remote sensing Article A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm Khalil Ur Rahman and Songhao Shang * State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China; [email protected] * Correspondence: [email protected]; Tel.: +86-10-6279-6674 Received: 25 October 2020; Accepted: 4 December 2020; Published: 8 December 2020 Abstract: Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25◦ spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography. Remote Sens. 2020, 12, 4009; doi:10.3390/rs12244009 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 4009 2 of 31 Keywords: blended precipitation datasets; weighted average least squares algorithm; regional and seasonal evaluation; complex topography; diverse climate; Pakistan 1. Introduction Precipitation estimates with high precision is essential to amend the regional and global scale hydrological and climate processes, and their impact [1,2]. Precipitation, ranked first by the Global Climate Observing System (GCOS), is extremely difficult to measure with high precision over the complex mountainous terrain and diverse climate across Pakistan and other similar regions [1–5]. Moreover, the temporal and spatial variations of precipitation add to the complexities in its precise estimation [6,7], particularly in poorly or ungauged catchments. Satellite precipitation datasets (SPDs) provide estimates of precipitation at a large scale as compared with rain gauges (RGs) and radars [8]. The performance of SPDs is significantly dependent on the retrieval algorithms and climatic regions [9,10]. Advancements in retrieval algorithms of SPDs and reanalysis precipitation products have been continuous [11–13]; however, there are still considerable sources and magnitude of errors [14–16]. Therefore, the assimilation of precipitation estimates from multiple sources SPDs into a blended dataset considering the weaknesses and strengths of an individual blending SPD is strongly recommended [2]. Considering a blended dataset, several efforts have been made in this regard, where the first blending was reported in the mid-1980s by merging radar-gauge precipitation [17]. The Global Precipitation Climatology Project (GPCP), an earlier attempt to blend satellite-gauge data, is a monthly temporal and 0.25◦ spatial resolutions dataset developed using a mean bias-corrected method and an inverse-error-variance weighting method [18]. Similarly, the Climate Prediction Center Merged Analysis of Precipitation (CMAP) having monthly temporal and 2.5◦ spatial resolutions with 17 years of availability period is developed by merging RGs, SPDs, and reanalysis datasets employing the maximum likelihood estimation method [19]. Since then, a number of approaches, such as improvements in calibration algorithms, adopting the relative weights techniques, reduction in sampling issues, application of dynamic methods to estimate SPDs weights, etc., have been employed to develop blended precipitation datasets (BPDs) having high-quality estimates [1–5,20–25]. Several studies have reported significantly improved performances of the BPDs in quantification and evaluation of precipitation estimates and also in hydrological and meteorological applications [26,27]. Several BPDs have been developed across different regions of the globe [2,5,23,28,29]. The methods used to develop BPDs include Bayesian model averaging [3,5,30], conditional merging [31,32], simple scaling method [32], data assimilation [12], variation approach [20], probability density function [21], simple model averaging [29], principal component analysis [24], neural network analysis [33], and the non-parametric kernel merging method [34]. A detailed description of techniques to blend SPDs is available in references [22,34–36]. Very limited studies have focused on the development and evaluation of BPDs across the complex topography and diverse climate of Pakistan. Muhammad et al. [37] developed a regional precipitation algorithm that incorporated the inconsistency issues and error of individual SPDs. The developed algorithm was based on regional performance weights augmented by the leave-one-out cross validation (LOOCV) and ensemble algorithm. They reported significant improvements in the developed regional precipitation algorithm as compared with individual SPDs. Rahman et al. [24] developed a BPD employing the sample t-test comparison and principal component analysis (PCA) to blend SPDs, i.e., Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B43-v7. The analyses depicted that the PCA-BPD outperformed all the SPDs and proved to be superior to the regional precipitation algorithm developed by Waseem et al. [37]. Similarly, Rahman et al. [1,3] developed BPDs using the dynamic Bayesian model averaging (DBMA) and Remote Sens. 2020, 12, 4009 3 of 31 dynamic clustered Bayesian averaging (DCBA), by utilizing precipitation estimates from TMPA 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), and Climate Prediction Center morphing technique (CMORPH) at a daily temporal scale for 16 years (2000–2015). Very recently, Rahman et al. [4] developed BPD using the weighted average least squares (WALS) method using the same combination of SPDs from 2000 to 2015. Overall, the results presented a comprehensively improved performance of WALS-BPD and DCBA-BPD as compared with DBMA-BPD. However, there is a specific pattern of error distribution across Pakistan, both spatially (high error in glacial and humid regions) and temporally (high error in monsoon season). The error shows a decreasing trend from DBMA-BPD to DCBA-BPD and WALS-BPD, but there is still a considerable magnitude of errors, which must be addressed. The above BPDs used the same set of SPDs in all climate regions. However, it is evident that different SPDs perform differently in diverse climate regions. The present study is an attempt to address the high magnitude of errors across glacial and humid regions by considering the spatial and temporal variations of SPDs, and develops a dynamic regional BPD (hereinafter, WALS-RBPD) by selecting appropriate blending SPDs for different climate regions and employing a robust and sophisticated