Managing Water Resources Research Articles

Study of Constraints in the Management of Dry and Hilly Lands in Flores Bajawa

Dry land is a landscape where water is stagnant for a long time or all the time. The use of dry land usually occurs on land, dry land, or uncultivated land. Dry land has the potential to become agricultural land, with 45% of Indonesia's territory consisting of hills and dry land, which are ideal areas for development. However, many obstacles arise in arid, dry regions. In addition to dry land, hills, or areas above ground, they have a positive impact on the agricultural system. Bajawa is a region in the Flores plateau that has a very extensive dryland area. The purpose of writing this article is to facilitate the resolution of problems or obstacles that occur in dryland and hilly areas (upland areas) with a dry climate. The method used in this research is a literature approach. (library research). Data collection in the research was conducted by reviewing and exploring several journals, books, and documents (both printed and electronic) as well as other data sources and information deemed relevant to the research or study. To support sustainable agriculture, dryland management must be sustainable. In land management, there are several issues that often arise. This includes soil fertility decline due to erosion and leaching, very steep topography, water availability, and production issues. Able to identify obstacles and determine solutions. As an alternative, one can use vegetative or mechanical soil management methods, enhance rainwater harvesting, manage water resources comprehensively, and plant drought-resistant crops.

Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye

Defining regions with similar characteristics for extreme precipitation is crucial for understanding the impacts of climate change, planning and managing water resources, and designing hydraulic structures. However, studies on the regionalization of extreme precipitation for Türkiye are limited, and regional extreme precipitation characteristics are not well defined. In this study, motivated by the need to contribute to this field, homogenous regions for extreme precipitation across Türkiye were determined using the latest version (V07) of Integrated Multi-satellitE Retrievals for GPM (IMERG). We initially validated IMERG V07 estimates using data from 214 ground-based stations and compared the results with its predecessor V06. The results revealed that IMERG showed some notable improvements from V06 to V07 for all seasons, especially in winter. During this season, the correlation coefficient increased from 0.57 to 0.64, the mean absolute bias decreased from 78.22 % to 69.27 %, and the RMSE decreased from 11.10 mm/day to 9.70 mm/day. In V07, while the trend of decreasing accuracy with increasing elevation observed in V06 continues, it has been shown that some notable improvements were achieved in continuous and categorical metrics. We then applied widely used non-hierarchical (K-means) and hierarchical (Ward's method) clustering techniques. To perform this, we first applied Principal Component Analysis (PCA) to reduce the number of variables related to extreme precipitation (e.g. amount, frequency, standard deviation, and seasonality) and geographic characteristics to identify the most significant variables for analysis. The K-means method delineated Türkiye into eight extreme precipitation regions, while the Ward's method resulted in six distinct extreme precipitation regions. We evaluated the results based on the existing extreme precipitation climatology literature for Türkiye and by associating them to known precipitation dynamics, and as a result, we recommended eight precipitation regions determined by the K-means. The identified precipitation regions are expected to contribute to future studies analyzing the effects of climate change and to regional studies on natural disasters resulting from extreme precipitation.

Development of intelligent systems for monitoring and management of agricultural enterprises

Modern agriculture faces a number of challenges, among which are the growing demand for food, the impact of climate change, limited natural resources and the need to ensure the environmental sustainability of production. In this context, the introduction of new technologies, in particular intelligent systems, becomes an important tool for improving the efficiency and competitiveness of agro-industrial enterprises. Intelligent Systems Based on Use artificial intelligence, machine learning, the Internet of Things (IoT) and big data are opening up new opportunities for monitoring and managing agricultural processes. The article presents the results of a study aimed at the development of intelligent systems that provide comprehensive monitoring of the state of fields and management of production processes at agricultural enterprises. The proposed system integrates data from various sources, such as sensors, drones, satellite images and other IoT devices, which allows you to create a detailed and up-to-date view of the state of crops, soils and climatic conditions. This, in turn, allows you to make prompt and sound management decisions that increase the productivity and efficiency of agricultural production. Particular attention is paid to the development of algorithms for processing big data and machine learning methods, which are used to analyze the data obtained and generate recommendations for optimizing the use of resources. This includes managing water resources, fertilizers, crop protection products, as well as predicting yields and identifying potential threats such as plant diseases or pests. The implementation of such systems allows not only to reduce resource costs, but also to minimize the environmental impact of agriculture by reducing the use of chemicals and water. Thus, the article makes a significant contribution to the development of intelligent technologies in agriculture, offering new approaches to the management and monitoring of agricultural enterprises. These approaches can form the basis for the development of future innovations in this area, contributing to improving the resilience and efficiency of agro-industrial production in response to modern challenges.

