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Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management. [ABSTRACT FROM AUTHOR]

The use of remote sensing for monitoring and managing droughts is examined in this review study. Drought has a significant impact on how water resources are managed and agricultural production is produced, and remote sensing is a vital technique for assessing and monitoring the severity of drought. A number of remote sensing data sources are discussed in the paper; including precipitation, groundwater and surface water storage, soil moisture, land surface temperature, evaporation, and agricultural indicators. With the use of these data sources, drought indices and indicators that measure the severity and spatiotemporal fluctuations of the drought may be developed. The novel approach of this review study emphasizes the benefits of using remote sensing to gain a full understanding of drought dynamics and to accurately capture fine-scale fluctuations in drought conditions. However, the study also highlights certain limitations, including issues related to data accessibility, data interpretation, and validation difficulties. It emphasizes the significance of using remote sensing to promote the developing policies and strategies to enhance drought resilience and adaptation. The importance of continuous research, technical development, and stakeholder cooperation in order to fully realize remote sensing's promise for tackling the complex problems associated with drought and promoting sustainable water resource management. [ABSTRACT FROM AUTHOR]

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province. [ABSTRACT FROM AUTHOR]

Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially in poorly-gauged basins and resource-limited settings. Therefore, it is necessary to examine new ways of constructing predictive models for runoff that can achieve satisfactory results, while also minimizing the data requirement and model construction time. In this study, the effectiveness of integrating the Random Forest (RF) as an important feature identifier with novel gradient boosted trees to achieve satisfactory results was examined for two adjacent catchments in Vietnam. Antecedent daily runoff in combination with daily and one-day antecedent rainfall was found to significantly influence the runoff at the outlet of the catchments. Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) were effective in predicting day-ahead runoff. For instance, CatBoost with NSE, d, r, and R2 values of 0.92, 0.98, 0.96, and 0.92, respectively, and XGBoost with NSE, d, r, and R2 values of 0.91, 0.98, 0.96, and 0.92, respectively, are well suited for predicting runoff. A comparative analysis of their results with previous studies revealed that the models were very effective since they were able to better reduce generalization errors at different calibration and validation phases. This study presents the integration of RF and gradient boosted trees as a simplified alternative to computationally expensive and data-intensive physically-based rainfall-runoff models. The practitioners can build upon the experimentation presented in this study to minimize the computational time requirement, construction process complexity, and data requirement, which are often serious constraints in physically-based rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e., Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains. [ABSTRACT FROM AUTHOR]

Arid regions are characterized by the fragility of their ecosystems, which are very vulnerable to climate change. The increase in aridity in these regions makes them more exposed to drought with serious consequences. This contribution analyzes the trend of aridity and drought in the Algerian steppes, during the period 1951–2022. Robust statistical tests (Mann–Kendall and Sen) and the Reconnaissance Drought Index (RDI) are used. Monthly rainfall data and monthly average temperature as well as potential evapotranspiration (PET) data from the Climatic Research Unit (CRU), characterized by a spatial resolution of 0.5°, were used. The results showed an increase in annual aridity, which led to an expansion of drylands. The change was characterized by the transition of 9.20% of the steppe surface from the semi-arid class to the arid class. At the same time, the entire subhumid class (1.67%) made the transition to the semi-arid class. On the other hand, a significant tendency towards drought over most of the steppe territory (81%) was recorded. The analysis of the results revealed the influence of precipitations and air temperatures in this enlargement. These changes have contributed to the degradation of the environment and natural resources and to an ecological and socio-economic imbalance. [ABSTRACT FROM AUTHOR]

This study aims to investigate the trend behavior of monthly maximum in daily rainfall categories in the Vu Gia-Thu Bon river basin located in central Vietnam. Daily maximum rainfall series from 12 rainfall stations for the period 1979–2018 were utilized to characterize six categories of the intensity of daily maximum rainfall: light (0–4 mm/day, category A), mild-moderate (4–16 mm/day, category B), moderate-heavy (16–32 mm/day, category C1), heavy (32–64 mm/day, category C2), heavy-torrential (64–128 mm/day, category D1), and torrential (≥ 128 mm/day, category D2). The new approach of the Innovative Trends Analysis was then applied to the six classified categories. The results revealed that category B had a dominant increasing trend (32% of rain events) for all the stations in January (5.85%) and February (3.44%). In March and April, category A was dominant with 45% and 20%, respectively. In July, category C1 was dominant with 25%, while in August and September, category C2 prevailed over all stations with 45% (all stations) and 20%, respectively. The categories D1 and D2 were observed at all stations in December and November, with 26% and 31% of events, respectively. These results indicate an increasing trend in the categories B, C1, C2, and D1. [ABSTRACT FROM AUTHOR]

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image–based index model was prepared and applied for the period 2009–2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future. [ABSTRACT FROM AUTHOR]

High-resolution sensors onboard satellites are generally reputed for rapidly producing land-use/land-cover (LULC) maps with improved spatial detail. However, such maps are subject to uncertainties due to several factors, including the training sample size. We investigated the effects of different training sample sizes (from 1000 to 12,000 pixels) on LULC classification accuracy using the random forest (RF) classifier. Then, we analyzed classification uncertainties by determining the median and the interquartile range (IQR) of the overall accuracy (OA) values through repeated k-fold cross-validation. Results showed that increasing training pixels significantly improved OA while minimizing model uncertainty. Specifically, larger training samples, ranging from 9000 to 12,000 pixels, exhibited narrower IQRs than smaller samples (1000–2000 pixels). Furthermore, there was a significant variation (Chi2 = 85.073; df = 11; p < 0.001) and a significant trend (J-T = 4641, p < 0.001) in OA values across various training sample sizes. Although larger training samples generally yielded high accuracies, this trend was not always consistent, as the lowest accuracy did not necessarily correspond to the smallest training sample. Nevertheless, models using 9000–11,000 pixels were effective (OA > 96%) and provided an accurate visual representation of LULC. Our findings emphasize the importance of selecting an appropriate training sample size to reduce uncertainties in high-resolution LULC classification. [ABSTRACT FROM AUTHOR]

