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The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. [ABSTRACT FROM PUBLISHER]

Quantitative use of satellite-derived rainfall products for various scientific applications often requires them to be accompanied with an error estimate. Rainfall estimates inferred from low earth orbiting satellites like the Tropical Rainfall Measuring Mission (TRMM) will be subjected to sampling errors of nonnegligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. The authors investigate sampling uncertainty of seasonal rainfall estimates from the active sensor of TRMM, namely, Precipitation Radar (PR), based on 11 years of PR 2A25 data product over the Indian subcontinent. In this paper, a statistical bootstrap technique is investigated to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space-time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall were found to exhibit seasonal variations. To give a practical example of the implications of the bootstrap technique, PR relative sampling errors over a subtropical river basin of Mahanadi, India, are examined. Results reveal that the bootstrap technique incurs relative sampling errors <; 33% (for the 2° grid), <; 36% (for the 1° grid), <; 45% (for the 0.5° grid), and <; 57% (for the 0.25° grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. The study compares resulting error estimates to those obtained from latin hypercube sampling. Based on this study, the authors conclude that the bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in situ validation data. This technique has wider implications for decision making before incorporating microwave orbital data products in basin-scale hydrologic modeling. [ABSTRACT FROM PUBLISHER]

• Evaluation of precipitation estimates from top down and bottom up approach.. • The VIC model is used to scrutinize the uncertainty of precipitation products. • IMERG depicts better performance annually over India. Land surface processes significantly influence weather thereby affecting terrestrial water balance. Even though land surface models (LSMs) offer a complementary means of examining fluxes, their simulations are often subjected to uncertainties from model parameters, model structure and forcing data. Of these, precipitation forcing uncertainty is known to significantly contribute to LSM output. This study examines the impact of different satellite-based precipitation products on LSM simulated soil moisture over India based on a data period of 8 years (2008 to 2015). The precipitation products used are namely, the Global Precipitation Measurement mission (GPM) integrated Multi-satellite Retrievals (IMERG) Late run, SM2RAIN-Climate Change Initiative (SM2RAIN-CCI) and SM2RAIN-Advanced SCATerometer (SM2RAIN-ASCAT). The first product is a multi-satellite precipitation product (top-down approach) with the remaining two products derived from surface satellite soil moisture (bottom up approach). The uncertainty in these three precipitation products are evaluated against simulations resulting from India Meteorological Department (IMD) precipitation dataset. The IMERG precipitation indicates a bias (mm) of 1.3014 and RMSE (mm) of 1.8024 during monsoon. Whereas for SM2RAIN-ASCAT it shows a bias of 0.0206 and RMSE of 0.9368 during monsoon and SM2RAIN-CCI which showed a bias of −0.3711 and RMSE of 0.9345 during monsoon. VIC simulated soil moisture using IMERG indicate a bias (m3/m3) of 0.0356 and RMSE (m3/m3) of 0.0402 during monsoon. This is in comparison to SM2RAIN-ASCAT which showed a bias of 0.0211 and RMSE of 0.0286 during monsoon and SM2RAIN-CCI which showed a bias of −0.0178 and RMSE of 0.0327 during monsoon. Our study has evaluated precipitation products derived from two different algorithms i.e top down and bottom up approach, it will provide insights on which algorithm is performing better in a country like India on different topographic condition. [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]

• Impacts of various DEMs on both channel and overland inundation are investigated. • Global DEMs under predict flood parameters and pose high sensitivity to flood risk. • Uncertainty in simulated flood depths are conspicuous in coastal areas. • Copious use of global DEMs falsify efficient flood management options. • CartoDEM in inundation mapping is laudably comparable to resampled LiDAR DEMs. Topographic data in the form of digital elevation models (DEMs) play a significant role in flood management. Despite the increasing availability of DEMs for large regions, there is a need to evaluate their performance at the inundation/flood level, while considering the overall complexity of flood models. The present study identifies, for the first time, the uncertainties generated in both river channel and overland flooding while considering a set of nine variants from various sources (LiDAR, Cartosat, SRTM, and ASTER) and grid resolutions (resampled versions) in the presence of discharge, rainfall, and tide boundary conditions for a severely flood-prone catchment in the Mahanadi River Basin, India. Extensive geostatistical analyses reveal the existence of significant biases with global DEMs i.e., SRTM and ASTER, whereas interestingly the LiDAR and Carto DEMs exhibit a high degree of isotropy. The global DEMs fail to capture several inundated spots; thus plummeting the flood inundation extents to a sufficient degree of unacceptability. Prominently, the inability in identifying high and very high flood depths (>1.5 m) over the coastal stretches results in large uncertainties in the majority of the grids. Our analysis reveals the existence of significant noise in global DEMs, which nullifies the hydrodynamic interaction during the coupling of 1-D and 2-D flood models in presence of tidal influence. We recommend that under unavailability of precise LiDAR DEMs, resampled and freely available Carto DEMs, that are as efficient as LiDAR if not more, be given higher preference. We caution against the copious usage of global DEMs for large data-scarce and flood-prone regions, as the DEM uncertainty may be substantially amplified at the inundation level during combined channel and overland flood simulations. Through this study, we would like to recommend the proposed framework as a guided step while selecting appropriate DEM for flood inundation mapping over large data scarce regions. [ABSTRACT FROM AUTHOR]