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The article substantiates and validates a methodological framework for adaptive water resource management in Ukraine, integrating a matrix of conceptual approaches (sectoral, ecosystem, integrated, adaptive) with an indicator-based assessment of water resource status. An integrated water resource status indicator (IWR) is proposed, constructed as the average of two normalized indicators: natural freshwater availability (a positive indicator) and the proportion of polluted/insufficiently treated discharges in the total volume (a negative indicator). The data are aggregated, interpreted using a threshold scale, and the results are linked to the management matrix to support differentiated decision-making. Testing on regional data demonstrated the adequacy of the calculations and the suitability of the IWR for interregional comparisons and for ranking priorities in management decisions in Ukraine’s water sector. It is found that the fastest improvements in the integrated assessment are achieved by reducing the share of polluted discharges; in several regions, disparities have been noted where high water availability is offset by poor wastewater treatment performance. It is recommended to combine the modernization of treatment facilities and nature-oriented solutions with integrated water resources management tools and stakeholder participation. Methodological warnings concern possible gaps and distortions in data in regions affected by military aggression impacts, highlighting the need for verification and restoration of water resource monitoring. The scientific novelty lies in combining indicator-based assessment with a management approach matrix within the adaptive cycle of «monitoring – assessment – correction», while the practical significance lies in the possibility of institutionalizing the approach in river basin management plans and prioritizing investments in water infrastructure restoration.

Water scarcity poses a significant global challenge, particularly in developing nations like Iran. Consequently, there is a pressing requirement for ongoing monitoring and prediction of water quality, utilizing advanced techniques characterized by low implementation costs, shorter timeframes, and high accuracy. In the present study, the investigation and forecasting of the monthly time series of a single-variable river water quality index have been addressed using ten water quality parameters. Daily monitoring data from four stations in the Dez River from 2010 to 2020 have been utilized to obtain the river water quality index value from the dataset. The Shannon entropy method has been employed to assign weights to each water quality parameter. Utilizing the integrated autoregressive integrated moving average (ARIMA) model, which ranks among the most extensively employed models for time series forecasting, and five deep learning models including Simple_RNN, LSTM, CNN, GRU, and MLP, the water quality index for the following year is predicted. The performance of the prediction models is evaluated using RMSE, MAE, MSE, and MAPE as evaluation metrics. The results indicate that the ARIMA model performs worse than the deep learning models, with the MSE, RMSE, MAE, and MAPE values for this model being 81.66, 9.037, 6.376, and 6.749, respectively. The deep learning models show results close to each other, demonstrating similar statistical index values. The outcomes of this study assist relevant decision-makers in planning and implementing necessary actions to enhance water quality, particularly freshwater resources in rivers.

Growing concerns over water availability arise from the problems of population growth, rapid industrialization, and human interferences, necessitating accurate streamflow estimation at the river basin scale. It is extremely challenging to access stream flow data of a transboundary river at a spatio-temporal scale due to data unavailability caused by water conflicts for assessing the water availability. Primarily, this estimation is done using rainfall-runoff models. The present study addresses this challenge by applying the soil and water assessment tool (SWAT) for hydrological modelling, utilizing high-resolution geospatial inputs. Hydrological modelling using remote sensing and GIS (Geographic Information System) through this model is initiated to assess the water availability in the Ganga River basin at different locations. The outputs are calibrated and validated using the observed station data from Global Runoff Data Centre (GRDC). To check the performance of the model, Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination ( R 2 ), and RSR efficacy measures are initiated in ten stations using the observed and simulated stream flow data. The R 2 values of eight stations range from 0.82 to 0.93, reflecting the efficacy of the model in rainfall-runoff modelling. Moreover, the results obtained from this hydrological modelling can serve as valuable resources for water resource planners and geographers for future reference.

