Historically, water resource systems have benefited people and economies in many ways. However, many areas lack the capacity to support and maintain resilient biodiverse ecosystems, and many are unable to satisfy the basic demands for drinking water and sanitation. These issues underscore the need for improved water resource systems. Inadequate infrastructure, excessive withdrawals from river flows, pollution from industry and agriculture, nutrient loading, salinization, infestations of exotic plants and animals, overfishing, alterations of the flood plain and habitat due to development, and modifications to the patterns of water and sediment flow are some of the causes of river pollution. In addition to discussing possible water-related concerns and outlining advancements in the field, this research paper offers an overview of the application of Integrated Water Resources Management (IWRM) in India. This study covers planning and management features in integrated policies, development plans, technical aspects, financial, economic, institutional, and governance, as well as the availability of water and related challenges. It helps shape future approaches to effective problem-solving in planning and management difficulties. In order to support effective problem-solving skills, this study makes recommendations for how to improve future IWRM capacity building. In order to improve effective problem-solving skills, the study identifies water availability and water-related issues, makes recommendations for future improvements in International Water Resources Management, and addresses issues with enabling environments, institutional frameworks, and management tools. Also, challenges such as rapid urbanization, water scarcity and pollution, flooding, wastewater management, climate change, and integrated approach highlight the importance of comprehensive planning and innovative solutions to ensure sustainable water management in India’s urban areas. These recommendations address issues with the enabling environment, institutional frameworks, and managerial tools.

Climate change is causing substantial damage to major crops in Pakistan's agricultural sector. The increasing frequency and intensity of droughts have raised adverse impacts on crop production and food security. The study analyzes historical data on rainfall patterns, temperature variations, and crop yields over the past few decades, revealing a clear trend of increasing agricultural drought incidents across various regions of Pakistan. This has led to crop production suffering due to water scarcity, inadequate irrigation facilities, and declining groundwater levels. Major staple crops like wheat, rice, and cotton have been adversely affected, leading to yield losses and diminished farmers' incomes. Climatic data, and crops yield data were collected from 1990 to 2021 Pakistan Metrological Department and Crop reporting service Punjab. Statistical correlation analysis were performed on rainfall patterns, temperature variations, and crop yields of wheat, rice, and cotton. The results reveled that due to shifting patterns of rainfall and increasing in temperature from decade to decade; wheat yield increased from 20 to 30 maunds per acre, rice yield increased 12 to 23 maunds per acre, cotton yield decreased 22 to 20 maunds per acre. Drought years 1998,1999,2000,2001,2002, 2015,2018 and 2019 significantly decreased the crop yields for all crops. The growing threat of agricultural drought poses a significant threat to Pakistan's crop production and food security. Smallholder farmers are particularly vulnerable due to lack of access to credit, technology, and insurance. Climate change has prompted farmers to adopt drought-resistant crop varieties and water conservation practices. A comprehensive approach involving policymakers, researchers, and communities is needed, including improved water management strategies and early drought monitoring systems.

Extreme rainfall analysis is crucial for flood hazard assessment. A common approach is to probabilistically analyze observed annual maximum rainfall values, which are then fitted to an extreme value distribution to estimate the probability of maximum rainfall. However, when continuous time series data are available, it is more effective to use methods that incorporate all rainfall data. Such a method is the point process (PP) method, which analyzes both the frequency of exceedances above a given threshold and the values of those exceedances. This study applies the PP method to uninterrupted daily rainfall records from 1901 to 2023 from the National Observatory of Athens meteorological station in Thiseion. Preliminary analysis using annual maxima did not reveal any significant trend but showed monthly seasonality in precipitation. A threshold rainfall of 10 mm was firstly selected for the PP model by examining the stability of parameter estimates across a range of values. Maximum likelihood estimation was then employed on 1509 rainfall data points over the 10 mm threshold using two different models: (a) a stationary model and (b) one that incorporates the observed seasonality into the shape and location parameters of the PP model. A likelihood ratio test for the two nested models confirmed that incorporating seasonality is statistically significant (p-value < 2.2 ∙ 10^-16). Finally, the results from the PP method were compared to those from the National Flood Risk Management Plans (NFRMPs) derived using the annual maximum approach method. The stationary model’s estimates for various return periods were consistent with the NFRMP values. However, the non-stationary model, which accounted for seasonality, produced estimates up to 16% higher for the upper 95% confidence interval. Therefore, the results of this study indicate that incorporating seasonality (non-stationarity) in the probabilistic extreme analysis of rainfall makes a significant difference in the rainfall design values.

