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WaterGAP

Summary

The global freshwater model WaterGAP calculates flows and storages of water on all continents of the globe (except Antarctica), taking into account the human influence on the natural freshwater system by water abstractions and dams. It supports understanding the freshwater situation across the world's river basins during the 20th and the 21st centuries, and is applied to assess water scarcity, droughts and floods and to quantify the impact of human actions on e.g. groundwater, wetlands, streamflow and sea-level rise. Modelling results of WaterGAP have contributed to international assessment of the global environmental situation including the UN World Water Development Reports, the Millennium Ecosystem Assessment, the UN Global Environmental Outlooks as well as to reports of the Intergovernmental Panel on Climate Change. WaterGAP contributes to the Intersectoral Impact Model Intercomparison Project ISIMIP,[1] where consistent ensembles of model runs by a number of global hydrological models are generated to assess the impact of climate change and other anthropogenic stressors on freshwater resources world-wide.

WaterGAP (Water Global Assessment and Prognosis)[2][3] was developed at the University of Kassel (Germany)[4] since 1996, while later on development has continued at Goethe University Frankfurt[5] and Ruhr University Bochum. It consists of both the WaterGAP Global Hydrology Model (WGHM)[6][7] and five water use models for the sectors irrigation,[8] livestock, households, manufacturing and cooling of thermal power plants.[9] An additional model component computes the fractions of total water use that are abstracted from either groundwater or surface waters (rivers, lakes and reservoirs).[10] The model runs with a temporal resolution of 1 day; WaterGAP 2 has a spatial resolution of 0.5 degree geographical latitude × 0.5 degree geographical longitude (equivalent to 55 km × 55 km at the equator)[3] and WaterGAP 3 a spatial resolution of 5 arc minutes x 5 arc minutes (9 km x 9 km).[11] Model input includes time series of climate data (e.g. precipitation, temperature and radiation) and information such as characteristics of surface water bodies (lakes, reservoirs and wetlands), land cover, soil type, topography and irrigated area.

WaterGAP Global Hydrology Model WGHM edit

 
Water storages (boxes) and flows (arrows) modelled for each grid cell of WGHM[3]

WGHM computes time-series of fast-surface and subsurface runoff, groundwater recharge and river discharge as well as storage variations of water in canopy, snow, soil, groundwater, lakes, wetlands and rivers.[3] Thus, it quantifies the total renewable water resources as well as the renewable groundwater resources of a grid cell, river basin, or country. Precipitation on each grid cell is transported through the different storage compartments, where water can also evapotranspirate. Location and size of wetlands, lakes and reservoirs are defined by the global lakes and wetland database (GLWD),[12] and the GRanD database of man-made reservoirs.[13][14] Groundwater storage is affected by diffuse groundwater recharge through the soil and by point recharge from surface water bodies.[10] Diffuse groundwater recharge is modeled as a function of total runoff, relief, soil texture, hydrogeology and the existence of permafrost or glaciers.[7] Cell runoff is routed downstream until it reaches the ocean or an internal sink. To allow a plausible representation of the actual freshwater situation, version 2.2d of WGHM is tuned against observed long-term mean annual streamflow at 1319 gauging stations.[3] Performance of WGHM with respect to streamflow observations has been compared in various studies to that of other global hydrological models for both Europe[15][16] and the globe,[17][18][19][20] while performance with respect to GRACE total water storage anomaly was compared globally[21][22] and for U.S. aquifers.[23]

 
Total renewable water resources, in mm/yr (1 mm is equivalent to 1 L of water per m2) (average 1981-2010).[3]
 
Total renewable groundwater resources, in mm/yr (1 mm is equivalent to 1 L of water per m2) (average 1981-2010), which are a part of the total renewable freshwater resources and the maximum that can be abstracted without depleting the groundwater.[3]

Water Use Models edit

In WaterGAP, modeling of water use refers to computation of water withdrawals (abstractions) from either groundwater or surface water bodies (lakes, reservoirs and rivers), of consumptive water uses (the fraction of the abstracted water that evapotranspires during use) and of the return flows to groundwater or surface water bodies. Consumptive irrigation water use is computed by the Global Irrigation Model[8][24] as a function of irrigated area[25] and climate in each grid cell. Livestock water use is calculated as a function of the animal numbers and water requirements of different livestock types. Domestic and manufacturing use are based on national values of water withdrawals at different points in time.[9] The temporal development of national household water use is based on statistical data modeled as a function of technological and structural change (the latter as a function of gross domestic product), taking into account population change. The temporal development of manufacturing water use takes into account technological change and the development of manufacturing gross value added. National values of domestic and manufacturing water use are downscaled to the grid cells using population density and urban population density, respectively.[9] Water use for cooling of thermal power plants takes into account the location and characteristics of thermal power plants.[9] Time series of monthly values of irrigation water use are computed, while all other uses are assumed to be constant throughout the year and to only vary from year to year. Based on sectoral water withdrawals and consumptive use as computed by the five water use models, the model component GWSWUSE calculates surface water abstractions from and return flows to groundwater and surface water as well as the total net abstraction from groundwater and from surface water in each grid cell.[10]