As a fundamental component of human existence, land is inextricably linked to human development, and its ecological functions are closely associated with multiple sustainable development goals. This paper presents a framework for constructing and optimizing ecological function space, with the Yangtze-to-Huaihe Water Diversion Project area serving as a case study. A comprehensive land ecological index system is established, encompassing natural foundation, land degradation, land production, ecological structure, and ecological protection. An identity-discrepancy-contrary connection method is employed to investigate changes in regional land ecological functions before (2013) and during (2017, 2020, and 2022) the project’s construction based on remote sensing data. The results indicated that the mean values of the land ecological index for each period were 0.1883, 0.1981, 0.2253, and 0.1370, respectively. The study calculated the connection, differences, and contradictions in the land ecological impacts across the counties, revealing a gradual decrease in differences and a growing prominence of contradictions. The land ecology of the Yangtze-to-Huaihe Water Diversion Project area is affected by the project construction, particularly within the construction area, showing an overall improvement. Most counties exhibited a trend of ecological improvement compared to the land ecology before the project’s construction. However, after the project implementation, most districts demonstrated a trend of ecological deterioration. As the distance from the construction canal increases, the characteristics of each section and stage vary, generally exhibiting an exponential decrease in the land ecological index. The study highlighted the significance of enhancing the land ecological pattern, improving water quality, increasing water supply along the project, and alleviating groundwater overexploitation. The study can serve as a reference for land ecological protection and restoration in water transfer areas and river basins worldwide.

Change in flow regime is one of the major reasons which influence the services offered by rivers and integrity of their aquatic ecosystems requiring certain amount of flow in the river known as environmental flow. In this study, the environmental flows described by Tennant’s method are correlated with a very versatile index defined in terms of Curve Number (CN) that incorporates hydrometeorological and geomorphological characteristics of catchment. Parameter CN is commonly known to be closely related with catchment characteristics (viz., land use, soil type, etc.). The rainfall-runoff data of 17 catchments from five major Indian river basins (i.e., Brahmani-Baitarani, Godavari, Mahanadi, Mahi, and Narmada) of low flow season (October–June) are used to derive the relationship between percentage of average annual flow (%AAF) and CN. The %AAF is seen to increase linearly with increasing CN, and vice versa, for all catchments and the correlation ( R ) is found to be greater than 0.7 for 16 (out of 17) catchments. The correlation between %AAF and CN is maximum for Dhariawad ( R 2  = 0.899) and Baronda ( R 2  = 0.814) catchments, indicating excellent relationship between %AAF and CN, which can be useful for the environmental flow prediction based on catchment characteristics. The existence of such a relationship is further strengthened by the relation established between the standard normal deviate of CN and the Standard Flow Index (SQI), a variation of %AAF, using the same field data.

The decay of rivers and river water pollution are common problems worldwide. However, many works have been performed on decaying rivers in India, and the status of the water quality is still unknown in Jalangi River. To this end, the present study intends to examine the water quality of the Jalangi River to assess ecological status in both the spatial and seasonal dimensions. To depict the spatiality of ecological risks, 34 water samples were collected from the source to the sink of the Jalangi River with an interval of 10 km while 119 water samples were collected from a secondary source during 2012–2022 to capture the seasonal dynamics. In this work, the seasonality and spatiality of change in the river’s water quality have been explored. This study used the eutrophication index (EI), organic pollution index (OPI), and overall index of pollution (OIP) to assess the ecological risk. The results illustrated that the values of OPI range from 7.17 to 588, and the values of EI exceed the standard of 1, indicating the critical situation of the ecological status of Jalangi River. The value of OIP ranges between 2.67 and 3.91 revealing the slightly polluted condition of the river water. The study signified the ecological status of the river is in a critical situation due to elevated concentrations of biological oxygen demand, chemical oxygen demand, and low concentrations of dissolved oxygen. The present study found that stagnation of water flow in the river, primarily driven by the eastward tilting of the Bengal basin, triggered water pollution and ecological risk. Moreover, anthropogenic interventions in the form of riverbed agriculture and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river. This decay needs substantial attention from the various stakeholders in a participatory manner.

