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In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the studies to address uncertainty, (4) multi-criteria approach for reducing uncertainty in hydrological forecasting and its applications (5) role of remote sensing data sources for hydrological forecasting and uncertainty handling, (6) selection of hydrological models for hydrological forecasting. Especially, a synthesis of the literature showed that approaches such as multi-data usage, multi-model development, multi-objective functions, and pre-/post-processing are widely used in recent studies to improve forecasting capabilities. This study reviews the current state-of-the-art and explores the constraints and advantages of using these approaches to reduce uncertainty. The comparative summary provided in this study offers insights into various methods of uncertainty reduction, highlighting the associated advantages and challenges for readers, scientists, hydrological modelers, and practitioners in improving the forecast task. A set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

Trends in river flow at national scale in Iran remain largely unclear, despite good coverage of river flow at multiple monitoring stations. To address this gap, this study explores the changes in Iranian rivers’ discharge using regression and analysis of variance methods to historically rich data measured at hydrometric stations. Our assessment is performed for 139 selected hydrometric stations located in Iranian data-rich basins that cover around 97% of the country’s rivers with more than 30 years of observations. Our findings show that most of Iran’s rivers (>56%) have undergone a downward trend (p–value

Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2–20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not available.

Ecohydrological studies in tropical peatland have mostly focused on a small number of non-native commercial species. However, studies of native species are urgently needed as they are considered to be a possible solution in the restoration of millions of hectares of degraded tropical peatlands. We investigated peatland species on Padang Island, Indonesia, to assess their responses to changing environmental factors, particularly the high fluctuation of the water table due to intensive peatland draining. We monitored the sap-flux velocity and radial growth of six trees (four native and one non-native peatland species) in high temporal resolution using sap-flux meters and dendrometers up to seven months. Monitoring also included hydrological and micrometeorological parameters. We found that sap-flux velocity in the monitored species was mainly controlled by air vapor pressure deficits, photosynthetic active radiation, and to some extent, by wind speed and water-table depth. We also observed a species-specific correlation between daily sap-flux velocity and daily radial growth. Non-native Acacia crassicarpa had up to twice the radial growth of native species. This growth of non-native species was significantly higher at a shallow water-table depth, while native species did not show the same relationship. The interaction between water-table depth, sap-flux velocity, and radial growth in daily timescale was not straightforward, presumably because of the complex carbon-allocation mechanism in trees.

Restoration and water table control on peatlands to limit fire risk are national priorities in Indonesia. The present study was initiated at Padang Island, Sumatra, to increase understanding on peatland hydrology in the tropic. At the pilot site, water table and precipitation were monitored at different stations. The results show variation in water table depths (WTDs) over time and space due to spatial and temporal variability in rain intensity and drainage networks. In part of the island, large-scale drainage for plantations led to deep WTD (−1.8 m) and high WTD recession rates (up to 3.5 cm/day). Around villages, farm-scale drainages had a smaller impact with a lower recession rate (up to 1.8 cm/day) and shallow WTD, typically below −0.4 m, the threshold for sustainable peatland management in Indonesia. The recession rates levelled off at 1.0 cm/day near the drained forest/plantation and at 0.5 cm/day near the farm. Deeper layers had much lower specific yield (Sy), 0.1 at −1.5 m depth, compared with top peat soils with Sy up to 0.3. Proximity to drainages extended discharge flow to deeper layers. The results highlighted the severity of peatland drainage impact on most coastal zones of Padang Island, which have intensive drainage networks. HIGHLIGHTS High spatial and temporal variability of water table was observed in Padang Island.; The variability was partially driven by variation in land use and farm drainages.; Recession rate near pulp plantations remained high (0.01 m/day) at −1.5 m depth.

Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

Middle Eastern, North African countries (MENA), and Central Asian countries are considered the countries most facing water and food scarcity. The current water exploitation indicates that a few countries are overexploiting their water resources and using the fossil water available. This study reviews each country’s renewable water resources volume and evaluates the resources available to expand the agricultural area. Different scenarios are considered, using both irrigated and rainfed farming options, for concluding the most sustainable farming method in each country. Different scenarios are considered using irrigated and rainfed farming options to recommend the most sustainable farming method for each country. Results show that the countries in the MENA and Central Asia can be divided into three main categories: (1) Countries whose expansion of agricultural area can only be applied by using fossil water resources (Bahrain, Egypt, Kuwait, Libya, Qatar, Saudi Arabia, Turkmenistan, United Arab Emirates, and Uzbekistan); (2) Countries where the agricultural area can be expanded to a certain limit, by sustainably using both irrigated and rainfed farming (Afghanistan, Algeria, Iran, Palestine, Jordan, Kazakhstan, Morocco, Oman, Syria, Tajikistan, Tunisia, and Yemen); (3) Countries that have enough renewable water resources to farm all their agricultural area (Lebanon, Iraq, Turkey, and Kyrgyzstan). However, the aim of this study and its results are only to assess the renewable water resources available to sustain the increased agricultural water demand by setting aside other agricultural factors that constrain the sector.

In this study, metal concentrations in the water column and surface sediment of the Sabalan Dam Reservoir (SDR) were determined. Moreover, heavy metal pollution index (HPI), contamination index (CI), heavy metal evaluation index (HEI), enrichment factor (EF), geo-accumulation index (Igeo), sediment quality guidelines (SQGs), consensus-based SQGs (C-BSQGs), and mean probable effect concentration quotients (mPECQs) were evaluated for water and sediments of SDR. It was observed that metal concentrations in river entry sediment were lower, but those in river entry water were higher than corresponding values in the vicinity of the dam structure. The HPI values of water samples taken from 10 m depth in the center of SDR exceeded the critical limit, due to high concentrations of arsenic. However, according to CI, the reservoir water was not contaminated. The HEI values indicated contamination of SDR water with metals at 10 m depth. A comparison of water quality indices revealed that HEI was the most reliable index in water quality assessment, while CI and HPI were not sufficiently accurate. For SQGs, As and Cu concentrations in sediments were high, but mPECQ, Igeo, and EF revealed some degree of sediment pollution in SDR. The calculated EF values suggested minor anthropogenic enrichment of sediment with Fe, Co, V, and Ni; moderate anthropogenic enrichment with As and Mn; and moderate to severe anthropogenic enrichment with Cu. A comparison of SQG values revealed that the threshold effect and probable effect levels were the most reliable metrics in the assessment of sediment toxicity. Statistical analysis indicated similarities between metal concentrations in the center of the reservoir and near to the dam structure, as a result of similar sediment deposition behavior at these points, while higher flow velocity at the river entry point limited deposition of fine particles and associated metals.

The majority of lake temperature studies have investigated climate-induced changes occurring at the lake surface, primarily by analyzing detailed satellite images of surface water temperature. Whilst essential to observe long-term change, satellite images do not provide information on the thermal environment at depth, thus limiting our understanding of lake thermal responses to a warming world. Long-term in situ observational data can fill some of the information gap, with depth-resolved field measurements providing a detailed view of thermal change throughout the water column. However, many previous studies that have investigated multi-decadal changes in lake temperature, both at the surface and at depth, have typically focused on north temperate lakes. Relatively few studies have investigated temperature variations in lakes situated north of the Arctic Circle, which is one of the most rapidly warming regions on Earth. Here, using a 60-year (1961–2020) observational data set of summer water temperature (July–September) from Lake Inari (Finland), we investigate changes in the thermal environment of this pristine lake. Our analysis suggests a statistically significant summer warming trend at the lake surface (+0.25°C decade⁻¹, p-value