Urban FEW Nexus Model for the Otun River Watershed

The food–energy–water (FEW) nexus has emerged as an alternative for managing resources in the food, energy, and water systems. However, there are limited case studies applying this approach in the Latin American and Caribbean region. This region stands to benefit significantly from the FEW nexus approach due to its heavy reliance on hydropower for electricity generation and unevenly distributed and poorly managed water resources. In this study, an urban FEW nexus framework was used in the Otun River Watershed (ORW) to evaluate changes in food, energy, and water demand for four scenarios. Additionally, regional climate models (RCMs) were used to forecast water availability in the ORW from 2030–2039. The results show that water demand could increase by 16% and energy demand will increase by roughly 15% for scenario 2, while water demand in scenario 3 will likely remain unchanged in relation to the current conditions (base scenario). Enhancing water resources management in the ORW will involve a variety of measures, including: implementing practices to reduce water losses in distribution systems, developing green infrastructure and decentralized wastewater systems, and embracing urban and peri-urban farming. Successful application of urban FEW nexus solutions requires involvement from stakeholders across the food, energy, and water systems.

Spatial assessment of the reproducibility of Indian summer monsoon rainfall regimes in multiple gridded rainfall products

The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India’s annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India’s socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called ‘rank score’ that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones.

Characteristics of Meteorological Drought Evolution in the Yangtze River Basin

Amid global climate change, recurrent drought events pose significant challenges to regional water resource management and the sustainability of socio-economic growth. Thus, understanding drought characteristics and regional development patterns is essential for effective drought monitoring, prediction, and the creation of robust adaptation strategies. Most prior research has analyzed drought events independently in spatial and temporal dimensions, often overlooking their dynamic nature. In this study, we employ a three-dimensional methodology that accounts for spatiotemporal continuity to identify and extract meteorological drought events based on a 3-month standardized precipitation evapotranspiration index (SPEI3). Measured by the SPEI3 index, the incidence of drought increased in the middle part of the basin, especially in some parts of Sichuan and Yunnan province, and the frequency of drought events decreased in the upper reaches. We evaluate drought events within the Yangtze River basin from 1980 to 2016 by examining five variables: chronology, extent, severity, duration, and epicenter locations. The results show that a total of 97 persisting drought events lasting at least 3 months have been identified in Yangtze River basin. Most events have a duration between 4 and 7 months. The findings indicate that while the number of drought events in the Yangtze River basin has remained unchanged, the intensity, duration, and severity of these events have shown a slight increase from 1980 to 2016. The drought events gradually moved from the western and southeastern parts of the basin to the central region. The most severe drought event occurred between January 2011 and October 2011, with a duration of 10 months and an affected area of 0.94 million km2, impacting over fifty percent of the basin. Changes in wetness and dryness in the Yangtze River basin are closely related to El Niño/Southern Oscillation (ENSO) events, with a positive correlation between the intensity of cold events and the probability of extreme drought. This study enhances our understanding of the dynamics and evolution of drought events in the Yangtze River basin, providing crucial insights for better managing water resources and developing effective adaptation strategies.

