Search

Climate change and rapid socioeconomic development have exacerbated the damage caused by hydrological droughts. To ensure effective drought defense and infrastructure development, it is essential to investigate variations in hydrological droughts. The primary objective of this study is to reconstruct the natural streamflow by using Soil and Water Assessment Tool (SWAT) hydrological modeling. The hydrological drought at different time scales (1, 3, 6, and 12 months) were measured using the streamflow drought index (SDI). The statistical parameters, including Nash–Sutcliffe Efficiency and the Coefficient of Determination, which yielded values of 0.84 and 0.82 during the calibration period and 0.78 and 0.76 during the validation period, respectively, showed a satisfactory SWAT model performance. Additionally, the Pettit test was used to identify a change point in streamflow within the 1991–2015 timeframe, leading to the division of the study period into two distinct phases: an undisturbed period (1991–1998) and a disturbed period (1999–2015). The SDI index‐based analysis revealed 9.39% moderate drought and 3.13% severe drought during the undisturbed period, while 11.76% moderate drought and 7.35% severe drought may happen due to the human influences that occurred in the disturbed period. These findings enhance the understanding of the hydrological drought variations in the Soan River basin for optimizing the water resources management system and effectively preventing and mitigating drought‐related damages. [ABSTRACT FROM AUTHOR]

Watershed management is necessary to conserve water resources because the watershed hydrological processes are more affected by climate and land use change, resulting in the problems of droughts, floods, soil erosion, etc. This study determined suitable alternatives that can ensure viable strategies for tackling the climate change impacts at the Soan River Basin (SRB). A framework was applied to assess the impacts of climate change and land use/cover change (LUCC) using the Soil and Water Assessment Tool (SWAT). A multi-criteria decision analysis (MCDA) was used to prioritize watershed management alternatives by comparing watershed management criteria and alternatives using the analytic hierarchy process (AHP). Framework findings showed a 69 and 31% decline in runoff, and a 58 and 42% increment in evapotranspiration (ET) due to climate change and LUCC, respectively. The top prioritized suitable alternatives were water harvesting structure (WHS) and vegetative cover (VC). Suitability analysis showed that 63.61 and 16.56% area of the SRB were moderately to highly suitable for WHS, respectively. For soil and water management, VC has been found suitable to moderately suitable for 72.68 and 26.75% of the basin area, respectively. So, there should be adoption of such measures which will assist in configuring the climate adaptive strategies. HIGHLIGHTS Climate change and LUCC were assessed through a framework using the SWAT model.; AHP was used for watershed management via suitable alternatives.; Water harvesting structures and vegetative cover were found to be the best alternatives for watershed management.; The selected alternatives can mitigate the climate change impacts on the watershed and may supplement to improve management practices.;

Management of the freshwater resources in a sustained manner requires the information and understanding of the surface water hydrology and streamflow is of key importance in this nexus. This study evaluates the performance of eight different precipitation products (APHRODITE, CHRS CCS, CHRS CDR, CHIRPS, CPC Global, GPCC, GPCP, and PERSIANN) for streamflow prediction in two sub-catchments (Chirah and Dhoke Pathan) of the data-scarce Soan River Basin (SRB) in Pakistan. A modified version of the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) known as HBV-light was used to generate streamflow. The model was separately calibrated and validated with observed and estimated precipitation data for streamflow simulation with optimized parameterization. The values of R2, NSE, KGE and PBIAS obtained during the calibration (validation) period for the Chirah sub-catchment were 0.64, 0.64, 0.68 and −5.6% (0.82, 0.81, 0.88 and 7.4%). On the other hand, values of R2, NSE, KGE, and PBIAS obtained during the calibration (validation) period for the Dhoke Pathan sub-catchment were 0.85, 0.85, 0.87, and −3.4% (0.82, 0.7, 0.73 and 6.9%). Different ranges of values were assigned to multiple efficiency evaluation metrics and the performance of precipitation products was assessed. Generally, we found that the performance of the precipitation products was improved (higher metrics values) with increasing temporal and spatial scale. However, our results showed that APHRODITE was the only precipitation product that outperformed other products in simulating observed streamflow at both temporal scales for both Chirah and Dhoke Pathan sub-catchments. These results suggest that with the long-term availability of continuous precipitation records with fine temporal and spatial resolutions, APHRODITE has the high potential to be used for streamflow prediction in this semi-arid river basin. Other products that performed better were GPCC, GPCP, and CHRS CCS; however, their scope was limited either to one catchment or a specific time scale. These results will also help better understand surface water hydrology and in turn, would be useful for better management of the water resources. [ABSTRACT FROM AUTHOR]