To remove nitrogen in cold conditions, we studied new nature-based treatment solutions using six pilot-scale reactors. The pilots were woodchip bioreactor (WBR), aquatic floating hook-moss (Warnstorfia fluitans) (MBR), and a combined woodchip and floating hook-moss hybrid unit (HBR) with an improved hydraulic design. The experiment was run in a climate room at temperatures of 10 °C and 5 °C and using mine water from two sites located in northern Finland. Unlike traditional horizontal flow woodchip bioreactors, in this study the hydraulic efficiency was improved from poor (λ = 0.06) in the woodchip bioreactor to satisfactory (λ = 0.51) in the hybrid unit by inserting two inner plates along the water flow and adding floating hook-moss. The hybrid bioreactor revealed the highest capability of nitrogen removal in all inorganic forms at T ≤ 10 °C with a mean HRT of 70.5 h. On average, 30–78 % of dissolved inorganic nitrogen was removed in the hybrid unit, which was 2 and 3 times more than in units consisting only of woodchip or floating hook-moss. The hybrid bioreactor revealed a maximum NO−3-N removal rate of 1.0–5.2 g m⁻³ d⁻¹ and a 21.8–99.7 % removal efficiency for an average incoming NO−3-N load of 40 g d⁻¹. The maximum NH⁺ ₄-N removal efficiency of 75.6 and 53 % took place in HBR and MBR, respectively, when the incoming NH⁺ ₄-N load was 23.6 ± 0.7 g d⁻¹ at 10 °C. Over the 154 days of the experiment, the hybrid unit removed a total of 2.95 kg DIN-N, which was 0.8 kg higher than the sum of the DIN-N mass removed in the individual woodchip (1.7 kg) and moss units (0.55 kg). The nitrogen content of the aquatic moss was higher in the hybrid unit compared to the moss unit, showing a higher contribution of N plant uptake. Overall, our results suggest that combining woodchips and aquatic moss in a hybrid unit with improved hydraulic efficiency using inner walls may enhance nitrogen removal in cold climate conditions.

Subarctic ecohydrological processes are changing rapidly, but detailed and integrated ecohydrological investigations are not as widespread as necessary. We introduce an integrated research catchment site (Pallas) for atmosphere, ecosystems, and ecohydrology studies in subarctic conditions in Finland that can be used for a new set of comparative catchment investigations. The Pallas site provides unique observational data and high-intensity field measurement datasets over long periods. The infrastructure for atmosphere- to landscape-scale research in ecosystem processes in a subarctic landscape has recently been complemented with detailed ecohydrological measurements. We identify three dominant processes in subarctic ecohydrology: (a) strong seasonality drives ecohydrological regimes, (b) limited dynamic storage causes rapid stream response to water inputs (snowmelt and intensive storms), and (c) hydrological state of the system regulates catchment-scale dissolved carbon dynamics and greenhouse (GHG) fluxes. Surface water and groundwater interactions play an important role in regulating catchment-scale carbon balances and ecosystem respiration within subarctic peatlands, particularly their spatial variability in the landscape. Based on our observations from Pallas, we highlight key research gaps in subarctic ecohydrology and propose several ways forward. We also demonstrate that the Pallas catchment meets the need for sustaining and pushing the boundaries of critical long-term integrated ecohydrological research in high-latitude environments.

This study examined changes in Iran’s river flows by applying regression and analysis of variance methods to long-term ground-truth data. Evaluations were performed for the country’s data-rich rivers, covering almost 97% of all rivers and including more than 35 years of measurements. The results showed that about 56% of Iran’s rivers have experienced a negative trend in mean annual flow that is approximately 2.5 times greater than that reported for world’s rivers, leading to a shift from perennial to intermittent for about 20% of rivers in Iran’s sub-basins. This reflects surface freshwater shortages in Iran caused by natural and, more importantly, anthropogenic disturbances. It may even indicate the development of new hydrological regimes which can have significant implications for future surface water storage in Iran. This research improves understanding of changes in Iran’s river flows and provides beneficial information for sustainable water resources management in the country.

Key Points: • Land use such as agriculture and peat extraction alter the physical and hydraulic properties of the peat more strongly than other land uses • The top 30 cm peat depth was most affected by agriculture and peat extraction, as indicated by the bulk density, specific yield, and porosity values • The van Genuchten-Mualem soil water retention model was applied successfully to different layers of peat under different land use Undisturbed peatlands are effective carbon sinks and provide a variety of ecosystem services. However, anthropogenic disturbances, especially land drainage, strongly alter peat soil properties and jeopardize the benefits of peatlands. The effects of disturbances should therefore be assessed and predicted. To support accurate modeling, this study determined the physical and hydraulic properties of intact and disturbed peat samples collected from 59 sites (in total 3,073 samples) in Finland and Norway. The bulk density (BD), porosity, and specific yield (Sy) values obtained indicated that the top layer (0–30 cm depth) at agricultural and peat extraction sites was most affected by land use change. The BD in the top layer at agricultural, peat extraction, and forestry sites was 441%, 140%, and 92% higher, respectively, than that of intact peatlands. Porosity decreased with increased BD, but not linearly. Agricultural and peat extraction sites had the lowest saturated hydraulic conductivity, Sy, and porosity, and the highest BD of the land use options studied. The van Genuchten-Mualem (vGM) soil water retention curve (SWRC) and hydraulic conductivity (K) models proved to be applicable for the peat soils tested, providing values of SWRC, K, and vGM-parameters (α and n) for peat layers (top, middle and bottom) under different land uses. A decrease in peat soil water content of ≥10% reduced the unsaturated K values by two orders of magnitude. This unique data set can be used to improve hydrological modeling in peat-dominated catchments and for fuller integration of peat soils into large-scale hydrological models.