The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station. [ABSTRACT FROM AUTHOR]

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope. [ABSTRACT FROM AUTHOR]

Floods are considered among the gravest natural disasters worldwide and have resulted in enormous human and material damage. The Manouba–Sijoumi basin (Northeast of Tunisia) is often flooded due to urban expansion, population growth and unplanned land use. This study aims to identify and to define the flood-prone areas of this basin for the 2003 and 2018 extreme events based on a Geographic Information System, a qualitative method (analytic network process-ANP) and a statistical model (frequency ratio-FR). The flood risk maps obtained by both models were validated using the receiver operating characteristic, the area under the curve (AUC) and inventory map. Areas of high and very high flood sensitivity are located mainly in urban settings, with an increase in risk between 2003 and 2018. The AUC values for both models were of the same significance (98%) for the year 2003 while those for the year 2018 were 94% and 98% for the ANP and FR models, respectively. This would imply that both models yielded reasonable results. However, the FR model showed an ability to reduce the uncertainty associated with expert judgements. The results indicate that the most influential factor on flooding in this area was land use/cover. Indeed, populations were largely settled in unsuitable sites for urbanization and in potentially flood-prone areas located mainly around the Sijoumi Sebkha, especially to the west and south of it. The findings of the study are of great value for policy makers and state authorities to achieve greater awareness and adopt strategies for environmental preparedness and management. [ABSTRACT FROM AUTHOR]

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models' performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time. [ABSTRACT FROM AUTHOR]

This ar ticle analyzes the spatial distribution of the values of six daily precipitation concentration indexes in the northeastern part of Algeria. The used indexes were the concentration index (CI) and the Gini index (GI) with the resolution of precipitation amounts 1, 5, and 10 mm. The values of the six indexes are calculated for the period 1980–2012 with 22 stations, and on the other hand, the correlations of the CI1 concentration index with geographical and rainfall variables are analyzed. The values of CI1, CI5, and CI10 and IG1, IG5, and IG10 have been mapped using the tools of the ArcGis10.02 programs. The CI1 concentration index correlates significantly with all the geographic and rainfall variables considered. A negative linear correlation was observed with a linear correlation coefficient r = − 0.67 (p value = 0.0006) between CI1 and altitude. The correlation between CI1 and the distance to sea with a linear correlation coefficient r = − 0.47 (p value = 0.026), the correlation between CI1 and latitude is positive with r = + 0.40 (p value = 0.032). On the other hand, CI1 is significantly positively correlated with average annual precipitation, the coefficient of variation, and the number of rainy days in the same period: r = 0.52 (p value = 0.023); r = − 0.25 (p value = 0.049) and r = 0.49 (p value = 0.022), respectively. [ABSTRACT FROM AUTHOR]

Hue Imperial City of Vietnam has a dense system of lakes and upgraded drainage infrastructure, but urban flooding still occurs and tends to increase. Hue Imperial City is a good area for studying the impact of lakes in reducing floods. The SCS-CN method was used to calculate the increase in surface flow, and the design capacity of reservoirs was based on TCVN 7957–2008 to demonstrate changes in the surface characteristics and siltation of the lakes. The study results showed that the lakes' characteristics vary significantly in an unfavorable direction for urban drainage, including the number of lakes, total lake area, lake capacity, interconnecting structure between lakes, and vegetation cover around lakes. In the southeast and southwest areas of the Imperial City, the number of lakes with significant changes in depth, flooding frequency, and duration was higher than in other areas of the region. About two-fifth of the lakes are regularly in a state of degradation. The effectiveness of using lakes for drainage, agricultural production, and tourism is not high. In the context of climate change and extreme rainfall, urban flooding in the Imperial City of Hue will be more severe if the function of lakes is not utilized correctly. Therefore, the main objective of this study is to analyze the relationship between changes in the characteristics of lakes and the urban flooding situation in Hue Imperial City. From there, the prioritized solution to reduce urban flooding in the area is to restore and protect the lakes and use them for regulation. [ABSTRACT FROM AUTHOR]

This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was used to investigate the interrelationships between critical hydrogeologic factors (and determine their relative weights) via a novel vulnerability index based on the DRASTIC model. The flexibility of GIS in handling spatial data was employed to delineate thematic map layers of the hydrogeologic factors and to improve the DRASTIC model. The hybrid MCDM model results show that net recharge (a key hydrogeologic factor) had the highest priority with a weight of 0.1986. In contrast, the topography factor had the least priority, with a weight of 0.0497. A case study validated the hybrid model using Anambra State, Nigeria. The resultant vulnerability map shows that 12.98% of the study area falls into a very high vulnerability class, 31.90% falls into a high vulnerability, 23.52% falls into the average vulnerability, 21.75% falls into a low vulnerability, and 9.85% falls into very low vulnerability classes, respectively. In addition, nitrate concentration was used to evaluate the degree of groundwater pollution. Based on observed nitrate concentration, the modified DRASTIC model was validated and compared to the traditional DRASTIC model; interestingly, the spatial model of the modified DRASTIC model performed better. This study is thus critical for environmental monitoring and implementing appropriate management interventions to protect groundwater resources against indiscriminate sources of pollution. [ABSTRACT FROM AUTHOR]

