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Study region: The NekooAbad Irrigation District in central Iran faces challenges due to inefficient surface water distribution. 82 million m3 of groundwater is extracted annually from 15,000 tube-wells, leading to decreased groundwater levels and increased energy consumption of 234 million kWh per year. Study focus: This study explores implementing automation in surface water distribution to reduce groundwater extraction and conserve energy. A hydraulic simulation model and a centralized Model Predictive Control approach were used to analyze the existing system and propose a recovery plan. The potential impact of automated surface water distribution on reducing groundwater extraction and energy conservation was evaluated through spatial assessment in GIS. New hydrological insights for the region: The results demonstrate that the introduction of automation can significantly improve surface water distribution and, accordingly, groundwater overexploitation and, consequently, energy conservation, particularly during water scarcity. Energy conservation increased by 42.3 %, 54.8 %, 56.2 %, and 57.7 % under normal conditions, with water shortages of less than 10 %, 10–15 %, and 15–20 %, respectively. However, as the surface water shortages intensified, the energy conservation rates decreased to 57.7 %, 43.7 %, 25.4 %, and 18.9 % for normal conditions, water shortages of 15–20 %, 20–30 %, 30–40 %, and over 40 %, respectively. The automation introduction effectively provided reliable surface water resources, prompting farmers to shut down pumping stations or reduce working hours. Even in extreme scenarios, the project achieved up to 18.9 % energy savings.

AbstractThe geologically complex Kyzylzharma groundwater field is located in the south-central part of Kazakhstan in the lower Syr Darya Basin. It supplies the 243,000 population of Kyzylorda City by drinking and agricultural water needs. Numerical modeling was used to predict the consequences of increased groundwater withdrawal for future water supply needs. The results displayed a mean squared error for the groundwater simulations of about 0.6 m and was thus acceptable. The validated transmissivity was between 2 × 10−7 and 2 × 10−5 m/d. These parameters showed that the planned groundwater withdrawal will result in a depression cone reaching 90–100 m below present levels in 2040. Maximum groundwater level drawdown may reach 130 m below present levels. This drawdown increases risks for brackish saltwater intrusion into the main groundwater aquifer. The results point at the depletion of groundwater resources in the Syr Daria Artesian Basin and the risks for groundwater quality, particularly increase in mineralization. The key outcome is the recognition that effective joint management of the Syr Daria Artesian Basin’s groundwater resources, involving both Kazakhstan and Uzbekistan, necessitates development and operation of a joint numerical aquifer model. Building this model is a crucial tool for assessing the sustainable groundwater resources in the region.

Groundwater is becoming increasingly important as surface water is decreasing and becoming more and more polluted. In particular, rural areas in the arid region of Central Asia face problems with both water quantity and quality. In view of this, we investigated the drinking water quality in the Maysky district in the Pavlodar region, Kazakhstan. The organoleptic properties, together with microbiological indicators, as well as organic and inorganic substances of drinking water before and after treatment, and tap water were studied and compared to recommended levels. The bacteriological indicators of the drinking water, especially, showed that the water represents health risks since the presence of bacteria of the genus Pseudomonas aeruginosa was confirmed. Water treatment reduced the total microbial count (TMC) indicator by 3.6 times. However, TMC still exceeded permissible levels in the tap water, indicating that the drinking water is sanitary and epidemiologically not acceptable. Pathogenic contamination of drinking water can severely affect weaker individuals and children. It has been estimated that the infant mortality rate in Kazakhstan is six times higher as compared to the EU and less than 30% of Kazakhstan’s population have access to safe water. Also, 50% of the population drink water that does not comply with the international standards, e.g., bacteriological levels. Thus, it is important to continuously monitor the groundwater quality to minimize health risks and work towards access to safe drinking water, in line with the UN SDGs.