Exploring dynamic vegetation changes and identifying the factors driving these changes are important for evaluating global ecosystem processes. Based on the pixel binary model, coefficient of variation, Theil-Sen median trend analysis, geographic detector, and Pearson correlation coefficient, this study analyzed vegetation cover variations and the factors influencing these changes in the Erhai Lake Basin, one of the most important plateau lakes in China. Vegetation cover exhibited a continuously increasing trend, with the proportion of high vegetation coverage consistently ranking first. Land cover is an effective explanatory factor for vegetation cover, and FVC shows obvious variation rules associated with elevation, land cover, population, and landform. It is important to highlight that the combination of two factors influences vegetation dynamics more significantly than one factor alone, with the interaction between land cover type and nightlight illumination being more powerful. These results enhance our understanding of the complex processes of vegetation cover variation in plateau lake catchments and offer a scientific reference for improving the spatial layout of vegetation in fragile ecosystems. [ABSTRACT FROM AUTHOR]

Pakistan's diverse landscapes and agrarian economy heavily rely on its water resources; however, groundwater scarcity poses a pressing challenge, particularly in the context of agriculture, industry, and households. Groundwater is the predominant water source that faces critical fluctuations influenced by seasonal precipitation changes and agricultural acts. Using Gravity Recovery and Climate Experiment (GRACE) based terrestrial water storage anomalies (TWSA), and water storage components from the Global Land Data Assimilation System (GLDAS), significant variations in groundwater storage are revealed. The analysis uncovers uneven groundwater storage patterns across the country, with distinct trends observed in different provinces. Over the past decade, at the national level, groundwater storage has significantly declined, Punjab showed notable decreases in groundwater storage attributed to overexploitation and rising water consumption; Balochistan, Khyber Pakhtunkhwa, Gilgit Baltistan, and Kashmir have declining trends, and Sindh experienced slightly higher levels of groundwater storage due to flood events. Furthermore, analyses showed that floods have led to substantial increases in storage, impacting all provinces, while droughts have caused significant drops in groundwater storage. These findings underscore the importance of sustainable groundwater management acts and emphasize the importance of provincial strategies and autonomy in developing integrated water resource management approaches, aligning with the country's National Water Policy. [ABSTRACT FROM AUTHOR]

The ecological environmental quality (EEQ) of the Yongding-Luan River Basin (YLRB) is pivotal to the ecological security of the Beijing-Tianjin-Hebei (JJJ) region's core area. Evaluating the EEQ and analyzing its changes are essential for regional ecological management. However, long-term ecological changes in the YLRB remain uncovered. In this study, we constructed a seamless Remote Sensing Ecological Index (RSEI) for the YLRB from 1986 to 2022 using time-series Landsat imagery on the Google Earth Engine (GEE) platform. The Sen + Mann-Kendall method was employed to analyze the spatiotemporal trends of EEQ, and the Geodetector was used to quantitatively assess the driving factors and their interactions. The results show that: 1) The mean RSEI of the YLRB increased from 0.486 in 1986 to 0.532 in 2022, marking a 9.5% rise and indicating a fluctuating upward trend. 2) The EEQ of the YLRB experienced three distinct phases: improvement, deterioration, and re-improvement. Improvements were predominantly in the western YLRB, while deterioration was mainly in the northern Xilinguole region and the southern urban expansion areas of Beijing, Langfang, Tianjin, and Tangshan. 3) The driving factor detection indicates that land use type and annual average precipitation are the primary driving factors of RSEI change in the YLRB. Furthermore, their interaction results in a significant effect on RSEI, with a maximum of 0.691. These findings align with the historical urban expansion in the YLRB and the environmental policies implemented by the Chinese government. The ecological evolution and driving factors identified in this study offer a scientific basis for regional ecological decision-making and management. [ABSTRACT FROM AUTHOR]

Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt breakpoint tests and the Double Mass Curve method were employed to identify the variation characteristics and breakpoints of precipitation (P), potential evapotranspiration (ET0), and runoff depth (R) in the Wanquan River Basin (WQRB) during the 1970–2016 period. The changes in runoff attributed to P, ET0, and the catchment characteristics parameter (n) were quantified using the elastic coefficient method based on the Budyko hypothesis. The results revealed that the P and R in the WQRB exhibited statistically insignificant decreasing trends, while ET0 displayed a significant increasing trend (p < 0.05). The breakpoint of runoff changes in the Jiabao and the Jiaji stations occurred in 1991 and 1983, respectively. The runoff changes show a negative correlation with both the n and ET0, while exhibiting a positive correlation with P. Moreover, it is observed that P and ET0 display higher sensitivity towards runoff changes compared to n. The decomposition analysis reveals that in the Dingan River Basin (DARB), human activities account for 53.54% of the runoff changes, while climate variability contributes to 46.46%. In the Main Wanquan River Basin (MWQRB), human activities contribute to 46.11%, whereas climate variability accounts for 53.89%. The research findings suggest that runoff is directly reduced by climate variability (due to decreased P and increased ET0), while human activities indirectly contribute to changes in runoff through n, exacerbating its effects. Rubber forest stands as the prevailing artificial vegetation community within the WQRB. The transformation of natural forests into rubber plantations constitutes the primary catalyst for the alteration of n in the WQRB. The research findings provide important reference for quantifying the driving force of hydrological changes caused by deforestation, which is of great significance for sustainable management of forests and water resources. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate and land use change on soil erosion is particularly important to the development and management of ecosystems. The purpose of this research was to differentiate the impacts of climate and land use alterations on soil erosion by using the InVEST model in the Anning River basin. The findings indicated a rise in average soil erosion from 42.78 t ha−1·a−1 to 49.84 t ha−1·a−1 over the decade from 2010 to 2020, with climate change accounting for 99.71% of the increase and land use change contributing 0.28%. The findings also indicated that the process of urbanization and the implementation of the Returning Grain to Forestry and Grass (RGFG) strategy were effective in decreasing soil erosion by 1.29 t ha−1·a−1 and 6.60 t ha−1·a−1, respectively. Four management measures were developed based on our results. The results of this study are not only of great significance for the environmental protection of a specific region, but also provide references for the mitigation of soil erosion in other regions of the world. [ABSTRACT FROM AUTHOR]

The ice phenomena are an inherent component of rivers in temperate, continental, and polar climate zones. Evident progress in global warming leads to a decrease in snow cover on land and ice phenomena in water bodies, disrupting the stability of the hydrological cycle and aquatic ecosystems. Although common observations indicate the disappearance of ice phenomena in rivers over recent decades, detailed quantitative research is lacking in many regions, especially in the temperate zone. In this paper, ice phenomena were analyzed on the rivers of southern Poland, located in the upland and mountain areas of the country, as no such studies have been conducted so far. The temporal changes in the annual number of days with ice (NDI) phenomena were studied in locations where ice phenomena were observed every year for at least 30 years between 1951 and 2021. Using straightforward but commonly accepted procedures, such as the Mann-Kendall test, statistically significant decreasing trends in the annual NDI were revealed for the majority of gauging stations. The Theil-Sen (TS) slope mean values were -1.66 (ranging from -3.72 to -0.56), -1.41 (from -3.22 to -0.29), and -1.33 (from -2.85 to -0.29) for the datasets representing the periods 1992–2020, 1987–2020, and 1982–2020, respectively. The results for the annual NDI were additionally presented within the context of meteorological characteristics such as annual and winter (Nov-Apr) air temperature, precipitation, and water temperature. Correlation and regression analyses revealed that the main factor triggering the decrease in NDI is the increase in the average winter air temperature. An increase in temperature by 1°C results in a decrease in NDI by up to twenty days. If these negative trends continue, ice phenomena may disappear completely from southern Polish rivers within few decades. [ABSTRACT FROM AUTHOR]

The climate change and extension of human activities are shedding more stresses on ecosystems. Ecological zoning could help manage the ecosystem and deal with environmental problems more effectively. Geology and topography could affect the ecology primarily and are vital perspectives on ecological zoning. It is worth preliminarily understanding the spatial-temporal patterns of ecologicalenvironmental attributes within various geological-topographical ecological zones (GTEZs). The objective of this study was to delineate GTEZs and present a spatial-temporal analysis on soil and land surface parameters within GTEZs. Firstly, Landsat imageries, high resolution satellite imagery products, digital elevation model, regional geological map, black soil thickness, soil bulk density, meteorological data, and ground survey were collected and conducted. Secondly, GTEZs in Hailun District were delineated according to geological and topographical background. Thirdly, soil composition, and monthly land surface temperature (LST), enhanced vegetation index (EVI), net primary productivity (NPP) were derived from ground survey and Landsat imageries. Finally, spatialtemporal patterns of various ecological-environmental attributes within different GTEZs were preliminarily revealed and analyzed. Results show that sand alluvial plain zone and silt-clay undulating plain zone mainly possess thick soil with finemedium granule and higher bulk density, and are mainly covered by crops and grass, vegetation flourish the most in August with the highest monthly EVI and NPP. While the sand-conglomerate hill zone, sandstone hill zone, and granite hill zone possess relatively thin soil with medium-coarse granule and lower bulk density, and are mainly covered by forest, vegetation flourish the most in June and July, and has the highest yearly total NPP. With thinner soil thickness and higher NPP, hill zones tend to have more vulnerability to disturbance and more contribution to carbon neutrality target. [ABSTRACT FROM AUTHOR]

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. [ABSTRACT FROM AUTHOR]

