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.
[1] The two-parameter Weibull distribution (2PWD), similar to an instantaneous unit hydrograph (IUH), is parameterized in terms of Horton catchment ratios on the basis of a geomorphologic model of catchment response. For this the shape and scale parameters of the Weibull distribution are expressed analytically in terms of Horton's catchment ratios. The two parameters of the IUH derived using Nash's model, which is a two-parameter gamma distribution (2PGD), are also expressed analytically in terms of Horton ratios. The performance of the proposed methods is tested for describing a synthetic unit hydrograph (SUH) under limited data conditions. A comparison is made with the unit hydrographs derived from the event data of two real catchments, and with the existing geomorphological based 2PGD for developing SUH given by Rosso (1984). The sensitivity analysis of the 2PWD to the nondimensional parameter b of the UH (a product of peak discharge and time to peak) shows b to be more sensitive to the shape parameter a than the scale parameter b. Further examination to find any similarity between the behavior of 2PWD and 2PGD showed that a in 2PWD corresponds to the shape parameter n in the 2PGD, and b behaves similar to the scale parameter k in the 2PGD. Finally, practical applicability of the proposed approach to ungauged catchments is tested using field data.
The Soil Conservation Service Curve Number (SCS-CN) method developed by the USDA-Soil Conservation Service (SCS, 1972) is widely used for the estimation of direct runoff for a given rainfall event from small agricultural watersheds. The initial soil moisture plays an important role in re-structuring of the SCS-CN method and enables us to prevent unreasonable sudden jump in runoff estimation and this has prompted the concept of soil moisture accounting (SMA) procedure to develop improved SCS-CN based models. Applying the concept of SMA procedure and changed parameterization, Michel et al. Water Resour Res 41(2):1-6 (2005) developed an improved SCS-CN model (MSCS-CN), which could be thought of an improvement over the existing SCS-CN method; however, their model still inherits several conceptual limitations and inconsistencies. Therefore, in this study an attempt is made to propose an improved SMA based SCS-CN-inspired model (MMSCS-CN) model incorporating a continuous function for initial soil moisture and test its suitability over the MSCS-CN and SCS-CN model using a large dataset from US watersheds. Using, Nash and Sutcliffe efficiency (NSE) and root mean square error (RMSE) of these models, the overall performance is further evaluated using rank grading system, and it is found that the MMSCS-CN scores highest mark (95; overall rank I) followed by MSCS-CN with 61 (overall rank II), and SCS-CN model with 51 mark (overall rank III) out of the maximum 105. This study shows Water Resour Manage that the proposed MMSCS-CN model has several advantages and performs better than the MSCS-CN and the existing SCS-CN model.
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