Regression-based machine learning approaches for estimating discharge from water levels in microtidal rivers

The challenges of managing water resources in tidal rivers, exacerbated by climate change and anthropogenic impacts, require innovative approaches for accurate estimation of hydrological parameters. In tidal rivers and estuaries, water levels depend primarily on river discharge and tidal dynamics. Microtidal estuaries are particularly complex due to the strong stratification and two-layer structure, which also affect the water level. This study investigates the potential of machine learning (ML) models for estimating discharge in the Neretva River, Croatia, using only water level data from multiple stations. Comparative analyzes were performed between simple supervised models - Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB) - and time series models - Long Short-Term Memory (LSTM) and LSTM-Attention. Both simulated and measured data sets were used for this purpose. The results show that time series models perform satisfactorily in the assessment of discharge and overcome the challenges faced by simple supervised models, especially under high flow scenarios. Overall, LSTM-Attention proves to be the best model when analyzing all error metrics with superior performance over the entire range of discharge values. It surpasses the overall LSTM model performance, with a percentage increase of above 9% in RMSE and MAE, above 0.2% in NSE, and above 0.1% in R for both simulated and measured datasets.

Spatio-temporal evaluation of MODIS temperature vegetation dryness index in the Middle East

Drought, a recurring meteorological event, can potentially cause devastating consequences for human populations, and its attributes vary significantly across diverse geographic areas. Therefore, recognizing drought events is paramount for strategically planning and managing water resource systems. In this study, the Temperature Vegetation Dryness Index (TVDI), derived using Moderate-Resolution Imaging Spectroradiometer (MODIS) data spanning from 2003 to 2022 in the Middle East, was used as the foundation for both trend and spectral analyses. To assess TVDI trends, the Mann-Kendall test and Sen's slope estimator were utilized, and harmonic analysis was conducted for spectral analyses. These methods were applied to a dataset comprising 258,087 pixels within the specified region, covering various time scales, including monthly and seasonal analyses. The monthly analyses indicated significant growth in March and April, with September showing the least significant increase, suggesting stability or decline. Geographically, upward trends were predominant in the northern Middle East, including Turkey, Syria, Iraq, western Iran, and eastern Jordan. Significant downward trends were observed in the southern Middle East during the warmer months. Seasonal assessments showed no significant TVDI trends in winter, but upward trends in the south, west, and northwest were identified during spring. The annual trend map indicates a long-term declining trend in TVDI for most regions within specific latitudes, particularly those below 34 degrees. The results of harmonic analysis revealed the presence of multiple cycles at a 95 % confidence level. Notably, there was a heightened prevalence of significant sinusoidal cycles, especially the 2–3-year cycles. This cycle was widespread in countries such as Iran, Oman, Yemen, and Turkey, as well as in the southern regions of Saudi Arabia and Egypt.

Analysis of Water Dynamics at the Effluent Treatment Station of Cooperoque: Evaluation of Precipitation, Evaporation, and Infiltration of Effluent from a Milk Refrigeration Station

Purpose: The study aims to analyze water dynamics at the Cooperoque Effluent Treatment Station, focusing on the interactions between precipitation, evaporation, and effluent infiltration to enhance water resource management. Theoretical reference: The research references various studies and methodologies related to the quantification of evaporation from lakes and reservoirs, particularly in semi-arid regions, utilizing the Jensen-Haise Method for evaporation estimation. The research aligns with previous studies, such as Mallmann (2014), on water infiltration in soil management, emphasizing the importance of understanding water dynamics for sustainable resource management. Method: The study employs the Jensen-Haise Method to calculate evaporation rates, covering the period from January 2023 to October 2024. It examines the effects of precipitation, evaporation, and effluent infiltration on tank levels. Results and conclusion: The results reveal that tank levels are influenced by precipitation, evaporation, and effluent infiltration, with more significant declines during dry months and smaller reductions during periods of high precipitation. Evaporation rates, calculated using the Jensen-Haise Method, are higher in warmer months, contributing to lower tank levels, while these rates decrease in colder months. A notable anomaly occurred in May 2024 when effluent infiltration exceeded the volume of generated effluents and rainfall due to the activation of an irrigation system. Key findings indicate that lake levels drop more during dry months, with a significant negative water balance observed in May 2024 due to excessive infiltration during heavy rainfall. The study concludes that adaptive water management strategies are necessary to address climatic variations and improve the efficiency of the effluent treatment system. Implications of research: The research provides valuable insights for developing adaptive water management strategies to address climatic variations and improve the efficiency of effluent treatment systems. Continuous monitoring is emphasized as crucial for sustainable water resource management. Originality/value: The study highlights the complex interactions between precipitation, evaporation, and effluent dynamics, offering a foundation for improved water management practices and contributing to the preservation of regional water resources. The study contributes to the understanding of water dynamics at effluent treatment stations, offering practical insights for managing water resources effectively. It highlights the importance of adaptive strategies and continuous monitoring to maintain pond levels and optimize the treatment process.

