquist J D Kavetski D Rupp D E Woods R A Freer J E Gutmann E D Wood A W Gochis D J Rasmussen R M Tarboton D G Mahat V Flerchinger G N and Marks D G A unified approach for process based hydrologic modeling 2 Model implementation and case studies Water Resour Res 51 2515 2542 doi 10 1002 2015WR017200 2015b Contreras S Hunink J and Baille A Building a Watershed Information System for the Campo de Cartagena basin Spain integrating hydrological modeling and remote sensing FutureWater Report 125 Tech rep FutureWater 2014 Corradini C Morbidelli R and Melone F On the interaction between infiltration and Hortonian runoff J Hydrol 204 52 67 doi 10 1016 S0022 1694 97 00100 5 1998 Dai A Drought under global warming a review Wiley Interdisciplinary Reviews Climate Change 2 45 65 doi 10 1002 wec 81 2011 de Jong S and Jetten V Distributed quantitative assessment of canopy storage capacity by Hyperspectral Remote Sensing available at http www geo uu nl dejong pdf files Interception by RS pdf last access 13 November 2014 2010 Deb S and Shukla M Soil hydrology land use and agriculture measurement and modelling Las Cruces doi 10 1079 9781845937973 0000 2011 Droogers P and Aerts J Adaptation strategies to climate change and climate variability A comparative study between seven contrast
24. 03 The application of degree day models is widespread in cryospheric models and is based on an empirical relationship between melt and air temperature Degree day models are easier to set up compared to energy balance models and only require air temperature which is mostly available and relatively easy to interpolate Hock 2005 Using a degree day modeling approach the daily potential snowmelt is calculated as follows 0 if T lt 0 avg t 7 _ DDF if Tavg t gt of pott 7 Equation 15 with Apott mm the potential snowmelt on day t and DDF mm C td7t a calibrated degree day factor for snow The actual snowmelt is limited by the snow storage at the end of the previous day and is calculated as Aactt min Apott SSt 1 Equation 16 with Aact Mmm the actual snowmelt on day t and SS _ mm the snow storage on day t 1 The snow storage from day t 1 is then updated to the current day t using the actual snowmelt Aact and the solid precipitation P Part of the actual snowmelt freezes within the snowpack and thus does not run off immediately When temperature is below the melting point meltwater that has frozen in the snowpack during t 1 is added to the snow storage as re SSi 1 Pset SSWi 1 if Tavge lt 0 a SS Pst E Aactt if Tavgt 20 Equation 17 with SS the snow storage on day t SS _ the snow storage on day t 1 P the solid precipitation on day t Aact the actual snowmelt on day t and SSW
25. 2 It should also be noted that soil moisture content is typically highly variable in space a very high correlation between point measurements and grid cell simulations of soil moisture may therefore not always be feasible Bramer et al 2013 fia 29 E 60 2 8 80 a o 100 G g E F f O 120 E a 140 160 Measured 20 Simulated 180 HEB Rainfall irrigation 0 4 1 L L L L L 4 L L 4 L 4 200 we we we sf S ss S Bi of a gt RS RS NN Y w X YNY LM g c S N Y D E S Y Figure 5 Measured and simulated daily root zone soil moisture content during the 2014 growing season Rainfall irrigation has been measured by the rain gauge that was attached to the moisture sensor 3 2 Snow and glacier fed river basins SPHY is being used in large Asian river basins with significant contribution of glacier melt and snowmelt to the total flow Immerzeel et al 2012 Lutz et al 2012 2014a The major goals of these applications are two fold e Assess the current hydrological regimes at high resolution e g assess spatial differences in the contributions of glacier melt snowmelt and rainfall runoff to the total flow e Quantify the effects of climate change on the hydrological regimes in the future and how these affect the water availability Rivers originating in the high mountains of Asia are considered to be the most meltwater dependent river systems on Earth Schaner et al 2012 In the regions surroundin
26. 2001 Karssenberg 2002 Karssenberg et al 2010 Schmitz et al 2009 2013 dynamic modelling framework PCRaster has been developed at Utrecht University PCRaster is targeted to the development and deployment of spatio temporal environmental models It allows users to develop their own simulation models for applications in environmental sciences such as e g hydrology ecology geography etc SPHY v2 0 is based on the 32 bit system architecture and therefore requires the 32 bit PCRaster 4 version SPHY v2 0 has been built and thoroughly tested using PCRaster 4 0 0 and it is therefore recommended to download and install this stable version of PCRaster More information about this version of PCRaster can be found at the link below http ocraster geo uu nl pcraster 4 0 0 In order to install PCRaster 4 0 0 it is mandatory to have successfully installed Python 2 7 6 and Numpy 1 8 0 during the previous two steps Section 0 and 4 2 2 To install PCRaster 4 0 0 you need to perform the following steps 1 Download the PCRaster version using this link http sourceforge net projects pcraster files PC Raster 4 0 0 pcraster 4 0 0 x86 32 zip download use_mirror heanet 2 Create a new folder on your hard disk where you prefer to install PCRaster For example c Program Files x86 PCRaster40 3 Unzip the contents of the file downloaded under 1 to this folder 4 To let your system recognize the existence of PCRaster the Environmental Var
27. 206 doi 10 1016 0921 8181 95 00046 1 1996 Lindstr m G Pers C Rosberg J Str maqvist J and Arheimer B Development and testing of the HYPE Hydrological Predictions for the Environment water quality model for different spatial scales Hydrol Res 41 295 319 doi 10 2166 nh 2010 007 2010 Liu Y Gupta H Springer E and Wagener T Linking science with environmental decision making Experiences from an integrated modeling approach to supporting sustainable water resources management Environ Model Softw 23 846 858 doi 10 1016 j envsoft 2007 10 007 2008 Lutz A F Droogers P and Immerzeel W Climate Change Impact and Adaptation on the Water Resources in the Amu Darya and Syr Darya River Basins Tech rep FutureWater Wageningen 2012 Lutz A F Immerzeel W W Gobiet A Pellicciotti F and Bierkens M F P Comparison of climate change signals in CMIP3 and CMIP5 multi model ensembles and implications for Central Asian glaciers Hydrol Earth Syst Sci 17 3661 3677 doi 10 5194 hess 17 3661 2013 2013 Lutz A F Immerzeel W W Shrestha A B and Bierkens M F P Consistent increase in High Asia s runoff due to increasing glacier melt and precipitation Nature Climate Change 4 587 592 doi 10 1038 nclimate2237 2014a Lutz A F Immerzeel W and Kraaijenbrink P Gridded Meteorological Datasets and Hydrological Modelling in the Upper Indus Basin FutureWater Report 130
28. 3 The SEAS model provided daily forecasts at a spatial resolution of 0 75 7 months ahead and was used to forecast streamflow up till the end of the melting season Figure 12 shows the bias between the total cumulative forecasted flow and observed flow for the 23 model runs that were executed during operational mode Although there are some bias fluctuations in the Rio Laja en Tucapel model runs it can be concluded that the bias decreases for each next model run for both locations which is a logical result of a decreasing climate forcing uncertainty as the model progresses in time It can be seen that the SPHY model streamflow forecasts for Canal Abanico which is downstream of Laja Lake are substantially better than for Rio Laja en Tucapel the most downstream location The reason for this has not been investigated during the demonstration study but since model performance for these two locations was satisfactory during calibration a plausible explanation could be the larger climate forecast uncertainty in the higher altitude areas Hijmans et al 2005 Rollenbeck and Bendix 2011 Vicu a et al 2011 McPhee et al 2010 Mendoza et al 2012 Ragettli and Pellicciotti 2012 Ragettli et al 2014 in the northeastern part of the basin that contributes to the streamflow of Rio Laja en Tucapel Additionally only two in situ meteorological stations were available during operational mode whereas during calibration 20 meteorological stations were avail
29. 6 7 1994 Nash J and Sutcliffe J River flow forecasting through conceptual models part A discussion of principles J Hydrol 10 282 290 doi 10 1016 0022 1694 70 90255 6 1970 Neitsch S L Arnold J G Kiniry J R and Williams J R Soil and Water Assessment Tool SWAT Theoretical Documentation version 2009 Tech rep Texas Water Resources Institute College Station Texas available at http twri tamu edu reports 201 1 tr406 pdf last access 04 June 2014 2009 Niu G Y Yang Z L Mitchell K E Chen F Ek M B Barlage M Kumar A Manning K Niyogi D Rosero E Tewari M and Xia Y The community Noah land surface model with multiparameterization options Noah MP 1 Model description and evaluation with local scale measurements J Geophys Res Atmos 116 D12109 doi 10 1029 2010JD015139 2011 Oogathoo S Prasher S Rudra R and Patel R Calibration and validation of the MIKE SHE model in Canagagigue Creek watershed in Agricultural and biosystems engineering for a sustainable world International Conference on Agricultural Engineering Hersonissos Crete Greece 2008 Parajka J and Bl schl G Spatio temporal combination of MODIS images potential for snow cover mapping Water Resour Res 44 W03406 doi 10 1029 2007W R006204 2008 Park C C World wide variations in hydraulic geometry exponents of stream channels An analysis and some observations J Hydrol
30. Model User Manual Tech rep Potsdam Institute for Climate Impact Research Potsdam 2000 Krysanova V Hattermann F Huang S Hesse C Vetter T Liersch S Koch H and Kundzewicz Z W Modelling climate and land use change impacts with SWIM lessons learnt from multiple applications Hydrolog Sci J 60 606 635 doi 10 1080 02626667 2014 925560 2015 Lall U Debates The future of hydrological sciences A common path forward One water One world Many climes Many souls Water Resour Res 50 5335 5341 doi 10 1002 2014WR015402 2014 Lambert J Daroussin J Eimberck M Le Bas C Jamagne M King D and Montanarella L Soil Geographical Database for Eurasia amp The Mediterranean Instructions Guide for Elaboration at scale 1 1 000 000 version 4 0 EUR 20422 EN Tech rep JRC Ispra Italy 2003 Lenaerts J T M van den Broeke M R D ry S J K6 nig Langlo G Ettema J and Munneke P K Modelling snowdrift sublimation on an Antarctic ice shelf The Cryosphere 4 179 190 doi 10 5194 tc 4 179 2010 2010 Liang X Lettenmaier D P Wood E F and Burges S J A simple hydrologically based model of land surface water and energy fluxes for general circulation models 99 14415 14428 doi 10 1029 94JD00483 1994 Liang X Wood E F and Lettenmaier D P Surface soil moisture parameterization of the VIC 2L model Evaluation and modification Global Planet Change 13 195
31. SPHY v2 0 Spatial Processes in Hydrology Model theory installation and data preparation October 2015 Author W Terink A F Lutz W W Immerzeel Report FutureWater 142 FutureWater Costerweg 1V 6702 AA Wageningen The Netherlands 31 0 317 460050 info futurewater nl www futurewater nl Acknowledgements The development and publication of the SPHY model source code its binaries GUIs and case studies has been supported through various research projects that were partly or completely funded by the following organizations e International Centre for Integrated Mountain Development ICIMOD e European Space Agency ESA e Asian Development Bank ADB e World Bank e Rijksdienst voor Ondernemend Nedeland RVO e NUFFIC We are very grateful to these organizations that made the development of the SPHY model possible We hope to continue to collaborate with these organizations in the future in order to further develop and improve the SPHY model and its interfaces 1 http www icimod org http www esa int ESA 3 http www adb org http www worldbank org 5 http www rvo nl https www nuffic nl en Table of contents Acknowledgements 1 2 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 3 1 3 2 3 3 4 1 4 2 5 1 5 2 5 3 5 4 5 5 5 6 Introduction Theory Background Modules Reference and potential evapotranspiration Dynamic vegetation process
32. Tech rep FutureWater Wageningen the Netherlands 2014b a A MacDonald M K Pomeroy J W and Pietroniro A Parameterizing redistribution and sublimation of blowing snow for hydrological models tests in a mountainous subarctic catchment Hydrol Process 23 2570 2583 doi 10 1002 hyp 7356 2009 Manning R On the flow of water in open channels and pipes Trans Inst Civ Eng Ireland 20 161 207 1989 McPhee J Rubio Alvarez E Meza R Ayala A Vargas X and Vicuna S An approach to estimating hydropower impacts of climate change from a regional perspective Watershed Management 2010 13 24 doi 10 1061 41143 394 2 2010 Meehl G A Covey C Taylor K E Delworth T Stouffer R J Latif M McAvaney B and Mitchell J F B THE WCRP CMIP3 Multimodel Dataset A New Era in Climate Change Research B Am Meteorol Soc 88 1383 1394 doi 10 1175 BAMS 88 9 1383 2007 Mendoza P A McPhee J and Vargas X Uncertainty in flood forecasting A distributed modeling approach in a sparse data catchment Water Resour Res 48 W09532 doi 10 1029 2011WR011089 2012 Morris E M andWoolhiser D A Unsteady one dimensional flow over a plane Partial equilibrium and recession hydrographs Water Resour Res 16 355 360 doi 10 1029 WR016i002p00355 1980 Myneni R and Williams D On the relationship between FAPAR and NDVI Remote Sens Environ 49 200 211 doi 10 1016 0034 4257 94 9001
33. XXXXxX which you defined in Section 5 1 In the example of Figure 24 it is EPSG 32737 Then click OK to do the reprojection After the reprojection is finished click OK and again OK and finally Close Project Edit View Layer Settings Plugins Vector Raster Database Web Processing Help DEBRA a o E Pea oo T Heatmap E aa aa 4 Ee K ig Ss ee eS 5 GB m G A CD amp n Ce e Terrain analysis gt g D re Ay s9 5 Zonal statistics Projections Warp Reproject T Assn projection Extract projection Batch mode for processing whole directory Input file dem Output file Active NUFFIC_Mozambique TEST SPHY input dem_pr tif Source SRS EPSG 4326 Target SRS EPSG 32737 Resampling method l Near No data values 0 Mask layer basins Memory used for caching 2048 YPaDaBAIYASLAS N Resize Ss Width 3000 B Height 3000 Use multithreaded warping implementation Load into canvas when finished gdalwarp overwrite s_srs EPSG 4326 t_srs EPSG 32737 of GTiff E Active NUFFIC_ Mozambique database HydroSheds_SRTM_30S dem tif E Active NUFFIC_Mozambique TEST SPHY input dem _pr tif Figure 24 Setting the files and Source and Target SRS in the Warp Tool 4 The next step involves resampling the projected dem from step 3 to the extent and spatial resolution of the clone map For this yo