 
Development of water abstractions (sum of return flows and consumptive use) and water consumption (the amount of water that is evapotranspired or incorporated in products, light colours) of the five water use sectors considered in WaterGAP for 1901–2010[26]
 
Water withdrawals around the year 2000, in mm/yr.[10]

Applications edit

WaterGAP has been applied to assess which areas of the world are and will be affected by water stress, and to estimate the world's freshwater balance.[3] In many studies, WaterGAP served to estimate the impact of climate change on the global freshwater system, e.g. on groundwater,[27][28][29] wetlands,[30] streamflow[31][32][33][34] and irrigation requirements.[35] Groundwater stress and depletion of groundwater resources were analyzed.[36][37] In addition, the alteration of ecologically relevant river flow characteristics and wetland dynamics due to human water use and dams was studied.[13][30] Time series of WaterGAP total water storage anomalies were used to process and interpret GRACE (Gravity Recovery and Climate Experiment) satellite measurement of the dynamic gravity of the Earth, as for the continents, the seasonal and longer-term gravity changes are to a large extent caused by changes of the water stored in groundwater, surface waters, soil and snow.[38][39] These time series also served to estimate the contribution of water storage variations on the continents to sea level rise.[40][41] WaterGAP results are also used in life-cycle assessments to take into account water stress at production sites.[42]

 
Groundwater stress: Groundwater withdrawals in 2010 in percent of renewable groundwater resources. Purple regions are likely to suffer from groundwater depletion, with declining groundwater tables.[37]
 
Global values of water resources and human water use (excluding Antarctica). Water resources 1961-90, water use around 2000.