Controlling the water level in irrigation channels is important for the efficient management of irrigation and water delivery. In this study, the proportional–integral–derivative (PID) controller was implemented in both the HEC-RAS boundary condition, as an internal model, and MATLAB, as an external model. In the latter, the Hydrologic Engineering Center’s (HEC) River Analysis System (HEC-RAS) model was automated for irrigation canals by coding in the MATLAB script. To test the new models, E1R1 (first right bank branch of the first eastern canal in the Dez irrigation network, Khuzestan Province, Iran) irrigation canal data were prepared in HEC-RAS. A flow pattern was provided to simulate the canal water levels. The results showed efficient control of the water level for both models. The maximum and average water depth deviations from the target value were 13% and 4%, respectively, which fall in the good agreement range. The fewer these indicators, the better the performance is. The efficiency and adequacy were close to the ideal value and in the good agreement classes. The equity indicator was 0.013, which is very close to its ideal value of zero, showing efficient water distribution in the tested system. According to the literature for the equity indicator, a range of 0–0.10 is good, a range of 0.11–0.25 is fair, and a range of greater than 0.25 is poor. The results showed that simple and fast implementation is the main advantage of the internal model; however, it is not suitable for implementing complex controllers. Conversely, the external model can be implemented for complicated algorithms without any limitations.

Abstract Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.

Study region: A densely populated urban area located in the 13th municipality of Tehran metropolis, Iran. Study focus: Bioretention cell is one of the low-impact development methods that aims to restore the hydrological cycle in city areas before urban development. However, the bioretention cell's hydrological performance can vary in urban environments. As a result, this research investigated the effectiveness of a bioretention cell in reducing runoff and recharging groundwater in a densely populated metropolitan area located in eastern Tehran, Iran. Groundwater and surface water modeling were conducted separately. The SWMM model was used for surface water modeling, while a novel approach that utilized the SWMM groundwater module was implemented to assess the bioretention cell's impact on groundwater recharge quantitatively. New hydrological insights for the region: The study found that implementing bioretention cells can significantly reduce total runoff volume, ranging from 75.6% to 60.7% for rainfall with a return period of 2–100 years. This reduction is due to increased infiltration from the bioretention cells, which can lead to a maximum monthly increase of 12.2–44.0 millimeters of groundwater table for the same rainfall events. The study highlights the effectiveness of retaining runoff through bioretention cells in mitigating flooding, restoring the hydrological cycle, and reviving aquifers in urban areas.

Study region: Bihar State, located in India’s eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focus: Recent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the region: The results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.

Wetting and drying climate sequences and anthropogenic vegetation changes can have profound effects on the hydrological cycle and sustainability of terrestrial ecosystems. China contains many different climates, from humid to extremely arid and it contributes to the global mitigation strategies of climate change. China is also an important contributor to global greening and carbon sequestration. In view of this, we estimated the spatial and temporal variation of leaf area index (LAI), dryness/wetness index (DWI), and soil moisture (SM) in arid (A), semi-arid (SA), semi-humid (SH), and humid (H) climate zones during 1981–2018 in China and analyzed the patterns of DWI and LAI effects on SM under different vegetation greenness. The results showed a significantly increasing trend of LAI in all climatic zones, and the contribution was from highest to lowest: SH (8.17 × 10-3 m2 m−2 year−1), SA (7 × 10-3 m2 m−2 year−1), H (4.71 × 10-3 m2 m−2 year−1), and A (3.98 × 10-3 m2 m−2 year−1). The SA and A zones are becoming wetter, while the soil of SH and H zones are drying. The dry/wet climate variation plays a decisive role in soil moisture, while the role of vegetation is limited. Additionally, the LAI3 (mean LAI from 2016 to 2018) thresholds were 0.25–0.41 and 0.84–0.86 for A and SA as a whole, respectively, as well as 0.47–0.54 and 0.93–1.18 for grassland, respectively. When vegetation exceeded this threshold, the effect of vegetation on soil moisture showed a shift from increasing to decreasing change. Overall, the results of the study provide improved understanding of the atmosphere-soil-vegetation interactions under climate change, as well as effects of vegetation restoration and water conservation efforts.