This study examines the relationship between the El Niño--Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) with Iran's precipitation patterns. Correlation analyses were conducted between ENSO indices (Nino1+2, Nino3, Nino3.4, and Nino4) and IOD phases, specifically the negative (NIOD) and positive phase (PIOD), using monthly and seasonal precipitation data from 91 synoptic stations across Iran from 1990 to 2018. On a monthly scale, the Kendall Tau correlation showed significant correlations at a 95% confidence level across most regions of Iran for all indices, except for the Nino3.4 index. Direct correlations up to 0.5 were noted with the Nino1+2 index and up to 0.3 with the Nino3 index, excluding the Caspian Sea coast. Inverse correlations of -0.3 with Nino4 and up to -0.5 with PIOD were found in all basins except the Caspian Sea. The results also indicated correlations up to 0.5 in many regions of Iran with the PIOD index. Seasonally, no significant correlations were observed between precipitation and the indices in winter, summer, and autumn. However, in spring, only the Nino1+2 and Nino4 indices showed significant correlations, with Nino1+2 ranging from 0.5 in the west to 0.3 in central and eastern Iran and Nino4 showing an inverse correlation up to -0.3. The Pearson correlation also demonstrated significant relationships between monthly precipitation and the indices, particularly for Nino1+2 and PIOD. Correlations up to 0.5 were observed across Iran, increasing to 0.7 in the Ghareh Ghowm basin. The PIOD index showed an inverse correlation, reaching -0.5 in the eastern and northeastern regions (Ghareh Ghowm basin). These findings highlight that the Nino1+2, Nino3, and NIOD indices positively correlate with Iran’s precipitation, while the Nino4 and PIOD indices show a negative correlation. Nino3.4 does not exhibit a significant relationship with precipitation in Iran. These insights are valuable for improving forecasting and water resource management.

Flood risk mapping is a key instrument for integrated flood risk management. The scope of this study was to create a methodology to assist in identifying flooding risk hotspots and potential flood-prone areas, thus providing a much quicker but reliable method for identifying areas with a high flooding risk. The methodology developed aimed to correlate the hydraulic simulation results obtained from a hydraulic analysis with topographic indexes that allowed the production of risk maps at the regional scale and for larger areas. Flooding susceptibility was assessed through the correlation of the hydraulic modelling results obtained from Hec-RAS with the Topographic Wetness Index (TWI), among other remote sensing indices (NDVI, NDWI, MNDWI, and fCover). The study area was set in northern Greece, in the region of Western Macedonia. The hydrological sub-basin has an area of 50.90 km² and is located in the catchment of Aiani. The first part of the study focused on integrating DTMs, remote sensing indexes, and geospatial data into a Geographic Information System (GIS) to generate high-quality cartographic information for flood risk analysis. The next phase was the implementation of the hydraulic analysis and modelling. Different flooding scenarios concerning different return periods were applied. The topographic indexes’ results were then calibrated, based on the hydraulic analysis outputs, in terms of i) thresholding and adjusting the acceptable value range, calibrating them based on the calculated inundation profiles for various return periods for a selected area, and ii) their correlation with other indices. The interpretation of the correlation between the TWI and the hydrological analysis as the flood risk potential offered a group of flood risk maps with distinct categorisations and classifications of risk areas (low to high), providing different flood zoning for different inundation areas according to different discharge flows.

At present the only relatively environmentally safe and economically feasible method is the utilisation of surplus produced water into the receiving horizon. The purpose of the presented study was to demonstrate the necessity of chemical compatibility control during mixing of formation and injected associated waters at the Zapolyarnoye oil and gas condensate field. Mixing takes place during injection of produced water into the receiving aquifer. The amount of associated water to be utilised is measured in hundreds of thousands of m³ per year. Discharge of such water into surface water bodies is prohibited due to its high salinity and sodium chloride ion salt composition. Mineralization of waters of the receiving horizon is 10.5 g/dm³. The source of associated waters are productive horizons of Jurassic age with mineralisation of more than 20 g/dm³. The following tasks were solved in the course of the study: hydrogeological features of the target horizon-receiver were analysed, composition and volumes of associated water injection at the field were studied, chemical compatibility of formation water of the receiving horizon and associated water was calculated by thermodynamic modelling under given formation conditions. The programme was developed on the basis of the normative document 39-229-89 «Water for oil reservoir flooding». We obtained that at formation pressure of 88.23 atm and formation temperature of 42^oC a sediment of 0.153 g/dm³ is formed in the receiving horizon when the share of associated water in the mixture reaches 40%. The formed sediment may cause colmatisation of the receiving horizon. Obtaining correct data on the chemical compatibility of two types of waters of different compositions mixed in reservoir conditions is one of the aspects of environmental protection under conditions of long-term anthropogenic impact on the geological environment of the region.

Floods have become increasingly frequent and severe, occurring more often and with greater intensity because of climate change. Pakistan, ranked as the eighth most vulnerable nation to climate change according to the Global Climate Risk Index, has been severely impacted by natural hazards like floods. During floods, vulnerable communities are heavily affected, which causes losses not only to the life of people but livestock, agriculture, infrastructure and socio-economic losses as well. In this study, a GIS-based multi-criteria approach is used to assess flood vulnerability in Dadu district and Sehwan city. The methodology involves Landsat-8 satellite images and data including census, agriculture and rainfall data). With the help of this, we created maps which were further divided into three categories (physical vulnerability, coping capacity and socio-economic vulnerabilities). In total, 14 criteria were selected to develop flood vulnerability maps. These criteria were then weighted with the help of the analytical hierarchy process (AHP), and supporting AHP criteria were standardized into spatial thematic layers. The results demonstrated that the northeast and southwest areas of Dadu district are highly to very highly vulnerable, the central and northwest parts of Dadu district are moderately to highly vulnerable, and southeast areas are low to moderately vulnerable to floods. With this approach, the outcomes of this study will suggest the most vulnerable areas that need the most attention for disaster preparedness. Our results will help policy makers to make communities more resilient and mitigate upcoming disastrous events.