Water demand and supply under future water development and climate change scenarios in the upper Blue Nile basin

ABSTRACT Integrated methodological approaches to analyzing water demand and supply under changing scenarios in a river basin are crucial for managing water resources. This study evaluates the effects of climatic change and socioeconomic development on the water system in the upper Blue Nile basin. An integrated methodological approach was used, including the Soil and Water Assessment Tool (SWAT) and the Water Evaluation and Planning (WEAP) models. Water development scenarios were created for two time periods: near future (2021–2040) and full development (2041–2060). During the current account period, the amount of water delivered for irrigation purposes was balanced, and existing hydropower plants generated energy at full capacity. However, the irrigation water supply in the near future and full development periods under climate change scenarios indicates a shortfall at the basin level. Despite the fact that the basin's hydropower energy production will increase during the scenario periods, under construction and planned hydropower plants are predicted to operate at less than full capacity. The study concluded that climate change-induced water availability and irrigation expansion would contribute to the basin-level water supply–demand mismatch. As a result, the overall findings recommended water resource management requirements for increasing availability of water, minimizing water demand, and reducing unmet demand.

Managed Aquifer Recharge for Sustainable Groundwater Management: New Developments, Challenges, and Future Prospects

The combined effect of climate change and increased water demand has put significant strain on groundwater resources globally. Managed aquifer recharge (MAR) has become an effective approach for addressing groundwater depletion problems and sustainable management of groundwater resources. This review article provides an extensive insight into the existing knowledge of MAR, including the main objectives and applications, implementation techniques (surface spreading, sub-surface, and induced recharge) being practiced over the years, risks and challenges associated with the MAR, and the developments in the field of MAR. This review also explores the potential of MAR in the Friuli Venezia Giulia (FVG) region, north-eastern Italy. An average increase in temperature and a decrease in precipitation and piezometric levels in the region suggest the development of a proper MAR plan to manage water resources in the decades to come. Additionally, a comparative analysis of studies published over the last 20 years, focusing on the quantitative and qualitative aspects of water resource management, is conducted to analyze the research trends in the field of MAR. The reviewed literature reveals a notable research trend towards the quantitative aspect compared to the qualitative one. This review also identifies a notable disparity in qualitative studies during the analysis of water quality parameters considered in different MAR studies. Based on this review, a prospective viewpoint to address the challenges and expand the scope of the field is presented. This calls for an optimized strategy that considers both water quality and quantity issues, along with incorporating environmental and socio-economic aspects within the framework of MAR.