Introduction: Runoff measurement and monitoring is a laborious, time-consuming, and costly task. Additionally, common runoff monitoring usually primarily provide water level, requiring information on the stage-discharge relation. Automatic equipment such as flow meter tipping bucket (TB) is a potential option to simplify and provide continuous runoff monitoring in small catchments. However, a proper description of how to size and adapt the design under different flow conditions is still lacking.Methodology: In this paper we present a novel standardized framework for the design of TB that can be used for low-cost and real-time runoff monitoring under many different conditions. The framework consists of an estimation of the runoff peak rate using the rational equation and a volumetric capacity estimate of the cavity based on runoff rate, operation speed, and inclination angle of TB when at resting position. The proposed framework was implemented in a case study where four TBs were designed for continuous runoff monitoring from experimental plots (100 m2) with different land use (sugarcane, soybean, and bare soil).Results: During field tests (five months), the designed TBs had a recovery rate of actual runoff ranging from 61% to 81% and were able to capture features poorly studied (starting/ending time and peak flow) that have potential importance in hydrological models.Discussion: The proposed framework is flexible and can be used for different environmental conditions to provide continuous runoff data records.

Identifying operational losses in irrigation canals can be difficult due to inaccurate simplification in designing and operating national guidelines. However, this study aims to provide a practical solution to this problem by identifying operational losses, which are the primary cause of off-farm irrigation water losses. The method involves simulating the daily delivered water to individual Irrigation Units (IUs) through off-take structures using hydraulic simulation. The daily agricultural water demand for individual IUs is then calculated using a crop growth model and irrigation system efficiency. This approach offers an effective way to accurately identify operational losses in agricultural water distribution systems. The Roodasht irrigation district in central Iran was used test proposed method. The water distribution simulation was conducted using an open-source Irrigation Conveyance System Simulation (ICSS) in three separate scenarios, including 29, 22, and 55 days, and each showed a typical operation based on history. The IUs’ agricultural water demand, at each off-take location, was calculated by the Aquacrop estimation including the existing information of the on-farm water efficiency depending on irrigation system. According to the study, the amount of water lost daily varied between 60% and 82%, 50–70%, and 44–61% in IUs that used drip, sprinkler, and surface water application systems, respectively, during normal operational scenarios. In situations where water was scarce, the water loss range was 4–87%, 68–80%, and 60–70%, respectively.The results of this study confirmed that losses in the conveyance and distribution systems varied according to the distance from the source and were often higher than the recommended guidelines for irrigation system design and operation (such as the 10–20% suggested in Iranian guidelines). The proposed methodology can be used to improve estimation of actual water losses for irrigation districts with similar operation systems and climatic conditions.

Inexpensive and efficient desalination is becoming increasingly important due to dwindling freshwater resources in view of climate change and population increase. Improving desalination techniques of brackish water using graphene-based materials has the possibility to revolutionize freshwater production and treatment. At the same time, graphene matter can be cheaply mass-produced from biowaste materials. In view of this, graphene material was obtained from a four-step production approach starting from rice husk (RH), including pre-carbonation, desilication, chemical activation, and exfoliation. The results showed that the produced samples contained a mixture of graphene layers and amorphous carbon. The activation ratio of 1:5 for carbonized RH and potassium hydroxide (KOH), respectively, provided higher graphene content than the 1:4 ratio of the same components, while the number of active layers remained unaffected. Further treatment with H2O2 did not affect the graphene content and exfoliation of the amorphous carbon. Preparation of the graphene material by the NIPS technique and vacuum filtration displayed different physicochemical characteristics of the obtained membranes. However, the membranes’ main desalination function might be related more to adsorption rather than size exclusion. In any case, the desalination properties of the different graphene material types were tested on 35 g/L saltwater samples containing NaCl, KCl, MgCl2, CaSO4, and MgSO4. The produced graphene materials efficiently reduced the salt content by up to 95%. Especially for the major constituent NaCl, the removal efficiency was high.