This research aims to determine the flood potential mapping within Golestan Province in Iran, applying six novel ensemble techniques guided by the multi-criteria decision-making (MCDM), bivariate statistics, and artificial neural network methods. The combinations of Combined Compromise Solution (COCOSO), Multi-Attributive Border Approximation Area Comparison (MABAC), and multilayer perceptron (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) were then generated. It is noted that this is the first application of COCOSO method in flood susceptibility assessment and its efficiency had not been evaluated before. In this regard, 10 flood influential criteria namely altitude, slope, aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, 240 flood points, and 240 non-flood points were employed for the modeling process, of which 70% of such data were chosen for training and remaining 30% for validating. The accuracy of proposed methods was tested by the area under the receiver operating characteristic (AUROC) curve. MABAC-WOE obtained the largest predictive precision (0.937), followed by MLP-WOE (0.934), COCOSO-WOE (0.923), MABAC-FR (0.921), MLP-FR (0.919), and COCOSO-FR (0.892), respectively. The high accuracy of all proposed models represents their capability in flood susceptibility assessment and can guide future flood risk management in the study location. [ABSTRACT FROM AUTHOR]

This study aims to assess, compare, and attribute the effects due to separate and combined land use/land cover (LULC) and climate changes on hydrological processes in a tropical catchment. The Soil and Water Assessment Tool (SWAT) model is set up and calibrated for a small contributing sub-basin of the Tana River Basin (TRB) in Kenya. The model is then applied to simulate the hydrological components (i.e., streamflow (FLOW), evapotranspiration (ET), soil water (SW), and water yield (WYLD)) for different combinations of LULC and climate scenarios. Land use data generated from Land Satellite 5 Thematic Mapper (Landsat 5TM) images for two different periods (1987 and 2011) and satellite-based precipitation data from the African Rainfall Climatology version 2 (ARC2) dataset are utilized as inputs to the SWAT model. The Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), percent bias (PBIAS), and the ratio of root mean square error to the standard deviation (RSR) for daily streamflow were 0.73, 0.76, 3.16%, and 0.51 in calibration period, respectively, and 0.45, 0.54, 12.53%, and 0.79 in validation period, respectively, suggesting that the model performed relatively good. An analysis of the LULC data for the catchment showed that there was an increase in agricultural, grassland, and forested land with a concomitant decrease in woodland and shrubland. Simulation results revealed that change in climate had a more significant effect on the simulated parameters than the change in LULC. It is shown that changes in LULC only had very minor effects in the simulated parameters. The monthly mean FLOW and WYLD decreased by 0.02% and 0.11%, respectively, while ET and SW increased by a monthly mean of 0.2% and 2.2%. Varying the catchment climate and holding the land use constant reduced FLOW, ET, SW, and WYLD by an average monthly mean of 43.2%, 21%, 13%, and 70%, respectively, indicating that climate changes have more significant effects on the catchment hydrological processes than changes in LULC. Thus, it is necessary to evaluate and identify the isolated and combined effects of LULC and climatic changes when assessing impacts on the TRB's hydrological processes. [ABSTRACT FROM AUTHOR]

The quantitative assessment of glacier changes and freshwater availability under future climate change is inevitable for sustainable water resources management and preventing natural disasters. Limiting the uncertainties in glacio-hydrological modeling and exploring spatiotemporal distributions of runoff and its components along the vertical profile are critical for such investigations. The present study quantifies glacio-hydrological changes using the Spatial Processes in HYdrology (SPHY) model forced by CMIP6 climate data (shared socioeconomic pathways: SSP126, SSP245, and SSP585) in the Upper Indus Basin (UIB) over twenty-first century. The model was calibrated based on in situ glacier changes, snow cover changes, and streamflow records to avoid the risk of equifinality. Variations in vertical distribution of runoff components and their impact on glacio-hydrology were investigated using the critical zone approach. The projected remaining glacier area is 55 ± 10%, 32 ± 17%, and 15 ± 5%, and freshwater availability is reduced by − 12 ± 5%, − 30 ± 7%, and − 36 ± 6% in 2100, compared with 2005–2014, under SSP126, SSP245, and SSP585, respectively. The average changes in snowmelt, glacier melt, baseflow, and rain-runoff contributions to total runoff under SSP245 are projected as 25 ± 15%, − 30 ± 11%, − 20 ± 16%, and 242 ± 71%, respectively. The critical zone (3500 – 5500 masl) contributes 63% of total runoff during the reference period, with a significant reduction (51–81%) in the projected period, indicating a diminishing influence of glacier runoff (with 50% reduction) in future hydrology compared with historical period. In turn, low flows (October–March) are projected to increase (9 − 58%), and high flows (April–September) will likely decrease (− 2 to − 13%). Warming temperature was identified as the dominant driver for the glacier area changes (r = − 0.85*) and total runoff (r = − 0.75*) at 0.05 level of significance. Our findings indicate a rainfall-runoff-dominant hydrological regime in future, highlighting critical freshwater availability conditions and associated socioeconomic risks in terms of agricultural applications and natural disasters. We recommend building infrastructure for water storage and conveyance in the Indus basin to prevent the adverse impacts of future climate change. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning (ML-DL) based models are widely used for rainfall-runoff prediction and they have potential to substitute process-oriented physics based numerical models. However, developing an ML model has also performance uncertainty because of inaccurate choices of hyperparameters and neural networks architectures. Thus, this study aims to search for best optimization algorithms to be used in ML-DL models namely, RMSprop, Adagrad, Adadelta, and Adam optimizers, as well as dropout techniques to be integrated into the Long Short Term Memory (LSTM) model to improve forecasting accuracy of rainfall-runoff modeling. A deep learning LSTMs were developed using 480 model architectures at two hydro-meteorological stations of the Mekong Delta, Vietnam, namely Chau Doc and Can Tho. The model performance is tested with the most ideally suited LSTM optimizers utilizing combinations of four dropout percentages respectively, 0%, 10%, 20%, and 30%. The Adagrad optimizer shows the best model performance in the model testing. Deep learning LSTM models with 10% dropout made the best prediction results while significantly reducing overfitting tendency of the forecasted time series. The findings of this study are valuable for ML-based hydrological models set up by identifying a suitable gradient descent (GD) optimizer and optimal dropout ratio to enhance the performance and forecasting accuracy of the ML model. [ABSTRACT FROM AUTHOR]

