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Using monthly weather statistics to generate daily data in a SWAT model application to West Africa

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  • Schuol, J.
  • Abbaspour, K.C.

Abstract

Most hydrologic models require daily weather data to run. While this information may be abundant in some parts of the world, in most parts such data is not available on daily basis. Distributed hydrologic models are particularly adversely affected by the lack of daily data or the existence of very inaccurate data as they impart large uncertainties to the model prediction. In this study we developed a daily weather generator algorithm (dGen) that uses the currently available 0.5° monthly weather statistics from the Climatic Research Unit (CRU). We tested dGen in two ways. First, we made a direct comparison of the measured and generated precipitation and maximum–minimum temperatures by looking at some long-term statistics in a few stations in West Africa. Second, we ran the model “Soil and Water Assessment Tool” (SWAT) with dGen-generated and measured daily weather data to simulate 25 years of annual and monthly river discharges at some gauging stations. The simulated river discharges were then compared with the measured ones. It was seen that using the dGen-simulated daily weather data resulted in a much better match with the measured discharge data than the measured daily weather data in combination with the SWAT internal weather generator WXGEN. WXGEN is used in SWAT to fill missing data using monthly statistics, which must be calculated from the existing daily data. For annual and monthly hydrological simulations, dGen-generated daily rainfall and temperature data appears to have a high degree of reliability.

Suggested Citation

  • Schuol, J. & Abbaspour, K.C., 2007. "Using monthly weather statistics to generate daily data in a SWAT model application to West Africa," Ecological Modelling, Elsevier, vol. 201(3), pages 301-311.
    • Handle: RePEc:eee:ecomod:v:201:y:2007:i:3:p:301-311
    DOI: 10.1016/j.ecolmodel.2006.09.028
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    References listed on IDEAS

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    1. Kuchar, Leszek, 2004. "Using WGENK to generate synthetic daily weather data for modelling of agricultural processes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 69-75.
    2. Hartkamp, A. D. & White, J. W. & Hoogenboom, G., 2003. "Comparison of three weather generators for crop modeling: a case study for subtropical environments," Agricultural Systems, Elsevier, vol. 76(2), pages 539-560, May.
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    Cited by:

    1. Abdouramane Gado Djibo & Harouna Karambiri & Ousmane Seidou & Ketevera Sittichok & Jean Paturel & Hadiza Saley, 2015. "Statistical seasonal streamflow forecasting using probabilistic approach over West African Sahel," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 699-722, November.
    2. repec:ipg:wpaper:2014-454 is not listed on IDEAS
    3. M. Mekonnen & A. Hoekstra & R. Becht, 2012. "Mitigating the Water Footprint of Export Cut Flowers from the Lake Naivasha Basin, Kenya," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3725-3742, October.
    4. Zhengfang Jiang & Baohong Lu & Zunguang Zhou & Yirui Zhao, 2024. "Comparison of Process-Driven SWAT Model and Data-Driven Machine Learning Techniques in Simulating Streamflow: A Case Study in the Fenhe River Basin," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    5. Saleh A Wasimi & Nahlah Abbas & Nadhir Al Ansari, 2018. "Climate Change Adaptation Considerations for Agriculture for North-East Iraq," Current Investigations in Agriculture and Current Research, Lupine Publishers, LLC, vol. 2(4), pages 236-242, May.
    6. Santosh Thampi & Kolladi Raneesh & T. Surya, 2010. "Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(15), pages 4567-4578, December.
    7. Huang, Feng & Li, Baoguo, 2010. "Assessing grain crop water productivity of China using a hydro-model-coupled-statistics approach: Part I: Method development and validation," Agricultural Water Management, Elsevier, vol. 97(7), pages 1077-1092, July.
    8. Vincent Simonin & Saeid Ashraf Vaghefi & Zeinab M. Abdelgadir & Dalya Eltayeb & Mohammed Ahmed M. Sidahmed & Jean-Pierre Monet & Nicolas Ray, 2023. "Present and Future Drinking Water Security and Its Impacts on Maternities: A Multi-Scale Assessment of Sudan," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    9. Elodie Blanc & Eric Strobl, 2014. "Is Small Better? A Comparison of the Effect of Large and Small Dams on Cropland Productivity in South Africa," The World Bank Economic Review, World Bank, vol. 28(3), pages 545-576.
    10. Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
    11. Cole, Matthew A. & Elliott, Robert J.R. & Strobl, Eric, 2014. "Climate Change, Hydro-Dependency, and the African Dam Boom," World Development, Elsevier, vol. 60(C), pages 84-98.
    12. Yu, Dan & Xie, Ping & Dong, Xiaohua & Su, Bob & Hu, Xiaonong & Wang, Kai & Xu, Shijin, 2018. "The development of land use planning scenarios based on land suitability and its influences on eco-hydrological responses in the upstream of the Huaihe River basin," Ecological Modelling, Elsevier, vol. 373(C), pages 53-67.
    13. Elodie Blanc & Eric Strobl, 2013. "The impact of climate change on cropland productivity: evidence from satellite based products at the river basin scale in Africa," Climatic Change, Springer, vol. 117(4), pages 873-890, April.

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