Articles | Volume 26, issue 1
https://doi.org/10.5194/hess-26-71-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-71-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
06 Jan 2022
Research article | Highlight paper |
| 06 Jan 2022
| 06 Jan 2022
Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Celray James Chawanda
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Jonas Jägermeyr
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Center for Climate Systems Research, Columbia University, New York,
NY 10025, USA
Climate Resilience, Potsdam Institute for Climate Impact Research
(PIK), Member of the Leibniz Association, 14412, Potsdam, Germany
Ann van Griensven
Hydrology and Hydraulic Engineering Department, Vrije Universiteit
Brussel (VUB), 1050 Brussels, Belgium
Water Science and Engineering Department, IHE Delft Institute for
Water Education, 2611 AX Delft, The Netherlands
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Manuscript not accepted for further review
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Cited articles
Abaci, O. and Papanicolaou, A. T.: Long-term effects of management practices
on water-driven soil erosion in an intense agricultural sub-watershed:
Monitoring and modelling, Hydrol. Process. Int. J., 23, 2818–2837, 2009.
Alemayehu, T., van Griensven, A., and Bauwens, W.: Evaluating CFSR and WATCH
Data as Input to SWAT for the Estimation of the Potential Evapotranspiration
in a Data-Scarce Eastern-African Catchment, J. Hydrol. Eng., 21, 05015028,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001305, 2016.
Alemayehu, T., van Griensven, A., Woldegiorgis, B. T., and Bauwens, W.: An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems, Hydrol. Earth Syst. Sci., 21, 4449–4467, https://doi.org/10.5194/hess-21-4449-2017, 2017.
Arnold, J., Bieger, K., White, M., Srinivasan, R., Dunbar, J., and Allen,
P.: Use of decision tables to simulate management in SWAT+, Water, 10,
713, https://doi.org/10.3390/w10060713, 2018.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large
Area Hydrologic Modeling and Assessment Part I: Model Development1, JAWRA J.
Am. Water Resour. Assoc., 34, 73–89,
https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M.
J., Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., and Van
Liew, M. W.: SWAT: Model use, calibration, and validation, Trans. ASABE, 55,
1491–1508, 2012.
Arnold, J. G., Kiniry, J. R., Srinivasan, R., Williams, J. R., Haney, E. B.,
and Neitsch, S. L.: SWAT 2012 input/output documentation, Texas Water
Resources Institute, 2013.
Asfaw, A., Simane, B., Hassen, A., and Bantider, A.: Variability and time
series trend analysis of rainfall and temperature in northcentral Ethiopia:
A case study in Woleka sub-basin, Weather Clim. Extrem., 19, 29–41,
https://doi.org/10.1016/j.wace.2017.12.002, 2018.
Bégué, A., Arvor, D., Bellon, B., Betbeder, J., De Abelleyra, D., P.
D. Ferraz, R., Lebourgeois, V., Lelong, C., Simões, M., and R.
Verón, S.: Remote Sensing and Cropping Practices: A Review, Remote
Sens., 10, 99, https://doi.org/10.3390/rs10010099, 2018.
Betrie, G. D., Mohamed, Y. A., van Griensven, A., and Srinivasan, R.: Sediment management modelling in the Blue Nile Basin using SWAT model, Hydrol. Earth Syst. Sci., 15, 807–818, https://doi.org/10.5194/hess-15-807-2011, 2011.
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen,
P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, A Completely
Restructured Version of the Soil and Water Assessment Tool, JAWRA J. Am.
Water Resour. Assoc., 53, 115–130,
https://doi.org/10.1111/1752-1688.12482, 2017.
Burakowski, E., Tawfik, A., Ouimette, A., Lepine, L., Novick, K., Ollinger,
S., Zarzycki, C., and Bonan, G.: The role of surface roughness, albedo, and
Bowen ratio on ecosystem energy balance in the Eastern United States, Agric.
For. Meteorol., 249, 367–376,
https://doi.org/10.1016/j.agrformet.2017.11.030, 2018.