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2 ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake. [ABSTRACT FROM AUTHOR]

Volcanic ash fallout is a recurrent environmental disturbance of flora ash deposits from the Icelandic volcano Eyjafjöll (2010) over large areas are responsible for several impacts on ecological processes, agricultural production, and human health in Western Europe. This study assessed the ash fall effects from the subject volcano on the surrounding flora as well as vegetative recovery at two different sites (Scotland and southern Sweden). For this purpose, we analyzed Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by Plants (FPAR) data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). The cited biophysical variables were most strongly influenced by ash cloud fallout, with the lowest maxima recorded for both sites during the 2010 eruption year. To confirm this impact, a statistical study with climate indicators was performed. The results showed a significant correlation between LAI and precipitation (R2 = 0.63, p-value = 0.0022) at site 1 (Scotland), while a weak non-significant correlation (R2 = 0.2248, p-value > 0.5) was observed at site 2. However, climatic data from both sites showed low correlations (R2 < 50%) with NDVI vegetation indicator. Despite the heavy rainfall and heat recorded during this period, our statistical results show us that ash cloud fallout affects vegetative development. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the suitable areas for agronomic practices using the analytic hierarchy process (AHP) and GIS technique in the Wabe Shebele basin, Ethiopia. Ten suitability determinant factors (i.e., soil depth, root zone plant-available water holding capacity (RZ-PAWHC), soil texture, major soil type, soil stoniness, slope and elevation, proximity to road and town, proximity to a water body) and two constraint layers (i.e., land use land cover and protected site) were employed for suitability analysis. The weights of determinant factors were determined in AHP based on experts' opinions and literature. Slope (30%) was shown to be the most determining element, followed by soil depth (22%), for agricultural land-use appropriateness analysis in the research region. Then, after weighted overlay analysis has been made, the basin was qualitatively categorized as highly suitable, moderately suitable, marginally suitable, currently unsuitable, and permanently unsuitable for agricultural production with 18.08, 29.33, 35.65, 7.89, and 9.05% landscape, respectively. It was determined that among land being used for agricultural activities, 36.59 and 34.56% were categorized under highly suitable and moderately suitable, respectively. As a result, given the available agricultural land, selecting agricultural commercialization as a development option, which entails more comprehensive assessments of a suitable property, may be critical. The findings demonstrated that AHP is an effective approach for identifying appropriate land for agricultural activities in the research region. Yet, land use policy is substantial for efficient agricultural land use and management. [ABSTRACT FROM AUTHOR]

This study examined landslide susceptibility, an increasingly common problem in mountainous regions across the world as a result of urbanization, deforestation, and various natural processes. The Rangit River watershed in Sikkim Himalaya is one of the most landslide-prone areas in India. The main objective of this study was to produce landslide susceptibility maps of the Rangit River watershed using novel ensembles of random forest tree (RFT) with support vector machine (RFT-SVM), artificial neural network (RFT-ANN), naïve Bayes tree (RFT-NBT), and logistic model tree (RFT-LMT). An inventory of landslides was created using historical landslide data, government and scientific studies, and Google Earth's high-resolution satellite images. The landslide/non-landslide locations were split 70/30 for training and validating the models, respectively. Eleven landslide conditioning factors were selected based on their predictive ability, determined using the information gain method, and each factor's importance was derived. A landslide susceptibility index was then estimated by weighted overlay using a model builder in a GIS (Geographic Information System) environment. Based on the area under the curve and statistical metrics, RFT-LMT was identified as the best model. The results showed that approximately 40% of the Rangit River watershed has high to very high susceptibility to landslides. This study's findings will be useful for policy-makers and land use planners in managing and mitigating future landslides in the study area. [ABSTRACT FROM AUTHOR]

