Evaluation of TRMM Precipitation Dataset over Himalayan Catchment: The Upper Ganga Basin, India

Abstract

Satellite based rainfall estimation techniques have emerged as a potential alternative to ground based rainfall measurements. The Tropical Rainfall Measuring Mission (TRMM) precipitation, in particular, has been used in various climate and hydrology based studies around the world. While having wide possibilities, TRMM rainfall estimates are found to be inconsistent with the ground based rainfall measurements at various locations such as the southwest coast and Himalayan region of India, northeast parts of USA, Lake Victoria in Africa, La Plata basin in South America, etc. In this study, the applicability of TRMM estimates is evaluated over the Upper Ganga Basin (Himalayan catchment) by comparing against gauge-based India Meteorological Department (IMD) gridded precipitation records. Apart from temporal evaluation, the ability of TRMM in capturing spatial distribution is also examined using three statistical parameters namely correlation coefficient (r), mean absolute error (MAE) and relative bias (RBIAS). In the results, the dual nature of bias is evident in TRMM precipitation with rainfall magnitude falling in the range from 100 to 370 mm representing positive bias, whereas, rainfall magnitude above 400 mm, approximately, representing negative bias. The Quantile Mapping (QM) approach has been used to correct the TRMM dataset from these biases. The raw TRMM precipitation is found to be fairly correlated with IMD rainfall for post-monsoon and winter season with R2 values of 0.65 and 0.57, respectively. The R2 value of 0.41 is obtained for the monsoon season, whereas least correlation is found for the pre-monsoon season with an R2 value of 0.24. Moreover, spatial distribution of rainfall during post-monsoon and winter season is captured adequately; however, the limited efficiency of TRMM is reflected for pre-monsoon and monsoon season. Bias correction has satisfactorily enhanced the spatial distribution of rainfall obtained from TRMM for almost all the seasons except for monsoon. Overall, the corrected TRMM precipitation dataset can be used for various climate analyses and hydrological water balance based studies in the Himalayan river basins.

Evaluation of Seasonal and Drought Conditions Effect on Satellite-based Precipitation Data Accuracy over Different Climatic Conditions

“…By minimizing the following equation, the best coefficients for approaching precise rainfall values from two sources are achievable. (1) In Equation 1, x is a factor that must be multiplied by the average monthly rainfall of GPM/TRMM. By changing it, the smallest RMSE can be obtained, and N is the number of records.…”

“…Water is a major part of a country's economic and social development, and rain is the principal source of freshwater on the Earth. Efficient management in this field needs accurate and precise information on precipitation and its temporal-spatial variations [1]. On the other hand, rain is the main factor in the water cycle and the balance of the Earth's energy [ 2 ] and directly impacts climate change [3].…”

Hydrologic Validation of MERGE Precipitation Products over Anthropogenic Watersheds

“…Precipitation is constantly referred as the most important water input to the hydrological cycle [1][2][3] as it regulates the renewable water resources distribution and consequently affects many aspects of human, ecological, and economic development [4]. In addition, its intensity, duration, and frequency abnormalities often result in natural disasters (e.g., floods, droughts, and landslides) [5], thus making its estimation indispensable for a variety of hydrological, as well as agronomic, meteorological, and climatological applications [6,7]; including the mitigation of extreme hydrological events and design of hydraulics structures [8].…”

“…According to Maggioni et al [21] this procedure is executed in five stages: (i) the Goddard profiling algorithm is used to calculate the PMW precipitation rates; (ii) the resulting data are cross calibrated and combined; (iii) the calibrated PMW data are used to create the TIR estimates; (iv) TIR and PMW data are combined; and (v) the resulting product is corrected for bias based on a global rain gauge network (not performed in the real time version). The latest TMPA version 3B42V7, which has shown considerable improvements over its previous versions, has been extensively used worldwide in a variety of hydrological applications [3,4,22,23]. However, regional errors are still found, leading to the development of a range of post analysis merging methods [24].…”

Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

“…The latter approach is appropriate for NEX-GDDP data. There is an effective method, quantile mapping (QM), which has been successfully applied for many precipitation bias-corrected studies [23][24][25] and is considered the most efficient method [26]. This study first attempted to combine the SVR methods for ensemble simulation and the QM method for bias correction on the basis of 21 NEX-GDDP precipitation models in the Han River basin to improve the reliability of future projections under the Representative Concentration Pathway (RCP)4.5 and RCP8.…”