Vulnerability maps of groundwater provide an efficient means of identifying environmental trends and prioritizing regions for prevention plans. The GIS-based DRASTIC method, however, does not consider the impact of contamination, so there is a need to modify it according to the specifics of the region and its contamination load. The aim of this study is to investigate a suitable DRASTIC modification for vulnerability assessment by changes in its rating and weighting systems. The goal is to explore and compare the impact of both objective and subjective weighting methods in the vulnerability assessment of a smaller aquifer situated beneath agricultural land. The frequency ratio (FR) method is used to adjust the DRASTIC index rates based on nitrate contamination as the main contamination from fertilizers extensively used in the study area. The DRASTIC parameters’ weights are determined using two objective and subjective methods, including Shannon entropy and single-parameter sensitivity analysis (SPSA), respectively. In total, five frameworks, including FR-DRASTIC, DRASTIC-Entropy, DRASTIC-SPSA, FR-SPSA, and FR-Entropy are developed and evaluated. We validate these proposed vulnerability indices based on the nitrate concentrations in 14 samples. The results show that the vulnerability map obtained from the FR-Entropy framework is superior, showing a 0.85 correlation with nitrate concentrations. Notably, Shannon entropy as an objective weighting method outperformed the subjective SPSA approach.

The authors wish to make the following corrections to their paper [...]

Semiarid Tunisia is characterized by agricultural production that is delimited by water availability and degraded soil. This situation is exacerbated by human pressure and the negative effects of climate change. To improve the knowledge of long-term (1980 to 2020) drivers for Land Use and Land Cover (LULC) changes, we investigated the semiarid Rihana region in central Tunisia. A new approach involving Google Earth Engine (GEE) was used to map LULC using Landsat imagery and vegetative indices (NDVI, MSAVI, and EVI) by applying a Random Forest (RF) classifier. A Rapid Participatory Systemic Diagnosis (RPSD) was used to consider the relation between LULC changes and their key drivers. The methodology relied on interviews with the local population and experts. Focus groups were conducted with practicians of the Regueb Agricultural Extension Services, followed by semi-structured interviews with 52 households. Results showed the following: (1) the RF classifier in Google Earth Engine had strong performance across diverse Landsat image types resulting in overall classification accuracy of ≥0.96 and a kappa coefficient ≥0.93; (2) rainfed olive land increased four times during the study period while irrigated agriculture increased substantially during the last decade; rangeland and rainfed annual crops decreased by 58 and 88%, respectively, between 1980 and 2021; (3) drivers of LULC changes are predominately local in nature, including topography, local climate, hydrology, strategies of household, effects of the 2010 revolution, associated increasing demand for natural resources, agricultural policy, population growth, high cost of agricultural input, and economic opportunities. To summarize, changes in LULC in Rihana are an adaptive response to these various factors. The findings are important to better understand ways towards sustainable management of natural resources in arid and semiarid regions as well as efficient methods to study these processes.

Problems with increasing heavy metal contents in natural waters are becoming a global issue. At the same time, improved methods for water treatment are becoming increasingly important. In this context, natural zeolites can be used to purify polluted water. In this paper, we investigated how the adsorption capacity of natural zeolites can be improved. Natural zeolites from the Shankanay district, Almaty, Kazakhstan, were used as adsorbent material for experiments on improving the water treatment of heavy metals. We found that the adsorption capacity for heavy metals was increased greatly by thermal activation using furnace treatment. The optimal thermal activation condition was about 550 °C for a duration of 2 h. However, the improved adsorption capacity for different heavy metals varied depending on the heat treatment temperature. Adsorption by the heat-treated zeolites at a temperature of 550 °C was 87% for nickel, 99% for copper and cadmium, and 100% for lead. Adsorption by heat-treated zeolites at a temperature of 500 °C was 78% for nickel, 98% for copper, 83% for cadmium, and 88% for lead. The residual concentration of heavy metals in the filtered water did not exceed the maximum permissible concentrations for drinking purposes. In all experiments, intense adsorption took place during the first 10 min representing 35 to 61% of the metal ions in the water. Adsorption properties were verified using adsorption capacity (BET), IR spectroscopy, and scanning electron microscopy. The study shows that modified Shankanay natural zeolites have great potential as a low-cost adsorbent material for purifying water from heavy metals.