The low water storage capacity caused water crisis in Pakistan; therefore, the country needs both small- and large-scale reservoirs to store surplus water resources. The construction of large dams in Pakistan could not be materialized due to financial and political constraints. However, the construction of multi-purpose small dams is the next best option to store water. To this end, there is a need to identify the best feasible sites in the country. The selection of feasible sites for multi-purpose dams must take into account multiple criteria, including engineering, socioeconomic and environmental. The current study utilizes the coupled Remote Sensing and Geographical Information System techniques to identify the feasible sites for multi-purpose small dams, considering the socioeconomic and environmental criteria in addition to the established engineering criteria in district Swat, Khyber Pakhtunkhwa. The suitability map considered nine engineering criteria, including rainfall distribution, slope, land use, curve number, runoff depth, soil, alluvial depth, closeness to streams, and drainage density, using the weights calculated from priority indices. The suitability map is divided into four classes, i.e., excellent, good, moderate, and unsuitable with the excellent and good classes area of 66.78 km2, and 195.75 km2. Twenty sites (based on accessibility and closeness to Swat River) from each class are selected that are situated in Kalam, Babozai, Bahrain, Madyan, Khwazakhela, Matta, Kabal, and Barikot areas of district Swat and evaluated using socioeconomic and environmental criteria, i.e., community well and no displacement cost, management ownership–private or public, biodiversity protection services, instrumental in groundwater recharge for the community, electricity generation for the local community, low maintenance cost, flood friendly, appropriate distribution of water resources, political well, and irrigation and drinking water potential. The top priority for these areas is electricity generation, flood protection, irrigation and drinking water capability, sustainable operation, low maintenance cost and political well. The current study demonstrated that socioeconomic and environmental criteria augment the engineering approach in identifying the best feasible site for multi-purpose small dams. These sites would not only store the water but would also provide important services (electricity generation, irrigation water, etc.) to the local community and economy. [ABSTRACT FROM AUTHOR]

Ca Mau and Kien Giang, the two provinces of the Mekong Delta bordering the Gulf of Thailand, are facing major environmental challenges affecting the agriculture and aquaculture sectors upon which many livelihoods in this region depend on. This study maps the suitability of these two provinces for paddy rice cultivation and shrimp farming according to soil characteristics and current and future environmental conditions for variables found to significantly influence the yield of those two sectors, i.e., the level of saltwater intrusion, water availability for rainfed agriculture, and the length of the growing period. Future environmental conditions were simulated using the MIKE 11 hydrodynamic model forced by four hydrodynamic scenarios, each one representing different extents of saltwater intrusion during both the dry and rainy seasons, while also considering the availability of water resources for rainfed agriculture. The suitability zoning was performed using a GIS-based analytic hierarchy process (AHP) approach, resulting in the categorisation of the land according to four suitability levels for each sector. The analysis reveals that paddy rice cultivation will become more suitable to Kien Giang province while shrimp farming will be more suitable to Ca Mau province if the simulated future environmental conditions materialise. A suitability analysis is essential for optimal utilisation of the land. The approach presented in this study will inform the regional economic development master plan and provide guidance to other delta regions experiencing severe environmental changes and wishing to consider potential future climatic and sea level changes, and their associated impacts, in their land use planning. [ABSTRACT FROM AUTHOR]

Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events and risk. Therefore, this study was undertaken to develop a flood vulnerability map and to assess the exposure of buildings to flood risk in Warsaw, the capital of Poland. This goal was pursued in four research phases. The thirteen flood predictors were evaluated using information gain ratio (IGR), and finally reduced to eight of the most causative ones and used for flood vulnerability mapping with three machine learning algorithms, Artificial Neural Network Multi-Layer Perceptron (ANN/MLP), Deep Learning Neural Network based approach—DL4j (DLNN-DL4j) and Bayesian Logistic Regression (BLR). These algorithms show a good predictive performance with the receiver operating curve (ROC) value of 0.851, 0.877 and 0.697, respectively. The buildings' exposure to flood was assessed in line with criteria established in European and national legal regulations. The introduced new buildings' flood hazard index (BFH) revealed a significant similarity of potential flood risk for both models, highlighting the greatest risk in zones with high vulnerability to flooding. Depending on the method used, the BFH value was 0.54 (ANN), 0.52 (DLNNs) or 0.64 (BLR). The holistic approach proposed in this study could assist local authorities in improving flood management. [ABSTRACT FROM AUTHOR]

In this study, we will attempt to spatialize the daily maximum temperature projections estimated for 2050 by the ALADIN-CLIMAT model by using three types of spatial interpolation methods (statistical interpolation by regression, deterministic interpolation by inverse distance weighting and geostatistical interpolation by Kriging) and their results. The 3 interpolation methods tested allow us to establish the following synthesis with regard to the summary table of estimated errors. For the (IDW), this method, simple to apply, presents low errors (0.31). However, the visual representation is not optimal with some inconsistencies in the interpolation result. On the other hand, geostatistical interpolation by kriging: in comparison with the IDW and linear regression methods, this method has the lowest errors (0.23), but visually, this is the interpolation map that includes the most inconsistencies. This result is not surprising, since the distribution of the NORTXAV variable does not meet the distribution normality conditions for applying kriging interpolation. However, statistical interpolation by linear regression: compared to the other methods applied, this method has the largest errors. Nevertheless, this model seems very reliable with an R2 of 91%. Moreover, the different parameters have been validated for the application of the model: linear relationship between x and y, absence of multicollinearity, normality/independence, and relative homoscedasticity of the residuals. However, upon visual analysis, some inconsistencies can also be detected, and one may wonder if there is a risk of over-interpreting the data within the mesh. In conclusion, we recommend the linear regression method as it seems to be the most efficient method to interpolate temperature projections. [ABSTRACT FROM AUTHOR]

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. [ABSTRACT FROM AUTHOR]

Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life, and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in the sustainable management of GWR. A comparative study was conducted to evaluate the performance of seven different ML models, such as random tree (RT), random forest (RF), decision stump, M5P, support vector machine (SVM), locally weighted linear regression (LWLR), and reduce error pruning tree (REP Tree) for GW level (GWL) prediction. The long-term prediction was conducted using historical GWL, mean temperature, rainfall, and relative humidity datasets for the period 1981–2017 obtained from two wells in the northwestern region of Bangladesh. The whole dataset was divided into training (1981–2008) and testing (2008–2017) datasets. The output of the seven proposed models was evaluated using the root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), correlation coefficient (CC), and Taylor diagram. The results revealed that the Bagging-RT and Bagging-RF models outperformed other ML models. The Bagging-RT models can effectively improve prediction precision as compared to other models with RMSE of 0.60 m, MAE of 0.45 m, RAE of 27.47%, RRSE of 30.79%, and CC of 0.96 for Rajshahi and RMSE of 0.26 m, MAE of 0.18 m, RAE of 19.87%, RRSE of 24.17%, and 0.97 for Rangpur during training, and RMSE of 0.60 m, MAE of 0.40 m, RAE of 24.25%, RRSE of 29.99%, and CC of 0.96 for Rajshahi and RMSE of 0.38 m, MAE of 0.24 m, RAE of 23.55%, RRSE of 31.77%, and CC of 0.95 for Rangpur during testing stages, respectively. Our study offers an effective and practical approach to the forecast of GWL that could help to formulate policies for sustainable GWR management. [ABSTRACT FROM AUTHOR]

The unique physiographic conditions, abundant rainfall, and anthropogenic disturbances in the mountainous Western Ghats (WG) region often cause landslides, which leads to loss of life and properties. The present study aims to generate a landslide susceptibility map for a mountainous WG region using novel hybrid methods. For this, a landslide inventory database is prepared with 82 landslide locations and twelve landslide contributing factors, viz., lithology, geomorphology, slope angle, soil texture, relative relief, distance from the streams, distance from the roads, distance from the lineaments, topographic wetness index (TWI), rainfall, land use/land cover, and slope curvature were used for modelling. Initially, four bivariate approaches, viz., information value (InfoV), evidential belief function (EBF), certainty factor (CF), and index of entropy methods (IoE), were applied to model landslide susceptibility, and the accuracy was estimated. Thereafter, these models were integrated with random forest (RF) to build hybrid models. The computed hybrid models were evaluated using ROC–AUC and confusion matrix-based statistical measures. Hybrid models show higher accuracy with ROC–AUC values ranging from 0.894 to 0.928 in the training phase and 0.906 to 0.925 in the testing phase. The RF-InfoV model was proven to be comparably efficient for landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

The central region of Vietnam has a tropical monsoon climate but often undergoes heat waves due to uncontrolled urbanization, foehn winds, and climate change. Water bodies are considered effective candidates for heat mitigation in cities through the water cooling island (WCI) effect. Quantifying the WCI capacity of water areas and related factors is necessary for sites with advantages of surface water. The current attempt used the WCI effect range (Lmax), temperature drop amplitude (ΔTmax), and temperature gradient (Gtemp) to investigate the cooling effect of 20 lakes in the Thanh Noi region, Hue City. Data derived from high-resolution Google Earth, Landsat-8 Satellite Imagery Data, and ground truth. The results show that the average water temperature of the 20 studied lakes was about 36.61 °C, lower than the average temperature in the area with an urban heat island (UHI) of about 2.82 °C. The mean Lmax was 150 m, ΔTmax was 1.52 °C, and Gtemp was 10.16 °C /km or 0.01 °C/m. Climate characteristics and human impacts had reduced the ability of the lakes to create WCI during the period when the lake water level was low. The factors that influenced the WCI significantly were the landscape shape index (LSI), the proportion of green (PG), and the percentage of impervious surfaces (PI). Most lakes with relatively simple LSI, high PG, and low PI obtained high WCI, suggesting that structural and landscape characteristics played a critical role in urban cooling. [ABSTRACT FROM AUTHOR]

Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood vulnerability for Warsaw, Poland, using three machine learning models, such as the Bayesian logistic regression (BLR), the artificial neural networks (ANN), and the deep learning neural networks (DLNNs). The perfomance of these three methods was assessed in order to select the best method for flood vulnerability mapping in densely urbanized city. Thus, initially, thirteen flood predictors were evaluated using the information gain ratio (IGR), and eight most important predictors were considered from model training and testing. The performance of the applied models and accuracy of the result was evaluated through the area under the curve (AUC) and statistical measures. By using the testing dataset, the result reveals that DLNN (AUC = 0.877) is the more performant model in comparison to ANN (AUC = 0.851) and BLR (AUC = 0.697). However, the BLR model has the lowest predictive capability. The results of the present study could be effectively used for the urban flood management strategies. [ABSTRACT FROM AUTHOR]

In this study, statistical and soft-computing methods are compared in forecasting groundwater levels under Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) in Tasuj Plain, Iran, for a near future period (2022–2027). A combination of general circulation models (GCMs) was used in the projection of precipitation and temperature in the future period. The estimation of climate variables for 2020–2044 period indicated that the temperature will increase, while the precipitation will decline. In simulation temporal groundwater level, wavelet-nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average (ARIMA), and wavelet-adaptive neuro-fuzzy inference system (ANFIS) models were used. The comparison of performance criteria for these models in the simulation of groundwater level demonstrated that the wavelet-NARX model with the R2 of 0.99 has shown better efficiency. Finally, the simulation of groundwater level through the wavelet-NARX model was carried out for different scenarios. The results indicated that future groundwater levels in Tasuj Plain would continue to decline by 3.12 m, 3.96 m, and 4.79 m, for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]