The effective management of water resources requires the precise distribution of streamflows in both temporal and spatial terms. In areas where direct measurements are lacking, data on average flows are often limited. This research aimed to evaluate the semi-distributed hydrological modeling of streamflows in the Huancané River using Hydro-BID. The model evaluation was conducted through qualitative analyses, such as time series graphs, and quantitative indicators, including Nash--Sutcliffe efficiency (NS), the correlation coefficient (r), the modified correlation coefficient (Rmod), and the overall volume error (OVE). The validation results showed good model performance and adequate representation of the seasonal dynamics of streamflows. For daily flows, the OVE was -12.48, the correlation coefficient (r) was 0.82, the modified correlation coefficient (Rmod) was 0.62, and the NS was 0.67. For monthly flows, the values were 12.52 for OVE, 0.90 for r, 0.68 for Rmod, and 0.79 for NS. The application of the calibrated parameters to data from the Putina station also showed good results, with an OVE of 14.29, an r of 0.85, an Rmod of 0.82, and an NS of 0.69 for monthly flows. In conclusion, the model demonstrates good performance in simulating flows in basins with limited information and can be useful for transferring flow information and assessing water availability in unmeasured areas, facilitating appropriate water resource planning in the Huancané River basin.

This work aims to present a case study in which Nature-based Solution (NbS) scenarios were tested using computational modelling to verify the effectiveness of such systems as an alternative for solving flooding problems in urban areas. This case study corresponds to a central area in the city of Frederiksberg, a municipality located in Denmark. Different scenarios for Nature-based Solutions were considered and evaluated based on the estimated flooding area defined based on precipitation, infiltration, and surface coverage. Scenario 1 consisted of increasing areas of vegetation cover. Scenario 2, in turn, consisted of infiltration trenches combined with linear gardens along the sidewalks. Scenario 3 was based on the use of rain gardens. For hydrological modeling, the Scalgo software was used, which combines Georeferenced Information System (GIS) tools with an analysis of flood spots based on the amount of precipitation, local topography, and infiltrated water. The flood spot analyses were carried out based on an intense rainfall event, resulting in a total rainfall of 50 mm. Water infiltration into soil was estimated based on the type of soil and type of coverage (paved or natural) using Horton's equation. The results obtained show that in the case of scenario 1, aimed at simply increasing vegetation cover, a reduction of 50 m³ of runoff was achieved, but it was not enough to significantly mitigate the flooded area on the main road, whose total flooded volume was 286 m³. Scenarios 2 and 3, in turn, were able to efficiently avoid flooding of the main road, with no flooding spots being observed in both, which was expected considering that the interventions in both scenarios enable increased infiltration and temporary storage of rainwater. The results of this study indicate a possible approach to be followed to compare different NbS with a view to its use as a measure of rainwater management.

Grid-based meteorological data products, such as reanalysis data, are essential for overcoming data scarcity in sparse and uneven hydrometeorological networks, especially in developing countries. However, these products require detailed validation to ensure their accuracy and reliability. This research evaluates the performance of ERA5, MERRA-2, and PERSIANN-CDR products in the Tambo basin, using monthly precipitation data from meteorological stations for the period 1985-2019 and applying two approaches: point values from stations and areal averages. The performance metrics considered are the Pearson correlation coefficient (CC) and percentage bias (PBIAS). In the station-based evaluation, ERA5 demonstrated excellent temporal correlation (with a CC between 0.83 and 0.91), but there was significant overestimation of precipitation (PBIAS between 72.0% and 217.5%). PERSIANN-CDR also overestimated precipitation (with a PBIAS between 8.0% and 86.9%) and showed low temporal correlation (with a CC between -0.14 and 0.15). In contrast, MERRA-2, despite underestimating precipitation (with a PBIAS between -30.8% and -77.9%), showed good temporal correlation (CC between 0.72 and 0.82). In the areal average evaluation, ERA5 had the best correlation (with a CC of 0.94) and a high bias (106.8%). MERRA-2 exhibited a low bias (a PBIAS of -53.5%) and a slightly lower correlation (0.88). PERSIANN-CDR had moderate bias (24.5%) and an intermediate correlation (0.70). These findings highlight the need to improve accuracy and reduce bias in products like ERA5 to optimize their use in hydrological applications and water resource management, thereby contributing to better forecasting and planning in water-scarce regions and helping to mitigate the impacts of climate change, especially in vulnerable areas.