Camberlin, P.: Nile Basin Climates, in: The Nile: Origin, Environments,
Limnology and Human Use, edited by: Dumont, H. J., Springer Netherlands,
Dordrecht, 307–333,
https://doi.org/10.1007/978-1-4020-9726-3_16, 2009.
Chawanda, C. J., Arnold, J., Thiery, W., and Griensven, A. van: Mass balance
calibration and reservoir representations for large-scale hydrological
impact studies using SWAT+, Clim. Change, 163, 1307–1327,
https://doi.org/10.1007/s10584-020-02924-x, 2020.
Chen, F. and Xie, Z.: Effects of crop growth and development on regional
climate: a case study over East Asian monsoon area, Clim. Dyn., 38,
2291–2305, https://doi.org/10.1007/s00382-011-1125-y, 2012.
Eldardiry, H. and Hossain, F.: Understanding Reservoir Operating Rules in
the Transboundary Nile River Basin Using Macroscale Hydrologic Modeling with
Satellite Measurements, J. Hydrometeorol., 20, 2253–2269,
https://doi.org/10.1175/JHM-D-19-0058.1, 2019.
Eltahir, E. A. B.: A Soil Moisture–Rainfall Feedback Mechanism: 1. Theory
and observations, Water Resour. Res., 34, 765–776,
https://doi.org/10.1029/97WR03499, 1998.
Estel, S., Kuemmerle, T., Levers, C., Baumann, M., and Hostert, P.: Mapping
cropland-use intensity across Europe using MODIS NDVI time series, Environ.
Res. Lett., 11, 024015, https://doi.org/10.1088/1748-9326/11/2/024015, 2016.
FAO: Using remote sensing in support of solutions to reduce agricultural
water productivity gaps, Rome, Italy, available
at: https://www.fao.org/3/i7315en/i7315en.pdf (last access: 20 February 2021), 2018.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar
topography mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183 (data available at: https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/, last access: 8 January 2021), 2007.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R.,
McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K.,
Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J.,
French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.:
The future of evapotranspiration: Global requirements for ecosystem
functioning, carbon and climate feedbacks, agricultural management, and
water resources, Water Resour. Res., 53, 2618–2626,
https://doi.org/10.1002/2016WR020175, 2017.
Gong, T. T., Lei, H. M., Yang, D. W., Jiao, Y., and Yang, H. B.: Effects of vegetation change on evapotranspiration in a semiarid shrubland of the Loess Plateau, China, Hydrol. Earth Syst. Sci. Discuss., 11, 13571–13605, https://doi.org/10.5194/hessd-11-13571-2014, 2014.
Ha, L. T., Bastiaanssen, W. G. M., Van Griensven, A., Van Dijk, A. I. J. M.,
and Senay, G. B.: Calibration of Spatially Distributed Hydrological
Processes and Model Parameters in SWAT Using Remote Sensing Data and an
Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin, Water,
10, 212, https://doi.org/10.3390/w10020212, 2018.
Haregeweyn, N., Tsunekawa, A., Nyssen, J., Poesen, J., Tsubo, M., Tsegaye
Meshesha, D., Schütt, B., Adgo, E., and Tegegne, F.: Soil erosion and
conservation in Ethiopia: A review, Prog. Phys. Geogr. Earth Environ., 39,
750–774, https://doi.org/10.1177/0309133315598725, 2015.
Haregeweyn, N., Tsunekawa, A., Poesen, J., Tsubo, M., Meshesha, D. T.,
Fenta, A. A., Nyssen, J., and Adgo, E.: Comprehensive assessment of soil
erosion risk for better land use planning in river basins: Case study of the
Upper Blue Nile River, Sci. Total Environ., 574, 95–108,
https://doi.org/10.1016/j.scitotenv.2016.09.019, 2017.
Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M.
G., Shepherd, K. D., Sila, A., MacMillan, R. A., Jesus, J. M. de, Tamene,
L., and Tondoh, J. E.: Mapping Soil Properties of Africa at 250 m
Resolution: Random Forests Significantly Improve Current Predictions, PLOS
ONE, 10, e0125814, https://doi.org/10.1371/journal.pone.0125814 (data available at: https://www.isric.org/projects/africa-soil-information-service-afsis, last access: 25 January 2021), 2015.
Hilker, T., Lyapustin, A. I., Tucker, C. J., Hall, F. G., Myneni, R. B.,
Wang, Y., Bi, J., de Moura, Y. M., and Sellers, P. J.: Vegetation dynamics
and rainfall sensitivity of the Amazon, P. Natl. Acad. Sci. USA, 111,
16041–16046, 2014.
Hurni, H.: Erosion-productivity-conservation systems in Ethiopia, Proceedings 4th International Conference on Soil Conservation, Maracay, Venezuela, 654–674, 1985.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020 (data available at: https://luh.umd.edu/, last access: 31 January 2021), 2020.
Iizumi, T., Kim, W., and Nishimori, M.: Modeling the Global Sowing and
Harvesting Windows of Major Crops Around the Year 2000, J. Adv. Model. Earth
Syst., 11, 99–112, https://doi.org/10.1029/2018MS001477, 2019.
Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J.,
Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J. A., Fuchs, K.,
Guarin, J. R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A. K., Kelly,
D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S.,
Moyer, E. J., Okada, M., Phillips, M., Porter, C., Rabin, S. S., Scheer, C.,
Schneider, J. M., Schyns, J. F., Skalsky, R., Smerald, A., Stella, T.,
Stephens, H., Webber, H., Zabel, F., and Rosenzweig, C.: Climate impacts on
global agriculture emerge earlier in new generation of climate and crop
models, Nat. Food, 2, 873–885, https://doi.org/10.1038/s43016-021-00400-y (data available at: https://doi.org/10.5281/zenodo.5062513),
2021.
Lange, S.: EartH2Observe, WFDEI and ERA-Interim data Merged and
Bias-corrected for ISIMIP (EWEMBI), GFZ Data Services [data], https://doi.org/10.5880/PIK.2016.004,
2016.
Leff, B., Ramankutty, N., and Foley, J. A.: Geographic distribution of major
crops across the world, Glob. Biogeochem. Cycles, 18, GB1009,
https://doi.org/10.1029/2003GB002108, 2004.
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping
Crop Cycles in China Using MODIS-EVI Time Series, Remote Sens., 6,
2473–2493, https://doi.org/10.3390/rs6032473, 2014.
Lin, J., Zhang, J., Gu, Z., Chen, J., and Lyu, H.: A new approach of
assessing soil erosion using the remotely sensed leaf area index and its
application in the hilly area, Yegetos, Int. J. Plant Res., 27, 1–12, 2014.
Lokupitiya, E., Denning, S., Paustian, K., Baker, I., Schaefer, K., Verma, S., Meyers, T., Bernacchi, C. J., Suyker, A., and Fischer, M.: Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands, Biogeosciences, 6, 969–986, https://doi.org/10.5194/bg-6-969-2009, 2009.
Lotsch, A., Friedl, M. A., Anderson, B. T., and Tucker, C. J.: Coupled
vegetation-precipitation variability observed from satellite and climate
records, Geophys. Res. Lett., 30, 1774, https://doi.org/10.1029/2003GL017506,
2003.
Lu, C. and Tian, H.: Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots and nutrient imbalance, Earth Syst. Sci. Data, 9, 181–192, https://doi.org/10.5194/essd-9-181-2017 (data available at: https://doi.org/10.1594/PANGAEA.863323), 2017.
M. El-Marsafawy, S., Swelam, A., and Ghanem, A.: Evolution of Crop Water
Productivity in the Nile Delta over Three Decades (1985–2015), Water, 10,
1168, https://doi.org/10.3390/w10091168, 2018.
Ma, T., Duan, Z., Li, R., and Song, X.: Enhancing SWAT with remotely sensed
LAI for improved modelling of ecohydrological process in subtropics, J.
Hydrol., 570, 802–815, https://doi.org/10.1016/j.jhydrol.2019.01.024, 2019.