Utilizing the power of sunlight through agro-photovoltaic fusion systems (APFSs) seamlessly blends sustainable agriculture with renewable energy generation. This innovative approach not only addresses food security and energy sustainability but also plays a pivotal role in combating climate change. This study assesses the feasibility and impact of APFS implementation in District Dir Lower, Pakistan, a region significant for its agriculture and energy needs. A quasi-experimental design was employed, comparing outcomes between a treatment group (with an APFS) and a control group (without an APFS). Stratified random sampling was used to select 400 participants, including farmers, residents, local authorities, and community leaders. Data were collected using structured questionnaires and analyzed employing paired t-tests, linear regression, analysis of variance (ANOVA), and Chi-square tests. The results show that the treatment group with an APFS exhibited significant improvements in farming practices (mean change = 4.20 vs. 2.80). Linear regression indicated a strong positive effect of APFSs on renewable energy production. The ANOVA results demonstrated significant mitigation of environmental challenges, and the Chi-square test showed a strong association between APFS implementation and community sustainability, resilience, and prosperity. It is concluded that APFS implementation significantly enhances farming practices, renewable energy production, and environmental sustainability, contributing to the resilience and prosperity of agricultural communities in District Dir Lower. These findings advocate for the broader adoption of APFSs in similar contexts to integrate sustainable agriculture with renewable energy generation. [ABSTRACT FROM AUTHOR]

Introduction: Land use land cover (LULC) change is an important factor driving global change, influenced by the interaction between human activities and natural ecosystems. The upper watershed of the Qingshui River is adversely affected due to anthropogenic activities. Therefore, analyzing the driving factors of land use changes in this area is crucial for ecological protection and sustainable development. Methods: Based on the long-term Landsat image data from 1990-2020, the spatiotemporal change characteristics of the LULC rate and its driving factors in the upper watershed of the Qingshui River basin were analyzed using the land use transfer matrix and dynamic degree of land use processes. The redundancy analysis was performed to investigate the links between LULC changes, socio-economic and climatic variables. Results: From 1990-2020, the area under waters and woodland decreased by -2.94 km² and -451.44 km², respectively. Meanwhile, grassland, arable land, construction land, and unused land area increased by 278.71 km², 115.72 km², 46.48 km² and 13.49 km². In terms of the proportion area to the total land (2334.10 km²), woodland accounted for 63.43-44.09% of the total land and was mainly distributed in the east; arable land was 17.00-21.96% and was largely distributed on both sides of the middle and lower parts of the basin; construction land comprised 1.03-3.02% and was generally found in flat areas near the downstream and water area was only 0.1-0.01% which primarily covered the Qingshui River and its tributaries from 1990-2020. Construction and unused lands showed the fastest rate of change, followed by water area and grasslands. Accelerated urbanization, rapid socio-economic development, and the farmland-to-forest policy were the main driving forces behind the change in LULC in the upper reaches of the Qingshui River. Discussion: The findings of this study can assist in planning strong management strategies for ecological protection and socio-economic sustainable development in the study area. [ABSTRACT FROM AUTHOR]

The hydrological cycle is significantly impacted by climatic changes, and understanding such changes using statistical or graphical methods is essential for proper management of water resources. This study performs the applications of two most recently proposed variants of popular Sen's Innovative Trend Analysis (ITA) methods namely Innovative Polygonal Trend Analysis (IPTA) and Innovative Trend Pivot Analysis (ITPA) for analysing the temporal trend of rainfall data of India. The rainfall data of All India along with five Homogeneous regions spanning from 1871–2016, are considered for this study. In order to determine the transition of changes throughout months and seasons, the trend length and trend slope were computed, which explicitly demonstrated a climatic shift in the rainfall of the Indian main land. The IPT and ITP analyses were carried out on both arithmetic mean (AM) and standard deviation (SD) based frameworks and SD of the data is found to be more decisive for monthly and seasonal rainfall over Indian regions in inducing climate non stationarity. The Central Northeast and North East regions show vulnerability to changing climate in rainfall magnitude and variability, while North West and West Central regions brings more risk as captured by ITPA method. [ABSTRACT FROM AUTHOR]

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (T max ), minimum temperature (T min ), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (T max , T min , and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of T max , T min , and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent for publication: Not applicable. Consent to participate: Not applicable. Ethics approval: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Residential buildings contribute 30% of the UK's total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings' energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building's energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the spatiotemporal variability and trends of hydro-climatic time series over the Modjo watershed, central Ethiopia using long-term records of precipitation, temperature, and river flow. In the study, the modified Mann–Kendall trend test was used to identify temporal trends, and Sen's slope estimator was employed to determine trend magnitude at multiple scales. Results of the study show that the spatial variability of annual and seasonal rainfall has decreased from north to south and west to east, whereas this was increased for the evapotranspiration (ETo) over the study watershed. Based on the trend tests, the annual and main rainy season flow exhibit significantly decreasing trends, with magnitudes of −19.2 mm/yr and −18.82 mm/yr, respectively, at a 95% confidence level. Whereas, the rainfall and ETo experienced monotonic trends during most of the wet season months and the annual total. The study highlights that the drop in river flow may be better explained by human activities, as seen by the weak association with climatic indicators. Water resources and irrigated agricultural production systems may be at risk in the area due to the unpredictability and declining trend in the river flow. Therefore, the study suggests appropriate watershed management, enhanced alternative water management techniques, and efficient use of the available water resources in the future. [ABSTRACT FROM AUTHOR]