34. able Moreover these operational meteorological stations were not installed at higher altitudes where precipitation patterns tend to be spatially very variable Wagner et al 2012 Rollenbeck and Bendix 2011 Fo 35 Canal Abanico ID 19 NS 0 41 Bias 4 0 Streamflow m s 25 Jan Mar May Jul Sep Nov Feb Apr Jun Aug Oct Dec 1000 Rio Laja en Tucapel ID 23 NS 0 67 Bias 9 4 io Q Q Q Q o oO o Streamflow m s N So oO 0 Jan Mar May Jul Sep Nov Feb Apr Jun Aug Oct Dec Figure 11 Daily observed vs SPHY simulated streamflow period 2007 2008 for the streamflow stations Canal Abanico ID 19 and Rio Laja en Tucapel ID 23 The Nash Sutcliffe NS and bias model performance indicators are shown as well Canal Abanico ID 19 5 T T T T Avg bias 4 2 Bias of cumulative sum L 4 Sep Oct Nov Dec Jan Feb Mar Rio Laja en Tucapel ID 23 60 y r r r r Avg bias 34 1 gt N o Bias of cumulative sum Q o 0 Sep Oct Nov Dec Jan Feb Mar Figure 12 Bias between total cumulative forecasted flow and observed flow for the 23 model runs that were executed between the end of September 2013 and March 2014 36 Fe Results are shown for the locations Canal Abanico ID 19 and Rio Laja en Tucapel ID 23 37 4 Installation of SPHY 4 1 General SPHY v2 0 can be either be installed as i a stand alone application where the user ca
35. alance of the first soil layer is SW SWy t 1 Pe i ETat RO iia LFit Percy Capt Equation 28 with SW and SW _ the water content in the first soil layer on days t and t 1 respectively Pe the effective precipitation on day t ET the actual evapotranspiration on day t RO the surface runoff on day t LF the lateral flow from the first soil layer on day t Perc the fia i9 percolation from the first to the second soil layer on day t and Cap the capillary rise from the second to the first soil layer on day t The second soil layer water balance is SW SW2 7 1 Percy Percz Cap Equation 29 with SW and SW _ the water content in the second soil layer on day t and t 1 respectively and Perc percolation from the second to the third soil layer on day t The third soil layer water balance is given as SW t SW3 7 1 Gchrg BF Equation 30 with SW and SW 1 the water content in the third soil layer on day t and t 1 respectively Gchrg groundwater recharge from the second to the third soil layer on day t and BF baseflow on day t If the glacier module is used then groundwater recharge consists of percolation from the second soil layer and percolated glacier melt otherwise only percolation from the second soil layer is taken into account The user can opt to run SPHY without the third soil layer groundwater This may be desirable if the user for example is mainly interested i
36. ameters Log Help Input raster layer Redassified Grid EPSG 32645 A R Aggregation method average z Propagate NULLs No v Weight according to area slower No X Align region to resolution default align to bounds in r region No v GRASS region extent xmin xmax ymin ymax 290289 0 387289 0 3065099 0 3214899 0 GRASS region cellsize leave 0 for default 200 000000 lica Output raster layer Save to temporary file mam Open output file after running algorithm 4 gt Figure 42 GRASS aggregation tool dialog box The resulting grid can be converted to a PCRaster map using step 8 from Section 5 3 5 7 Other static input maps Similar as the DEM you can reproject and resample other static model input data and convert them to PCRaster format maps using the reprojection and resampling functions in QGIS step 1 9 from Section 5 3 Note that different data types are used for PCRaster maps You can convert maps from one data type to another using the command line functions boolean 58 A nominal ordinal scalar directional or Idd For example to convert the scalar type landuse map to a nominal landuse map type pcrcalc landuse_nominal map nominal landuse map Table 5 Data types used in SPHY data type description domain Example attributes boolean boolean 0 false 1 true suitable unsuitable visible non visible nominal classified 2 whole values soil classes land
37. ange 0 000000 4 gt maximum value for range 0 000000 new value for range 0 000000 aaa 4 gt operator for range ol lt v Lookup Table Fixed table 3X 3 operator for table 0 min lt value lt max X replace no data values Yes X new value for no data values 0 000000 replace other values No v new value for other values 41 gt 0 000000 ee Reclassified Grid Save to temporary file Open output file after running algorithm Figure 40 Reclassify tool dialog box Now we aggregate the fine resolution grid with glaciers to the model resolution This can be done using the r resamp stats tool selected under Processing Toolbox gt GRASS commands gt Raster gt r resamp stats fia s7 Processing Toolbox resamp st H Recently used algorithms E GRASS GIS 7 commands 159 geoalgorithms El Q GRASS commands 167 geoalgorithms E Raster r Q rresamp stats Resamples raster layers to a coarser grid using aggregation Figure 41 GRASS aggregation tool In the dialog box set the fine resolution glacier grid as input raster layer and choose aggregation method average Import the processing extent from the clone map and set the cell size to the model resolution in the screenshot below it is 200m as in the example of the Trisuli case study KA r resamp stats Resamples raster layers to a coarser grid using aggrd mo H a amp 5 Fa y J J j L Par
38. as installed in a soybean field to monitor root zone water content shortly after 01 May 2014 which is the start of the soybean growing season The sensor measures volumetric moisture content for every 10cm of the soil profile up to a depth of 60cm It is also equipped with a rain gauge measuring the sum of rainfall and applied irrigation water which was used as an input to SPHY Soil moisture measured over the extent covered by the crop root depth was averaged and compared to simulated values Figure 5 Since this study was a demonstration project only an initial model calibration was performed The model was in this case most sensitive for the crop coefficient Kc affecting the evaporative demand for water As can be seen in Figure 5 the temporal patterns as measured by the soil moisture sensor are well simulated by the SPHY model Based on daily soil moisture values a Nash Sutcliffe Nash and Sutcliffe 1970 model efficiency coefficient of 0 6 was found indicating that the quality of prediction of the SPHY model is good Foglia et al 2009 Soil moisture simulations could be further improved by conducting a full model calibration adjusting the soil physical parameters Kati SWic SWipr3 aNd SW yr4 2 Remotely sensed sensed evapotranspiration can be used in the calibration process Immerzeel and Droogers 2008 although such data are often not available on these small scales as ET is a very complex variable to assess Samain et al 201
39. ate and land use change impacts irrigation planning and droughts iii can be used for catchment and river basin scale applications as well as farm and country level applications and has a flexible spatial resolution and iv can easily be implemented Implementation of SPHY is relatively easy because i it is open source ii input and output maps can directly be used in GIS iii it is set up modular in order to switch on off relevant irrelevant processes and thus decreases model run time and data requirements iv it needs only daily precipitation and temperature data as climate forcing v it can be forced with remote sensing data and vi it uses a configuration file that allows the user to change model parameters and choose the model output that needs to be reported The objectives of this manual are e Introduce and present the SPHY model v2 0 e Present the SPHY model v2 0 theory and demonstrate some typical applications e Provide the steps that are required to install the SPHY model as a standalone application e Learn how to prepare model data for a SPHY model for your own area of interest The model source code is in the public domain open access and can be obtained from the SPHY model website free of charge www sphy model org The peer reviewed open access publication of the SPHY model can be found at http www geosci model dev net 8 2009 2015 gmd 8 2009 2015 padf Terink et al 2015 Table 1 Pros and cons
40. avg t Equation 24 where DDFpc mm C7td7t is a degree day factor for debris covered glaciers and Fpc is the fraction of debris covered glaciers within the fractional glacier cover of a grid cell The total glacier melt per grid cell Agrac mm is then calculated by summing the melt from the debris covered and debris free glacier types and multiplying by the fractional glacier cover according to Aguact Acrt Ance GlacF Equation 25 2 6 2 Glacier runoff In SPHY a fraction of the glacier melt percolates to the groundwater while the remaining fraction runs off The distribution of both is defined by a calibrated glacier melt runoff factor GlacROF that can have any value ranging from 0 to 1 Thus the generated runoff GRo mm from glacier melt is defined as GRo Agact GlacROF Equation 26 2 6 3 Glacier percolation The percolation from glacier melt to the groundwater Gperc mm is defined as Gperet Agiact 1 GlacROF Equation 27 The percolated glacier water is added to the water that percolates from the soil layers of the non glacierized part of the grid cell Section 2 7 1 and 2 7 6 which eventually recharges the groundwater 2 7 Soil water processes 2 7 1 Soil water balances The soil water processes in SPHY are modeled for three soil layers Figure 2 being i the first soil layer root zone ii second soil layer subzone and iii third soil layer groundwater layer The water b
41. aw is an index that relates the baseflow response to changes in groundwater recharge Lower values for w therefore correspond to areas that respond slowly to groundwater recharge whereas higher values indicate areas that have a rapid response to groundwater recharge The baseflow recession coefficient is generally used as a calibration parameter in the SPHY model but a good first approximation of this coefficient can be calculated using the number of baseflow days Neitsch et al 2009 2 3 sw BED Equation 47 24 Fa with BFD d the number of baseflow days which is defined as the number of days required for baseflow recession to decline 2 8 Routing After calculating the different runoff components the cell specific total runoff QTot is calculated by adding these different runoff components Depending on the modules being switched on the different runoff components are i rainfall runoff RRo ii snow runoff SRo iii glacier runoff GRo and iv baseflow BF Rainfall runoff is the sum of surface runoff RO Section 2 7 3 and lateral flow from the first soil layer LF Section 2 7 4 If the groundwater module is not used then baseflow is calculated as being the lateral flow from the second soil layer QTot is eventually calculated according to QTot RRo SRo GRo BF Equation 48 with QTot mm the cell specific total runoff RRo mm rainfall runoff SRo mm snow runoff GRo mm glacier runoff and BF
42. c is then used in Equation 52 and Equation 53 to calculate the accumulated streamflow and updated storage respectively Qaccu t accufractionflux Fair Sact t Qfrac t Equation 52 Sact t 1 accufractionstate Fair Sact t Qfract Equation 53 with Sact m and Sactt 1 m the actual storage and updated storage to be used in the next time step respectively and Qaccu m d the accumulated streamflow on day t without flow delay taken into account Since Qfrac is always equal to 1 for the non lake cells the accufractionflux function becomes equal to the accuflux function used in the previous section This actually means that for the river network the same routing function from Section 2 8 1 is used and that Equation 52 and Equation 53 only apply to lake reservoir cells In order to account for non linearity and slower responding catchments the same kx coefficient is used again This involves applying Equation 51 as a last step after Equation 52 and converting the units from m d to m s Since the accufractionflux and accufraction state functions are more complex to compute the use of these functions increases model run time fia 27 3 Applications The SPHY model has been applied and tested in various studies including real time soil moisture predictions in lowlands operational reservoir inflow forecasting in mountainous catchments irrigation scenarios in the Nile basin and climate change impact studies in the snow gla
43. cier rain dominated Himalayan region Some example applications will be summarized in the following sections 3 1 Irrigation management in lowland areas As SPHY produces spatial outputs for the soil moisture content in the root zone and the potential and actual evapotranspiration ET it is a useful tool for application in agricultural water management decision support By facilitating easy integration of remote sensing data crop growth stages can be spatially assessed at different moments in time The SPHY dynamic vegetation module ensures that all relevant soil water fluxes correspond to crop development stages throughout the growing season Spatially distributed maps of root water content and ET deficit can be produced enabling both the identification of locations where irrigation is required and a quantitative assessment of crop water stress SPHY has been applied with the purpose of providing field specific irrigation advice for a large scale farm in western Romania comprising 380 individual fields and approximately ten different crops Contrary to the other case studies highlighted in this paper a high spatial resolution is very relevant for supporting decisions on variable rate irrigation The model has therefore been set up using a 30m resolution covering the 2013 and 2014 cropping seasons on a daily time step Optical satellite data from Landsat 8 USGS 2013 were used as input to the dynamic vegetation module Soil properties were deriv
44. culated the same way as the travel time for lateral flow Equation 41 2 7 6 Groundwater recharge Water that percolates from the second to the third soil layer will eventually reach the shallow aquifer This process is referred to as groundwater recharge hereafter If the glacier module is used as well then glacier melt that percolates also contributes to the groundwater recharge Groundwater recharge often does not occur instantaneously but with a time lag that depends on the depth of the groundwater table and soil characteristics SPHY uses the same exponential decay weighting function as proposed by Venetis 1969 and used by Sangrey Harrop Williams and Klaiber 1984 in a precipitation groundwater response model This approach has also been adopted in the SWAT model Neitsch et al 2009 using fia 23 1 1 Gchrg 1 expe W2 perc exp ew Gchrg _ Equation 44 with Gchrg mm and Gchrg _ mm the groundwater recharge on days t and t 1 respectively dgw d is the delay time and wz perc mm is the amount of water that percolates from the second to the third layer on day t 2 7 7 Baseflow After groundwater recharge has been calculated SPHY calculates baseflow which is defined as the flow going from the shallow aquifer to the main channel Baseflow only occurs when the amount of water stored in the third soil layer exceeds a certain threshold BFihresn that can be specified by the user Baseflow calculation i
