Evaluation of Gridded Multi-Satellite Precipitation (TRMM-3B42-V7) Estimation Performance in the Upper Indus Basin (UIB)

Khan, Asim Jahangir; Koch, Manfred; Chinchilla, Karen Milena

doi:10.20944/preprints201808.0340.v1

2018

DOI: 10.20944/preprints201808.0340.v1

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Evaluation of Gridded Multi-Satellite Precipitation (TRMM-3B42-V7) Estimation Performance in the Upper Indus Basin (UIB)

Asim Jahangir Khan¹,

Manfred Koch²,

Karen Milena Chinchilla³

Abstract: The present study aims to evaluate the capability of the TRMM-3B42-(V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus basin (UIB) and the analysis of the dependency of the estimates’ accuracies on the time scale. To that avail statistical analyses and comparison of the TMPA- products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the time scale is analysed by comparisons of daily, monthly, seasonal … Show more

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Cited by 5 publications

References 30 publications

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The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

et al. 2020

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Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

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The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

et al. 2020

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Hydrological Modeling Response to Climate Model Spatial Analysis of a South Eastern Europe International Basin

Skoulikaris

Αnagnostopoulou

Lazoglou

2019

Climate

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One of the most common questions in hydrological modeling addresses the issue of input data resolution. Is the spatial analysis of the meteorological/climatological data adequate to ensure the description of simulated phenomena, e.g., the discharges in rainfall–runoff models at the river basin scale, to a sufficient degree? The aim of the proposed research was to answer this specific question by investigating the response of a spatially distributed hydrological model to climatic inputs of various spatial resolution. In particular, ERA-Interim gridded precipitation and temperature datasets of low, medium, and high resolution, i.e., 0.50° × 0.50°, 0.25° × 0.25°, and 0.125° × 0.125°, respectively, were used to feed a distributed hydrological model that was applied to a transboundary river basin in the Balkan Peninsula, while all the other model’s parameters were maintained the same at each simulation run. The outputs demonstrate that, for the extent of the specific basin study, the simulated discharges were adequately correlated with the observed ones, with the marginally best results presented in the case of precipitation and temperature of 0.25° × 0.25° spatial analysis. The results of the research indicate that the selection of ERA-Interim data can indeed improve or facilitate the researcher’s outputs when dealing with regional hydrologic simulations.

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Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management

et al. 2020

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Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

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Evaluation of Gridded Multi-Satellite Precipitation (TRMM-3B42-V7) Estimation Performance in the Upper Indus Basin (UIB)

Cited by 5 publications

References 30 publications

The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Hydrological Modeling Response to Climate Model Spatial Analysis of a South Eastern Europe International Basin

Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management

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