Makowski, D., Nesme, T., Papy, F., and Doré, T.: Global agronomy, a new
field of research. A review, Agron. Sustain. Dev., 34, 293–307,
https://doi.org/10.1007/s13593-013-0179-0, 2014.
Metzner, J. R. and Barnes, B. H.: Decision table languages and systems, Academic Press, ISBN 978-1483205366, 2014.
Molnár, D. K. and Julien, P. Y.: Estimation of upland erosion using GIS,
Comput. Geosci., 24, 183–192,
https://doi.org/10.1016/S0098-3004(97)00100-3, 1998.
Monteith, J. L.: Radiation and crops, Exp. Agric., 1, 241–251, 1965.
Msigwa, A., Komakech, H. C., Verbeiren, B., Salvadore, E., Hessels, T.,
Weerasinghe, I., and van Griensven, A.: Accounting for Seasonal Land Use
Dynamics to Improve Estimation of Agricultural Irrigation Water Withdrawals,
Water, 11, 2471, https://doi.org/10.3390/w11122471, 2019.
Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M.,
Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M.,
Maignan, F., McCabe, M. F., Reichle, R., Reichstein, M., Rodell, M.,
Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.:
Evaluation of global observations-based evapotranspiration datasets and IPCC
AR4 simulations, Geophys. Res. Lett., 38, 3–10,
https://doi.org/10.1029/2010GL046230, 2011.
Muthoni, F. K., Odongo, V. O., Ochieng, J., Mugalavai, E. M., Mourice, S.
K., Hoesche-Zeledon, I., Mwila, M., and Bekunda, M.: Long-term
spatial-temporal trends and variability of rainfall over Eastern and
Southern Africa, Theor. Appl. Climatol., 137, 1869–1882,
https://doi.org/10.1007/s00704-018-2712-1, 2019.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Williams, J. R., and King, K.
W.: SWAT theoretical documentation, Soil Water Res. Lab. Grassl., 494,
234–235, 2005.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
water assessment tool theoretical documentation version 2009, Texas Water
Resources Institute, 2011.
Ngetich, K. F., Mucheru-Muna, M., Mugwe, J. N., Shisanya, C. A., Diels, J.,
and Mugendi, D. N.: Length of growing season, rainfall temporal
distribution, onset and cessation dates in the Kenyan highlands, Agric. For.
Meteorol., 188, 24–32, https://doi.org/10.1016/j.agrformet.2013.12.011,
2014.
Nile Basin Initiative: Land use/cover in the Nile Basin – Nile Basin Water
Resources Atlas, available at: https://atlas.nilebasin.org/treatise/land-usecover-in-the-nile-basin/ (last access: 8 January 2021), 2016.
Nkwasa, A., Chawanda, C. J., Msigwa, A., Komakech, H. C., Verbeiren, B., and
van Griensven, A.: How Can We Represent Seasonal Land Use Dynamics in SWAT
and SWAT+ Models for African Cultivated Catchments?, Water, 12, 1541,
https://doi.org/10.3390/w12061541, 2020.
Nkwasa, A.: Agricultural land use and crop management implementation in large-scale SWAT+ model applications, Zenodo [code], https://doi.org/10.5281/zenodo.5797553, 2021.
O'Neal, M. R., Nearing, M. A., Vining, R. C., Southworth, J., and Pfeifer,
R. A.: Climate change impacts on soil erosion in Midwest United States with
changes in crop management, Catena, 61, 165–184, 2005.
Onyutha, C. and Willems, P.: Spatial and temporal variability of rainfall in the Nile Basin, Hydrol. Earth Syst. Sci., 19, 2227–2246, https://doi.org/10.5194/hess-19-2227-2015, 2015.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000 – Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modeling, Glob. Biogeochem.
Cycles, 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010.
Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D.:
Characterizing the spatial patterns of global fertilizer application and
manure production, Earth Interact., 14, 1–22, 2010.
Qi, J., Zhang, X., Yang, Q., Srinivasan, R., Arnold, J. G., Li, J.,
Waldholf, S. T., and Cole, J.: SWAT ungauged: Water quality modeling in the
Upper Mississippi River Basin, J. Hydrol., 584, 124601,
https://doi.org/10.1016/j.jhydrol.2020.124601, 2020.