Climate change–induced precipitation unpredictability has disastrous consequences for closed basins with sensitive hydrological equilibrium. Identifying changes and trends in precipitation characteristics in such catchments is an important step towards minimizing potential disasters. This study explores the precipitation variability of the Konya Closed Basin (Turkey), an over-exploited semi-arid basin. For this purpose, monthly precipitation data for the period 1971–2020 (50 years) of 11 meteorological observation gauges operated by the Turkish State Meteorological Service in the basin were utilized. Precipitation data were examined using two graphical innovative trend methods namely innovative polygon trend analysis (IPTA) and trend polygon star concept (TPSC) approaches. With these methods, periodic features in monthly precipitation data and trend transitions between months can be interpreted visually and linguistically. IPTA and TPSC methods were applied separately to both arithmetic mean data and standard deviation for each month's precipitation data of 11 stations. A general increasing trend in precipitation was obtained in most of the months for the stations used in the IPTA application. Most of the TPSC arrows, which were distributed throughout all four regions for both the arithmetic mean and standard deviation, were in the I and III regions. Based on the TPSC graphs, the longest transition arrows between two consecutives occurred in the first months of autumn (highly increasing) and summer (highly decreasing) in most of the stations. The results of this study are of great importance for the policies to be sustained regarding the ongoing climate change in the agricultural sector in KCB, which is both a water scarce basin and a region with intensive agricultural activities. [ABSTRACT FROM AUTHOR]

Nature-based solutions (NbSs) are considered to form an innovative stormwater management approach that has living resolutions grounded in natural processes and structures. NbSs offer many other environmental benefits over traditional grey infrastructure, including reduced air pollution and climate change mitigation. This review predominantly centers on the hydrological aspect of NbSs and furnishes a condensed summary of the collective understanding about NbSs as an alternatives for stormwater management. In this study, which employed the CIMO (Context, Intervention, Mechanism, Outcome) framework, a corpus of 187 NbS-related publications (2000–2023) extracted from the Web of Science database were used, and we expounded upon the origins, objectives, and significance of NbSs in urban runoff and climate change, and the operational mechanisms of NbSs (including green roofs, permeable pavements, bioretention systems, and constructed wetlands), which are widely used in urban stormwater management, were also discussed. Additionally, the efficacy of NbSs in improving stormwater quality and quantity is discussed in depth in this study. In particular, the critical role of NbSs in reducing nutrients such as TSS, TN, TP, and COD and heavy metal pollutants such as Fe, Cu, Pb, and Zn is emphasized. Finally, the main barriers encountered in the promotion and application of NbSs in different countries and regions, including financial, technological and physical, regulatory, and public awareness, are listed, and future directions for improving and strategizing NbS implementation are proposed. This review gathered knowledge from diverse sources to provide an overview of NbSs, enhancing the comprehension of their mechanisms and applications. It underscores specific areas requiring future research attention. [ABSTRACT FROM AUTHOR]

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R2 and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

This study investigated the temporal and spatial variations of precipitation duration and intensity in Jiangsu Province from 1960 to 2020 using the IDW spatial interpolation method and Kendall's tau trend test, based on daily precipitation data collected from 22 meteorological stations. Additionally, a Pearson correlation analysis was conducted to examine the correlations between the occurrence rate and contribution rate of precipitation with different durations and grades, as well as five large-scale climate indices. The results indicated the following trends: (1) An increase in the precipitation duration corresponded to a decrease in the occurrence rates, while the contribution rates initially increased and then decreased. The province was predominantly characterized by 1–3 days of light rainfall, with a higher probability of short-duration heavy rainfall in northern Jiangsu. (2) From 1960 to 2020, most stations experienced decreasing trends in the precipitation duration occurrence and contribution rates, but heavy rainfall increased, suggesting a shift to short-duration heavy precipitation. (3) The Arctic Oscillation (AO) notably negatively correlates with the 9-day occurrence rate of precipitation (9dOR), while it positively correlates significantly with the occurrence rate of moderate rainfall (MROR). The North Atlantic Oscillation (NAO) exhibits a significant positive correlation with the 2-day occurrence rate of precipitation (2dOR) and a notable negative correlation with the 9-day occurrence rate of precipitation (9dOR). The PDO (Pacific Decadal Oscillation) has shown significant positive correlations with the 2-day precipitation occurrence rate (2dOR) and contribution rate (2dCR), a negative correlation with the light rainfall occurrence rate (LROR), and significant positive correlations with both the moderate and heavy rainfall occurrence rates (MROR and HROR, respectively). The AO, NAO, and PDO are potential climate factors that influence changes in the precipitation structure in Jiangsu Province. These research findings offer valuable insights for regional water resource management, flood risk assessment, and predicting future precipitation trends under climate change scenarios. [ABSTRACT FROM AUTHOR]

Water is an essential entity of life, and its major sources are surface and groundwater. However, surface water is excluded long ago for drinking purposes due to high contamination through different human activities such as the common release of domestic and industrial wastewater into nearby running water sources. No doubt industries are the economic backbone of any country; however, due to the lack of proper waste disposal policies in under-developing and developing countries, their common activity is disposing of wastewater into nearby surface water sources. This is causing great harm to not only the surface water but also the groundwater resources since the major source of groundwater recharge is through these surface waters. Rawat Industrial State is the second-largest industrial zone in Pakistan. Due to poor waste disposal policies, their wastewater is released into nearby streams. To check the linkage of surface water as a recharging source of groundwater, we applied an electrical resistivity survey including 12 profiles throughout the waste release zone and collected 18 water samples to find the maximum affective depth of these contaminants. Our results have confirmed that all the major aquifers in the study area are receiving their recharge through these contaminated streams, and it is polluting the groundwater up to the sampled depth of 350 ft. [ABSTRACT FROM AUTHOR]

This study focuses on a hydro-meteorological variable, streamflow, and its vulnerability to global warming. In this study, we analyze monthly streamflow in 40 catchments across four major South Indian River basins: Cauvery, Godavari, Krishna, and Mahanadi, within the Indian Peninsular region. The chosen catchments have available streamflow series records ranging from 41 to 49 years, spanning from 1970 to 2018. To uncover trends in the streamflow datasets, an innovative technique called Innovative Polygon Trend Analysis (IPTA) is used. IPTA detects trend transitions between successive time series parts and identifies possible trend pieces within a year, crucial for agricultural and irrigation practices. Results reveal diverse polygon patterns across different catchments, reflecting non-systematic streamflow variation. Non-systematic changes occur primarily in the Cauvery basin, followed by the Godavari, Krishna, and Mahanadi basins. Varied trends—increasing and decreasing—are dispersed across different catchments. Increasing trends were generally observed in the standard deviation of streamflow, while decreasing trends were more widespread in the mean of streamflow. Notably, the Godavari basin displays more prevalent decreasing trends in the majority of catchments for different months, followed by Cauvery and Krishna. Mahanadi basin has more catchments showing an increasing trend than other basins. The results of this study provide deeper insight into the streamflow dynamics within these four basins. [ABSTRACT FROM AUTHOR]

This editorial paper takes the form of a concise report and delves into a critical and intricate issue essential for the sustainability of agriculture. It centers on the intricate relationship between agri-cultural water resource management and agronomical practices, as well as their ability to adapt to the impacts of climate change while ensuring both the quantity and quality of crop yields. Specifically, this paper serves as a synopsis of how the far-reaching consequences of climate change for water resources impact agricultural production. It also highlights primary adaptation strategies for managing agricultural water resources, as drawn from the existing literature. Such strategies are designed to counteract the potentially adverse impacts of climate change on the rural sector. Fur-thermore, this brief report offers a valuable overview of the 17 selected papers featured in this Special Issue (SI) on Water, published by MDPI. These papers serve as exemplars of cutting-edge approaches to adaptability in water resource management and resilient crop production systems, as these fields attempt to thrive in an ever-changing environmental landscape. [ABSTRACT FROM AUTHOR]

In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin. [ABSTRACT FROM AUTHOR]

Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling. [ABSTRACT FROM AUTHOR]

Accurate prediction of future streamflow in flood-prone regions is crucial for effective flood management and disaster mitigation. This study presents an innovative approach for streamflow projections in deep learning (DL) environment by integrating the quantitative Land-Use Land-Cover (LULC) overlaid with flow accumulation values and the various Global Climate Model (GCM) simulated data. Firstly, the Long Short Term Memory (LSTM) model was developed for the streamflow prediction of Greater Pamba River Basin (GPRB) in Kerala, India for 1985 to 2015 period, considering the climatic inputs. Then, the flow accumulation-weighted LULC integration was considered in modelling, which substantially improves the accuracy of streamflow predictions including the extremes of all the three stations, as the model accounts for the geographical variety of land cover types towards the streamflow at the sub-basin outlets. Subsequently, Reliability Ensemble Averaging (REA) technique was used to create an ensemble of three candidate GCM products to illustrate the spectrum of uncertainty associated with climate projections. Future LULC changes are accounted in regional scale based on the sub-basin approach by means of Cellular-Automata Markov Model and used for integrating with the climatic indices. The basin-scale streamflow projection is done under three climate scenarios of SSP126, SSP245 and SSP585 respectively for lowest, moderate and highest emission conditions. This work is a novel approach of integrating quantified LULC with flow accumulation and other climatic inputs in a DL environment against the conventional techniques of hydrological modelling. The DL model can adapt and account for shifting hydrological responses induced by changes in climatic and LULC inputs. The integration of flow accumulation with changes in LULC was successful in capturing the flow dynamics in long-term. It also identifies regions that are more likely to experience increased flooding in the near future under changing climate scenarios and supports decision-making for sustainable water management of the Greater Pamba Basin which was the worst affected region in Kerala during the mega floods of 2018. [ABSTRACT FROM AUTHOR]

Erosion is primarily caused by precipitation and runoff, while erodibility results from the type of the formations at the catchment scale, which is subject of this study. The objective of this study was to investigate the relationship between geologic units and erosion intensity. Initially, two models of MPSIAC and Fargas were used to study erosion intensity. The Fargas model was presented based on the theoretical principles of two erosion and the erodibility factors, while MPSIAC model was based on 9 factors. BLM method was used to separate the intensities of a type of erosion according to the S.S.F table. RUSLE model was used to estimate soil erosion in the studied sub-basins. To determine the risk of erosion using the value coefficient, Fargas model was used. In the MPSIAC model, coefficients were considered for each of the nine factors. Based on the results of MPSIAC model, 79% of the basin surface had high erosion intensity and 21% had very high erosion intensity, while according to Fargas model, 0.3% had moderate erosion intensity, 10% had high erosion intensity, and 4.7% and 85% were characterized with severe erosion and very severe erosion, respectively. Moreover, in both models, 18% corresponded to very high erosion intensity (very severe), while 7.2% had high erosion intensity. In Fargas model, E1t1, E1t2, E2ig3, Q2f, Qt1, and Qt2 had very strong erosion intensity, whereas, in MPSIAC model, E1t1, Qt1, and Qt2 had high erosion intensity on their surfaces. One reason for this phenomenon could be the erodibility of the formations. Most of E1t2 and Q2t are highly eroded, which contributes to a large amount of drainage in addition to the erodibility of the formations. [ABSTRACT FROM AUTHOR]

Groundwater quality risk assessment is vital to protect this precious resource, because increasing anthropogenic and agricultural activities combined with limited precipitation deteriorate the groundwater quality particularly in the arid regions. Therefore, the assessment of groundwater quality using hydro-chemical and spatial analysis can provide the guidelines for efficient management of groundwater resources. In present study, a total of 87 samples were collected from various pumping wells in district Multan, Pakistan. These samples were analysed for groundwater quality parameters like electrical conductivity (EC), total dissolve solids (TDS), pH, Na+, Ca2+ + Mg2+, Cl−, CO32−, HCO3−, sodium adsorption ratio (SAR), sodium percentage (Na%), total hardness (TH), residual sodium carbonate (RSC) and Kelly's ratio (KR). The Wilcox, United States Salinity Laboratory (USSL) and permeability index (PI) diagrams were drawn to classify the water into excellent, good, marginal and poor-quality groundwater for irrigation obligatory. Using the ArcGIS vs 10, an ordinary kriging method with best fit semivariogram model was applied for preparation of spatial distribution maps. According to Wilcox classification, 40% of groundwater samples fall in 'Excellent to a Good' category. USSL diagram showed that 27% of groundwater samples fall in 'Medium Salinity' and 'Low sodium hazard' (C2S1) class. The PI values were found in the range of 22 to 95 meq/L with an average value of 58.5 meq/L. Similarly, the spatial analysis showed that upper part (northeast and northwest) of the Multan District have good quality of groundwater for irrigation. Furthermore, the finding may help to make the spatial management decision for groundwater in the region. [ABSTRACT FROM AUTHOR]

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance. [ABSTRACT FROM AUTHOR]

Climate change and human activities significantly impact the hydrological cycle, particularly in regions with numerous large-scale reservoirs. Recognizing the limitations of the reservoir module in the original SWAT model, this study presents an improved reservoir module based on a dispatch function to enhance runoff simulation. Its performance is validated by simulating daily runoff in the Jinsha River Basin, China. The scenario simulation approach is employed to quantitatively analyze the influences of climate change and human activities on runoff. And downscaled Global Climate Models (GCMs) are utilized to predict runoff for the next three decades. The results show that (1) the improved SWAT model outperforms the original model in runoff simulation; (2) during the test period, reservoir regulations caused a reduction of 26 m3/s in basin outlet runoff, while climate change led to an increase of 272 m3/s; and (3) future changes in basin outlet runoff over the next 30 years exhibit a high level of uncertainty, ranging from −5.6% to +11.0% compared to the base period. This study provides valuable insights into the hydrological impacts of climate change and human activities, highlighting the importance of incorporating an improved reservoir module in hydrological modeling for more accurate predictions and assessments. [ABSTRACT FROM AUTHOR]

