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.

Background: Emotional distress in Breast cancer patients may interfere with the ability to cope effectively with breast cancer, its physical symptoms and its treatment. This in turn causes significant increase in psychiatric morbidity leading to poor adjustment in the patients. This study aimed to evaluate the effectiveness of the psycho-educational program on emotional distress and mental adjustment among women with Breast Cancer. A quasi-experimental design (one group pre\posttest) was used to achieve the aim of the study. A convenient sample of 50 adult women diagnosed with breast cancer. This study was conducted at the outpatient department of the clinical oncology center at the Nasser Institute Hospital. Data were collected by using three tools: structured interview questionnaire sheet, Hospital Anxiety and Depression Scale (HADS), and Mini-Mental Adjustment to Cancer Scale (Mini-MAC). Results: The result revealed that there was reduction in the mean score of anxiety and depression of the studied subjects post program than before with highly statistically significant differences. There was also a statistically significant increase in the total mean scores of all mental adjustment subscales among the studied women after participation in the psycho-educational program than before. Conclusion: The psycho-educational intervention program was the key element in reducing emotional distress and improving mental adjustment for women with breast cancer. The study recommended generalization of psycho-educational intervention programs for all patients with breast cancer.

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are available. This study investigated the influence of varying lengths of streamflow observation records on model calibration and evaluated the usefulness of a signature-based calibration approach in conceptual rainfall-runoff model calibration. Scenarios of continuous short-period observations were used to emulate poorly gauged catchments. Two approaches were employed to calibrate the HBV model for the Brue catchment in the UK. The first approach used single-objective optimization to maximize Nash–Sutcliffe efficiency (NSE) as a goodness-of-fit measure. The second approach involved multiobjective optimization based on maximizing the scores of 11 signature indices, as well as maximizing NSE. In addition, a diagnostic model evaluation approach was used to evaluate both model performance and behavioral consistency. The results showed that the HBV model was successfully calibrated using short-term datasets with a lower limit of approximately four months of data (10% FRD model). One formulation of the multiobjective signature-based optimization approach yielded the highest performance and hydrological consistency among all parameterization algorithms. The diagnostic model evaluation enabled the selection of consistent models reflecting catchment behavior and allowed an accurate detection of deficiencies in other models. It can be argued that signature-based calibration can be employed for building adequate models even in data-poor situations.

<p>In response to recent major flood events in Ireland, the authorities have prioritised the development of a national flood forecasting model for use as a tool in flood risk management. Accurate flood predictions by this model require high resolution spatiotemporal rainfall data. One source for this type of data is the remote sensing estimated precipitation provided by the Global Precipitation Measurement (GPM) satellite. The GPM has ability to detect and estimate all forms of precipitation using a range of advanced instruments, including Microwave and Radar technologies. This study evaluates the accuracy of detecting the large rainfall events which occurred in Ireland during the period 2014-2021 by three Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products (i) early run ; (ii) late run; and (iii) final run. The satellite estimates of these events have been assessed using five statistical indices applied to various temporal scales; hourly, daily, and monthly. The results showed that, for satellite detection, all of the three IMERG products had an acceptable detection accuracy of the large rainfall events. In particular, the calibrated product – final run product – outperformed the other near-real-time products in terms of estimation error and bias. Overall, the results indicate that IMERG satellite precipitation products can be used with confidence to detect large events over high latitude areas such as Ireland. Besides, they have a high potential for coupling with in-situ data to improve the accuracy of the integrated flood forecasting model.</p>

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.