45. date the snow storage SS mm in the model in order to initialize it for the forecasting period Figure 10 shows the snow storage as simulated by the SPHY model during the snow melting season in the Laja basin These maps clearly show the capability of SPHY to simulate the spatial variation of snow storage with more snow on the higher elevations and a decrease in snow storage throughout the melting season Discharge precipitation and temperature data were collected using in situ meteorological stations In order to calculate the lake outflow accurately the SPHY model was initialized with water level measurements retrieved from reflected Global Navigation Satellite System GNSS signals in Laja Lake Static data that were used in the SPHY model consisted of soil characteristics derived from the Harmonized World Soil Database HWSD Batjes et al 2009 and land use data obtained from the GLOBCOVER Bontemps et al 2011 product The SPHY model was set up to run at a spatial resolution of 200m Figure 11 shows the observed vs simulated daily streamflow for two locations within the Laja River basin for the historical period 2007 2008 It can be seen that model performance is quite satisfactory for both locations with volume errors of 4 and 9 4 for the Abanico Canal downstream of Lake Laja and Rio Laja en Tucapel respectively The NS coefficient which is especially useful for assessing the simulation of high discharge peaks is less satisfac
46. djustable SPHY model parameters is shown in Appendix 1 Table 6 The SPHY model provides a wealth of output variables that can be selected based on the preference of the user Spatial output can be presented as maps of all the available hydrological processes i e actual evapotranspiration runoff generation separated by its components and groundwater recharge These maps can be generated on a daily basis but can also be aggregated at monthly or annual time periods Time series can be generated for each cell in the study area Time series often used are streamflow actual evapotranspiration and recharge to the groundwater 2 2 Modules SPHY enables the user to turn on off modules processes that are relevant irrelevant for the area of interest This concept is very useful if the user is studying hydrological processes in regions where not all hydrological processes are relevant A user may for example be interested in studying irrigation water requirements in central Africa For this region glacier and snow melting processes are irrelevant and can thus be switched off The advantages of turning off irrelevant modules are two fold i decrease model run time and ii decrease the number of required model input data It should be noted however that the hydrologic model structure should be specific to the catchment s characteristics Pomeroy et al 2007 Clark et al 2008 Niu et al 2011 Essery et al 2013 Clark et al 2015a 2015b It
47. e evapotranspiration on day t and Kc the crop coefficient The effects of both crop transpiration and soil evaporation are integrated into the Kc If the dynamic vegetation module in SPHY is not used then the user can opt i to use a single constant Ke throughout the entire simulation period or ii to use a pre defined time series of crop coefficients as model input Plausible values for Kc can be obtained from the literature Allen et al 1998 FAO 2013 However vegetation is generally very dynamic throughout the fia 3 year It is therefore more realistic to use a pre defined time series of crop coefficients or to use the dynamic vegetation module instead of a single constant Kc This can be adjusted according to the user s preferences Kc can be estimated using remotely sensed data Rafn et al 2008 Contreras et al 2014 In the dynamic vegetation module Kc is scaled throughout the year using NDVI and the maximum and minimum values for Kc which are crop specific These values for Kc can easily be obtained from Allen et al 1998 Then Kc is calculated using NDVI NDVImin NDVI max NDVI nin Kc KCmin KCmax Kemin i Equation 3 with NDVImax and NDVI pin the maximum and minimum values for NDVI vegetation type dependent This approach shows the flexibility of SPHY in using remote sensing data e g NDVI as input to improve model accuracy 2 4 Dynamic vegetation processes 2 4 1 Maximum ca
48. e storage Williams 1975 or Muskingum Gill 1978 routing methods to obtain river streamflow But the Manning equation also requires river bed dimensions which are generally unknown on the spatial scale that SPHY generally is applied on Therefore SPHY calculates for each cell the accumulated amount of water that flows out of the cell into its neighboring downstream cell This can easily be obtained by using the accuflux PCRaster built in function which calculates for each cell the accumulated specific runoff from its upstream cells including the specific runoff generated within the cell itself If only the accuflux function is used then it is assumed that all the specific runoff generated within the catchment on one day will end up at the most downstream location within one day which is not plausible Therefore SPHY implements a flow recession coefficient kx that accounts for flow delay which can be a result of channel friction Using this coefficient river flow in SPHY is calculated using the three equations shown below fia 25 _ QTot 0 001 A Tot ar ote 24 3600 Equation 49 Qaccut accuf lux F4ir QTot Equation 50 Qrout t 1 kx g Qaccu t kx Qrout t 1 Equation 51 with QTot m s the specific runoff on day t QTot the specific runoff in mm on day t A m the grid cell area Qaccur m s the accumulated streamflow on day t without flow delay taken into account Q ourr m s the routed streamflow
49. ed from the Harmonized World Soil Database Batjes et al 2012 which for Romania contains data from the Soil Geographical Database for Europe Lambert et al 2003 Using the Van Genuchten equation Van Genuchten 1980 soil saturated water content field capacity and wilting point were determined for the HWSD classes occurring at the study site Elevation data was obtained from the EU DEM data set EEA 2014 and air temperature was measured by two on farm weather stations In irrigation management applications like these a model should be capable of simulating the moisture stress experienced by the crop due to insufficient soil moisture contents which manifests itself by an evapotranspiration deficit potential ET actual ET gt 0 Figure 4 shows the spatial distribution of ET deficit as simulated by the SPHY model for the entire farm on 03 April 2014 When SPHY is run in an operational setting this spatial information can be included in a decision support system that aids the farmer in irrigation planning for the coming days 28 ffs Legend Farm fields ET deficit mm 0 01 E 0 33 0 66 E 0 98 E 1 30 Figure 4 Spatial distribution of evapotranspiration ET deficit as simulated by the SPHY model for a Romanian farm on 03 April 2014 Transparency means no ET deficit For calibration purposes field measurements of soil moisture and or actual ET are desired In this case study one capacitance soil moisture sensor w
50. eir corresponding drainage components are surface runoff lateral flow and baseflow SPHY simulates for each cell precipitation in the form of rain or snow depending on the temperature Precipitation that falls on land surfaces can be intercepted by vegetation and evaporated in part or whole The snow storage is updated with snow accumulation and or snowmelt A part of the liquid precipitation is transformed in surface runoff whereas the remainder infiltrates into the soil The resulting soil moisture is subject to evapotranspiration depending on the soil properties and fractional vegetation cover while the remainder contributes to river discharge by means of lateral flow from the first soil layer and baseflow from the groundwater layer Melting of glacier ice contributes to the river discharge by means of a slow and fast component being i percolation to the groundwater layer that eventually becomes baseflow and ii direct runoff The cell specific runoff which becomes available for routing is the sum of surface runoff lateral flow baseflow snowmelt and glacier melt If no lakes are present then the user can choose a simple flow accumulation routing scheme for each cell the accumulated amount of water that flows out of the cell into its neighboring downstream cell is calculated This accumulated amount is the amount of water in the cell itself plus the amount of water in upstream cells of the cell and is calculated using the flow directi
51. emotely Sensed Data in Idaho J Irrig Drain E ASCE 134 722 729 doi 10 1061 ASCE 0733 9437 2008 134 6 722 2008 Ragettli S and Pellicciotti F Calibration of a physically based spatially distributed hydrological model in a glacierized basin On the use of knowledge from glaciometeorological processes to constrain model parameters Water Resour Res 48 W03509 doi 10 1029 2011WR010559 2012 Ragettli S Cort s G Mcphee J and Pellicciotti F An evaluation of approaches for modelling hydrological processes in highelevation glacierized Andean watersheds Hydrol Process 28 5674 5695 doi 10 1002 hyp 10055 2014 Ragettli S Pellicciotti F Immerzeel W Miles E Petersen L Heynen M Shea J M Stumm D Joshi S and Shrestha A Unraveling the hydrology of a Himalayan watershed through integration of high resolution in situ data and remote sensing with an advanced simulation model Adv Water Resour 78 94 111 doi 10 1016 j advwatres 2015 01 013 2015 Refshaard J and Storm B MIKE SHE Danish Hydraulic Institute Horsholm 1995 Regonda S K Rajagopalan B Clark M and Zagona E A multimodel ensemble forecast framework Application to spring seasonal flows in the Gunnison River Basin Water Resour Res 42 W09404 doi 10 1029 2005WR004653 2006 Reid T D Carenzo M Pellicciotti F and Brock B W Including debris cover effects in a distributed model of glacier ablation J Ge
52. enaeeeeeeeeeeee 10 Figure 2 SPHY modeling concepts The fluxes in grey are only incorporated when the groundwater module is not used Abbreviations are explained in the text ee eeeeeeeeeeeeeeeee 11 Figure 3 Modules of the SPHY model that can be switched On Off ceccceeeteeeteeeeeteeeetees 13 Figure 4 Spatial distribution of evapotranspiration ET deficit as simulated by the SPHY model for a Romanian farm on 03 April 2014 Transparency means no ET deficit 0 eects 29 Figure 5 Measured and simulated daily root zone soil moisture content during the 2014 growing season Rainfall irrigation has been measured by the rain gauge that was attached to the MOISTUFE SEMSOM recorsi aeaa aaa aaa a a a e a a Ea a aaa a aaa adaa ai 30 Figure 6 Average monthly observed and SPHY simulated flow 1998 2007 for the Chatara major discharge measurement location in the Ganges basin Lutz et al 2014a Metrics are calculated based on monthly time stepS sssseesssnesserneessnnnssennnsnnnnnnnennnnnnnnnnennnennnnnnnnnneennnnnnenn 31 Figure 7 The contribution of glacier melt a snowmelt b and rainfall c to the total flow for major streams in the upstream basins of the Indus Ganges Brahmaputra Salween and Mekong during 1998 2007 Lutz et al 20148 sessssssssssssssssssissssrrssssrnssrirnnsrinnssinnnnnnnnnennnnt 32 Figure 8 Observed and simulated average fractional snow cover in the upper Indus basin The values repr
53. er y rredass Creates a new map layer whose category values are based upon a reclassification o Y tredass area greater Reclassifies a raster layer selecting areas larger than a user specified Y tredass area lesser Redassifies a raster layer selecting areas lower than a user specified size Y trecode Recodes categorical raster maps Y tregression line Calculates linear regression from two raster layers y a b x X report Reports statistics for raster layers v r resamp interp Resamples a raster map layer to a finer grid using interpolation Y rresamp stats Resamples raster layers to a coarser grid using aggregation V r resample GRASS raster map layer data resampling capability using nearest neighbors Y rrescale Rescales the range of category values in a raster layer Y trescale eq Rescales histogram equalized the range of category values in a raster layer wv rros Generates three or four raster map layers showing 1 the base perpendicular rate of Y nseries Makes each output cell value a function of the values assigned to the corresponding Y slope Generates raster maps of slope from a elevation raster map Y slope aspect Generates raster layers of slope aspect curvatures and partial derivatives fr Y spread Simulates elliptically anisotropic spread on a graphics window and generates a raster stats Generates area statistics for raster layers Q rsum Sums up the raster cell values V r su
54. ervable slp Slope of grid cell mm Observable Sew Groundwater recharge delay time day Free Qgw Baseflow recession coefficient day Free BF tresh Threshold for baseflow to occur mm Free kx Flow recession coefficient Free fa 7
55. es 2 4 1 Maximum canopy storage 2 4 2 Interception Snow processes 2 5 1 Snow and rainfall 2 5 2 Snowmelt refreezing and storage 2 5 3 Snow runoff Glacier processes 2 6 1 Glacier melt 2 6 2 Glacier runoff 2 6 3 Glacier percolation Soil water processes 2 7 1 Soil water balances 2 7 2 Actual evapotranspiration 2 7 3 Surface runoff 2 7 4 Lateral flow 2 7 5 Percolation 2 7 6 Groundwater recharge 2 7 7 Baseflow Routing 2 8 1 Runoff routing 2 8 2 Lake reservoir routing Applications Irrigation management in lowland areas Snow and glacier fed river basins Flow forecasting Installation of SPHY General Installing SPHY as a stand alone application 4 2 1 Python 2 7 6 32 bit 4 2 2 Numpy 1 8 0 32 bit 4 2 3 PCRaster 4 0 32 bit 4 2 4 SPHY v2 0 source code Build your own SPHY model Select projection extent and resolution Clone map DEM and Slope Delineate catchment and create local drain direction map Preparing stations map and sub basins map Glacier fraction map 2 7 10 10 12 13 14 14 15 16 16 17 18 18 18 19 19 19 19 20 21 22 23 23 24 25 25 26 28 28 30 34 38 38 38 38 40 41 43 44 44 44 45 50 51 56 5 7 Other static input maps 5 8 Meteorological forcing map series 6 References Copyright 4 58 59 60 70 Tables Table 1 Pros and cons of some well known hydrological models including the SPHY model A categorization is made between i proc
56. esent the 9 year average for 46 8 day periods during 2000 2007 33 Figure 9 a SPHY simulated snow cover 2000 2007 and b MODIS observed snow cover ZO OOK 2OOF oss ca cites estan dba stacey cane sl beiaddanednce dak aa vant cabin padres dee ed pe aera aaa iaaa oaa 33 Figure 10 Snow storage mm as simulated by the SPHY model on 12 August left and 01 October right during the snow melting season of 2013 in the Laja River basin 00008 35 Figure 11 Daily observed vs SPHY simulated streamflow period 2007 2008 for the streamflow stations Canal Abanico ID 19 and Rio Laja en Tucapel ID 23 The Nash Sutcliffe NS and bias model performance indicators are shown as Well cccceesseceseeneeeeseeeeeesseeeees 36 Figure 12 Bias between total cumulative forecasted flow and observed flow for the 23 model runs that were executed between the end of September 2013 and March 2014 Results are shown for the locations Canal Abanico ID 19 and Rio Laja en Tucapel ID 23 cee 36 ra 5 Figure 13 Figure 14 Figure 15 Figure 16 package Figure 17 Figure 18 Figure 19 System properties to set Environmental Variables cccccccsseeeesssteeeeessteeeeeeaes 39 Setting the Path variable cccccccccesssececessneeeceesneeeeesaeeeeseeaeeeeseaeeeessaeeseeesaeeeeeeaas 40 Adding the Python27 installation folder to the Path system variables 2 40 Illustration of selectin
