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Nowadays, large-scale and remotely sensed products have been widely used, and this study aims to investigate the effect of precipitation and evapotranspiration (ET) products on the performance of hydrological models, especially the water balance in Gheshlagh and Baghmalek basins in Iran with two different climates and model structures. For this purpose, five large-scale precipitation products, including CHIRPS, ERA, IMERG, TRMM, Terra and ET named GLEAM, SSEbop, MODIS, Terra, and Era, were implemented in five designed scenarios. These scenarios depend on how this information is applied and used in the model calibration process. Moreover, to evaluate the parametric behavior of these models, the uncertainties of their parameters were evaluated by the GLUE method. The parameters of the developed scenarios model with uncertain behavior were also measured in the main model. The results showed that in the absence of ground observations of precipitation or ET, the use of large-scale products in the mountainous and highland watersheds, where such measurements are not possible, is a suitable alternative for wrapping up the water balance equation. Using these products as the model input maintains model performance at the reference model level. The results also indicate that in the simultaneous calibration of ET and streamflow, the use of ET products improves the simulation of ET but reduces the accuracy of the simulated streamflow. No precipitation or ET product shows its best behavior in all conditions and all regions. This issue implies various products' functional values in different regions. [ABSTRACT FROM AUTHOR]

Investigation of the effects of sloping dry and wet immobile beds on downstream dam-break flows is of great importance given the destructive power of these flows. Such investigation can lead to the adoption of appropriate measures and timely control strategies. In this study, numerical simulations of experimental wave propagation were carried out using four models, i.e. k − ω -SST-RANS, realizable k − ε -RANS, Smagorinsky-LES, and k − ω -SST-DES on sloped beds, for the first time as its novelty, with respect to upstream water and different downstream tail-water depths. The k − ω -SST-DES exhibited the best performance in the simulation of wave peak patterns and mean velocities. Taylor diagrams showed that all models were similar in estimating the highest R values and lowest centered root-mean-square errors for dry beds. However, for wet beds with higher downstream tail-water depths, the CRMSE were higher. For lower depths, the standard deviations of the models were generally closer to those of the flume experimental measurements conducted by the experimental study. The present study has shed light on the levels of turbulence models' accuracy in simulation of dam-break flows on dry and wet sloping beds, and can be taken as a basis for further understanding of the flood flow behavior in future studies. [ABSTRACT FROM AUTHOR]

Improving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE. [ABSTRACT FROM AUTHOR]