This study examined the effect of different attributes on regionalization of potential evapotranspiration (ETp) in Urmia Lake Basin (ULB), Iran, using the region of influence (RoI) framework. Data for the period 1997–2016 from 30 weather stations were selected for the analysis. To achieve similarity between stations, climate, geographical, and statistical attributes were selected. To determine the effect of each attribute, the Shannon entropy weighting method was used. The results showed that attribute weighting had a significant impact on ETp clustering. Among the groups studied, the most significant effect of weighting was observed in the statistical attributes category. Among all attributes, skewness coefficient (CS) was the most useful in determining similarity between stations. Based on the results, ULB can be divided into three homogeneous regions. Proximity of weather stations did not always indicate similarity between them, but by weighting the stations in addition to weighting the attributes, more accurate estimates of ETp in the basin were obtained. Overall, the results demonstrate potential for application of the RoI approach in regionalization of ETp, by assigning a weight to weather stations and to influencing attributes.

For accurate forecasting of extreme events in rivers, streamflow time series with sub‐daily temporal resolution (1–6 hour) are preferable, but discharge time series for long rivers are usually available at daily or monthly resolution. In this study, the scaling properties of hourly and daily streamflow time series were measured. As an innovation, the effects of extreme values on the multifractal behavior of these series were evaluated. Interestingly, both hourly and daily discharge records led to nearly identical scaling trends and identical crossover times. Daily and hourly discharge time series appeared to be non‐stationary when the timescale ranged from 75 to 366 days. Otherwise, the signals may be considered stationary time series. In addition, the results indicated that the extreme values strongly contribute to the multifractality of the series. The width of singularity spectra decreased considerably when the extreme events were removed from both hourly and daily discharge records.

In an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Hydrological connections between aquifers and boreal mires need to be better understood for protection of this type of wetland. Three-dimensional (3D) models have so far been sparsely used for such systems. This study investigated the effect of parameterisation with global sensitivity analysis on groundwater-surface water (GW-SW) interactions in a boreal esker-aapa mire system. Sensitivity analysis by the elementary effect (Morris) method was applied to a 3D steady-state hydrological model built with the fully-integrated HydroGeoSphere code. Parameter sensitivity with respect to various model outputs was explored, providing comprehensive insights into the most hydrologically relevant parameters. The results indicated that depending on model outputs the most influential model parameters varied. They also revealed existence of feedback in terms of interdependence of parameters between the esker aquifer and surrounding aapa mires. The properties of the mire (or peatland) landscape affected groundwater levels in the unconfined aquifer and, conversely, esker-mire interactions depended on esker hydraulic characteristics. This implies that accurate representation of both systems is required and that reliable determination of GW-SW interactions may be impeded by parameter interactions. In this study, the van Genuchten functions were used to represent hydraulic properties of unsaturated flow domain and the results revealed that formal sensitivity analysis methods based on random sampling might not be appropriate for evaluating parametric sensitivity. If the van Genuchten parameters ranges are large, randomly sampled values may not always produce physically realistic water retention characteristics. Overall, our results demonstrate that the computationally efficient elementary effect method is a suitable tool for investigating the parametric sensitivity of integrated models, enhancing modelling of boreal groundwater-dependent ecosystems.

