Study regionThis study considers Iran, encompassing hyper-arid to humid hydroclimates and major agricultural plains. Using 70 synoptic stations (2001–2022), we collocated station observations with satellite/reanalysis predictors from the Global Precipitation Measurement (GPM) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS), and the Copernicus Climate Change Service (C3S).Study focusAgricultural drought monitoring benefits from combining indicators of meteorological forcing and land-surface response, yet many studies rely on a single index or combine indices without an operational integration logic. We propose a dual-index framework for Iran integrating the Soil Moisture Deficit Index (SMDI) and the 3-month Standardized Precipitation–Evapotranspiration Index (SPEI-3).New hydrological insights for the regionWe combine stability selection with leakage-safe forward expanding cross-validation and a held-out most-recent test window to compare Light Gradient Boosting Machine (LightGBM), Random Forest, Elastic Net, and a feature-tokenizer Transformer. SMDI is estimated more reliably (best RMSE = 0.80, R² = 0.82) than SPEI-3 (best RMSE = 0.96, R² = 0.55). Uncertainty is quantified from held-out test absolute errors via empirical quantiles (50% and 90%); for SMDI, ∼50% of predictions fall within ∼0.5 index units and ∼90% within ∼1–1.5 units. These quantile error bands are attached as confidence qualifiers to the monthly drought classes in the monitoring framework, where SMDI anchors severity and SPEI-3 supports early-warning escalation.

Effective management of water resources and preservation of aquatic ecosystems are pressing global challenges. With the ongoing impacts of climate change and the increasing demands on water resources, there is a growing need for targeted restoration of degraded inland waters and those experiencing declining levels. To achieve meaningful outcomes, it is essential to establish measures for evaluation the effectiveness of restoration efforts accurately. Such metrics enable clear insights into restoration progress and guide the adaptive management needed for sustainable water resource management. This study addresses critical gaps in current methodologies by introducing two novel, tensor-based approaches to assess inland water restoration programs. Using the Normalized Difference Water Index (NDWI) derived from satellite imagery, these methods significantly enhance spatio-temporal analysis and visualization of water level dynamics, providing more precise insights into restoration impacts over time. The methods are applied to evaluate the effectiveness of the project connecting the Zarineh River to the Simineh River, one of the restoration program of Urmia Lake. The analysis using two newly introduced operators reveals significant water level patterns in the southeastern part of Lake Urmia. First, a substantial increase in water coverage was observed on the left side of the study area in 10 of the 12 months following restoration, indicating the program's effectiveness. Conversely, a reduction in water presence on the right side was noted during 5 months, suggesting areas that need further intervention. These findings demonstrate the value of these methods for tracking water level variations and assessing restoration outcomes effectively.

Actual evapotranspiration (ET_a) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ET_a is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ET_a values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ET_a, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R² as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ET_a estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ET_a within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.

In recent years, in addition to water resources’ quantity, their quality has also received much attention. In this study, the quality of the urban water distribution network in northwestern Iran was evaluated using the water quality index (WQI) method. Then, some important trace elements were investigated, and finally, the health risk assessment was evaluated for both carcinogenic elements (Ni, Cd, Cr, Pb, and As) and non-carcinogenic elements (Ca, Mg, Na, K, F, NO₃, and Cu) using carcinogenic risk (CR) and hazard quotient (HQ), respectively. In the present study, the WQI was calculated based on both World Health Organization (WHO) and Iranian drinking water standards. Comparing the results of these standards revealed that the WQI based on the Iranian standard was slightly higher. Regarding the calculated WQI for the study region, the status of water quality for drinking consumption is in the good water quality class (25 < WQI < 50). It was observed that Cu and Cd have the highest and lowest concentrations in all sampling points, respectively. Hazard Index (HI) results showed that the non-carcinogenic substances studied had a low risk for both adults and children (<1.0). However, the CR results showed that Ni, Cd, and As were above the desired level for both children and adults. The results of this study can be applied for efficient water management and human health protection programs in the study area.