57. esses that are integrated ii field of application iii scale of application and iv implementation ce eceeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeees 9 Table 2 LAlmax values for different vegetation types Sellers et al 1996 ceeeeeeseeeereees 15 Table 3 Station locations used for calibration and validation of the SPHY model in HICAP Lutz et al 2014a Three stations were used for calibration for 1998 2007 Five stations were used for an independent validation for the same period The Nash Sutcliffe efficiency NS and bias metrics were calculated at a monthly time step eee ee eeteee ee eeee ee ee eeeeeeeeeeeeeeeeeneaeeeeneaees 32 Table 4 Resampling settings based on the layer data type eee eeeeeeeeeeenneeeeeenaeeeeeenaeeeeeeaas 49 Table 5 Data types used in SPHY cccccceeeceeseeeeeeee cee eeeeaeeeeeeeseeeeesaaeeeeaaeseneeeseaeeesaeeneaeeseeeees 59 Table 6 Overview of SPHY model parameters The last column indicates whether the parameter is observable or can be determined by calibration free 71 Figures Figure 1 Illustration of SPHY sub grid variability A grid cell in SPHY can be a partially covered with glaciers or b completely covered with glaciers or c1 free of snow or c2 completely covered with snow In the case of c1 the free land surface can consist of bare Soil vegetation OF OPEN Water 2 0 0 ccceeececeeece cece eeeeeeeaeeeeeeeeeseeaeaaeceeeeeeeseeaaaaeceeeeeeesee
58. for predicting the hydraulic conductivity of unsaturated soils Soil Sci Soc Am J 44 892 898 1980 VanderKwaak J E and Loague K Hydrologic response simulations for the R 5 catchment with a comprehensive physics based model Water Resour Res 37 999 1013 doi 10 1029 2000WR900272 2001 Venetis C A STUDY ON THE RECESSION OF UNCONFINED ACQUIFERS International Association of Scientific Hydrology Bulletin 14 119 125 doi 10 1080 02626666909493759 1969 Verbunt M Gurtz J Jasper K Lang H Warmerdam P and Zappa M The hydrological role of snow and glaciers in alpine river basins and their distributed modeling J Hydrol 282 36 55 doi 10 1016 S0022 1694 03 00251 8 2003 Vicu a S Garreaud R D and McPhee J Climate change impacts on the hydrology of a snowmelt driven basin in semiarid Chile Climatic Change 105 469 488 doi 10 1007 s10584 010 9888 4 2011 Von Hoyningen Huene J Die Interzeption des Niederschlags in landwirtschaftlichen Pflanzenbestanden Arbeitsbericht Deutscher Verband fur Wasserwirtschaft und Kulturbau DWK 1981 Wada Y Van Beek L P H Van Kempen C M Reckman J W T M Vasak S and Bierkens M F P Global depletion of groundwater resources Geophys Res Lett 37 L20402 doi 10 1029 2010GL044571 2010 Wagener T Sivapalan M Troch P A McGlynn B L Harman C J Gupta H V Kumar P Rao P S C Basu N B and Wilson J S The future of
59. g the Himalayas and the Tibetan Plateau large human populations depend on the water supplied by these rivers Immerzeel et al 2010 However the dependency on meltwater differs strongly between river basins as a result of differences in climate and differences in basin hypsometry Immerzeel and Bierkens 2012 Only by using a distributed hydrological modeling approach that includes the simulation of key hydrological and cryospheric processes and inclusion of transient changes in climate snow cover glaciers and runoff can appropriate adaptation and mitigation options be developed for this region Sorg et al 2012 The SPHY model is very suitable for such goals and has therefore been widely applied in the region For application in this region SPHY was set up at a 1km spatial resolution using a daily time step and forced with historical air temperature Tavg Tmax Tmin and precipitation data obtained from global and regional data sets e g APHRODITE Yatagai et al 2012 Princeton Sheffield Goteti and Wood 2006 TRMM Gopalan et al 2010 or interpolated WMO station data from a historical reference period For this historical reference period SPHY was calibrated and validated using observed streamflow For the future period SPHY was forced with downscaled climate change projections obtained from general circulation models GCMs as 30 ic available through the Climate Model Intercomparison Projects e g CMIP3 Meehl et al 2
60. g the Python installation folder during installation of the Numpy E E E E E O A E E A E T 41 Adding the PCRaster bin folder to the Path system variables c ccsseeeeeeeees 42 Editing or creating the PYTHONPATH variable for the PCRaster package 42 Command prompt view of testing a successful installation of PCRaster after entering the perceale command renarena RA EE E A A vane 43 Figure 20 Figure 21 Figure 22 Figure 23 Figure 24 Figure 25 Figure 26 Figure 27 Figure 28 Figure 29 Figure 30 Figure 31 Figure 32 Figure 33 Figure 34 Figure 35 Figure 36 Figure 37 Figure 38 Figure 39 Figure 40 Figure 41 Figure 42 Command line menu for clone creation cccccceeesceeeeeeeeeeeeeeeeceeeeeeeeeesaeeeeaeeteneeees 45 Opening the Processing Toolbox ccccccecsseceeeseneeeceeeeeeeeseeeeeesaeeeeesseeeensneeeeess 45 Selecting the Advanced interface in the Processing Toolbox ssassn 46 Weal OOl osos a a E E A emeeene ae 47 Setting the files and Source and Target SRS in the Warp TOOlI sseeeseeeereees 47 Selecting the Resampling tool in the Processing TOOIDOX csccccsssseeeessteeeeeeaes 48 Setting the Resampling tool Options ccccccccsseceeeseneeeeecseeeseeseeeeecaeeeeesseeseseaas 48 Translate tool convert raster fOrma eeeceeeeeeceeeeeeeeeeeeeeeeeeeeseeeeeeseeeeeeeeeeeeeeeeeeaees 49 Setting the Translate Options
61. hical User Interfaces GUIs FutureWater report 143 Tucker C J Red and photographic infrared linear combinations for monitoring vegetation Remote Sens Environ 8 127 150 doi 10 1016 0034 4257 79 90013 0 1979 USGS Landsat 8 U S Geological Survey Fact Sheet 2013 3060 Tech rep available at http pubs usgs gov fs 2013 3060 last access 15 June 2014 2013 USGS Water Resources Applications Software available at http water usgs gov software lists alphabetical last access 30 April 2014 2014 fia e7 van Beek L and Bierkens M The Global Hydrological Model PCR GLOBWB Conceptualization Parameterization and Verification Tech rep Department of Physical Geography Utrecht University Utrecht available at http vanbeek geo uu nl suppinfo vanbeekbierkens2009 pdf last access 24 November 2014 2008 van Dam J C Huygen J Wesseling J G Feddes R A Kabat P van Walsum P E V Groenendijk P and van Diepen C A Theory of SWAP version 2 0 Simulation of water flow solute transport and plant growth in the Soil Water Atmosphere Plant environment Tech rep DepartmentWater Resources Wageningen Agricultural University 1997 Van Der Knijff J M Younis J and De Roo A P J LISFLOOD a GIS based distributed model for river basin scale water balance and flood simulation Int J Geogr Inf Sci 24 189 212 doi 10 1080 13658810802549154 2010 Van Genuchten M A closed form equation
62. hydrology An evolving science for a changing world Water Resour Res 46 W05301 doi 10 1029 2009W R008906 2010 Wagner P D Fiener P Wilken F Kumar S and Schneider K Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions J Hydrol 464 465 388 400 doi 10 1016 jhydrol 2012 07 026 2012 Wheeler T and von Braun J Climate change impacts on global food security Science 341 508 513 doi 10 1126 science 1239402 2013 Wigmosta M S Vail L W and Lettenmaier D P A distributed hydrology vegetation model for complex terrain Water Resour Res 30 1665 1680 doi 10 1029 94WR00436 1994 Williams J HYMO flood routing J Hydrol 26 17 27 doi 10 1016 0022 1694 75 90122 5 1975 Yatagai A Kamiguchi K Arakawa O Hamada A Yasutomi N and Kitoh A APHRODITE Constructing a Long Term Daily Gridded Precipitation Dataset for Asia 68 fa Based on a Dense Network of Rain Gauges B Am Meteorol Soc 93 1401 1415 doi 10 1175 BAMS D 11 00122 1 2012 69 Copyright Redistribution and use of the SPHY model source code or its binary forms with or without modification are permitted provided that the following conditions are met 1 Redistributions of source code must retain this copyright notice this list of conditions and the following disclaimer 2 Redistributions in binary form must reproduce the above copyright notice this list of c
63. iables need to be updated again The steps to get to your system Environmental Variables are shown in Section 0 steps 1 4 1 http pcraster geo uu nl fa 41 5 It is now required to add the bin directory of the extracted PCRaster package to the Path system variable Figure 17 In our example it is the folder c Program Files x86 PCRaster40 bin 6 Click OK 7 The next step involves setting the PYTHONPATH environment variable In the same system variables window check the existence of a PYTHONPATH variable If it exists then edit the variable by adding the path Figure 18 of the Python directory of the extracted PCRaster package which is in our example c Program Files x86 PCRaster40 python Otherwise click New to create it and add PYTHONPATH as Variable name and add the Python directory folder as the Variable value 8 Click OK and OK to complete the installation of PCRaster 9 The successful installation of PCRaster can be tested as follows a Open a command prompt b Type pcrcalc c You should see the command prompt view as is shown in Figure 19 10 To test the combination of PCRaster and Python a Open a command prompt b Type python c This opens the Python interactive console d Type import pcraster e If no errors are shown then installation has been completed successfully Edit System Variable s Variable name Path Variable value BHC Program Files x86 PCRaster40 bin fa o
64. ibed in the sections below 5 1 Select projection extent and resolution First you need to start a new project within QGIS Give it a useful name and save your project regularly during the steps in the following sections Because all calculations in SPHY are metric you will need to project your data in a metric coordinate system In the example of the Pungwe basin we chose the WGS84 UTM Zone 36 South projection EPSG 32736 Define the minimum and maximum x and y values in the projection that you have chosen that cover the entire area you want to model Then define the spatial resolution of your model The choice of resolution will be a tradeoff of the resolution of your input data computation resources availability number of runs you intend to do and required detail for your modelling purpose For your reference the model for the Pungwe case study has an extent of 275 x 255 km For this model the spatial resolution is 1000 x 1000 m and thus the model contains 70 000 grid cells Running this model at a daily time step for 5 years takes about 5 minutes In order to create your own model you need to setup the directory structure This means you need to create a new SPHY model directory containing the SPHY model source py files and in that directory you need to create a new input and output directory 5 2 Clone map You will need to define a clone map which is a map in PCRaster format with the model extent and resolution This map i
65. ient Free Kemax Maximum crop coefficient Free Kein Minimum crop coefficient Free NDVImax Maximum NDVI Observable NDVImin Minimum NDVI Observable FPARmax Maximum fraction of absorbed photosynthetically active radiation Free FPARmin Minimum fraction of absorbed photosynthetically active radiation Free Terit Temperature threshold for precipitation to fall as snow e Free DDF Degree day factor for snow mm c7 day Free ssc Water storage capacity of snowpack mmmm Free GlacF Glacier fraction of grid cell Observable DDFc1 Degree day factor for debris free glaciers mm c day Free DDFpc Degree day factor for debris covered glaciers mm c day Free Foy Fraction of GlacF that is debris free Observable Foc Fraction of GlacF that is covered with debris Observable GlacROF Fraction of glacier melt that becomes glacier runoff Free SW sat Saturated soil water content of first soil layer mm Observable SW1 fc Field capacity of first soil layer mm Observable SW pr3 Wilting point of first soil layer mm Observable SW pF4 2 Permanent wilting point of first soil layer mm Observable Kati Saturated hydraulic conductivity of first soil layer mm day Observable SW sat Saturated soil water content of second soil layer mm Observable SW fe Field capacity of second soil layer mm Observable Ksat 2 Saturated hydraulic conductivity of second soil layer mm day Observable SW3 sat Saturated soil water content of groundwater layer mm Obs
66. igure 15 Adding the Python27 installation folder to the Path system variables 4 2 2 Numpy 1 8 0 32 bit Numpy stands for Numerical Python and is a fundamental package for scientific computing with Python It has especially been developed to work with raster data arrays which is also the basis of the PCRaster dynamic modelling framework in which SPHY has been developed SPHY requires a Numpy version that works with the 32 bit version of Python 2 7 6 which is Numpy 1 8 0 32 bit This package can be downloaded using the link below http sourceforge net projects numpy files NumPy 1 8 0 numpy 1 8 0 win32 superpack python2 7 exe download After downloading the Numpy package it can be installed by double clicking on the downloaded file If Python 2 7 6 has been installed correctly in the previous step then the Python installation folder will be found automatically during the installation of Numpy In our example case this folder was c Python27 see example Figure 16 a fa numpy 1 8 0 Python 2 7 is required for this package Select installation to use Python Version 2 7 found in regis PYTHON Powered Python Directory Installation Ditectory C Python27 Lib site packages Figure 16 Illustration of selecting the Python installation folder during installation of the Numpy package 4 2 3 PCRaster 4 0 32 bit SPHY is written in the Python programming language using the PCRaster Karssenberg et al
67. in plate spline tin Thin plate spline global d GDAL OGR 45 geoalgorithms w GRASS commands 160 geoalgorithms Models 0 geoalgorithms Orfeo Toolbox Image analysis 83 geoalgorithms QGIS geoalgorithms 103 geoalgorithms SAGA 2 1 2 235 geoalgorithms Scripts 0 geoalgorithms Loo Advanced interface v Figure 22 Selecting the Advanced interface in the Processing Toolbox Use your own DEM or otherwise the DEM provided in the database You will need to project your DEM in the model s projection and resample the DEM to model resolution and extent You can do that using the following steps 1s 2 46 Drag the DEM inside the QGIS canvas Use the Warp tool in QGIS to reproject the DEM to the Coordinate Reference System CRS of your basin EPSG XXXXXxX This can be found under Raster gt Projections gt Warp Reproject see Figure 23 Within the Warp tool you need to select the Input file the Output file and the Target SRS The Input file is the layer that you need to reproject which is in this case the dem The Output file is the file to which you want to save the reprojected dem in GeoTiff format tif Give it a useful name and save it in a directory that is useful In the example of Figure 24 the reprojected dem is saved under the SPHY input directory with the name dem_pr tif Finally it is important that you select the correct Target SRS EPSG