References edit

  1. ^ "The Inter-Sectoral Impact Model Intercomparison Project". ISIMIP. Retrieved 2022-02-28.
  2. ^ Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., Siebert, S. (2003): Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 48(3), 317-338.
  3. ^ a b c d e f g h Müller Schmied, Hannes; Cáceres, Denise; Eisner, Stephanie; Flörke, Martina; Herbert, Claudia; Niemann, Christoph; Peiris, Thedini Asali; Popat, Eklavyya; Portmann, Felix Theodor; Reinecke, Robert; Schumacher, Maike (2021-02-23). "The global water resources and use model WaterGAP v2.2d: model description and evaluation". Geoscientific Model Development. 14 (2): 1037–1079. Bibcode:2021GMD....14.1037M. doi:10.5194/gmd-14-1037-2021. hdl:11250/2984567. ISSN 1991-9603.
  4. ^ "Goethe-Universität — WaterGAP". www.uni-frankfurt.de. Retrieved 2022-04-20.
  5. ^ "Goethe-Universität — WaterGAP". www.uni-frankfurt.de. Retrieved 2021-08-29.
  6. ^ Döll, P., Kaspar, F., Lehner, B. (2003): A global hydrological model for deriving water availability indicators: model tuning and validation. Journal of Hydrology, 270 (1-2), 105-134.
  7. ^ a b Döll, P., Fiedler, K. (2008): Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci., 12, 863-885.
  8. ^ a b Döll, P., Siebert, S. (2002): Global modeling of irrigation water requirements. Water Resources Research, 38(4), 8.1-8.10, doi:10.1029/2001WR000355
  9. ^ a b c d Flörke, Martina; Kynast, Ellen; Bärlund, Ilona; Eisner, Stephanie; Wimmer, Florian; Alcamo, Joseph (February 2013). "Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study". Global Environmental Change. 23 (1): 144–156. doi:10.1016/j.gloenvcha.2012.10.018.
  10. ^ a b c d Döll, P., Hoffmann-Dobrev, H., Portmann, F.T., Siebert, S., Eicker, A., Rodell, M., Strassberg, G., Scanlon, B. (2012): Impact of water withdrawals from groundwater and surface water on continental water storage variations. J. Geodyn. 59-60, 143-156, doi:10.1016/j.jog.2011.05.001
  11. ^ Eisner, S. (2015): Comprehensive evaluation of the WaterGAP3 model across climatic, physiographic, and anthropogenic gradients. PhD dissertation, University of Kassel, Germany
  12. ^ Lehner, B., Döll, P. (2004): Development and validation of a database of lakes, reservoirs and wetlands. Journal of Hydrology, 296 (1-4), 1-22.
  13. ^ a b Döll, P., Fiedler, K., Zhang, J. (2009): Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrol. Earth Syst. Sci., 13, 2413-2432.
  14. ^ Lehner, B., Reidy Liermann, C., Revenga, C., Vörösmary, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rödel, R., Sindorf, N., Wisser, D. (2011): High resolution mapping of the world's reservoirs and dams for sustainable river flow management. Frontiers in Ecology and the Environment, 9(9), 494-502.
  15. ^ Gudmundsson, L., T. Wagener, L. M. Tallaksen, and K. Engeland (2012), Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, doi:10.1029/2011WR010911
  16. ^ Gudmundsson, L., et al. (2012), Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe, J. Hydrometeorol., 13(2), 604–620, doi:10.1175/JHM-D-11-083.1
  17. ^ Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P. (2017): A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, doi:10.5194/essd-9-389-2017.
  18. ^ Zaherpour, J., Gosling, S. N., Mount, N., Müller Schmied, H., Veldkamp, T. I. E., Dankers, R., Eisner, S., Gerten, D., Gudmundsson, L., Haddeland, I., Hanasaki, N., Kim, H., Leng, G., Liu, J., Masaki, Y., Oki, T., Pokhrel, Y., Satoh, Y., Schewe, J., Wada, Y. (2018): Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts. Environmental Research Letters 13, 065015, doi:10.1088/1748-9326/aac547
  19. ^ Veldkamp, T. I. E., Zhao, F., Ward, P. J., de Moel, H., Aerts, J. C. J. H., Müller Schmied, H., Portmann, F. T., Masaki, Y., Pokhrel, Y., Liu, X., Satoh, Y., Gerten, D., Gosling, S. N., Zaherpour, J., Wada, Y. (2018): Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study. Environmental Research Letters 13, 055008, doi:10.1088/1748-9326/aab96f
  20. ^ Krysanova, V., Zaherpour, J., Didovets, I., Gosling, S.N., Gerten, D., Hanasaki, N., Müller Schmied, H., Pokhrel, Y., Satoh, Y., Tang, Q., Wada, Y. (2020): How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change. Climatic Change 163, 1353-1377, doi:10.1077/s10584-020-02840-0
  21. ^ Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., van Beek, L. P. H., Wiese, D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Döll, P., Bierkens, M. F. P. (2018): Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences of the United States of America 115, 6, E1080-E1089, doi:10.1073/pnas.1704665115
  22. ^ Scanlon, B. R., Zhang, Z., Rateb, A., Sun, A., Wiese, D., Save, H., Beaudoing, H., Lo, M. H., Müller Schmied, H., Döll, P., van Beek, R. Swenson, S., Lawrence, D., Croteau, M., Reedy, R. C. (2019): Tracking seasonal fluctuations in land water storage using global models and GRACE satellites. Geophysical Research Letters 46 (10), 5254-5264, doi:10.1029/2018GL081836
  23. ^ Rateb, A., Scanlon, B.R., Pool, D.R., Sun, A., Zhang, Z., Chen, J., Clark, B., Faunt, C.C., Haugh, C.J., Hill, M., Hobza, C., McGuire, V.L., Reitz, M., Müller Schmied, H., Sutanudjaja, E.H., Swenson, S., Wiese, D., Xia, Y., Zell W. (2020): Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers. Water Resources Research 56 (12), e2020WR027556, doi:10.1029/2020WR027559