Study region: Northeastern Iran. Study focus: In northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic and geogenic contamination. We characterize the sources of groundwater contamination by employing an integrated approach that can be applied to the identification of large-scale contamination sources in other regions. An existing dataset of georeferenced water quality parameters from 676 locations in northeast of Iran was analyzed to investigate the geochemical properties of groundwater. Gridding of the parameters graphically illustrates the areas affected by high concentrations of As, Cl⁻, Cr, Fe, Mg²⁺, Na⁺, NO₃⁻, Se, and SO₄^2-. We then identified potential anthropogenic and geogenic contamination sources by employing random forest (RF) regression modeling. New hydrological insights for the region: Random forest (RF) models show that the major ions, As, Cr, Fe, and Se content of groundwater are mainly determined by geology in the study area. Modeling also links groundwater NO₃⁻ contamination with sewage discharge into aquifers as well as the application of nitrogenous and animal-waste fertilizers. Areas of high salinity result from evaporate deposits and irrigation return flow. Medium to high non-carcinogenic health risk is found in areas with high concentrations of geogenic As and Cr in groundwater. Our approach can be applied elsewhere to analyze regional groundwater quality and associated health risks as well as identify potential sources of contamination.

Rainwater harvesting (RWH) has been recognized as one of the most reliable and efficient methods for water supply, especially in arid and semi-arid regions (ASARs) facing freshwater scarcity. Nevertheless, due to the inherent uncertainty of input data and subjectivity involved in the selection of influential parameters, the identification of RWH potential areas is a challenging procedure. In this study, two approaches for locating potential RWH sites were implemented. In the first approach, a frequently-used method of the multi-criteria decision analysis and geographic information system (MCDA-GIS) was utilized, while, in the second approach, a novel strategy of integrating the soil and water assessment tool (SWAT) model as a hydrology model into an MCDA-GIS method was proposed to evaluate its performance in locating potential RWH sites. The Mashhad Plain Basin (MPB) was selected as a case study area. The developed potential RWH maps of the two approaches indicated similar patterns for potential RWH areas; in addition, the correlation coefficient (CC) between the two obtained maps were relatively high (i.e., CC = 0.914) revealing that integration of SWAT as a comprehensive hydrologic model does not necessarily result in very different outputs from the conventional method of MCDA-GIS for RWH evaluation. The overlap of developed maps of the two approaches indicated that 3394 km² of the study area, mainly located in the northern parts, was identified as high-potential RWH areas. The performed sensitivity analysis indicated that rainfall and slope criteria, with weights of 0.329 and 0.243, respectively, had the greatest sensitivity on the model in the first approach while in the second approach, the criterion of runoff coefficient (with weights of 0.358) had the highest impact. Based on results from the identification of the potential locations for conventional RWH techniques, pond and pan techniques are the most proper options, covering high-potential areas of RWH more effectively than other techniques over MPB.

The growth of Iran's agricultural sector in the past few decades has exerted enormous pressure on its aquifers. There is a strong disparity between economic development and natural resource endowments, which threatens water and food security. In this paper, we used a multiregional input-output (MRIO) framework to assess the virtual water flows in Iran. We also estimate the internal and external water footprint of regions compared to their water availability. The results show that the northern part of the country, with no water scarcity, imported virtual water through the trade of goods and services, while severely water-scarce regions were net virtual water exporters. Iran had a net export of 1811 Mm3 per annum. While blue water resources (surface and groundwater) accounted for 92.2% of the national water footprint, 89.1% of total exports were related to the agriculture sector, contributing to only 10.5% of the national income. The results suggest that policy-makers should reconsider the current trade policy regarding food production liberalization in order to make Iran's limited water resources available for producing industrial goods, which can contribute more to the economy.

The strong desire for achieving self-sufficiency in developing and mostly water-scarce regions has endangered socioeconomic and environmental sustainability. South Khorasan is particularly exposed to such insecurities, largely due to its limited water resource endowments and its comparatively intensive agriculture. In this paper, we apply the water footprint accounting method (WFA) along with a regional input-output (IO) model to analyze the efficiency of the total (direct + indirect) water consumption in dierent economic sectors and water footprint of the region in 2011. Results show that agriculture is responsible for more than 95% of water consumption in the area, while it accounts for just 27% of value-added. Additionally, this sector has the largest contribution to water footprint composition (92%) when compared to other sectors. Three water-saving scenarios are simulated by the use of IO economic model and water footprint accounting method. Applying the proper cropping pattern has the greatest impact on water conservation with 348.46 Mm³ per year. A 10% increase in water productivity contributes nearly twice as much as reducing the exports and increasing the imports of agricultural crops by 10% in saving water with 115.23 and 65.49 Mm³, respectively. The most significant contribution in each water-saving strategy comes from the agriculture sector since it has the largest direct and indirect water-use coefficient. The results of this study can help local policymakers take appropriate measures to improve the efficiency of water resource utilization, taking into consideration social, economic, and environmental sustainability.

