The vital demand of reliable climatic and hydrologic data of fine spatial and temporal resolution triggered the employment of reanalysis datasets as a surrogate in most of the hydrological modelling exercises. This study examines the performance of four widely used reanalysis datasets: ERA-Interim, NCEP-DOE R2, MERRA and CFSR, in reproducing the spatio-temporal characteristics of observed daily precipitation of different stations spread across Ethiopia, East Africa. The appropriateness of relying on reanalysis datasets for hydrologic modelling, climate change impact assessment and regional modelling studies is assessed using various statistical and non-parametric techniques. ERA-Interim is found to exhibit higher correlation and least root mean square error values with observed daily rainfall, which is followed by CFSR and MERRA in most of the stations. The variability of daily precipitation is better captured by ERA, CFSR and MERRA, while NCEP-DOE R2 overestimated the spread of the precipitation data. While ERA overestimates the probability of moderate rainfall, it is seemingly better in capturing the probability of low rainfall. CFSR captures the overall distribution reasonable well. NCEP-DOE R2 appears to be outperforming others in capturing the probabilities of higher magnitude rainfall. Climatological seasonal cycle and the characteristics of wet and dry spells are compared further, where ERA seemingly replicates the pattern more effectively. However, observed rainfall exhibits higher frequency of short wet spells when compared to that of any reanalysis datasets. MERRA relatively underperforms in simulating the wet spell characteristics of observed daily rainfall. CFSR overestimates the mean wet spell length and mean dry spell length. Spatial trend analysis indicates that the northern and central western Ethiopia show increasing trends, whereas the Central and Eastern Ethiopia as well as the Southern Ethiopia stations show either no trend or decreasing trend. Overall, ERA-Interim and CFSR are better in depicting various characteristics of daily rainfall in Ethiopian region.
Abstract Conflicts between increasing irrigated agricultural area, commercial crops, shifting cultivation and ever increasing domestic and industrial demand has already been a cause of tension in the society over water in the Ganga River Basin, India. For the development of sustainable water resource strategies, it is essential to establish interaction between landuse changes and local hydrology through proper assessment. Precisely, seeing how change in each LULC affects hydrologic regimes, or conversely evaluating which LULC shall be appropriate for the local hydrological regime can help decision makers to incorporate in the policy instruments. In this study, hydrologic regimes of the Ganga River basin have been assessed with landuse change. Catchment hydrologic responses were simulated using Soil and Water Assessment Tool (SWAT). Meteorological data from IMD of 0.25° × 0.25° spatial resolution were taken as the climate inputs. Simulated stream flow was compared at different gauge stations distributed across the Gang River and its tributaries. Urbanization has been the topmost contributor to the increase in surface runoff and water yield. While increased irrigation demands were the dominant contributor to the water consumption and also added to the increased evapotranspiration. This study can be important tool in quantifying the changes in hydrological components in response to changes made in landuse in especially basins undergoing rapid commercialization. This shall provide substantive information to the decision makers required to develop ameliorative strategies.
Radiation is a variable that governs many hydrological and phenological processes, but its measurements are not made routinely. To overcome this problem, continuous hydrological models that include evapotranspiration, snowmelt (using solar radiation data) and plant growth modules have applied different strategies to generate daily radiation data. In this paper, artificial neural networks (ANNs), temperature-based (TB) and stochastic (ST) approaches for estimation of solar radiation have been used and compared. These three approaches have been applied to the Ammameh Catchment, an alpine subcatchment of the Jadjroud River, in Iran. Results reveal better performance for ANNs than for TB and ST. However, the TB method because of its capability to generalize results and to be easily linked with hydrological models appears to be a good candidate to be applied in the catchments where the climatological data are limited.
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