68. ing Int min 1 5ET Scan Equation 11 with Int mm the intercepted water on day t and ET mm the reference evapotranspiration on day t Finally the canopy storage is updated by subtracting the interception Scan Scan Int Equation 12 2 5 Snow processes For each cell a dynamic snow storage is simulated at a daily time step adopted from the model presented by Kokkonen et al 2006 The model keeps track of a snow storage which is fed by precipitation and generates runoff from snowmelt Refreezing of snowmelt and rainfall within the snowpack are simulated as well 2 5 1 Snow and rainfall Depending on a temperature threshold precipitation is defined as falling in either solid or liquid form Daily snow accumulation which is defined as solid precipitation is calculated as S Pe if Tavg t lt Terit a 0 if Tavgt gt Terit Equation 13 with P mm the snowfall on day t Pe mm the effective precipitation on day t Tayg C the mean air temperature on day t and Terit C a calibrated temperature threshold for precipitation to fall as snow The precipitation that falls as rain is defined as liquid precipitation and is calculated as E a if Tavge gt a oO if Tavgt S Terit Equation 14 with P mm being the amount of rainfall on day t 16 fe 2 5 2 Snowmelt refreezing and storage To simulate snowmelt the well established and widely used degree day melt modeling approach is used Hock 20
69. ing river basins Phys Chem Earth Pt A B C 30 339 346 doi 10 1016 j pce 2005 06 015 2005 Droogers P and Bouma J Simulation modelling for water governance in basins Int J Water Resour D 30 475 494 doi 10 1080 07900627 2014 903771 2014 Droogers P and Immerzeel W W Wat is het beste model H2O Tijdschrift voor watervoorziening en waterbeheer 4 38 41 2010 Droogers P Immerzeel W W Terink W Hoogeveen J Bierkens M F P van Beek L P H and Debele B Water resources trends in Middle East and North Africa towards 2050 Hydrol Earth Syst Sci 16 3101 3114 doi 10 5194 hess 16 3101 2012 2012 EEA EU DEM layers Copernicus data and information funded by the European Union European Environmental Agency Tech rep 2014 Endrizzi S Dall Amico M Gruber S and Rigon R GEOtop Users Manual User Manual Version 1 0 Tech rep Department of Physical Geography University of Zurich Zurich 2011 Endrizzi S Gruber S Dall Amico M and Rigon R GEOtop 2 0 simulating the combined energy and water balance at and below the land surface accounting for soil freezing snow cover and terrain effects Geosci Model Dev 7 2831 2857 doi 10 5194 gmd 7 2831 2014 2014 F EPA Modeling at EPA available at http www epa gov epahome models htm last access 30 September 2014 2014 Essery R Morin S Lejeune Y and B M nard C A comparison of 1701 snow models using
70. ion Interception is calculated on a daily basis if the dynamic vegetation module is used and consists of the daily precipitation plus the intercepted water remaining in the canopy storage from the previous day First the canopy storage is updated with the amount of precipitation of the current day Scan Scan _ P Equation 8 with Scan mm the canopy storage on day t Scan _ mm the canopy storage on day t 1 and P mm the amount of precipitation on day t The portion of precipitation that cannot be stored in the canopy storage is known as precipitation throughfall or effective precipitation according to Pe max 0 Scan Scanmaxt Equation 9 E 15 with Pe mm the effective precipitation on day t and Scan mm the canopy storage on day t This equation shows that precipitation throughfall only occurs if the water stored in the canopy exceeds the maximum canopy storage After the effective precipitation has been calculated the canopy storage is updated as Scan Scan Pe Equation 10 The remaining amount of water stored in the canopy is available for interception and the amount of water that will be intercepted depends on the atmospheric demand for open water evaporation A commonly used value for the atmospheric demand for open water evaporation is 1 5 Allen et al 1998 which is derived from the ratio between 1 and the mean pan evaporation coefficient Kp 0 65 The interception can now be calculated us
71. is therefore essential that the user knows which catchment characteristics and processes should be included in their modeling framework Figure 3 represents an overview of the six modules available glaciers snow groundwater dynamic vegetation simple routing and lake reservoir routing All modules can run independently of each other except for the glacier module If glaciers are present then snow processes are relevant as well Verbunt et al 2003 Singh and Kumar 1997 Since melting glacier water percolates to the groundwater layer the glacier module cannot run with the groundwater module turned off Two modules are available for runoff routing i a simple flow accumulation routing scheme and ii a fractional flow accumulation routing scheme used when lakes reservoirs are present The user has the option to turn off routing or to choose between 12 i one of these two routing modules All hydrological processes incorporated in the SPHY model are described in detail in the following sections Glaciers Dynamic vegetation 4 Snow z z I Simple routing Groundwater Lake reservoir routing Figure 3 Modules of the SPHY model that can be switched on off 2 3 Reference and potential evapotranspiration Despite the good physical underlying theory of the Penman Monteith equation Allen et al 1998 for calculating the reference evapotranspiration ET its major limitation is the high data demand for energy based me
72. ity is a suction force H and is therefore often expressed in cm negative water column The pF value is simply a conversion of the suction force H and is calculated as pF logi9 H Equation 33 Soils that are at field capacity generally have a pF of 2 meaning 100cm of water column and soils that are at permanent wilting point have a pF of 4 2 or 16000cm of water column The permanent wilting point is often referred to as the point where the crop dies In SPHY it is assumed that the linear decline in rootwater uptake starts at a pF of 3 1000cm water column Therefore ETredary is calculated as SWyt SW pF4 2 ETredarys SW pr3 SW1 pF4 2 Equation 34 with ETredgry t the reduction in rootwater uptake due to water shortage on day t SW mm the actual soil water content in the first soil layer on day t and SW prz mm and SW pr4 2 mm the soil water content in the first soil layer at pF3 and pF4 2 respectively ETredgry can therefore have values ranging between 0 and 1 where a value of 1 represents optimal plant growing conditions and 0 means no rootwater uptake at all ETredg is eventually used in Equation 32 to calculate the ET 2 7 3 Surface runoff Since the SPHY model runs on a daily time step the model does not account for sub daily variability in rainfall intensities Therefore the Hortonian runoff process Beven 2004 Corradini et al 1998 which refers to infiltration excess over
73. land flow is considered less important For this reason SPHY uses the saturation excess overland flow process known as Hewlettian runoff Hewlett 1961 to calculate surface runoff Surface runoff is calculated from the first soil layer RO es SW sar if SW gt oan 0 if SW lt SWy sat Equation 35 fu 21 with RO mm surface runoff SW mm the water content in the first soil layer and SW sat mm the saturated water content of the first soil layer 2 7 4 Lateral flow Lateral flow is substantial in catchments with steep gradients and soils with high hydraulic conductivities Beven 1981 Beven and Germann 1982 Sloan and Moore 1984 In SPHY it is assumed that only the amount of water exceeding field capacity can be used for lateral flow Therefore the drainable volume of water excess water needs to be calculated first w SM SWire if SW gt SW Lesc 0 if SW lt Wi Equation 36 with W exc mm the drainable volume of water from soil layer L SW mm the water content in soil layer 1 and SW fe mm the field capacity of soil layer l According to Sloan and Moore 1984 the lateral flow at the hillslope outlet can be calculated as LF W excfrac Viat Equation 37 with LF mm lateral flow from soil layer 1 W excfrac the drainable volume of water as a fraction of the saturated volume and viat mm d the flow velocity at the outlet In SPHY the drainable volume as a fraction of the satu
74. lation Method Scale Down 1 Bilinear Interpolation Output extent xmin xmax ymin ymax 120849 0 727849 0 8196188 0 8583188 0 p Cellsize Use lay p canvas extent peen Select extent on canvas Grid E Active NUFFIC_Mozambique TEST SPHY input dem_res tif Open output file after running algorithm Use min covering extent from input layers Figure 26 Setting the Resampling tool options s fa Table 4 Resampling settings based on the layer data type Layer data type Continuous Preserve Data Type No Interpolation Method scale o Bilinear Interpolation Method scale Down Bilinear Example layer Classified No Majority Nearest Landuse neighbor 7 Since the projected dem that we want to resample is continuous data we select Bilinear Interpolation for both the interpolation methods and we uncheck the Preserve Data Type option For the Grid we select the projected dem from step 3 For the Output extent we use the layer extent see Figure 26 of the clone map For the Cellsize cell length you can fill in the value that you determined in Section 5 1 Then save the resampled Grid as GeoTiff in the Grid in a useful directory In the example of Figure 26 the file is saved as dem_res tif under the directory SPHY input Finally click Run to finish the resampling If these steps are performed correctly then your resampled dem should have the same e
75. loping forested watersheds Water Resour Res 20 1815 1822 doi 10 1029 WR020i012p01815 1984 Smedema L and Rycroft D Land Drainage Planning and Design of Agricultural Drainage Systems Cornell University Press 1983 Sorg A Bolch T Stoffel M Solomina O and Beniston M Climate change impacts on glaciers and runoff in Tien Shan Central Asia Nature Climate Change 2 725 731 doi 10 1038 nclimate1592 2012 Sperna Weiland F C van Beek L P H Kwadijk J C J and Bierkens M F P The ability of a GCM forced hydrological model to reproduce global discharge variability Hydrol Earth Syst Sci 14 1595 1621 doi 10 5194 hess 14 1595 2010 2010 Strasser U Bernhardt M Weber M Liston G E and Mauser W Is snow sublimation important in the alpine water balance The Cryosphere 2 53 66 doi 10 5194 tc 2 53 2008 2008 Taylor K E Stouffer R J and Meehl G A An Overview of CMIP5 and the Experiment Design B Am Meteorol Soc 93 485 498 doi 10 1175 BAMS D 11 00094 1 2012 Terink W A F Lutz G W H Simons W W Immerzeel P Droogers 2015 SPHY v2 0 Spatial Processes in HYdrology Geoscientific Model Development 8 2009 2034 doi 10 5194 gmd 8 2009 2015 Terink W A F Lutz G W H Simons 2015a SPHY Spatial Processes in HYdrology Case studies for training FutureWater report 144 Terink W A F Lutz W W Immerzeel 2015b SPHY Spatial Processes in HYdrology Grap
76. map Select TargetSRS EPSG 32737 Select Outsize 25 No data 0 al fal Expand Gray X Srewin Prjwin Sds v Creation Options Profile Default Iv Name Value ei Validate Help Load into canvas when finished gdal_translate of PCRaster E Active NUFFIC_Mozambique TEST SPHY input dem_res tif E Active NUFFIC_Mozambique TEST SPHY input dem map oc ae Figure 28 Setting the Translate options el Select the raster file to save the results to Lookin J E Active NUFFIC_Mozambique TEST SPHY input SOO FF A My Computer R wio File name dem map Files of type PCRaster Raster File map MAP Figure 29 Saving the translated raster as a PCRaster Raster File map Now you should have the DEM in the model resolution and extent and in PCRaster format The slope map can be derived from the DEM using the slope command This can be done in the Windows Command line window by typing pcrcalc slope map slope dem map 5 4 Delineate catchment and create local drain direction map You can now use the DEM you created in the previous section to generate a local drain direction LDD map for your own model area 50 Pe To create a flow direction map or local drain direction LDD you can use the pcraster command Iddcreate Type the following command in the Windows Comma
77. mm baseflow from the third soil layer or lateral flow from the second soil layer In order to obtain river discharge QTot needs to be routed through a flow direction network SPHY allows the user to opt between the use of a simple routing scheme Section 2 8 1 or a more complex routing scheme Section 2 8 2 that involves the calculation of lake outflow through Q h relations Both methods require a flow direction network map which can be obtained by delineating a river network using PCRaster or GIS software in combination with a digital elevation model DEM 2 8 1 Runoff routing In hydrology streamflow routing is referred to as the transport of water through an open channel network Since open channel flow is unsteady streamflow routing often involves solving complex partial differential equations The St Venant equations Brutsaert 1971 Morris and Woolhiser 1980 are often used for this but these have high data requirements related to the river geometry and morphology which are unavailable for the spatial scale SPHY is generally applied on Additionally solving these equations requires the use of very small time steps which result in large model calculation times The use of very small time steps in the St Venant equations is required to provide numerical stability Other models such as e g SWAT Neitsch et al 2009 use the Manning equation Manning 1989 to define the rate and velocity of river flow in combination with the variabl
78. mpatibility us Modular set up _ Computational a efficient Climate forcing b 4 requirements Flexible output reporting options Graphical user a _ interface in GIS 2 Currently in development More climate variables are required if the model is run in energy balance mode Only if run in combination with LISFLOOD FP NA information not available Z 1 Z 2 Theory 2 1 Background SPHY is a spatially distributed leaky bucket type of model and is applied on a cell by cell basis The main terrestrial hydrological processes are described in a conceptual way so that changes in storages and fluxes can be assessed adequately over time and space SPHY is written in the Python programming language using the PCRaster Karssenberg et al 2001 Karssenberg et al 2010 Karssenberg 2002 Schmitz et al 2009 Schmitz et al 2013 dynamic modeling framework SPHY is grid based and cell values represent averages over a cell Figure 1 For glaciers sub grid variability is taken into account a cell can be glacier free partially glacierized or completely covered by glaciers The cell fraction not covered by glaciers consists of either land covered with snow or land that is free of snow Land that is free of snow can consist of vegetation bare soil or open water The dynamic vegetation module accounts for a time varying fractional vegetation coverage which affects