Separations of meltwater discharge in a snowpack by artificial rain-on-snow experiments

In temperate regions, snow and its meltwater constitute primary freshwater resources and snowmelt isotopes offer valuable insights into understanding the snowmelt processes including the timing and contribution of snowmelt to the soil and watershed in spring. Assessing the storage and movement of liquid water within natural snowpacks, a previously unquantified aspect, holds significance for predicting natural hazards and managing water resources for agricultural purposes and ecosystem health. The escalating occurrence of rain-on-snow (ROS) events, attributed to winter warming, has the potential to trigger natural hazards and surface runoff into major river systems in temperate climate regions. End member mixing calculations (EMMC) based on isotopic and chemical tracers were employed to quantify the proportions of rainwater, meltwater, and pore water within the snowpack discharge. In this study, artificial rain-on-snow experiments involving conservative anions and stable water isotopes were conducted at the surface of snowpack to differentiate each component (rainwater, pore water, and snowmelt) within the discharge collected at the bottom of the snowpack. Pore water content exhibited a shift from 1.1 ± 1.1% (± 1σ, N = 23) after the initial artificial ROS event to 2.8 ± 1.2% (± 1σ, N = 19) following the spray in our experiment. Based on the EMMC, the contributions of rainfall, pore water, and snowmelt to the meltwater discharge were 2,620.2 L (63.3%), 829.0 L (20.0%), and 687.4 L (16.6%), respectively. Notably, contrary to prior studies, our experimental results suggest that rainwater reached the bottom through multiple rapid flow channels before matrix flow occurred. This experimental approach provides additional insights into the dynamics of water percolation in snowpacks during rain-on-snow events.

Assessment of Soil Moisture through Climatic Variables Using Remote Sensing Data in Semi-Arid Sokoto State, Nigeria

Soil moisture availability is an indispensable requirement for not only the growth of crops but also other living organisms in any ecological settings This study determined soil moisture condition in Sokoto State using climate-based water index. Satellite-based estimations of mean monthly rainfall data was obtained from the USGS/FEWS database for each of the local government areas. Also, approximations of monthly values of minimum and maximum air temperatures (AIRS) were downloaded from the NASA website. The latter variables were utilised in the computation of potential evapotranspiration using Hargreaves equation. It was very clear from the results that water surplus occurred in the months of July, August and September across almost all the locations. In addition, places lying in the southern part of the State such as Kebbe, Yabo, Tambuwal and Shagari recorded higher positive values of moisture availability in contrast to their counter parts in the extreme north bordering Niger republic. Thus, this study revealed slight spatio-temporal variation in soil moisture condition across the entire study area decreasing from the southern locations towards the north. Hence, the research could help improve agricultural planning and suggest irrigation during dry periods. Consequently, the findings could offer useful insights for managing water resources and improving farming in dry areas.

Impacts of hydroelectric stations on vegetation phenology in the Lancang River Basin

Hydropower projects can be critical in regulating ecological systems and managing water resources. The combined effects of natural conditions and human activities make the impact of hydropower on vegetation phenology much more complex. In this study, we selected four representative hydropower stations (Huangdeng (HD), Xiaowan (XW), Nuozhadu (NZD), and Jinghong (JH)) along the Lancang River to assess the effects of dams on vegetation phenology. The buffer zone analysis and Geodetector methods were used to evaluate the spatiotemporal variations of vegetation phenology and to analyze the possible causes of vegetation phenology. This study found that: (1) Throughout the study area, the relative contributions of factors affecting the start of the growing season (SOS) were ranked as air temperature (50.46 %), elevation (50.16 %), precipitation (49.90 %), and human activities (34.45 %). (2) The SOS of the rugged areas (HD) had been significantly delayed since water storage. The disparities of vegetation phenology in different areas had been identified by introducing the Core Impact Zone (CIZ) and the Peripheral Impact Zone (PIZ). Within the CIZ, the most pronounced delay was observed in grasslands (+6.57 days/yr). The delay days in the PIZ was 2.68 days/yr, which revealed that the effect of water storage diminished with increasing distance from the water source. (3) Human activities significantly influenced the vegetation phenology around the economically developed region (JH). This research provided new insights into the impact of hydropower development on vegetation phenology in river basins.