Rajib, A., Kim, I. L., Golden, H. E., Lane, C. R., Kumar, S. V., Yu, Z., and
Jeyalakshmi, S.: Watershed Modeling with Remotely Sensed Big Data: MODIS
Leaf Area Index Improves Hydrology and Water Quality Predictions, Remote
Sens., 12, 2148, https://doi.org/10.3390/rs12132148, 2020.
Raymond, P. A., Oh, N.-H., Turner, R. E., and Broussard, W.:
Anthropogenically enhanced fluxes of water and carbon from the Mississippi
River, Nature, 451, 449–452, https://doi.org/10.1038/nature06505, 2008.
Rounsevell, M. D. A., Annetts, J. E., Audsley, E., Mayr, T., and Reginster,
I.: Modelling the spatial distribution of agricultural land use at the
regional scale, Agric. Ecosyst. Environ., 95, 465–479,
https://doi.org/10.1016/S0167-8809(02)00217-7, 2003.
Schuol, J. and Abbaspour, K. C.: Calibration and uncertainty issues of a
hydrological model (SWAT) applied to West Africa, Adv. Geosci., 9, 137–143,
2006.
Schuol, J., Abbaspour, K. C., Yang, H., Srinivasan, R., and Zehnder, A. J.
B.: Modeling blue and green water availability in Africa, Water Resour.
Res., 44, 1–18, https://doi.org/10.1029/2007WR006609, 2008.
Siad, S. M., Iacobellis, V., Zdruli, P., Gioia, A., Stavi, I., and
Hoogenboom, G.: A review of coupled hydrologic and crop growth models,
Agric. Water Manag., 224, 105746,
https://doi.org/10.1016/j.agwat.2019.105746, 2019.
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global map of
irrigation areas version 5, Rheinische Friedrich-Wilhelms-Univ. Bonn Ger.
Agric. Organ. U. N. Rome Italy, 2, 1299–1327, available at: https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/ (last access: 16 February 2021), 2013.
Sietz, D., Conradt, T., Krysanova, V., Hattermann, F. F., and Wechsung, F.:
The Crop Generator: Implementing crop rotations to effectively advance
eco-hydrological modelling, Agric. Syst., 193, 103183,
https://doi.org/10.1016/j.agsy.2021.103183, 2021.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrol.
Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Souza, V. F. C. de, Bertol, I., and Wolschick, N. H.: Effects of soil
management practices on water erosion under natural rainfall conditions on a
Humic Dystrudept, Rev. Bras. Ciênc. Solo, 41, 1–14, https://doi.org/10.1590/18069657rbcs20160443, 2017.
Srinivasan, R., Zhang, X., and Arnold, J.: SWAT ungauged: hydrological
budget and crop yield predictions in the Upper Mississippi River Basin,
Trans. ASABE, 53, 1533–1546, 2010.
Srivastava, A., Kumari, N., and Maza, M.: Hydrological Response to
Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity
Model, Water Resour. Manag., 34, 3779–3794,
https://doi.org/10.1007/s11269-020-02630-4, 2020.
Sugita, M., Matsuno, A., El-Kilani, R. M. M., Abdel-Fattah, A., and Mahmoud,
M. A.: Crop evapotranspiration in the Nile Delta under different irrigation
methods, Hydrol. Sci. J., 62, 1618–1635,
https://doi.org/10.1080/02626667.2017.1341631, 2017.
Sundborg, A. and White, W. R.: Sedimentation problems in river basins,
France, ISBN 9789231020148, 1982.
Swain, A.: Challenges for water sharing in the Nile basin: changing
geo-politics and changing climate, Hydrol. Sci. J., 56, 687–702,
https://doi.org/10.1080/02626667.2011.577037, 2011.
Tamene, L. and Le, Q. B.: Estimating soil erosion in sub-Saharan Africa
based on landscape similarity mapping and using the revised universal soil
loss equation (RUSLE), Nutr. Cycl. Agroecosystems, 102, 17–31,
https://doi.org/10.1007/s10705-015-9674-9, 2015.