79. mperature index melt model Ann Glaciol 54 311 321 doi 10 3189 2013A0G63A537 2013 Hijmans R J Cameron S E Parra J L Jones P G and Jarvis A Very high resolution interpolated climate surfaces for global land areas Int J Climatol 25 1965 1978 doi 10 1002 joc 1276 2005 2 fia Hock R Temperature index melt modelling in mountain areas J Hydrol 282 104 115 doi 10 1016 S0022 1694 03 00257 9 2003 Hock R Glacier melt a review of processes and their modelling Prog Phys Geog 29 362 391 doi 10 1191 0309133305pp453ra 2005 Hooghoudt S Bijdragen tot de kennis van eenige natuurkundige grootheden van den grond No 7 Algemeene beschouwing van het probleem van de detailontwatering en de infiltratie door middel van parallel loopende drains greppels slooten en kanalen Versl Landbouwkd Onderz 46 515 707 1940 Hunink J Niadas l Antonaropoulos P Droogers P and de Vente J Targeting of intervention areas to reduce reservoir sedimentation in the Tana catchment Kenya using SWAT Hydrolog Sci J 58 600 614 doi 10 1080 02626667 2013 774090 2013 Immerzeel W and Droogers P Calibration of a distributed hydrological model based on satellite evapotranspiration J Hydrol 349 411 424 doi 10 1016 j jnydrol 2007 11 017 2008 Immerzeel W Lutz A and Droogers P Climate Change Impacts on the Upstream Water Resources of the Amu and Syr Darya River Basins Tech rep Fu
80. n SPHY is based on the steady state response of groundwater flow to recharge Hooghoudt 1940 and the water table fluctuations that are a result of the non steady response of groundwater flow to periodic groundwater recharge Smedema and Rycroft 1983 The SWAT model Neitsch et al 2009 assumes a linear relation between the variation in groundwater flow baseflow and the rate of change in water table height according to dBF Keat 10 dt UL w Gchrg BF gy Gchrg BF Equation 45 with BF mm the groundwater flow baseflow into the main channel on day t K a mm d the hydraulic conductivity of the shallow aquifer u the specific yield of the shallow aquifer Low m the distance from the subbasin divide for the groundwater system to the main channel Gchrg mm the amount of groundwater Equation 44 recharge entering the shallow aquifer on day t and a y the baseflow recession coefficient Equation 45 can be integrated and rearranged to calculate baseflow according to BF 0 if SW Ss en BF exp 9 Gchrg 1 exp 9 if SW gt BFenresh Equation 46 with BF mm the baseflow into the channel on day t and BF _ mm the baseflow into the channel on day t 1 Since this equation has proven its success in the SWAT model Neitsch et al 2009 throughout many applications worldwide this equation has been adopted in the SPHY model as well The baseflow recession coefficient
81. n run the model throughout the command prompt or as ii a an integrated application plugin in QGIS where the model can be run using a Graphical User Interface GUI The GUI has been developed as a plugin in QGIS and has the advantage that changing the model input and output as well as changing model parameters is more clear and user friendly Furthermore the use of the plugin allows you to store and visualize your model input and output in the user friendly and world wide used QGIS Geographical Information System GIS which is in the public domain The name of this SPHY model plugin is SphyPlugin v1 0 and is compatible with SPHY v2 0 The installation of the SPHY model plugin is not part of this manual and is described in the SPHY GUIs manual Terink et al 2015b This manual Section 4 2 describes the installation of SPHY v2 0 as a stand alone application 4 2 Installing SPHY as a stand alone application In order to install SPHY as a stand alone application it is required to have a PC with a windows operating system The software packages that are required to run the SPHY model as stand alone application are 1 Python 2 7 6 32 bit 2 NumPy 1 8 0 32 bit 3 PCRaster 4 0 32 bit 4 SPHY v2 0 source code These packages need to be installed in the same order as shown above and the installation of the each package is described in the following sections The Python NumPy and PCRaster software packages can also be do
82. n simulating soil moisture conditions in the root zone instead of evaluating for instance the contribution of baseflow to the total routed river flow In that case only the two upper soil layers are used where the bottom boundary of soil layer two is controlled by a seepage flux positive outward and instead of baseflow from the third soil layer water leaves the second soil layer through lateral flow With the groundwater module turned off the water balance for the second soil layer is SW SW 4 1 Perca LF Cap Seep Equation 31 with LF lateral flow from the second soil layer and Seep seepage in or out of the second soil layer positive is outgoing The units for all water balance terms are in mm 2 7 2 Actual evapotranspiration Evapotranspiration refers to both the transpiration from vegetation and the evaporation from soil or open water As was mentioned in Section 2 3 the Kc accounts for both the crop transpiration and soil evaporation The additional use of the dynamic vegetation module accounts for a time variable vegetation cover meaning that the role of evaporation becomes more dominant as soon as vegetation cover decreases Many limiting factors e g salinity stress water shortage water excess diseases can cause a reduction in potential evapotranspiration ET resulting in the actual evapotranspiration rate ET Since SPHY is a water balance model SPHY only accounts for stresses related to water
83. nd line window pcrcalc Idd map Iddcreate dem map 1 31 1e31 1e31 1e31 This command should also fill the sinks in the DEM to avoid that pits are generated in the depression in the DEM which could hamper the water to flow to the basin s outlet A good way to test if the LDD map is correct is to calculate for each cell how many cells are upstream You can do this using the pcraster command accuflux Type pcrcalc accuflux map accuflux ldd map 1 Drag the newly generated accuflux map to the QGIS canvas Check if the stream network is complete and all branches are connected to the outlet point If the generated LDD is not entirely correct and not all streams are connected toward the downstream outlet point this happens because during the creation of the LDD map pits have been generated where depressions in the landscape are present More details on the LDD generation can be found in the PCRASTER online manual There are multiple ways to overcome the problem of pit generation The first and most easy option is to try this command in the Windows Command line window pcrcalc Idd map Iddrepair ldd map If this does not solve the correct creation of the Idd map then you can try the following options e Test different values for the parameters in the Iddcreate command e Remove pits manually by changing the values for those cells e Use a map with the streams present in your study area and burn them into the DEM to force the other
84. nmask Calculates cast shadow areas from sun position and elevation raster map Y r surf area Surface area estimation for rasters surf contour Surface generation program from rasterized contours Y r surf gauss Creates a raster layer of Gaussian deviates Y rsurf idw Surface interpolation utility for raster layers Y r surf random Produces a raster layer of uniform random deviates whose range can be expre Y r thin Thins non zero cells that denote linear features in a raster layer y rto vect Converts a raster into a vector layer Y r topidx Creates topographic index layer from elevation raster layer tunivar Calculates univariate statistics from the non null cells of a raster map W r walk Outputs a raster layer showing the anisotropic cumulative cost of moving based on fric Vector v W v drape Converts vector map to 3D by sampling of elevation raster map y v kernel Generates a raster density map from vector point data using a moving kernel or opti Y v neighbors Makes each cell value a function of attribute values and stores in an output rast v sample Samples a raster layer at vector point locations to rast attribute Converts rasterize a vector layer into a raster layer v to rast value Converts rasterize a vector layer into a raster layer Figure 35 Selecting the v to rast attribute tool from the Processing Toolbox random Creates a raster la Y rrandom raster Create ranc Y
85. nopy storage SPHY allows the user to use the dynamic vegetation module in order to incorporate a time variable vegetation cover and corresponding rainfall interception In order to calculate the rainfall interception the canopy storage needs to be calculated using a time series of NDVI Carlson and Ripley 1997 The first step involves the calculation of the fraction photosynthetically active radiation FPAR FPAR can be calculated using a relation between NDVI and FPAR which was found by Peng et al 2012 and described by Sellers et al 1996 according to SR gt SRrmin F PAR max F FPARmin FPAR mi FPAR min 0 95 ii SR max SRmin t Equation 4 with _ 1 NDVI 1 NDVI Equation 5 and FPARmax and FPAR min having values of 0 95 and 0 001 respectively An FPAR of 0 95 is equivalent to the maximum LAI for a particular class and an FPAR of 0 001 is equivalent to a minimum LAI In order to calculate FPAR an NDVI time series is required The second step is the calculation of the leaf area index LAI which is eventually required to calculate the maximum canopy storage Scanmax According to Monteith 1973 LAI for vegetation that is evenly distributed over a surface can be calculated using a logarithmic relation between LAI and FPAR according to log 1 FPAR max log 1 FPARmax 14 fa LAI LAI Equation 6 with LAI the leaf area index and LAI ma the maximum leaf area index vegeta
86. ocations tif lescl Open output file after running algorithm SS Figure 36 Setting the options in the v to rast attribute tool i A g Translate Convert format Batch mode for processing whole directory Input Layer Rasterized Output file E Active NUFFIC_Mozambique TEST SPHY input locations map Target SRS Outsize No data Expand Srewin Prjwin Sds v Creation Options Profile Default Name Validate Help Load into canvas when finished gdal_translate of PCRaster E Active NUFFIC_ ee ee tif E Active NUFFIC_Mozambique TEST SPHY input locations may Figure 37 Editing the command for Translation Load into canvas when finished E Active NUFFIC_Mozambique TEST SPHY input locations tif gdal_translate of PCRaster l E Active NUFFIC_Mozambique TEST SPHY input locations map Figure 38 Adding the ot Float32 syntax to the command for Translation The resulting locations map is of the Float82 data format scalar As can be seen Table 5 from it is required to have a nominal format for station files This can be achieved by typing the following command in the Windows Command line pcrcalc locs map nominal locations map You can use locs map and Idd map to delineate the catchments of the points in locs map Use the subcatchment command for that pc
87. on network If lakes are present then the fractional accumulation flux routing scheme is used fa 11 depending on the actual lake storage a fraction of that storage becomes available for routing and is extracted from the lake while the remaining part becomes the updated actual lake storage The flux available for routing is routed in the same way as in the simple flow accumulation routing scheme As input SPHY requires static data as well as dynamic data For the static data the most relevant are digital elevation model DEM land use type glacier cover lakes reservoirs and soil characteristics The main dynamic data consist of climate data such as precipitation temperature and reference evapotranspiration Since SPHY is grid based optimal use of remote sensing data and global data sources can be made For example the Normalized Difference Vegetation Index NDVI Tucker 1979 Carlson and Ripley 1997 Myneni and Williams 1994 can be used to determine the leaf area index LAI in order to estimate the growth stage of land cover For setting up the model streamflow data are not necessary However to undertake a proper calibration and validation procedure flow data are required The model could also be calibrated using actual evapotranspiration soil moisture contents and or snow covered area SCA Section 3 2 contains an example application in which the SPHY model has been calibrated using MODIS snow cover images An overview of the a
88. on of SPHY is discussed in detail in Section 3 3 Four different relations can be chosen to calculate the lake outflow from the lake level height or lake storage being i an exponential relation ii a first order polynomial function iii a second order polynomial function and iv a third order polynomial function The user needs to supply maps containing the coefficients used in the different functions The lake reservoir routing scheme simply keeps track of the actual lake storage meaning that an initial lake storage should be supplied Instead of the simple accuflux function described in the previous section the lake reservoir routing scheme uses the PCRaster functions accufractionstate and accufractionflux The accufractionflux calculates for each cell the amount of water that is transported out of the cell while the accufractionstate calculates the amount of water that remains stored in the cell For non lake cells the fraction that is transported to the next cell is always equal to 1 while the fraction that is transported out of a lake reservoir cell depends on the actual lake storage Each model time step the lake storage is updated by inflow from upstream Using this updated storage the lake level and corresponding lake outflow can 26 ffs be calculated using one of the four relations mentioned before The lake outflow can then be calculated as a fraction Qfrac of the actual lake storage Instead of using Equation 50 Qfra
89. onditions and the following disclaimer in the documentation and or other materials provided with the distribution 3 Any changes modifications improvements and or simplifications of the source code should be sent to FutureWater 4 Any redistribution of source code or binary form should be reported to FutureWater 5 Any application publication and or presentation of results generated by using the Software should be reported to FutureWater THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS AS IS AND ANY EXPRESS OR IMPLIED WARRANTIES INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT INDIRECT INCIDENTAL SPECIAL EXEMPLARY OR CONSEQUENTIAL DAMAGES INCLUDING BUT NOT LIMITED TO PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES LOSS OF USE DATA OR PROFITS OR BUSINESS INTERRUPTION HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY WHETHER IN CONTRACT STRICT LIABILITY OR TORT INCLUDING NEGLIGENCE OR OTHERWISE ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE 70 ffs Appendix 1 Input and Output Table 6 Overview of SPHY model parameters The last column indicates whether the parameter is observable or can be determined by calibration free Acronym Description Units Parameter determination Ke Crop coeffic