Advanced Monthly Rainfall Forecasting in Southern Lazio: Integrating Climatic Indices, Classical or Deep Neural Networks, and a Feature Selection Algorithm

Accurately predicting monthly precipitation is crucial for managing water resources, particularly in regions with complex climates like Southern Lazio, Italy. This study addresses the challenges of selecting relevant climate parameters and optimizing model complexity for precise forecasting. Four models, based on Bi-directional Long Short-Term Memory (BI-LSTM) and Radial Basis Function (RBF) neural networks, were compared, focusing on their ability to predict monthly precipitation using diverse inputs such as mean temperature, relative humidity, sea level pressure, radiation, precipitation change, and 12 climate indices. One significant challenge was determining the most impactful subset of input variables from a large dataset to enhance model performance without overfitting. To address this, the Particle Swarm Optimization (PSO) feature selection algorithm was employed, significantly improving model accuracy by identifying the optimal subset of input variables. The comparative analysis revealed that PSO-based models consistently outperformed those using all input variables, with the PSO-RBF model achieving a Kling-Gupta Efficiency (KGE) value of up to 0.80 during testing. Notably, both PSO-BI-LSTM and PSO-RBF models demonstrated strong performance in predicting precipitation peaks, achieving KGE values of 0.74. The study highlights the efficacy of the RBF model combined with PSO, which optimizes fewer parameters compared to the more complex BI-LSTM network while reducing input variables. This approach is not only less computationally intensive but also easier to implement, particularly in regions with limited instrumentation. These findings suggest that the PSO-RBF model is a reliable and efficient tool for monthly precipitation prediction in challenging environments.

Drought forecasting with regionalization of climate variables and generalized linear model

AbstractSpring drought forecasting is essential in South Korea for managing water resources reliably and cultivating agricultural products efficiently, as seasonal rainfall difference often drives water shortage during spring. In the current study, a novel scheme for spring drought forecasting was suggested by extensively searching appropriate predictors from the global climate variable: here mean sea level pressure (MSLP) of the winter season due to its time lag for forecasting. The target series was estimated with the median of the spring precipitation series of the weather stations over South Korea, called the accumulated spring precipitation (ASP). A number of points of the MSLP data were detected as significant cross‐correlation with the ASP and also the points were regionally grouped. Therefore, the regionalization for the high correlation points was performed, resulting in three regions, such as Arctic Ocean (R1), South Pacific (R2), and South Africa (R3). The R1 and R2 regions are located at the places where climate indices have been developed such as Arctic Oscillation and North Atlantic Oscillation for R1 and the indicator of El‐Nino and Southern Oscillation for R2. The generalized linear model (GLM) was adopted in ASP drought forecasting with the driven three regionalized indices as the predictors of the ASP. The result indicates that the regionalized indices can produce a good performance in forecasting the ASP. The forecasting result can be employed as a good tool for managing water resources and planning better cultivation in agriculture industries.

Water Volume and 3D Topographic Analysis of Hub Dam, Karachi, Using Remote Sensing and Global Bathymetry Data

