Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and other lessons learned, as well as future directions of using SPPs for drought applications

Permeable Pavement Systems for Effective Management of Stormwater Quantity and Quality: A Bibliometric Analysis and Highlights of Recent Advancements

In recent years, there has been growing interest in the field of permeable pavement systems (PPS), especially in the scope of stormwater management as a sustainable urban drainage system (SUDS). In this study, a comprehensive bibliometric analysis followed by a systematic review were conducted to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in the field of PPS. Relevant publications over 22 years (2000–2021) were retrieved from the Web of Science database for analysis. Results revealed that slight modifications within the PPS layers or incorporation of innovative filters could result in improved contaminant removal efficiency. Impermeable soils and PPS pore size were the main limiting factors affecting the permeability and infiltration rates. A combination of maintenance procedures was presented and proven effective in mitigating clogging effects, mostly occurring at the upper 1.5–2.5 cm of the PPS. Although partial replacement of the PPS mix design with recycled aggregates improved the overall permeability, the compressive strength was slightly compromised. The present study also discusses several evolving aspects for water quality improvements, innovative investigations that include recycled aggregates, and other lessons learned and future research directions in the area of PPS. Findings from the conducted analysis provide researchers, designers, urban planners, and even municipalities with research gaps and technical deficiencies in implementing and investigating PPS

Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models’ performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area

Permeable Pavement Systems for Effective Management of Stormwater Quantity and Quality: A Bibliometric Analysis and Highlights of Recent Advancements

In recent years, there has been growing interest in the field of permeable pavement systems (PPS), especially in the scope of stormwater management as a sustainable urban drainage system (SUDS). In this study, a comprehensive bibliometric analysis followed by a systematic review were conducted to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in the field of PPS. Relevant publications over 22 years (2000–2021) were retrieved from the Web of Science database for analysis. Results revealed that slight modifications within the PPS layers or incorporation of innovative filters could result in improved contaminant removal efficiency. Impermeable soils and PPS pore size were the main limiting factors affecting the permeability and infiltration rates. A combination of maintenance procedures was presented and proven effective in mitigating clogging effects, mostly occurring at the upper 1.5–2.5 cm of the PPS. Although partial replacement of the PPS mix design with recycled aggregates improved the overall permeability, the compressive strength was slightly compromised. The present study also discusses several evolving aspects for water quality improvements, innovative investigations that include recycled aggregates, and other lessons learned and future research directions in the area of PPS. Findings from the conducted analysis provide researchers, designers, urban planners, and even municipalities with research gaps and technical deficiencies in implementing and investigating PPS

Meta-Analysis of the Performance of Pervious Concrete with Cement and Aggregate Replacements

In recent years, pervious concrete (PC) has gained much attention as one of the strategies for low-impact development (LID) in pavements due to its structural, economic, and road-user benefits. This study sought to review and evaluate changes in the mechanical, hydraulic, and durability performance of PC produced with cement and aggregate replacements. A meta-analysis was conducted to elucidate the feasible range of the replacement percentage and the number of materials that could be used to replace cement and aggregates; single or binary replacements were considered. Results indicated that cement-replacing materials, industrial wastes (IWA), and recycled aggregates (RA) met the minimum requirement for the mechanical, hydraulic, and durability properties of PC. The use of a single cement replacement material provided PC with better performance than when cement was replaced with two or more materials or when cement alone was used. Industrial waste was found to be a better replacement to aggregates than RA. The combined replacement of cement and aggregates with IWA and other cement-replacing materials was the most effective method for improving the mechanical, hydraulic, and durability performance of PC. Replacements of up to 40% was considered viable for cement replacement, while up to 50% replacement was considered practical for aggregate and combined replacement. PC incorporating different cement-replacing materials exhibited equivalent or improved mechanical properties and maintained hydraulic performance compared to cement-based PC. Nonetheless, limited studies are available on the durability performance of PC made with cement and/or replacements. Thus, the durability of PC coupled with the applicability of replacement materials acquired from different locations need to be evaluated to address the viability of producing more durable PC with the use of replacements

Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models’ performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area

Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE

Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE

Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain, Algeria

In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl− > SO42− > HCO3− > NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater