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]

The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change.

This study evaluates the spatiotemporal rainfall variability over the semimountainous Soan River Basin (SRB) of sub-Himalayan Pothwar region, Pakistan. The temporal rainfall trend analysis of sixteen rain gauges was performed on annual basis with long-term (1981–2016) data. The results depicted that there is substantial year-to-year and season-to-season variability in rainfall patterns, and rainfall patterns are generally erratic in nature. The results highlight that most of the highland rainfall stations showed decreasing trends on annual basis. The central and lowland stations of the study area recorded an increasing trend of rainfall except for Talagang station. The average annual rainfall of the study area ranges between 492 mm and 1710 mm in lowland and high-altitude areas, respectively. Of the whole year’s rainfall, about 70 to 75% fall during the monsoon season. The rainfall spatial distribution maps obtained using the inverse distance weighting (IDW) method, through the GIS software, revealed the major rainfall range within the study area. There is a lack of water during postmonsoon months (November–February) and great differences in rainfall amounts between the mountainous areas and the lowlands. There is a need for the rational management of mountainous areas using mini and check dams to increase water production and stream regulation for lowland areas water availability. The spatiotemporal rainfall variability is crucial for better water resource management schemes in the study area of Pothwar region, Pakistan.

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]

The quantitative description of relationships and propagation between different forms of drought at multiple spatiotemporal scales in various geographical locations is informative for early drought warning systems. This study intends to evaluate the historical hydrometeorological drought from 1984–2015 in the Soan River Basin, which is a critical water source for the Pothwar region of Pakistan. The reconnaissance drought index (RDI) and standardized runoff index (SRI) are used to characterize meteorological and hydrological droughts, respectively. The spatiotemporal variations of the RDI and SRI demonstrated that 2000 and 2010 were extremely dry and wet years, respectively. The results further reveal that the frequency of hydrometeorological drought events was higher in a shorter time scale (3 and 6 months), while durations featured longer timescales (9 and 12 months). The RDI and SRI time series showed a significant decreasing trend in terms of the Mann–Kendal and Sen slope estimator (SSE) results. Cross-correlation analysis for RDI and SRI with a time lag acknowledged the existence of a sequence between the RDI and SRI and a positive relationship between the two indices. The findings of this study could be helpful for better understanding drought variability and water resource management. [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]