Background: Karachi one of the largest and fastest-growing cities in the world, the city faces severe water scarcity and aggravated by climate change, excessive groundwater extraction in dense urban areas and inefficient water distribution systems. The Hub Dam, along with the Keenjhar Lake, is the source of water for Karachi's population as well as for the industrial and urban agricultural sectors. It is therefore important to have current information on the dam's storage capacity, its topographic features and the hydrological dynamics of its catchment area, which remains poorly understood to date. Objectives: This study's primary objectives are (1) to assess the hydrography of Hub Dam using global bathymetric and remote sensing data and (2) to create a detailed 3D model of the dam to facilitate analysis of its hydrological features. (3) Also generate data-driven insights to guide improvements in Karachi’s urban water management policies. Methods: The methodology integrates global bathymetric data with remote sensing and Google Earth Engine (GEE) for analysing Hub Dam’s hydrological and topographic characteristics. Data processing and visualization were conducted in Google Colab by python codes. This process enabling an efficient workflow for handling and analysing large datasets. Results: Hub Dam 3D model is developed. The approaches is enhancing visualization of the dam's water volume and terrain features to support detailed hydrological analysis. 3D volumetric analysis estimates with total water volume at approximately 79.954 · 106 m3. It was found that currently, the Hub Dam reservoir has enough water to fulfil Karachi needs for 503 days, a critical reserve for urban planning, resource management. Using a topographic analysis that includes surrounding slopes and watershed features, key elements affecting dam performance were identified. This result helps to adjust the filling of the dam with water. The Hub Dam's ability to store water was confirmed through a preliminary volume assessment. Conclusion: The study successfully achieved its objectives. The results obtained highlight the importance of adaptive well informed strategies for sustaining well managed water resources in response to environmental pressures. The current study further advance rational water management by integrating remote sensing and data-driven methodologies into this area of knowledge. That is, the effectiveness of Using Remote Sensing and Global Bathymetry Data has been repeatedly proven by other studies in different areas of knowledge. However, in the current study, this scientific approach has successfully demonstrated the efficient and rapid acquisition of accurate, up-to-date information on reservoir capacity and hydrological characteristics, which opens up new opportunities for efficient water management.

Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

Investigation of the formation and variability of dissolved inorganic carbon and dissolved organic carbon in the water of a small river (on the example of the Styr River, Ukraine).

This paper presents the results of a study on the dynamics in the concentrations of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) in water samples collected from the Styr River between 2019 and 2022. The concentrations of DIC and DOC were measured using an Elementar liqui TOC II analyzer. The study methodology involved analyzing the changes in DIC and DOC concentrations and their relationship with flow rates, temperature, seasonality, and other indicators such as hydrogen pH levels, total alkalinity (TA), and total dissolved solids (TDS). The purpose of this article is to identify patterns in the formation and changes of DIC and DOC concentrations in the Styr River. The concentrations of DIC and DOC in the samples ranged from 1.55 to 4.93mM and 0.49 to 1.43mM, respectively, with DOC accounting for an average of 22% of the total dissolved carbon content. The highest DOC concentrations were observed in summer, while the highest DIC concentrations were observed in winter. Based on the results, it can be concluded that water flow and temperature have an impact on DOC concentration, while flow, temperature, and pH affect DIC concentration. There was no correlation between DIC and DOC concentrations, but a strong positive relationship (r = 0.9056, p < 0.001) was found between DIC and TA concentrations. Therefore, the main factors influencing DIC in the Styr River are those that affect the carbonate equilibrium, such as leaching of carbonate and silicate rocks, CO2 absorption from the atmosphere, and changes in pH. Additionally, the concentration of DOC is influenced by biological activity and is higher during the warm season. These findings can be used to develop a strategy for managing water resources in the Styr River basin and to assess and predict the ecological state of the river.

Bridging ESG and SDGs Clean Air and Water Through CSR Committee: A New Pathway for Sustainability in the Utility Sector

This study aims to bridge Environmental, Social, and Governance (ESG) principles with the Sustainable Development Goals (SDGs), particularly related to clean air and water, through the role of the Corporate Social Responsibility (CSR) committee in the utility sector. As global awareness of climate change rises, utility companies worldwide are adopting ESG strategies to reduce carbon emissions, improve energy efficiency, and manage water resources. This research proposes a new evaluation framework to assess the contribution of utility companies to achieving SDGs focused on air and water quality. Using data from annual reports and Refinitiv LSEG, the study examines the relationship between ESG scores, asset size, debt-to-equity ratio, and the moderating role of CSR committees in achieving SDG goals. Regression analysis results indicate that ESG scores have a significant positive influence on company contributions to achieving SDGs, especially in terms of clean air and water quality. Additionally, the CSR committee acts as an effective moderator in optimizing asset use and managing company debt to meet sustainability goals. This study provides both theoretical and practical contributions to support evidence-based decision-making for more sustainable policies in the utility sector.