Twine, T. E., Kucharik, C. J., and Foley, J. A.: Effects of Land Cover
Change on the Energy and Water Balance of the Mississippi River Basin, J.
Hydrometeorol., 5, 640–655,
https://doi.org/10.1175/1525-7541(2004)005<0640:EOLCCO>2.0.CO;2, 2004.
van Griensven, A., Ndomba, P., Yalew, S., and Kilonzo, F.: Critical review of SWAT applications in the upper Nile basin countries, Hydrol. Earth Syst. Sci., 16, 3371–3381, https://doi.org/10.5194/hess-16-3371-2012, 2012.
Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., and Peng, Y.:
Comparison of different vegetation indices for the remote assessment of
green leaf area index of crops, Remote Sens. Environ., 115, 3468–3478,
https://doi.org/10.1016/j.rse.2011.08.010, 2011.
Waha, K., Müller, C., Bondeau, A., Dietrich, J. P., Kurukulasuriya, P.,
Heinke, J., and Lotze-Campen, H.: Adaptation to climate change through the
choice of cropping system and sowing date in sub-Saharan Africa, Glob.
Environ. Change, 23, 130–143,
https://doi.org/10.1016/j.gloenvcha.2012.11.001, 2013.
Waha, K., Dietrich, J. P., Portmann, F. T., Siebert, S., Thornton, P. K.,
Bondeau, A., and Herrero, M.: Multiple cropping systems of the world and the
potential for increasing cropping intensity, Glob. Environ. Change, 64,
102131, https://doi.org/10.1016/j.gloenvcha.2020.102131, 2020.
Wang, L., Good, S. P., and Caylor, K. K.: Global synthesis of vegetation
control on evapotranspiration partitioning, Geophys. Res. Lett., 41,
6753–6757, https://doi.org/10.1002/2014GL061439, 2014.
Williams, J. R. and Berndt, H. D.: Sediment yield prediction based on
watershed hydrology, Trans. ASAE, 20, 1100–1104, 1977.
Williams, J. R. and Singh, V.: The EPIC Model, Computer models of watershed
hydrology, Water Resour. Publ. Highl. Ranch Colo., 909–1000, 1995.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J.,
Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of
continental Africa using Google Earth Engine cloud computing, ISPRS J.
Photogramm. Remote Sens., 126, 225–244,
https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017.
Yin, X. and Struik, P. C.: C3 and C4 photosynthesis models: An overview from
the perspective of crop modelling, NJAS – Wagening. J. Life Sci., 57,
27–38, https://doi.org/10.1016/j.njas.2009.07.001, 2009.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., and Liu, Z.:
Monitoring the response of vegetation phenology to precipitation in Africa
by coupling MODIS and TRMM instruments, J. Geophys. Res.-Atmos., 110, D12103,
https://doi.org/10.1029/2004JD005263, 2005.
Zhang, Y., Wu, Z., Singh, V. P., He, H., He, J., Yin, H., and Zhang, Y.:
Coupled hydrology-crop growth model incorporating an improved
evapotranspiration module, Agric. Water Manag., 246, 106691,
https://doi.org/10.1016/j.agwat.2020.106691, 2021.
Zhao, W., Fu, B., and Qiu, Y.: An Upscaling Method for Cover-Management
Factor and Its Application in the Loess Plateau of China, Int. J. Environ.
Res. Public. Health, 10, 4752–4766, https://doi.org/10.3390/ijerph10104752,
2013.
Short summary
We present an approach on how to incorporate crop phenology in a regional hydrological model using decision tables and global datasets of rainfed and irrigated cropland with the associated cropping calendar and management practices. Results indicate improved temporal patterns of leaf area index (LAI) and evapotranspiration (ET) simulations in comparison with remote sensing data. In addition, the improvement of the cropping season also helps to improve soil erosion estimates in cultivated areas.
We present an approach on how to incorporate crop phenology in a regional hydrological model...