Water challenges-especially in developing countries-are set to be strained by population explosion, growing technology, climate change and a shift in consumption pattern toward more water-intensive products. In these situations, water transfer in virtual form can play an important role in alleviating the pressure exerted on the limited water resources-especially in arid and semi-arid regions. This study aims to quantify the 10-year average of virtual water trade and the water footprint within South Khorasan-the third largest province in Iran-for both crops and livestock products. The virtual water content of 37 crops and five livestock is first estimated and the water footprint of each county is consequently measured using a top-down approach. The sustainability of the current agricultural productions is then assessed using the water scarcity (WS) indicator. Results of the study show that in spite of the aridity of the study area, eight out of 11 counties are net virtual water exporters. Birjand-the most populous county-is a net virtual water importer. The 10-year average water footprint of the region is measured as 2.341 Gm³ per year, which accounts for 2.28% of national water footprint. The region's average per capita water footprint however, with 3486 m³, is 115% higher than the national ones. Crop production and livestock production are responsible for 82.16% and 17.84% of the total water footprint. The current intensive agricultural practices in such an arid region have resulted in a water scarcity of 206%-which is far beyond the sustainability criteria. This study gives the water authorities and decision-makers of the region a picture of how and where local water resources are used through the food trade network. The generated information can be applied by the regional policymakers to establish effective and applicable approaches to alleviate water scarcity, guarantee sustainable use of water supplies, and provide food security.

Atmospheric stability conditions over the water surface can affect the evaporative and convective heat fluxes from the water surface. Atmospheric instability occurred 72.5% of the time and resulted in 44.7 and 89.2% increases in the average and maximum estimated evaporation, respectively, when compared to the neutral condition for a small shallow lake (Binaba) in Ghana. The proposed approach is based on the bulk-aerodynamic transfer method and the Monin-Obukhov similarity theory (MOST) using standard meteorological parameters measured over the surrounding land. For water surface temperature, a crucial parameter in heat flux estimation from water surfaces, an applicable method is proposed. This method was used to compute heat fluxes and compare them with observed heat fluxes. The heat flux model was validated using sensible heat fluxes measured with a 3-D sonic anemometer. The results show that an unstable atmospheric condition has a significant effect in enhancing evaporation alongside the sensible heat flux from water surfaces.

An unsteady fully three-dimensional model of Lake Binaba (a shallow small reservoir) in semi-arid Upper East Region of Ghana has been developed to simulate its temperature dynamics. The model developed is built on the Reynolds Averaged Navier-Stokes (RANS) equations, utilizing the Boussinesq approach. As the results of the model are significantly affected by the physical conditions on the boundaries, allocating appropriate boundary conditions, particularly over a water surface, is essential in simulating the lake's thermal structure. The thermal effects of incoming short-wave radiation implemented as a heat source term in the temperature equation, while the heat fluxes at the free water surface, which depend on wind speed, air temperature, and atmospheric stability conditions are considered as temperature boundary condition. The model equations were solved using OpenFOAM CFD toolbox. As the flow is completely turbulent, which is affected by the complex boundary conditions, a new heat transfer solver and turbulence model were developed to investigate the spatial and temporal distribution of temperature in small and shallow inland water bodies using improved time-dependent boundary conditions. The computed temperature values were compared with four days of observed field data. Simulated and observed temperature profiles show reasonable agreement where the root mean square error (RMSE) over the simulation period ranges from 0.11 to 0.44 °C in temporal temperature profiles with an average value of 0.33 °C. Results indicate that the model is able to simulate the flow variables and the temperature distribution in small inland water bodies with complex bathymetry.