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Abstract:Evapotranspiration is an important hydrological process and its estimation usually needs measurements of many weather variables such as atmospheric pressure, wind speed, air temperature, net radiation and relative humidity. Those weather variables are not easily obtainable from in situ measurements in practical water resources projects. This study explored a potential application of downscaled global reanalysis weather data using mesoscale modelling system 5 (MM5). The MM5 is able to downscale the global data down to much finer resolutions in space and time for use in hydrological investigations. In this study, the ERA-40 reanalysis data are downscaled to the Brue catchment in southwest England. The results are compared with the observation data. Among the studied weather variables, atmospheric pressure could be derived very accurately with less than 0Ð2% error. On the other hand, the error in wind speed is about 200-400%. The errors in other weather variables are air temperature (<10%), relative humidity (5-21%) and net radiation (4-23%). The downscaling process generally improves the data quality (except wind speed) and provides higher data resolution in comparison with the original reanalysis data.The evapotranspiration values estimated from the downscaled data are significantly overestimated across all the seasons (27-46%) based on the FAO Penman-Monteith equation. The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period). There are clear patterns among some weather variables and they could be used to correct the biases in the downscaled data from either short-term in situ measurements or through regionalization from surrounding weather stations. Artificial intelligence tools could be used to map the downscaled data directly into evapotranspiration or even river runoff if rainfall data are available. This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encouraged.

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Abstract:Sustainable water resources management require scientifically sound information on precipitation, as it plays a key role in hydrological responses in a catchment. In recent years, mesoscale weather models in conjunction with hydrological models have gained great attention as they can provide high-resolution downscaled weather variables. Many cumulus parameterization schemes (CPSs) have been developed and incorporated into three-dimensional Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model 5 (MM5). This study has performed a comprehensive evaluation of four CPSs (the Anthes-Kuo, Grell, Betts-Miller and Kain-Fritsch93 schemes) to identify how their inclusion influences the mesoscale model's precipitation estimation capabilities. The study has also compared these four CPSs in terms of variability in rainfall estimation at various horizontal and vertical levels. For this purpose, the MM5 was nested down to resolution of 81 km for Domain 1 (domain span 21 ð 81 km) and 3 km for Domain 4 (domain span 16 ð 3 km), respectively, with vertical resolutions at 23, 40 and 53 vertical levels. The study was carried out at the Brue catchment in Southwest England using both the ERA-40 reanalysis data and the land-based observation data. The performances of four CPs were evaluated in terms of their ability to simulate the amount of cumulative rainfall in 4 months in 1995 representing the four seasonal months, namely, January (winter), March (spring), July (summer) and October (autumn). It is observed that the Anthes-Kuo scheme has produced inferior precipitation values during spring and autumn seasons while simulations during winter and summer were consistently good. The Betts-Miller scheme has produced some reasonable results, particularly at the small-scale domain (3 km grid size) during winter and summer. The KF2 scheme was the best scheme for the larger-scale (81 km grid size) domain during winter season at both 23 and 53 vertical levels. This scheme tended to underestimate rainfall for other seasons including the small-scale domain (3 km grid size) in the mesoscale. The Grell scheme was the best scheme in simulating rainfall rates, and was found to be superior to other three schemes with consistently better results in all four seasons and in different domain scales.

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