Using Monthly Weather Statistics to Generate Daily Data in a SWAT Model Application to West Africa

Swiss Federal Institute of Aquatic Science and Technology (Eawag), Überlandstrasse 133, P.O. Box 611, 8600 Dübendorf, Switzerland
Ecological Modelling (Impact Factor: 2.32). 03/2007; 201(3-4):301-311. DOI: 10.1016/j.ecolmodel.2006.09.028

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.

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    • "). A weather generator developed by [36] was used to fill the gaps due to missing data. Daily river discharge values for Kajang streamflow station were obtained from the Department of Irrigation and Drainage (DID) Malaysia. "
    02/2016; 8(2):132-136. DOI:10.7763/IJET.2016.V8.872
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    • "For the climate data, we used the monthly gridded climate data (potential evapotranspiration and precipitation) from the CRU TS 3.1 dataset (Harris et al., 2013). The CRU data have already been used in many studies on climate and hydrological modeling in the region (Paturel et al., 2010, 1995; Schuol and Abbaspour, 2007). In addition to the physical characteristics of the watersheds, two main climate characteristics (Table 1) are considered: the annual rainfall and the annual potential evapotranspiration. "
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    ABSTRACT: Hydrological observation networks in the West African region are not denseand reliable. Furthermore, the few available discharge data often present significant gaps.The Volta basin, the second largest transboundary basin in the region, is a typical exampleof a basin with inadequate hydrological In this study, a prediction approach to determine monthly discharge inungauged watersheds is developed. The approach is based on the calibration of two con-ceptual models for gauged watersheds and an estimation of models’ parameters from thephysical and climatic characteristics of the watersheds. The models’ parameters were deter-mined for each ungauged watershed through two different methods: the multiple linearregressions and the kriging method. The two methods were first validated on five gaugedwatersheds and then applied to the three ungauged watersheds. The application of the two hydrological models onthe eight watersheds helped to produce relevant monthly runoff and to establish the annualhydrological balances from 1970 to 2000 for both gauged and ungauged watersheds. Thedeveloped method in this study could therefore help estimate runoff time series, which areof crucial importance when it comes to design hydraulic structures such as small reservoirs.
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    • "The topography was derived from the Digital Elevation Model of 90 m resolution obtained from the Shuttle Radar Topography Mission of the NASA (Jarvis et al., 2008). Soil classes were extracted from the global 10 km resolution database of the United Nations Food and Agriculture Organization (FAO) (FAO, 1995; Schuol and Abbaspour, 2007) and the land cover data was obtained from the 300 m resolution global dataset GlobCover (ESA, 2006). Meteorological data (daily precipitation, maximum and minimum temperatures and wind speed) were obtained from national sources: "
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    ABSTRACT: Unmitigated anthropogenic climate change is set to exacerbate current stresses on water resources management and creates the need to develop strategies to face climate change impacts on water resources, especially in the long term. Insufficient information on possible impacts on water availability limits the organization and promotion of efforts to adapt and improve the resilience and efficiency of water systems. To document the potential impacts of climate change in the region of Mendoza, Argentina, we perform a hydrological modeling of the Mendoza River watershed using a SWAT model and project climate change scenarios to observe hydrological changes. The results show the impact of higher temperature on glaciers as river flow increases due to glacier melting; at the same time, runoff decreases as precipitation is reduced. Furthermore, the runoff timing is shifted and an earlier melting becomes more important in more pronounced climate change scenarios. Scenarios show a reduction in water availability that ranges between 1 and 10%. An additional scenario under stronger climate change conditions without glaciers data shows a reduction of the river flow by up to 11.8%. This scenario would correspond to a future situation in which glaciers have completely melted. These situations would imply a reduction in the water availability and the possibility of future unsatisfied water uses, in particular for irrigation, which received most of the available water in Mendoza, on which agricultural activities and regional economy depends.
    Environmental Science & Policy 11/2014; 43. DOI:10.1016/j.envsci.2014.01.002 · 3.02 Impact Factor
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