Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan

Abstract

Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000–2015 and 2007–2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil’s U, and the modified Kling–Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25° scale during 2007–2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil’s U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil’s U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil’s U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region.

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

“…SM2RAIN is a satellite precipitation product based on soil moisture [60]. Influenced by ice and snow, the satellite sensor signal is unstable, and the accuracy of precipitation estimation is seriously reduced [61]. Therefore, SM2RAIN has a poor ability to reproduce the winter precipitation.…”

A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm

“…The elevation decreases to 0 m towards the extreme south at the Arabian Sea [24,39]. Pakistan is blessed with a very diverse climate, which changes abruptly from glacial to humid, arid, and hyper-arid nature [40], while there are four dominant seasons, i.e., winter, autumn, spring, and summer. Therefore, Pakistan is divided into four main climate regions, i.e., glacial, humid, arid, and hyper-arid regions (Figure 1b), according to the climatic variations [1, 3,4,40].…”

“…Pakistan is blessed with a very diverse climate, which changes abruptly from glacial to humid, arid, and hyper-arid nature [40], while there are four dominant seasons, i.e., winter, autumn, spring, and summer. Therefore, Pakistan is divided into four main climate regions, i.e., glacial, humid, arid, and hyper-arid regions (Figure 1b), according to the climatic variations [1, 3,4,40]. The RGs distributed in glacial, humid, arid, and hyper-arid regions are named as GR-RGs, HR-RGs, AR-RGs, HAR-RGs, respectively.…”

“…The melted water from these snow and glaciers is the primary source of water in the Indus River and its tributaries, which is used for domestic, agriculture, and industrial sectors. However, excessive melting of snow and glaciers have caused devastating flood events in the history of Pakistan, for example, the 2010 flood, which brutally damaged the economy and infrastructure of the country and took thousands of lives [40].…”

An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

“…The distribution of GPGs in Pakistan is non-homogenous and very limited/scarce, which is not enough to understand the precipitation pattern, its spatial and temporal variability over different climate regions [48]. In the last few decades, the number of GPGs has significantly increased, but the density is still not able to cope with scientific requirements [49]. Therefore, the application of SPPs and development of MSPDs are of paramount importance over Pakistan as an alternative precipitation source for hydrological and meteorological applications.…”