90. ophys Res Atmos 117 D18105 doi 10 1029 2012JD017795 2012 Rigon R Bertoldi G and Over T M GEOtop A Distributed Hydrological Model with CoupledWater and Energy Budgets J MHydrometeorol 7 371 388 doi 10 1175 JHM497 1 2006 Rockstr m J Falkenmark M Lannerstad M and Karlberg L The planetary water drama Dual task of feeding humanity and curbing climate change Geophys Res Lett 39 L15401 doi 10 1029 2012GL051688 2012 Rollenbeck R and Bendix J Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological field observations Atmos Res 99 277 289 doi 10 1016 j atmosres 2010 10 018 2011 Samain B Simons G W H Voogt M P Defloor W Bink N J and Pauwels V R N Consistency between hydrological model large aperture scintillometer and remote sensing based evapotranspiration estimates for a heterogeneous catchment Hydrol Earth Syst Sci 16 2095 2107 doi 10 5194 hess 16 2095 2012 2012 Samaniego L Kumar R and Attinger S Multiscale parameter regionalization of a grid based hydrologic model at the mesoscale Water Resour Res 46 W05523 doi 10 1029 2008WR007327 2010 Sangrey D A Harrop Williams K O and Klaiber J A Predicting Ground Water Response to Precipitation J Geotech Eng ASCE 110 957 975 doi 10 1061 ASCE 0733 9410 1984 110 7 957 1984 66 r Schaner N Voisin N Nijssen B and Let
91. ow SRo mm is generated when the air temperature is above melting point and no more meltwater can be frozen within the snowpack according to Aactt Pie ASSW if Tavgt gt 0 E 0 if Tavgt lt 0 Equation 21 with ASSW mm the change in meltwater stored in the snowpack according to ASSW SSW SSW 1 Equation 22 2 6 Glacier processes Since the SPHY model usually operates at a spatial resolution between 250m and 1km the dynamics of glaciers such as ice flow cannot be resolved explicitly Therefore glaciers in SPHY are considered melting surfaces that can completely or partly cover a grid cell 2 6 1 Glacier melt Glacier melt is calculated with a degree day modeling approach as well Hock 2005 Because glaciers that are covered with debris melt at different rates than debris free glaciers Reid et al 2012 a distinction can be made between different degree day factors for both types The daily melt from debris free glaciers Ac mm is calculated as 0 if T lt 0 avg t 7 Tavgt DDFor Fc if Tavgt gt 0 Acit Equation 23 with DDF mm C 1d a calibrated degree day factor for debris free glaciers and Fc the fraction of debris free glaciers within the fractional glacier cover GlacF of a grid cell The daily melt from debris covered glaciers Apc mm is calculated in a similar way but with a different degree day factor 18 a F _ Tavgt DDFpc Foc if Tavgt gt 0 DEET 0 if T lt 0
92. p FAO and IIASA Rome Italy and Laxenburg Austria 2009 Batjes N Dijkshoorn K van Engelen V Fischer G Jones A Montanarella L Petri M Prieler S Teixeira E Wiberg D and Shi X Harmonized World Soil Database version 1 2 Tech rep FAO and IIASA Rome Italy and Laxenburg Austria 2012 Beven K Kinematic subsurface stormflow Water Resour Res 17 1419 1424 doi 10 1029 WR017i005p01419 1981 Beven K Robert E Horton s perceptual model of infiltration processes Hydrol Process 18 3447 3460 doi 10 1002 hyp 5740 2004 Beven K and Germann P Macropores and water flow in soils Water Resour Res 18 1311 1325 doi 10 1029 W R018i1005p01311 1982 Bierkens M F P and van Beek L P H Seasonal Predictability of European Discharge NAO and Hydrological Response Time J Hydrometeorol 10 953 968 doi 10 1175 2009JHM1034 1 2009 Biswas A K and Tortajada C Future Water Governance Problems and Perspectives Int J Water Resour D 26 129 139 doi 10 1080 07900627 2010 488853 2010 Bontemps S Defourny P van Bogaert E Arino O Kalogirou V and Ramos Perez J GLOBCOVER 2009 Products Description and Validation Report Tech rep ESA 2011 Bowling L C Pomeroy J W and Lettenmaier D P Parameterization of Blowing Snow Sublimation in a Macroscale Hydrology Model J Hydrometeorol 5 745 762 doi 10 1175 1525 7541 2004 005 lt 0745 POBSIA gt 2 0 CO 2 2004
93. rated volume is calculated as i Wrexe Lexcfrac SW sat SW fc Equation 38 The velocity of flow at the outlet viat mm d depends on both the saturated hydraulic conductivity K a mm d and the slope of the hill slp and is defined as Yat Koat slp Equation 39 The slope slp in SPHY is calculated for each grid cell as the increase in elevation per unit distance According to Neitsch et al 2009 only a fraction of lateral flow will reach the main channel on the day it is generated if the catchment of interest has a time of concentration greater than 1 day This concept is also implemented in the SPHY model and uses a lateral flow travel time TTiag d to lag a portion of lateral flow release to the channel 1 LF LF LF _ 1 1 exp Ti ag Equation 40 with LF mm the amount of lateral flow entering the channel on a given day LF mm the lateral flow Equation 37 generated within the cell on a given day and LF _ mm the lateral flow lagged from the previous day SPHY assumes the lateral flow travel time to be dependent 22 i on the field capacity SW mm saturated content SW sat mm and the saturated conductivity Katt mm d according to SW sat SW TTiag A ma sat l Equation 41 A longer lateral flow travel time will result in a smoother streamflow hydrograph 2 7 5 Percolation If the groundwater module is used then water can percolate f
94. rcalc catchment map subcatchment Idd map locs map F 55 5 6 Glacier fraction map The glacier fraction map can be calculated from a vector file with glacier outlines In QGIS from the Processing toolbox select the v to rast value tool like in the previous section Select your glacier outlines as vector input layer and convert it to raster at the same extent of the clone map Set the cellsize at a lower value than your model resolution For example if your model cell size is 200 m select 20 m for the converted raster The nodata values need to be reclassified to zeros To do this use SAGA s Reclassify tool from the Processing toolbox You can easily find it by typing Reclassify in the search field Processing Toolbox redassify amp Recently used algorithms E amp SAGA 228 geoalgorithms Grid Tools amp Redassify grid values Figure 39 Reclassify tool In the dialog box set all values to 0 0 and set replace no data values to Yes set new value for no data values to 0 0 and set replace other values to No Select an output filename and click Run se fia E Reclassify grid values fee eel Ex Parameters Log Help Grid Rasterized layer EPSG 32645 a Method 0 single z old value for single value change 0 000000 new value for single value change J 4 gt 4 gt 0 000000 operator for single value change 0 M minimum value for r
95. rom the first to the second soil layer and from the second to the third soil layer If the user decides to run SPHY without the groundwater module percolation only occurs from the first to the second soil layer In SPHY water can only percolate if the water content exceeds the field capacity of that layer and the water content of the underlying layer is not saturated A similar approach has been used in the SWAT model Neitsch et al 2009 The water volume available for percolation to the underlying layer is calculated as 0 if SW SSW fc or SWi41 2 SWi41 sat Wrexe SWi 1sat SWi41 if SW SW fe gt SWit1 sat SWist SW SW fo else Equation 42 with W exc mm the drainable volume of water from layer 1 SW mm the water content in layer L SWifc mm the field capacity of layer 1 SW mm the water content in layer L 1 and SWi 1 sat Mm the saturated water content of layer L 1 Only a certain amount of Wy exc will percolate to the underlying soil layer depending on the percolation travel time TTperc d This approach follows the storage routing methodology which is also implemented in the SWAT model Neitsch et al 2009 1 Wi perc Wiexc exp Equation 43 with w perc mm the amount of water percolating to the underlying soil layer Since the speed at which water can move through the soil is mainly dependent on the saturated hydraulic conductivity Kat the travel time for percolation is cal
96. rredass Creates a new maf Y rredass area greater Reda v rredass area lesser Redas v rrecode Recodes categorice y rregression line Calculates Y report Reports statistics fo Y r resamp interp Resamples z Y rresamp stats Resamples rz Y resample GRASS raster ma Y rrescale Rescales the range Y r rescale eq Rescales histog Y rros Generates three or fo JZ vto rast attribute Converts rasterize a vector layer into a raster layer a Parameters Log Help Input vector layer locations EPSG 32737 Source of raster values attr Name of column for attr parameter data type must be numeric id z GRASS region extent xmin xmax ymin ymax 120849 0 727849 0 8196 188 0 8583188 0 GRASS region cellsize leave 0 for default 1000 000000 Y rsum Sums up the raster ce Y rsunmask Calculates cast st Y r surf area Surface area est r surf contour Surface gene y r surf gauss Creates a raste Y r surfidw Surface interpolat Y r surf random Produces a ra Y r thin Thins non zero cells th y r to vect Converts a raster i V r topidx Creates topographii Y runivar Calculates univariat Y r walk Outputs a raster laye B Vector v Q v drape Converts vector ma w v kernel Generates a raster v neighbors Makes each cell D weanmnin Camnlan ranbas l b Advanced parameters Rasterized E Active NUFFIC_Mozambique TEST SPHY input l
97. rsity of water related challenges are large and are expected to increase in the future Wagener et al 2010 Lall 2014 Even today the ideal condition of having the appropriate amount of good quality water at the desired place and time is most often not satisfied Biswas and Tortajada 2010 Droogers and Bouma 2014 It is likely that climate variability and change will intensify food insecurity by water shortages Wheeler and Braun 2013 and loss of access to drinking water Rockstr m et al 2012 Current and future water related challenges are location and time specific and can vary from impact of glacier dynamics Immerzeel et al 2011 economic and population growth Droogers et al 2012 floods or extended and more prolonged droughts Dai 2011 amongst others In response to these challenges hydrologists and water resource specialists are developing modeling tools to analyze understand and explore solutions to support decision makers and operational water managers Pechlivanidis et al 2011 Despite difficulties in connecting the scientific advances in hydrological modeling with the needs of decision makers and water managers progress has been made and there is no doubt that modeling tools are indispensable in what is called good water governance Droogers and Bouma 2014 Liu et al 2008 The strength of hydrological models is that they can provide output at high temporal and spatial resolutions and for hydrological processes that
98. s of 8 days 5 days for the last period to generate 46 different average snow cover maps For example period 1 is the average snow cover for 01 08 January for 2000 until 2008 whereas period 2 is the average snow cover for 09 16 January for 2000 until 2008 etc The SPHY model was run for 2000 2007 at a daily time 22 fi step and for each 1 x 1km grid cell the average snow cover was calculated for the same 46 periods as in the MODIS observed snow cover data set Subsequently these simulated snow cover maps were resampled to 0 05 spatial resolution which is the native resolution of the MODIS product Figure 8 shows the basin average observed and simulated fractional snow cover for the 46 periods during 2000 2007 and Figure 9 shows the same at the 0 05 grid cell level As a final step the baseflow recession coefficient agw and routing coefficient kx were calibrated to match the simulated streamflow with the observed streamflow 0 7 MODIS 0 6 gt o w a a Fractional snow cover o K 0 1 0 4 4 4 4 4 L 4 4 i 5 10 15 20 25 30 35 40 45 Period Figure 8 Observed and simulated average fractional snow cover in the upper Indus basin The values represent the 9 year average for 46 8 day periods during 2000 2007 a SPHY simulated snow cover 2000 2007 gh o 9 oF 0 Ni s Se a roo OF b PSF VP oF 9 oF o_o 0 Tor oh PF oF oF oF oh oF OF Figure 9 a SPHY simulated snow cover 20
99. s used as the template for your model You can create a clone map using PCRaster s mapattr command in the Windows Command line window Make sure you are in the model s input directory This can be done using commands as for example e c enter gt go to your c drive e cdc SPHY input enter gt go to the SPHY input directory on your c drive e d enter gt go to your d drive e cddASPHY input enter gt go to the SPHY input directory on your d drive e etc If you are in the model s input directory then type following in the Command line mapattr clone map 44 a You will enter a menu where you can set the clone map s properties E Command Prompt mapattr clone map mys als e ls o gt amp x creation of map clone map umber of rows NOT SET number of columns NOT SET data type boo lean cell representation small integer projection y increases from bottom to top x upperleft corner y upperleft corner cell length angle degrees gt file id ACTIONS keyboard keys action Enter Select ArrowDown j LineDown ArrowJp k LineUp Quit u UndoLastEdit Figure 20 Command line menu for clone creation Change the settings of the number of rows number of columns check if the y values in your model projection increase from bottom to top or from top to bottom define the x and y values of the upperleft corner of your model s extent and define the cell length spatial resolution
100. shortage or water excess If there is too much water in the soil profile then the plant is unable to extract water because of oxygen stress Bartholomeus et al 2008 The calculation of evapotranspiration reduction due to water excess oxygen stress is quite complex and requires a substantial number of plant and soil properties e g soil temperature root dry weight plant respiration and minimum gas filled soil porosity Bartholomeus et al 2008 that are generally not available for the spatial scale that SPHY is applied on Therefore SPHY uses an 20 ffs evapotranspiration reduction parameter ETredwe that has a value of 0 if the soil is saturated and otherwise it will have a value of 1 This parameter is used in the following equation to calculate the actual evapotranspiration ET ETp t ETredwet ETredgry Equation 32 with ET mm the actual evapotranspiration on day t ET mm the potential evapotranspiration on day t and ETred and ETredg the reduction parameters for water excess and water shortage conditions respectively ETred4 is calculated using the Feddes equation Feddes et al 1978 which assumes a linear decline in rootwater uptake if the water pressure head drops below a critical value This critical value can be determined using the soil water retention curve pF curve which relates the moisture content of the soil to its binding capacity This relation is unique for each soil type The binding capac
101. struction of process based stochastic spatio temporal models and data assimilation Environ Model Softw 25 489 502 doi 10 1016 j envsoft 2009 10 004 2010 Kauffman S Droogers P Hunink J Mwaniki B Muchena F Gicheru P Bindraban P Onduru D Cleveringa R and Bouma J Green Water Credits exploring its potential to enhance ecosystem services by reducing soil erosion in the Upper Tana basin Kenya International Journal of Biodiversity Science Ecosystem Services amp Management 10 133 143 doi 10 1080 21513732 2014 890670 2014 Kokkonen T Koivusalo H Jakeman A and Norton J Construction of a Degree Day Snow Model in the Light of the Ten Iterative Steps in Model Development in Proceedings of the iEMSs Third Biennial Meeting Summit on Environmental Modelling and Software Environmental Modelling and Software Society Burlington USA 2006 F 63 Kozak J A Ahuja L R Green T R and Ma L Modelling crop canopy and residue rainfall interception effects on soil hydrological components for semi arid agriculture Hydrol Process 21 229 241 doi 10 1002 hyp 6235 2007 Krysanova V Muller Wohlfeil D l and Becker A Development and test of a spatially distributed hydrological water quality model for mesoscale watersheds Ecol Model 106 261 289 1998 Krysanova V Wechsung F Arnold J Srinivasan R and Williams J PIK Report Nr 69 SWIM Soil and Water Integrated
