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

Asim Khan,Manfred Koch,Karen Chinchilla

doi:10.3390/cli6030076

Abstract

The present study aims to evaluate the capability of the Tropical Rainfall Measurement Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), version 7 (TRMM-3B42-V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus Basin (UIB) by analyzing the dependency of the estimates’ accuracies on the time scale. To that avail, various statistical analyses and comparison of Multisatellite Precipitation Analysis (TMPA) products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the aggregation time scale is analyzed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA Tropical Rainfall Measurement Mission (TRMM) precipitation estimates for the UIB, as well as high numbers of false alarms and miss ratios. The correlation of the TMPA estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e., time scale. There is a predominant trend of underestimation of the TRMM product across the UIB at most of the gauge stations, i.e., TRMM-estimated rainfall is generally lower than the gauge-measured rainfall. For the seasonal aggregates, the bias is mostly positive for the summer but predominantly negative for the winter season, thereby showing a slight overestimation of the precipitation in summer and underestimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA estimates and gauge data, the use of the former in hydrological watershed modeling undertaken by the authors may be a valuable alternative in data-scarce regions like the UIB, but still must be taken with a grain of salt.

Highlights

The continued improvements in computation capabilities and the subsequent increase in the development of spatially explicit and distributed models for expressing environmental phenomena have necessitated the provision of more intensive and improved data for environmental variables both in space and time
The assessment of the reliability of the Tropical Rainfall Measurement Mission (TRMM) estimates and their comparisons with the rain data from gauge station presented has been done by three different methodologies: (1) a statistical analysis, based on r, relative bias error (rBIAS), mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) for daily, monthly, annual and seasonal data aggregates; (2) categorical statistics of daily data by computing Ac, frequency bias index (FBI), probability of detection (POD), false alarm ratios (FAR), critical success index (CSI), and true skill statistics (TSS); and (3) a visual comparison for monthly, annual, and seasonal data
The results of the TRMM-assessment based on the statistical measures r, rBIAS, MBE, MAE, and RMSE are given for daily data aggregation in Table 3, for monthly and annual data aggregation in

Summary

Introduction

The continued improvements in computation capabilities and the subsequent increase in the development of spatially explicit and distributed models for expressing environmental phenomena have necessitated the provision of more intensive and improved data for environmental variables both in space and time. As data with an acceptable gridded resolution of daily climatic variables are critical for hydrological and water resources modeling [6,7], managing the gaps in the data appropriately is the first stage of most climatological, environmental, and hydrological studies [2] This step is necessary to improve the spatial resolution for sparse gauge station data sets before using them as an input for spatially-distributed rainfall-runoff models, because the gauge-based interpolation methods, commonly used in hydrologic models, usually do not cover the spatial heterogeneity of the variability of climatic variables in the catchment. These errors in the interpolated data field have the potential to significantly bias model calibrations and water balance calculations [6]

Methods

Results

Conclusion