Momentum exchange in the mixing region between the floodplain and the main channel is an essential hydraulic process, particularly for the estimation of discharge. The current study investigated various data mining models to estimate apparent shear stress in a symmetric compound channel with smooth and rough floodplains. The applied predictive models include random forest (RF), random tree (RT), reduced error pruning tree (REPT), M5P, and the distinguished hybrid bagging-M5P model. The models are constructed based on several correlated physical channel characteristic variables to predict the apparent shear stress. A sensitivity analysis is applied to select the best function tuning parameters for each model. Results showed that input with six variables exhibited the best prediction results for RF model while input with four variables produced the best performance for other models. Based on the optimised input variables for each model, the efficiency of five predictive models discussed here was evaluated. It was found that the M5P and hybrid bagging-M5P models with the coefficient of determination (R2) equal to 0.905 and 0.92, respectively, in the testing stage are superior in estimating apparent shear stress in compound channels than other RF, RT and REPT models.

Catchment storage sustains ecologically important low flows in headwater systems. Understanding the factors controlling storage is essential in analysis of catchment vulnerability to global change. We calculated catchment storage and storage sensitivity of streamflow for 61 boreal headwater catchments in Finland. We also explored the connection between computed storage indices and low flow conditions. The relationships between selected climate, snow, and catchment characteristics and calculated storage properties and low flows were investigated, in order to assess the importance of different factors that render catchments vulnerable to climate and environmental change. We found that the most sensitive areas to climate change were located in the southern boreal coastal zone, with fine‐grained soils and agricultural areas. In contrast, catchments in the middle and northern boreal zone, with till and peatland soils and higher snow water equivalent values, were less sensitive under current conditions. In addition, we found a threshold at a snow to precipitation ratio of 0.35. Above that threshold, summer low flows were generally sensitive to changes in snow conditions, whereas below that threshold catchment characteristics gained importance and the sensitivity was more directly related to changes in temperature and timing of rainfall. These findings suggest that a warming climate will have pronounced impacts on hydrology and catchment sensitivity related to snow quantity and snow cover duration in certain snow to precipitation ratio zones. Moreover, land use activities had an impact on storage properties in agricultural and drained peatland areas, resulting in a negative effect on low flows.

In separate analyses of the impacts of land use change and climate change, a scenario-based approach using remote sensing and hydro-climatological data was developed to assess changes in hydrological indices. The data comprised three Landsat TM images (1988, 1998, 2008) and meteorological and hydrological data (1983–2012) for the Aligudarz and Doroud stations in the Marboreh watershed, Iran. The QUAC module and supervised classification maximum likelihood (ML) algorithm in ENVI 5.1 were used for remote sensing, the SWAT model for hydrological modelling and the Mann-Kendall and t test methods for statistical analysis. To create scenarios, the study period was divided into three decades (1983–1992, 1993–2002, 2003–2012) with clearly different land use/land cover (LULC). After hydrological modelling, 10 hydrological indices related to high and low flow indices (HDI and LDI) were analysed for seven scenarios developed by combining pre-defined climate periods and LULC maps. The major changes in land use were degradation of natural rangeland (−18.49%) and increasing raid-fed farm area (+16.70%) and residential area (+0.80%). The Mann-Kendall test results showed a statistically significant (p

Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.

With the development of shipping in the Caspian Sea, the need for high-quality data on wave characteristics has increased. In the present work, the analysis of maximum wave height at coastal stations in Kazakhstan was carried out using wave and wind data and numerical modeling with the SWAN model. The influence of stationary wind and its direction on the wave height was determined. The results show that the changes in the parameters of the wind wave depend on the wind speed and direction. At wind speeds below 10 m/s, the predicted wave height can reach 3 m in the Middle Caspian basin. In the Northern Caspian basin, where the depths are shallow, the waves do not exceed 2 m. The study output produced the first wind zoning map for the Kazakh part of the Caspian Sea.

Significance Iran is facing a state of water bankruptcy that threatens its socioeconomic development and natural environments. Using an exceptionally rich measured groundwater dataset, we illustrate the extent and severity of Iran’s groundwater depletion and salinization problems during the 2002 to 2015 period, when the number of groundwater extraction points nearly doubled. Iran’s nonrenewable groundwater withdrawal was about 66 million m3 in 1965, which cumulatively grew to approximately 133 × 103 million m3 in 2019. This increase is about 3.4 times the capacity of the famous Three Gorges Dam in China. Groundwater decline due to extensive overexploitation of nonrenewable groundwater and rising salinity levels are documented in almost all subbasins, pointing to dire, worsening water security risks across the country., Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran’s widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km3/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km3/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating “very high salinity hazard” for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.