The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions.

Highly accurate and real-time estimation of precipitation over large areas remains a fundamental challenge for the hydrological and meteorological community. This is primarily attributed to the high heterogeneity of precipitation across temporal and spatial scales. Rapid developments in remote sensing technologies have made the quantitative measurement of precipitation by satellite sensors a significant data source. The Global Precipitation Measurement (GPM) mission makes precipitation data with high temporal and spatial resolutions available to different users. The objective of this study is to evaluate the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) V06 (Early, Late, and Final) satellite precipitation products (SPPs) at high latitudes. Ground-based observation data across Finland were used as a reference and compared with IMERG data from 2014 to 2019. Three aspects were evaluated: the spatial coverage of the satellite estimates over Finland; the accuracy of the satellite estimates at various temporal scales (half-hourly, daily, and monthly); and the variation in the performance of SPPs over different spatial regions. The results showed that IMERG SPPs can be used with high confidence over Southern, Eastern, and Western Finland. These SPPs can be used with caution over the region of the historical province of Oulu but are not recommended for higher latitudes over Lapland. In general, the IMERG-Final SPP performed the best, and it is recommended for use because of its low number of errors and high correlation with ground observation. Furthermore, this SPP can be used to complement or substitute ground precipitation measurements in ungauged and poorly gauged regions in Southern Finland.

Abstract. The influence of topographical features and rainfall intensity on the accuracy of precipitation values estimated by earth observing satellites has attracted attention in the past decade. Assessment of rainfall products delivered by the Integrated Multi-satellitE Retrievals of Global precipitation measurement (IMERG) against ground observations has risen as an important endeavour since the accuracy of these products remain unreliable. This study comprehensively evaluated the three GPM IMERG products (near and post-real-time), over the period March 2014 to June 2018. The evaluation approaches were carried out for different seasons, rainfall intensities, topographical features, and hydrological regions over an extremely arid and semiarid country of Saudi Arabia. In general, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and estimated errors. The evaluation results showed that for seasonal-based evaluation, the precipitation products exhibited better performance in spring and summer, while having relatively lower accuracy and higher biases in fall and winter. In addition, the results showed that the IMERG products had high performance in capturing the various rainfall intensities, with light rain having the highest accuracy. This is particularly important for arid regions as most of the rainfall is of the low-intensity class. Overall, the higher the rainfall intensity, the higher the detection errors in the IMERG products. Moreover, the hydrological evaluation results showed that the hydrological regions with low density of rain gauge stations hinders the proper evaluation of satellite products and tends to underestimate the performance of the products. Furthermore, the accuracy of the precipitation products was affected by topography to different extents. IMERG precipitation products exhibited high detection accuracy over moderate elevation areas (inland regions); whereas it had poor performance over flat plains (coastal regions) and high altitudes (foothills and mountainous regions). The outcomes of this evaluation could help developers in improving the GPM IMERG calibration to achieve better detection accuracy over arid and semiarid regions. More importantly, these results are of interest for local authorities to help manage development activities and to plan precautionary measures for extreme rainfall events.

Accurate rainfall measurement is a challenge, especially in regions with diverse climates and complex topography. Thus, knowledge of precipitation patterns requires observational networks with a very high spatial and temporal resolution, which is very difficult to construct in remote areas with complex geological features such as desert areas and mountains, particularly in countries with high topographical variability such as Chile. This study evaluated the performance of the near-real-time Integrated Multi-satellite Retrievals for GPM (IMERG) Early product throughout Chile, a country located in South America between 16° S–66° S latitude. The accuracy of the IMERG Early was assessed at different special and temporal scales from 2015 to 2020. Relative Bias (PBIAS), Mean Absolute Error (MAE), and Root-Mean-Squared Error (RMSE) were used to quantify the errors in the satellite estimates, while the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were used to evaluate product detection accuracy. In addition, the consistency between the satellite estimates and the ground observations was assessed using the Correlation Coefficient (CC). The spatial results show that the IMERG Early had the best performance over the central zone, while the best temporal performance was detected for the yearly precipitation dataset. In addition, as latitude increases, so do errors. Also, the satellite product tends to slightly overestimate the precipitation throughout the country. The results of this study could contribute towards the improvement of the IMERG algorithms and open research opportunities in areas with high latitudes, such as Chile.

This paper evaluates the effect of mix design parameters on the mechanical, hydraulic, and durability properties of pervious geopolymer concrete (PGC) made with a 3:1 blend of granulated blast furnace slag (GBFS) and fly ash (FA). A total of nine PGC mixtures were designed using the Taguchi method, considering four factors, each at three levels, namely, the binder content, dune sand addition, alkaline-activator solution-to-binder ratio (AAS/B), and sodium hydroxide (SH) molarity. The quality criteria were the compressive strength, permeability, and abrasion resistance. The Taguchi and TOPSIS methods were adopted to determine the signal-to-noise (S/N) ratios and to optimize the mixture proportions for superior performance. The optimum mix for the scenarios with a compressive strength and abrasion resistance at the highest weights was composed of a binder content of 500 kg/m3, dune sand addition of 20%, AAS/B of 0.60, and SH molarity of 12 M. Meanwhile, the optimum mix for the permeability-dominant scenario included a 400 kg/m3 of binder content, 0% of dune sand addition, 0.60 of AAS/B, and 12 M of SH molarity. For a balanced performance scenario (i.e., equal weights for the responses), the optimum mix was similar to the permeability scenario with the exception of a 10% dune sand addition. An ANOVA showed that the binder content and dune sand addition had the highest contribution toward all the quality criteria. Multivariable regression models were established to predict the performance of the PGC using the mix design factors. Experimental research findings serve as a guide for optimizing the production of PGC with a superior performance while conducting minimal experiments.