Abstract The use of organic mulch is important for urban green applications. For urban areas in arid and semiarid regions receiving short high-intensive rainfall, rainfall characteristics, and soil slope play an important role for mulch functioning. These properties of mulch were studied. For this purpose, rainfall simulation experiments using organic mulching were conducted in Jiufeng National Forestry Park to analyze the influence of organic mulch under different slope and heavy rainfall events. The results showed that soil water content displayed a decreasing tendency with increasing mulch application. Compared to bare soil, a mulch application of 0.25 kg/m2 and 0.50 kg/m2 led to maximum soil water content and maximum runoff decrease occurred for 0.50 kg/m2 mulch. Higher application rate of mulch displayed less soil water content and greater runoff. The runoff amount and runoff generation rate decreased by 28–83% and 21–83%, respectively, as compared to bare soil. With a mulch application of 0.25–1.00 kg/m2, soil drainage accounted for 56–60% of total rainfall. Overall, an efficient mulch application was found to be 0.25–0.50 kg/m2. The results of this study are relevant for arid and semiarid urban regions that experience heavy rainfall.

Soil physicochemical properties and macropore spatial structure affect saturated hydraulic conductivity (Ks). However, due to regional differences and long measurement time, Ks is tedious to quantify. Therefore, it is of great importance to find simplified but robust methods to predict Ks. One possibility is to use pedo-transfer functions (PTFs). Along this line, stratified sampling was carried out in six typical forestlands in the rocky mountain area of Northern China. Penetration experiments and industrial CT scanning were combined to explore the distribution characteristics of regional Ks and its influencing factors. Based on this, we compared three Ks PTF models by multiple linear regression for Ks prediction. The results indicated that: (1) Ks decreased with increasing soil depth, which followed the order coniferous forest < broad-leaved forest < mixed forest, and the change range of mixed forest was greater than that of homogeneous forest. (2) Soil bulk density, water content, sand, silt, organic matter, total nitrogen, total phosphorus, and total potassium were significantly correlated with Ks (p < 0.05). In addition, stand type and soil depth had a certain impact on soil physicochemical properties that affected Ks. (3) Soil macropore structure, such as number density, length density, surface area density, and volume density, all decreased with increasing soil depth. They were all significantly positively correlated with Ks (p < 0.001). (4) The best predictability and universality for PTFs was achieved for PTFs containing bulk density, organic matter content, and total phosphorus. Only PTFs containing parameters of macropore spatial structure did not yield high predictability of Ks. The findings of this study contribute to the understanding of forest hydrological infiltration processes in rocky mountain forests in Northern China, and provide theoretical support for the prediction and management of water loss and soil erosion and the enhancement of water conservation functions.

Providing safe drinking water to people in developing countries is an urgent worldwide water problem and a main issue in the UN Sustainable Development Goals. One of the most efficient and cheapest methods to attain these goals is to promote the use of slow sand filters. This review shows that slow sand filters can efficiently provide safe drinking water to people living in rural communities not served by a central water supply. Probably, the most important aspect of SSF for developing and less-developed countries is its function as a biological filter. WASH problems mainly relate to the spread of viruses, bacteria, and parasites. The surface and shallow groundwater in developing countries around urban areas and settlements are often polluted by domestic wastewater containing these microbes and nutrients. Thus, SSF’s function is to treat raw water in the form of diluted wastewater where high temperature and access to nutrients probably mean a high growth rate of microbes and algae but probably also high predation and high efficiency of the SSF. However, factors that may adversely affect the removal of microbiological constituents are mainly low temperature, high and intermittent flow rates, reduced sand depth, filter immaturity, and various filter amendments. Further research is thus needed in these areas, specifically for developing countries.

Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops’ biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0 from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d−1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d−1).

Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993–2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM+, and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.