Water resources planning is currently designed to meet emerging challenges and encompassing a more comprehensive and integrated water management. The main focus of current water resources planning is to promote good water resource conditions through the implementation of innovative water policies. To achieve this goal, it is necessary to characterize and assess the status of water resources, monitor the implementation of planned actions, disseminate information and support the decision-making process. Indicators are often regarded as appropriate tools for supporting these tasks, although their use in this context is not imperative. The present study intends to explore the stakeholder perspective on the role of indicators in the water resources planning and management processes and their strategic environmental assessment process using a Portuguese case study. To achieve this objective, semi-structured interviews with the various stakeholder groups involved were conducted. The treatment of the obtained data using content analysis shows that the interviewees consider indicators to be an essential tool for supporting water resources planning. Some participants express concerns and suggest changes to the selection models and to the communication and decision-support capacity of the sets of indicators used. All stakeholders in the process consider that improvements to the indicators to be used in future processes are possible. In light of the results, it can be concluded that there is a long way to go for indicators to be used to promote better communication with stakeholders and to support decision-making processes; simultaneously, they can form the basis for the review and development of the subsequent cycle of the planning process.

For decades, rivers have been used for transporting pollutants loaded with heavy metals (HMs) causing severe pollution in downstream. The current study aimed to review the levels and sources of 10 HMs, viz. As, Pb, Cd, Cr, Fe, Mn, Cu, Co, Ni, and Zn in the surface water of the rivers in Bangladesh. The PRISMA criteria were used to conduct a systematic review of the available literature published between 2001 and 2020, and thus a total of 55 documents were finally selected for review. The mean concentration of each HM exceeding the threshold limits as per World Health Organization (WHO), the United States Environmental Protection Agency (USEPA), and Department of Environment (DoE), Bangladesh standards were higher in the last decade (2011–2020) than in the previous one (2001–2010). Most HM concentrations in water were found above the threshold limits in three divisions (Dhaka, Rajshahi, and Chattogram). The Buriganga River in Dhaka has been the top polluted river in Bangladesh. Among the 10 HMs, six metals (As, Pb, Cd, Cr, Fe, and Mn) exceeded the limit set by WHO, USEPA, and DoE in all three seasons, where mean values of most of the HMs were found to be the highest in the summer season. Statistical analyses identified possible sources of HMs such as natural weathering, electroplating, fertilizers and pesticides, mining and manufacturing, textiles, coal mining and burning, batteries, and paint industries. Strong legislations and regulations, awareness programs, continuous monitoring, and comprehensive research are urgently needed to control riverine HMs pollution in Bangladesh.

BackgroundLarge-river decision-makers are charged with maintaining diverse ecosystem services through unprecedented social-ecological transformations as climate change and other global stressors intensify. The interconnected, dendritic habitats of rivers, which often demarcate jurisdictional boundaries, generate complex management challenges. Here, we explore how the Resist–Accept–Direct (RAD) framework may enhance large-river management by promoting coordinated and deliberate responses to social-ecological trajectories of change. The RAD framework identifies the full decision space of potential management approaches, wherein managers may resist change to maintain historical conditions, accept change toward different conditions, or direct change to a specified future with novel conditions. In the Upper Mississippi River System, managers are facing social-ecological transformations from more frequent and extreme high-water events. We illustrate how RAD-informed basin-, reach-, and site-scale decisions could: (1) provide cross-spatial scale framing; (2) open the entire decision space of potential management approaches; and (3) enhance coordinated inter-jurisdictional management in response to the trajectory of the Upper Mississippi River hydrograph.ResultsThe RAD framework helps identify plausible long-term trajectories in different reaches (or subbasins) of the river and how the associated social-ecological transformations could be managed by altering site-scale conditions. Strategic reach-scale objectives may reprioritize how, where, and when site conditions could be altered to contribute to the basin goal, given the basin’s plausible trajectories of change (e.g., by coordinating action across sites to alter habitat connectivity, diversity, and redundancy in the river mosaic).ConclusionsWhen faced with long-term systemic transformations (e.g., > 50 years), the RAD framework helps explicitly consider whether or when the basin vision or goals may no longer be achievable, and direct options may open yet unconsidered potential for the basin. Embedding the RAD framework in hierarchical decision-making clarifies that the selection of actions in space and time should be derived from basin-wide goals and reach-scale objectives to ensure that site-scale actions contribute effectively to the larger river habitat mosaic. Embedding the RAD framework in large-river decisions can provide the necessary conduit to link flexibility and innovation at the site scale with stability at larger scales for adaptive governance of changing social-ecological systems.