In order to assess the effects of climate change and land use change on Rawal Dam, a major supply of water for Rawalpindi and Islamabad, this study uses hydrological modeling at the watershed scale. The HEC-HMS model was used to simulate the hydrological response in the Rawal Dam catchment to historical precipitation. The calibrated model was then used to determine how changes in land use and climate had an impact on reservoir inflows. The model divided the Rawal Dam watershed into six sub-basins, each with unique features, and covered the entire reservoir's catchment area using data from three climatic stations (Murree, Islamabad Zero Point and Rawal Dam). For the time spans of 2003–2005 and 2006–2007, the model was calibrated and verified, respectively. An excellent fit between the observed and predicted flows was provided by the model. The GCM (MPI-ESM1-2-HR) produced estimates of temperature and precipitation under two Shared Socioeconomic Pathways (SSP2 and SSP5) after statistical downscaling with the CMhyd model. To evaluate potential effects of climate change and land use change on Rawal Dam, these projections, along with future circumstances for land use and land cover, were fed to the calibrated model. The analysis was carried out on a seasonal basis over the baseline period (1990–2015) and over future time horizon (2016–2100), which covers the present century. The findings point to a rise in precipitation for both SSPs, which is anticipated to result in an increase in inflows throughout the year. SSP2 projected a 15% increase in precipitation across the Rawal Dam catchment region until the end of the twenty-first century, while SSP5 forecasted a 17% increase. It was determined that higher flows are to be anticipated in the future. The calibrated model can also be utilized successfully for future hydrological impact assessments on the reservoir, it was discovered. [ABSTRACT FROM AUTHOR]

In the present study, the optimal placement contamination warning systems (CWSs) in water distribution systems (WDSs) was investigated. To this end, we developed a novel optimization model called WOA-SCSO, which is based on a hybrid nature-inspired algorithm that combines the whale optimization algorithm (WOA) and sand cat swarm optimization (SCSO). In the proposed hybrid algorithm, the SCSO operators help to find the global optimum solution by preventing the WOA from becoming stuck at a local optimum point. The effectiveness of the WOA-SCSO algorithm was evaluated using the CEC′20 benchmark functions, and the results showed that it outperformed other algorithms, demonstrating its competitiveness. The WOA-SCSO algorithm was finally applied to optimize the locations of CWSs in both a benchmark and a real-world WDS, in order to reduce the risk of contamination. The statistically obtained results of the model implementations on the benchmark WDS showed that the WOA-SCSO had the lowest average and standard deviation of the objective functions in 10 runs, 131,754 m3 and 0, respectively, outperforming the other algorithms. In conclusion, the results of applying the developed optimization model for the optimal placement of CWSs in the Dortmund WDS showed that the worst-case impact risk could be mitigated by 49% with the optimal placement of at least one sensor in the network. These findings suggest that the WOA-SCSO algorithm can serve as an effective optimization tool, particularly for determining the optimal placements of CWSs in WDSs. [ABSTRACT FROM AUTHOR]

The implications of climate change on water resources are of major concerns worldwide. Arid regions of the world need proper management for the water resources as climatic changes are bringing severe problems for their sustainability. Pothohar area of Pakistan is the most essential in this regard. A data set of 30 years of temperature, precipitation, and river discharge across the Pothohar Plateau were obtained from Pakistan Meteorological Department and Water and Power Development Authority, respectively. Hydro-meteorological data were subjected to a series of statistical methods for detecting trend and abrupt point changes. A rising trend in mean annual temperatures were noticed in all stations with most significant change in Jhelum and Attock stations (p < 0.05) during the period of 30 years while precipitation trends were found irregular, representing both scenarios of flash floods and droughts with non-significant change (p > 0.05). In addition, 2000 and 2001 are drought years in 30 years of time series, and the driest years were seen from 1999 to 2002. The discharge of River Haro and Soan were found downward significantly (p > 0.05), posing a threatening challenge to the future of water resources in this area. The correlation coefficient test revealed a substantial relationship between mean annual precipitation and river discharge, although the linear regression model demonstrated a diminishing trend with mean annual precipitation. Based on the analysis, it is concluded that modern techniques of irrigation and agriculture should be adopted to protect the future of the Pothohar area from climate change related extreme situations. [ABSTRACT FROM AUTHOR]