102. t M Bathurst J Cunge J O Connell P and Rasmussen J An introduction to the European Hydrological System Systeme Hydrologique Europeen SHE 2 Structure of a physically based distributed modelling system J Hydrol 87 61 77 1986 ADB Consultants Report Regional Technical Assistance Water and Adaptation Interventions in Central and West Asia Tech rep 2012 Allen R G Pereira L S Raes D and Smith M Crop evapotranspiration Guidelines for computing crop water requirements FAO Irrigation and drainage paper 56 1998 Andersson E User guide to ECMWF forecast products Version 1 1 Tech rep ECMWF available at http old ecmwf int products forecasts guide user_guide pdf last access 02 August 2014 2013 Bartholomeus R P Witte J P M van Bodegom P M van Dam J C and Aerts R Critical soil conditions for oxygen stress to plant roots Substituting the Feddes function by a process based model J Hydrol 360 147 165 doi 10 1016 j jnydrol 2008 07 029 2008 Bastiaanssen W Allen R Droogers P Da Urso G and Steduto P Twenty five years modeling irrigated and drained soils State of the art Agr Water Managent 92 111 125 doi 10 1016 j agwat 2007 05 013 2007 Batjes N Dijkshoorn K van Engelen V Fischer G Jones A Montanarella L Petri M Prieler S Teixeira E Wiberg D and Shi X Harmonized World Soil Database version 1 1 Tech re
103. tenmaier D P The contribution of glacier melt to streamflow Environ Res Lett 7 034029 doi 10 1088 1748 9326 7 3 034029 2012 Schmitz O Karssenberg D van Deursen W and Wesseling C Linking external components to a spatiotemporal modelling framework Coupling MODFLOW and PCRaster Environ Model Softw 24 1088 1099 doi 10 1016 j envsoft 2009 02 018 2009 Schmitz O Karssenberg D de Jong K de Kok J L and de Jong S M Map algebra and model algebra for integrated model building Environ Model Softw 48 113 128 doi 10 1016 j envsoft 2013 06 009 2013 Sellers P J Tucker C J Collatz G J Los S O Justice C O Dazlich D A and Randall D A A Revised Land Surface Parameterization SiB2 for Atmospheric GCMS Part II The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data J Climate 9 706 737 doi 10 1175 1520 0442 1996 009 lt 0706 ARLSPF gt 2 0 CO 2 1996 Sheffield J Goteti G and Wood E F Development of a 50 Year High Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling J Climate 19 3088 3111 doi 10 1175 JCLI3790 1 2006 Singh P and Kumar N Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan river J Hydrol 193 316 350 doi 10 1016 S0022 1694 96 03142 3 1997 Sloan P G and Moore D Modeling subsurface stormflow on steeply s
104. thods This brought Hargreaves and Samani 1985 to derive the modified Hargreaves equation that is based on temperature only For this reason this equation has also been implemented in the SPHY model according to ET 0 0023 0 408 Ra Tayg 17 8 TD S vg Equation 1 with Ra MJm 2d the extraterrestrial radiation Tavg C the average daily air temperature and TD C the daily temperature range defined as the difference between the daily maximum and minimum air temperature The constant 0 408 is required to convert the units to mm and Ra can be obtained from tables Allen et al 1998 or equations using the day of the year and the latitude of the area of interest According to Allen et al 1998 ET is the evapotranspiration rate from a reference surface with access to sufficient water to allow evapotranspiration at the potential rate The reference surface is a hypothetical grass reference crop with specific characteristics The potential evapotranspiration ET has no limitations on crop growth or evapotranspiration from soil water and salinity stress crop density pests and diseases weed infestation or low fertility Allen et al 1998 determined ET by the crop coefficient approach where the effects of various weather conditions are incorporated into ET and the crop characteristics in the crop coefficient Kc using ET ETpe Ke Equation 2 with ET mm the potential evapotranspiration on day t ET mm the referenc
105. tion type dependent This means that the maximum and minimum LAI values are related to the maximum and minimum of FPAR Table 2 shows the LAlmax values for a certain number of vegetation types Table 2 LAlmax values for different vegetation types Sellers et al 1996 Vegetation type Broadleaf evergreen trees Broadleaf deciduous trees Mixed trees Needleleaf evergreen trees High latitude deciduous trees Grass with 10 40 woody cover Grass with lt 10 woody cover Shrubs and bare soil Moss and lichens Bare Cultivated 5 7 7 7 8 8 5 5 5 5 5 6 For vegetation that is concentrated in clusters the linear relation from Goward and Huemmrich 1992 is often used However since SPHY is generally applied using grid cell resolutions between 250m and 1km we can assume that the effect of having vegetation concentrated in clusters is negligible Therefore the calculation of LAl in SPHY is done using the logarithmic relation of Monteith 1973 Equation 6 The next step involves the calculation of the maximum canopy storage Scanmax mm Many different relations between Scan and LAI can be found in the literature depending on the vegetation type Jong and Jetten 2010 The best results for crop canopies are shown by Kozak et al 2007 and are archived by Von Hoyningen Huene 1981 who derived the following relation between Scan and LAI ScaNmax 0 935 0 498LAI 0 00575LAI Equation 7 2 4 2 Intercept
106. tory for these locations Hydropower companies however have more interest in expected flow volumes for the coming weeks months than in accurate day to day flow simulations and therefore the NS coefficient is less important in this case If the NS coefficient is calculated for the same period on a monthly basis then the NS coefficients are 0 53 for the Abanico Canal and 0 81 for Rio Laja en Tucapel It is likely that SPHY model performance would even have been better if a full model calibration would have been performed A Legend E Lake J Streamflow stations C Laja River Basin Rivers Snow storage mm 0 E 250 EE 500 EE 750 E 1000 Fa E Lake Streamflow stations CI Laja River Basin Rivers Snow storage mm 0 E 250 JEN J I 500 0 10 20 30 40 km Mill 750 Figure 10 Snow storage mm as simulated by the SPHY model on 12 August left and 01 October right during the snow melting season of 2013 in the Laja River basin The hydropower company s main interest is the model s capacity to predict the total expected flow for the coming weeks during the melting season October 2013 through March 2014 To forecast streamflow during the snow melting season the SPHY model was forced with gridded temperature and precipitation data from the European Centre for Medium range Weather Forecasts ECMWF Seasonal Forecasting System SEAS Andersson 201
107. tureWater Wageningen 2012 Immerzeel W W and Bierkens M F P Asia s water balance Nat Geosci 5 841 842 doi 10 1038 ngeo1643 2012 Immerzeel W W Droogers P de Jong S M and Bierkens M F P Large scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing Remote Sens Environ 113 40 49 doi 10 1016 j rse 2008 08 010 2009 Immerzeel W W van Beek L P H and Bierkens M F P Climate change will affect the Asian water towers Science 328 1382 1385 doi 10 1126 science 1183188 2010 Immerzeel W W Beek L P H Konz M Shrestha A B and Bierkens M F P Hydrological response to climate change in a glacierized catchment in the Himalayas Climatic Change 110 721 736 doi 10 1007 s10584 011 0143 4 2011 Irrisoft Database and on line Applications in Irrigation Drainage amp Hydrology available at http www irrisoft org last access 07 May 2014 2014 Karssenberg D The value of environmental modelling languages for building distributed hydrological models Hydrol Process 16 2751 2766 doi 10 1002 hyp 1068 2002 Karssenberg D Burrough P A Sluiter R and de Jong K The PCRaster Software and Course Materials for Teaching Numerical Modelling in the Environmental Sciences T GIS 5 99 110 doi 10 1111 1467 9671 00070 2001 Karssenberg D Schmitz O Salamon P de Jong K and Bierkens M F A software framework for con
108. u need to type resampling in the Processing Toolbox search window see Figure 25 fa 47 Processing Toolbox f x resampling El Recently used algorithms amp Resampling El Q GRASS commands 160 geoalgorithms Raster r wy resample GRASS raster map layer data resampling capability using nearest neighbors E SAGA 2 1 2 235 geoalgorithms Grid Tools Resampling 4 Figure 25 Selecting the Resampling tool in the Processing Toolbox 5 Then double click Resampling under SAGA gt Grid Tools to open the Resampling tool as shown in Figure 26 6 Within this tool you need to select the Grid file that you want to resample the Interpolation Methods for scaling up and for scaling down the Output extent the Cellsize and the Grid to which you want to save the resampled file You also need to check or uncheck the Preserve Data Type option You can use Table 4 to determine which options to set for the Preserve Data Type and the Interpolation Methods for scaling up and for scaling down resampling Recently used algorithms Resampling GRASS commands 160 geoalgorithm E Raster Parameters Log Help Y resample GRASS raster mz B SAGA 2 1 2 235 geoalgorithms Grid Grid Tools dem_pr EPSG 32737 m amp Resampling Preserve Data Type Interpolation Method Scale Up 1 Bilinear Interpolation Interpo
109. use classes order discharge stations administrative regions ordinal classified order 2 2 whole values succession stages income groups scalar continuous lineair 10 10 real values elevation temperature directional continuous O to 2 pi radians or to aspect directional 360 degrees and 1 no direction real values local drain 1 9 codes of drain drainage networks wind direction to directions directions neighbour cell 5 8 Meteorological forcing map series Meteorological forcing map series are series of input maps with the time step indicated in each filename The filenames have a strict format with 8 characters before a dot and three characters behind the dot For example the average temperature maps can have the format tavg0000 001 tavg0000 002 etc To generate forcing data you have two options 1 interpolate point station data to grids at the model extent and resolution and convert to PCRaster grid format 2 resample existing gridded meteorological data products to model extent and resolution and convert to PCRaster grid format Depending on the number of time steps in your model you will probably need to write a script to batch this process and repeat it automatically for multiple time steps A script like this can be created in any scripting language like for example Python or R This procedure is automated in the SPHY preprocessor plugin Terink et al 2015b F 59 6 References Abbot
110. utz et al 2014a We use one parameter set for the entire domain which inherently means some stations perform better than others In the particular case of the upper Indus another possible explanation could be uncertainty in air temperature forcing in the highest parts of the upper Indus basin locations Dainyor bridge Besham Qila and Tarbela inflow in Table 3 since especially in this area the used forcing data sets are based on very sparse observations SPHY allowed the assessment of the current contribution of glacier melt and snowmelt to total flow Figure 7 and how total flow volumes and the intra annual distribution of river flow will change in the future Lutz et al 2014a Chatara Koshi basin Ganges 5 000 Nash Sutcliffe 0 87 4 000 Pearson 0 94 Bias 7 9 3 000 2 000 Q ms 1 000 0 J FMAM JJ AS ON D Observed flow Simulated flow Figure 6 Average monthly observed and SPHY simulated flow 1998 2007 for the Chatara major discharge measurement location in the Ganges basin Lutz et al 2014a Metrics are calculated based on monthly time steps F Table 3 Station locations used for calibration and validation of the SPHY model in HICAP Lutz et al 2014a Three stations were used for calibration for 1998 2007 Five stations were used for an independent validation for the same period The Nash Suitcliffe efficiency NS and bias metrics were calculated at a monthly time step
111. wnloaded from the SPHY model website as zip files Download additional software The login credentials that are required for downloading software and data can be obtained from http www sphy nl software download sphy 4 2 1 Python 2 7 6 32 bit SPHY requires the installation of the Python programming language PCRaster has been developed using the 2 7 6 version of Python Since SPHY has been developed using the 32 bit version of PCRaster 4 0 it is required to install the 32 bit version of Python 2 7 6 which can be downloaded from the internet using the link below https www python org ftp python 2 7 6 python 2 7 6 msi 1 http www qgis org en site https www python org 38 PS After downloading Python it can be installed by double clicking the downloaded file During installation it will be asked where to install Python You can choose any location that you prefer As an example to be used in this manual we have installed Python in the folder c Python27 A final installation step includes setting the environmental variables In order to do this follow the steps below 1 Goto start then control panel and type environment in the top right search window Click on Edit the system environmental variables Click Environmental Variables in the bottom right of this window Figure 13 Under system variables select the Path variable and click Edit Figure 14 In order let your system know the existence
112. xtent and spatial resolution as your clone map The final step involves converting the GeoTiff format to the PCRaster map format This can be done using the Translate function under Raster gt Conversion gt Translate Convert Format see Figure 27 Project Edit View Layer Settings Plugins Vector Raster Database Web Processing Help gt i Raster calculator A D 8RRA YS PRAAR Georeferencer z A ee Ga o Heatmap abc ab abc ab Eas A i g o ed a X S Interpolation o amp Ee ie MA QQ ea Terrain analysis gt BP GO 7 Q Ug W Zn O Zonalstatistics gt sin pds ex Projections a ad PY i Conversion Sy Rasterize Vector to raster a n ra Extraction gt Polygonize Raster to vector ome E Favourites A K 4 Translate Convert format y M Micrellaneniic Figure 27 Translate tool convert raster format 9 In the Translate box see Figure 28 make sure that you select the Input Layer result from step 7 and set the Output Layer The Output Layer should be save as PCRaster Raster File format map In the example of Figure 29 we save it in the SPHY input directory with the name dem map Finally click OK and OK and OK and Close to finish this step 49 Z Translate Convert format zX Batch mode for processing whole directory Input Layer Grid z Select Output file E Active NUFFIC_Mozambique TEST SPHY input dem

Terink, W., A.F. Lutz, W.W. Immerzeel. 2015. SPHY v2.0

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