  24. ^ Portmann, F. T. (2017): Global irrigation in the 20th century: extension of the WaterGAP Global Irrigation Model (GIM) with the spatially explicit Historical Irrigation Data set (HID), Frankfurt Hydrology Paper, 18, 131 pp.
  25. ^ Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., Scanlon, B.R. (2015): A global dataset of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci., 19, 1521-1545. doi:10.5194/hess-19-1521-2015
  26. ^ Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P. (2016): Impact of climate forcing uncertainty and human water use on global and continental water balance components. Proc. IAHS, 374, 53-62. doi:10.5194/piahs-374-53-2016
  27. ^ Döll, P. (2009): Vulnerability to the impact of climate change on renewable groundwater resources: a global-scale assessment. Environ. Res. Lett., 4, 036006 (12pp). doi:10.1088/1748-9326/4/3/035006
  28. ^ Portmann, F.T., Döll, P., Eisner, S., Flörke, M. (2013): Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environ. Res. Lett. 8, 024023. doi:10.1088/1748-9326/8/2/024023
  29. ^ Reinecke, R., Müller Schmied, H., Trautmann, T., Andersen, L. S., Burek, P., Flörke, M., Gosling, S. N., Grillakis, M., Hanasaki, N., Koutroulis, A., Pokhrel, Y., Thiery, W., Wada, Y., Satoh, Y., Döll, P. (2021): Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study. Hydrol. Earth Syst. Sci., 25, 787–810. doi:10.5194/hess-25-787-2021
  30. ^ a b Döll, P., Trautmann, T., Göllner, M., Müller Schmied, H. (2020): A global-scale analysis of water storage dynamics of inland wetlands: Quantifying the impacts of human water use and man-made reservoirs as well as the unavoidable and avoidable impacts of climate change. Ecohydrology, 13, e2175. doi:10.1002/eco.2175
  31. ^ Döll, P., Zhang, J. (2010): Impact of climate change on freshwater ecosystems: a global-scale analysis of ecologically relevant river flow alterations. Hydrol. Earth Syst. Sci., 14, 783-799.
  32. ^ Döll, P., Müller Schmied, H. (2012): How is the impact of climate change on river flow regimes related to the impact on mean annual runoff? A global-scale analysis. Environ. Res. Lett., 7 (1), 014037 (11 pp). doi:10.1088/1748-9326/7/1/014037
  33. ^ Eisner, S., Flörke, M., Chamorro, A., Daggupati, P., Donnelly, C., Huang, J., Hundecha, Y., Koch, H., Kalugin, A., Krylenko, I., Mishra, V., Piniewski, M., Samaniego, L., Seidou, O., Wallner, M., Krysanova, V. (2017): An ensemble analysis of climate change impacts on stream flow seasonality across 11 large river basins. Climatic Change, doi:10.1007/s10584-016-1844-5
  34. ^ Döll, P., Trautmann, T., Gerten, D., Müller Schmied, H., Ostberg, S., Saaed, F., Schleussner, C.-F. (2018): Risks for the global freshwater system at 1.5 °C and 2 °C global warming. Environ. Res. Lett., 13, 044038. doi:10.1088/1748-9326/aab7
  35. ^ Döll, P. (2002): Impact of climate change and variability on irrigation requirements: a global perspective. Climatic Change, 54(3), 269-293
  36. ^ Döll, P., Müller Schmied, H., Schuh, C., Portmann, F., Eicker, A. (2014): Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites. Water Resour. Res., 50, 5698–5720, doi:10.1002/2014WR015595
  37. ^ a b Herbert, C., Döll, P. (2019): Global assessment of current and future groundwater stress with a focus on transboundary aquifers. Water Resour. Res., 55, 4760-4784. doi:10.1029/2018WR023321
  38. ^ Schmidt, R., Schwintzer, P., Flechtner, F., Reigber, Ch., Güntner, A., Döll, P., Ramillien, G., Cazenave, A., Petrovic, S., Jochmann, H., Wünsch, J. (2006): GRACE observations of changes in continental water storage. Global and Planetary Change, 50, 112-126.
  39. ^ Kusche, J., et al. (2009): Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model. J Geod, 83, 903-913
  40. ^ Cáceres, D., Marzeion, B., Malles, J.H., Gutknecht, B., Müller Schmied, H., Döll, P. (2020): Assessing global water mass transfers from continents to oceans over the period 1948–2016. Hydrol. Earth Syst. Sci., 24, 4831-4851. doi:10.5194/hess-24-4831-2020
  41. ^ Horwath, M., Gutknecht, B. D., Cazenave, A., Palanisamy, H. K., Marti, F., Marzeion, B., Paul, F., Le Bris, R., Hogg, A. E., Otosaka, I., Shepherd, A., Döll, P., Cáceres, D., Müller Schmied, H., Johannessen, J. A., Nilsen, J. E. Ø., Raj, R. P., Forsberg, R., Sandberg Sørensen, L., Barletta, V. R., Simonsen, S. B., Knudsen, P., Andersen, O. B., Ranndal, H., Rose, S. K., Merchant, C. J., Macintosh, C. R., von Schuckmann, K., Novotny, K., Groh, A., Restano, M., Benveniste, J. (2022): Global sea-level budget and ocean-mass budget, with a focus on advanced data products and uncertainty characterisation, Earth System Science Data, 14, 411-447, doi:10.5194/essd-14-411-2022
  42. ^ Boulay, A. Bare, J., de Camillis, C., Döll, P., Gassert, F., Gerten, D., Humbert, S., Inaba, A., Itsubo, N., Lemoine, Y., Margni, M., Motoshita, M., Núñez, M., Pastor, A.V., Ridoutt, B., Schnecker, U., Shirakawa, N., Vionnet, S., Worbe, S., Yoshikawa, S., Pfister, S. (2015): Consensus building on the development of a stress-based indicator for LCA-based impact assessment of water consumption: outcome of the expert workshops. Int J Life Cycle Assess. doi:10.1007/s11367-015-0869-8

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