![]() ![]() As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. ![]() The result revealed that the performance of different products varies with climate. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson-Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research-University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. ![]() Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. The details of the methodology and the results will be discussed in our work.The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Evaluation of the simulations also shows a good agreement with streamflow observations in the outlet of the catchment, comparable to the results of more complex models and to traditional methodologies using the same WEAP model. The results show that with just 6 HRUs it is possible to reduce up to about a 10% the relative within variance of the catchment this contrasts with about 40 to 50 HRUs used in the most common methodologies applied in similar basins. Through principal component analysis and a hierarchical clustering analysis, the HRUs were defined. The hydrological parameters required by the WEAP model were calculated from gridded datasets of land- use and basin slope. For the HRU delineation, meteorological data was taken from WRF simulations at 1km of resolution, where a bias-correction method was applied to the precipitation data before processing. The methodology is tested using the Water Evaluation And Planning System (WEAP Yates et al., 2005) model for the Alicahue River Basin, a small catchment in Central Andes, in Chile. ![]() We present a quantitative methodology to construct HRUs based on cluster analysis using gridded meteorological data and hydrological parameters of the target model. If the aggregation process is not properly done, areas of extreme or interesting hydrologic behavior can be neglected. There are no reports of HRUs delineation using meteorological information as suggested by Flügel more than 20 years ago, even though, those datasets are easily accessible today. HRUs are usually constructed by the intersection of main attributes of land-use and soil type in a sub-basin, without considering the climate variables. In its original conception, HRUs are homogeneous structured elements having similar climate, land-use, soil and/or pedotransfer properties, hence a homogeneous hydrological response under equivalent meteorological forcing. Most of the semi-distributed models use the basic concept of Hydrological Response Unit (HRU Leavesley et al., 1983 Flügel, 1995). Although complex hydrological models with detailed physics are every day more common, lumped and semi-distributed models are still used for many applications and offer some advantages, for instance its reduced computational cost. ![]()
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