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Abstract The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications. [ABSTRACT FROM AUTHOR]

• Highlights the sampling effects of GPM orbital through hydrologic modeling. • A multidimensional error model is used to generate synthetic GPM orbital data. • The VIC model is used to scrutinize the impact of GPM orbital data sampling. • An insight into optimum integration time of number of satellites in a constellation. Global precipitation estimation depends on constellations of mainly passive microwave sensors along with satellite radars and constellations of geosynchronous earth orbit infrared sensors. With passive microwave constellations, a persisting challenge is the limited sampling, which impacts applications such as hydrological modeling. This study presents a framework to examine the impact of the Global Precipitation Measurement (GPM) orbital sampling on streamflow simulations. Orbital data from the GPM Dual Precipitation Radar (DPR) 2A-Ku is used within the Variable Infiltration Capacity (VIC) model to derive ensembles of runoff simulations. Ensembles of synthetic precipitation fields are generated at different sampling intervals which are representative of GPM orbital data products with the Satellite Rainfall Error Model (SREM-2D) over the Hirakud catchment in the Indian subcontinent over a period of two years (2015–2016). The NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based gridded rainfall product is considered as the reference rainfall for our study. Sampling uncertainty in orbital precipitation data is shown to amplify in streamflow simulations. The statistical analysis indicates that increasing sampling rates tend to generate more realistic land surface fluxes. The results from our study indicate that streamflow simulations from VIC model using precipitation forcing at 6-hourly integration outperforms the simulations as compared to 3-hourly and 12-hourly forcing integration times. This is exemplified by the statistical indices, with mean relative error of 0.17 for simulations with 3-hourly forcing, 0.12 for simulation with 6-hourly forcing, and 0.57 for 12-hourly forcing. The relative Root-Mean-Square-Error (RMSE) for 3-hourly, 6-hourly, 12-hourly for 2015 are 0.31, 0.25, 1.43 and for 2016 are 0.25, 0.15, 1.06, respectively. It can be noticed that both mean relative error and relative RMSE show a similar pattern for both years. A generic framework is therefore proposed to study the manifestation of satellite constellation sampling uncertainty in hydrologic simulations, which are crucial for future missions such as the CubeSat-based Temporal Experiment for Storms and Tropical Systems, and the Time-Resolved Observations of precipitation structure and storm intensity with a constellation of smallsats. [ABSTRACT FROM AUTHOR]

• Evaluated performance of precipitation forcing data (TMPA, GDAS, CHIRPS, MSWEP) to simulated soil moisture. • Validation data was used from ESA-CCI soil moisture and CTP-SMTMS dataset for years 2010–2012. • Evaluated graphical and statistical comparison of results. • Simulated soil moisture forced with GDAS precipitation showed overall superior performance. Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India. [ABSTRACT FROM AUTHOR]