Under the inaccessibility of optimum networks and lack of well-organized rain gauge data, the provided high-resolution satellite estimates serve as a vital baseline for hydro climate-associated studies. In the rugged topography of South Gojjam Basin, satellite rainfall estimates from the African Rainfall Climatology (ARC2), Climate Hazard Group Infrared Precipitation with Stations Data (CHIRPSv2), African Rainfall Estimations Algorithm (REFv2), and Tropical Applications of Meteorology using Satellite (TAMSATv3) were evaluated and compared with rain gauge rainfall data. Satellite rainfall estimates were evaluated at spatiotemporal scales with observed data using approaches of point observed rainfall data and areal averaged rainfall comparisons for the period of 2001–2018. The basin was divided into two parts, according to geographical region, to identify the effect of topographical variation. Besides, the spatial map of the annual satellite rainfall estimate pattern was illustrated with observed rainfall data for comparison. The result showed that TAMSATv3 and RFEv2 rainfall estimates showed the best performance than ARC2 and CHIRPSv3 products at daily time scales and in both spatial scales (i.e., higher, and lower elevations). On a monthly and wet season timescale, the CHIRPSv2 product was outperformed, even though the four satellite products performed better. CHIRPSv2 and TAMSATv3 products presented overestimated rainfall in the lower elevation region at daily and monthly scales, while ARC2 and RFEv2 products were underestimated at all spatiotemporal scales. Overall, CHIRPSv2 and TAMSATv3 satellite rainfall estimates showed good relation to the rain gauge rainfall data in the rugged topography and a limited number of rain gauges in the South Gojjam basin. Thus, this study decided that CHIRPSv2 and TAMSATv3 satellite rainfall data could be used as an alternative to rain gauge rainfall on a monthly and wet season time scale for hydroclimate studies. [ABSTRACT FROM AUTHOR]

Flood is one of the most destructive natural hazards associated with substantial damage in various world regions. In this study, Iran’s Kalan basin was divided into ten sub-watersheds. Then, 13 flood-related morphometric criteria, i.e., drainage density (Dd), bifurcation ratio (Rb), frequency of streams (Fs), slope (S), time of concentration (Tc), form factor (Rf), shape factor (Bs), Gravelius factor (Rc), elongation ratio (Re), roughness factor (RR), mean elevation (H), and drainage density of mainstream (D), were derived for each sub-watershed. Afterward, the picture fuzzy-analytic hierarchy process (PF-AHP) and picture fuzzy-linear assignment model (PF-LAM) were combined to assign weights to selected morphometric criteria and rank the sub-watersheds based on the level of flood susceptibility. The results showed that Ag and Aj sub-watersheds were the most susceptible sub-watersheds to flooding. In contrast, Ac and Ai sub-watersheds had the lowest susceptibility to flooding. Prioritization and ranking of sub-watersheds from the perspective of flood susceptibility can be a powerful tool in terms of flood prevention and in providing the most effective mitigation measures. [ABSTRACT FROM AUTHOR]

The delta of the Mekong River is one of the largest in the world, with the Mekong River carrying a large amount of sediments in its Region of Freshwater Influence (ROFI). This study investigates the flow structure and movement of both suspended and bedload sediments in the ROFI of the Lower Mekong Delta (LMD) in order to identify areas prone to sediment accretion and erosion. This is accomplished by applying the three-dimensional Coastal and Regional Ocean COmmunity (CROCO) model and then calculating the sediment budget of different stretches of the coastline. The model outputs, depicting areas experiencing sediment accretion and erosion along the coastline of the LMD, are then compared against observations obtained during the period 1990–2015 and demonstrate the ability of the model to identify areas particularly prone to erosion and where preventive actions against coastal erosion should focus. [ABSTRACT FROM AUTHOR]

Mangroves belong to the most productive ecosystem on the Earth as they provide essential services in stabilizing shorelines, preventing erosion, carbon sequestration, and climate change. In recent decades, mangroves in the Andaman Islands have gone through enormous changes because of natural and anthropogenic activities. The main objective of this study was to investigate the extended areas of mangroves in the Andaman region and explore the major factors affecting their degradation. For this, we used the archives of the Landsat imageries in the Google Earth Engine cloud platform to identify the mangrove areas derived from a random forest model. The combined seven different Landsat spectral indices based on their ratios and Shuttle Radar Topography Mission (SRTM) data were used to delineate the mangrove-occupied areas. The resulting map of Andaman mangrove extents shows that the total mangrove area in 2000 was 1412 km2, 1164 km2 in 2010, and 887 km2 in 2020. The mangrove loss occurred by 25% between 2000 and 2020 in mangrove regions; because of the tsunami in 2004 in the Andaman Islands, severe degradation of the mangroves was observed in South Andaman Islands. In contrast, 5% of the mangrove areas were recovered in 2020 in the South Andaman region. The overall accuracy achieved during the mangrove classification in 2000 was 100%; for 2010, it was 99.7%, and for 2020 it was 99.9%, respectively. The mangrove areas were depleting due to several factors from the north to the south Andaman Islands. Furthermore, the results were analyzed with the possible landuse changes. Mangrove depletion occurred in the north Andaman region as a result of deforestation and agricultural conversion, whereas it is severely affected in the South Andaman region due to natural calamities. The results of this study are useful for understanding the landuse dynamics that influence the mangroves in the Andaman Islands region. Sufficient measures are necessary to maintain the sustainable management of mangroves for maintaining the coastal ecosystem stability. [ABSTRACT FROM AUTHOR]

Accurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e., river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model's performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management. [ABSTRACT FROM AUTHOR]

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5–22% using traditional recharge maps, while those are 9–23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was>2500 mS/cm in the high groundwater vulnerability zones, while it was <1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices. [ABSTRACT FROM AUTHOR]

Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the "poor" to "very poor" bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality. [ABSTRACT FROM AUTHOR]

Changes in the streamflow pattern raise a plethora of implications on the morphological, economic, social, and cultural aspects of an entire river basin. Trend analysis of hydrological time series is the most common method to identify these changes. Several methods have been proposed to identify the trends in time series. In the present study, Mann–Kendall (MK), innovative trend analysis (ITA), and revised innovative trend analysis (RITA) were used to identify the trend in discharge and sediment load at two gauging sites of the lower Drava River Basin, during 1988–2017 with an objective to compare and understand the limitations of these methods. Innovative trend analysis provided greater visualization of the trend of different magnitude of discharges and sediment load as compared to Mann–Kendall test. However, it was observed that due to certain assumptions, ITA produced significant trends when the trends were insignificant. The significance of trends obtained from RITA was in close agreement with the Mann–Kendall test. In this regard, the method of determining the significance of trend in ITA needs to be rechecked and revised. [ABSTRACT FROM AUTHOR]

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t + 1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2) values of 168.94 m3/s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3/s, 0.67, 0.84, 0.7023 and 148.55 m3/s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis. [ABSTRACT FROM AUTHOR]

Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials. [ABSTRACT FROM AUTHOR]

A comprehensive overview of flow characteristics in natural channels with bedforms is a vital issue in river management projects. Pool-riffle sequences as common bedforms in the gravel-bed rivers significantly impact flow characteristics and turbulence intensity. The present study was taken by field investigation in the Babolroud River, Iran. A 95 m reach with variable width was chosen in this river and velocity components and shear stress were obtained in different sections. Quadrant analysis was also applied to determine the dominant bursting event in the pool section. The results revealed a phase shift for stream-wise velocity, near-bed velocities, and bed shear stress versus bed profile. In the pool, vertical velocity components were oriented downward near the bed and upward near the water surface, while in the riffle section vectors were oriented towards the bed. The findings of quadrant analysis demonstrated the ejections and sweeps as a dominant event close to the bed and water surface, respectively. [ABSTRACT FROM AUTHOR]

One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters. [ABSTRACT FROM AUTHOR]

Many regions of Iraq were suffering from (meteorological and agricultural) drought. Middle Euphrates region is one of these regions. Therefore, this study carried out to monitor this phenomenon in this region during period from 1988 to 2018. The Reconnaissance Drought Index (RDI) and the Vegetation Condition Index (VCI) have been utilized for monitoring the meteorological and agricultural droughts, respectively. The final results showed that the overall drought percentage in the study area had been increased compared with no drought percentage. The percentage of the no drought area was ranged from 8% (in 1988 and 2000) to 17% (in 2018). The severe and extreme drought classes recorded a higher ratio in the area followed by moderate and mild droughts. There is a high level of the dry season in the region, especially in its southern and western parts. The reason for drought particularly in these parts is due to the geological composition of its soils, where it consists sandy soils that are consistently subjected to erosion by the wind. Additionally, the high porosity of these soils adversely influenced on their water maintenance limit particularly water of precipitation. In addition to the absence of a source of surface water in these parts of the region and its dependence on the water of some wells with high salinity and unsuitable for agriculture. [ABSTRACT FROM AUTHOR]

Empirical frequency distribution of daily precipitation amounts can be fitted by a negative exponential distribution, because anywhere there are many small daily totals and few large ones. Therefore, the cumulative percentages of days with precipitation, sorted in increasing order according to their amounts, against the cumulative percentage of the rainfall amounts that they contribute are fitted by positive exponential curves Y = aXebx, a and b constants. Based on these curves, the Concentration Index (CI) evaluates the contribution of the rainiest days to the total amount. In this study the CI has been calculated for 15 meteorological stations in Da Nang city and Quang Nam province in Central Coast Vietnam, for the 1979–2016 period. The results show high values of CI, ranging from 0.62 to 0.72. Conversely, the linear correlation between altitude and CI is negative (R = -0.60, p < 0.01). There are no correlations between the latitude nor the annual mean number of precipitation days and the CI. CI change for the sub-periods of 1979–1997 and 1998–2016 is also analyzed. [ABSTRACT FROM AUTHOR]

The ecological sustainable development and planning of groundwater resources is an excessive challenge for many countries currently facing water insufficiency. The main focus of this work was to determine the direction of groundwater flow, head value, and water level using the steady-state finite difference model (MODFLOW software) in basaltic formations in Maharashtra, India. The MODFLOW model was integrated with ground data using Geographic Information System (GIS) for sustainable groundwater resource management in the hard rock terrain. The MODFLOW-2005 model simulated the interaction between heads and time in 2014–18 by steady-state conditions. In this present study, four observation wells were selected. During the field survey, four observation wells have been monitored regularly as per the Central Groundwater Board guidelines. MODFLOW software has been conceptualized as a double-layered rigid and fractured aquifer area feast over 18,312 m × 11,265 m area. This research demonstrates that the integration of GIS, conventional fieldwork, and mathematical model can support to understand groundwater demand and supply in a better way. [ABSTRACT FROM AUTHOR]