The availability of global satellite‐based precipitation datasets provides an asset to accomplish precipitation dependent analysis where gauge based precipitation datasets are not available or limited. In this study, we have taken three most popular and globally accepted satellite‐based daily gridded (0.25° × 0.25°) precipitation datasets such as Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Satellite Soil Moisture to Rain (SM2RAIN‐ASCAT) and Tropical Rainfall Measuring Mission (TRMM now available as Global Precipitation Measurement [GPM]) for 10 years (2007–2016) time‐series durations to test their reliability across India. The India Meteorological Department (IMD) observed daily gridded (0.25° × 0.25°) precipitation data have been taken as reference data to compare the other three satellite‐based gridded precipitation datasets by developing standard extreme precipitation indices (SEPIs). The precipitation extremity has been tested in the wet season (June–July–August–September) and throughout the year. We have also analysed the extreme behaviour of precipitation (in both upper and lower tails) using quantile‐quantile (Q–Q) regression analysis after selecting 33 random precipitation grids across India. The overall analysis results showed that all satellite‐based datasets have significant spatial heterogeneity in estimating precipitation extremes accurately which varies across India. Among all satellite‐precipitation datasets, TRMM found closer to IMD than SM2RAIN‐ASCAT and CHIRPS. The frequency based SEPIs showed that CHIRPS, TRMM and SM2RAIN‐ASCAT have similarities to IMD precipitations. The intensity‐based SEPIs show that TRMM and CHIRPS have significant similarities with IMD precipitations. The wet season‐based analysis results showed that TRMM and CHIRPS are closer to IMD precipitations than SM2RAIN‐ASCAT satellite‐precipitations. Overall TRMM and CHIRPS datasets performed well across most regions in India, while SM2RAIN‐ASCAT dataset has performed poorly in India, especially for extreme precipitation cases. Q–Q plots show that each satellite‐based precipitation dataset captured most of extreme cases in different quantile intervals with respect to IMD precipitation; however, SM2RAIN‐ASCAT has slightly under‐performed at many regions in India.

Modeling hydrologic processes for depression-dominated areas such as the North American Prairie Pothole Region is complex and reliant on a clear understanding of dynamic filling-spilling-merging-splitting processes of numerous depressions over the surface. Puddles are spatially distributed over a watershed and their sizes, storages, and interactions vary over time. However, most hydrologic models fail to account for these dynamic processes. Like other traditional methods, depressions are filled as a required preprocessing step in the Soil and Water Assessment Tool (SWAT). The objective of this study was to facilitate hydrologic modeling for depression-dominated areas by coupling SWAT with a Puddle Delineation (PD) algorithm. In the coupled PD-SWAT model, the PD algorithm was utilized to quantify topographic details, including the characteristics, distribution, and hierarchical relationships of depressions, which were incorporated into SWAT at the hydrologic response unit (HRU) scale. The new PD-SWAT model was tested for a large watershed in North Dakota under real precipitation events. In addition, hydrologic modeling of a small watershed was conducted under two extreme high and low synthetic precipitation conditions. In particular, the PD-SWAT was compared against the regular SWAT based on depressionless DEMs. The impact of depressions on the hydrologic modeling of the large and small watersheds was evaluated. The simulation results for the large watershed indicated that SWAT systematically overestimated the outlet discharge, which can be attributed to the failure to account for the hydrologic effects of depressions. It was found from the PD-SWAT modeling results that at the HRU scale surface runoff initiation was significantly delayed due to the threshold control of depressions. Under the high precipitation scenario, depressions increased the surface runoff peak. However, the low precipitation scenario could not fully fill depressions to reach the overflow thresholds in the selected sub-basins. These results suggest the importance of depressions as gatekeepers in watershed modeling.