Abstract Nitrate pollution in groundwater is a serious problem in many parts of the world. However, due to the diffuse and common spatially over-lapping character of potential several non-point pollution sources, it is often difficult to distinguish main nitrate sources responsible for the pollution. For this purpose, we present a novel methodology applied to groundwater for an intensely polluted area. Groundwater samples were collected monthly from April 2017 to March 2018 in Shimabara City, Nagasaki, Japan. Soil samples were collected seasonally at soil surface and 50 cm depth at 10 locations during the same period. Sequential extraction by water and extract agents was performed using calcium phosphate for anions and strontium chloride for cations. Mean nitrate concentration in groundwater close to a livestock waste disposal site (hereinafter called “LWDS”) was 14.2 mg L−1, which is exceeding Japanese drinking water standards (10 mg L−1). We used coprostanol concentration, which is a fecal pollution indicator, to identify pollution sources related to livestock waste. For this purpose, we measured coprostanol (5β) and cholestanol (5α) and then calculated the sterol ratio (5β/(5β + 5α)). The ratios for three groundwater sampling sites were 0.28, 0.26, and 0.10, respectively. The sterol ratios indicated no pollution (

Pond water is used in everyday life by many people in Bangladesh, however, without sufficient and reliable information regarding water quality and pollution status. For this reason, geospatial analysis and mapping of water quality indices such as metal (MI), contamination (Cd), and physicochemical water quality index (WQI) were assessed to improve the understanding of potential pollution sources. Samples were collected from twenty randomly selected ponds situated in Jashore Sadar Upazila, Bangladesh. Nineteen (19) water quality parameters were measured, including pH, temperature, EC, TDS, total suspended solids (TSS), chloride, alkalinity, total hardness, salinity, Fe, Mn, Pb, Cr, Cd, Co, Zn, Ag, Ni, and Cu. The average concentration of Fe, Mn, Pb, Cd, and Ag was much higher than recommended standards. The WQI ranged from 1.59-5.27, Cd from -0.19-18.28, and MI from 7.81-26.28. The spatial distribution of MI indicates that the south-western and south-eastern region of the study area are stands out with a very high pollution pressure. The spatial distribution of Cd, follows the same trend as for MI. A multitude of different types of pollution sources contributes to the high pollution load such as, municipal wastewater, leachate from landfills, small industry wastewater and stormwater, and agricultural runoff. The studied pond water is highly polluted and not suitable for household use and fish consumption.

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.

Peatlands are unique ecosystems that contain massive amounts of carbon. These ecosystems are incredibly vulnerable to human disturbance and climate change. This may cause the peatland carbon sink to shift to a carbon source. A change in the carbon storage of peatlands may result in surface deformation. This research uses the interferometric synthetic aperture radar (InSAR) technique to measure the deformation of the peatland’s surface in south Sweden in response to the seasonal and extreme weather conditions in recent years, including the unprecedented severe drought in the summer of 2018. The deformation map of the study area is generated through a time-series analysis of InSAR from June 2017 to November 2020. Monitoring the peatland areas in this region is very important as agricultural and human activities have already caused many peatlands to disappear. This further emphasizes the importance of preserving the remaining peat sites in this region. Based on the InSAR results, a method for calculating the carbon flux of the peat areas is proposed, which can be utilized as a regular monitoring approach for other remote areas. Despite the severe drought in the summer of 2018, our findings reveal a significant uplift in most of the investigated peat areas during the study period. Based on our estimations, 86% of the peatlands in the study area experienced an uplift corresponding to about 47 000 tons of carbon uptake per year. In comparison, the remaining 14% showed either subsidence or stable conditions corresponding to about 2300 tons of carbon emission per year during the study period. This emphasizes the importance of InSAR as an efficient and accurate technique to monitor the deformation rate of peatlands, which have a vital role in the global carbon cycle.

Nitrate pollution in groundwater is a severe problem in Shimabara Peninsula, Nagasaki Prefecture, Japan. Previous studies have investigated water quality characteristics in the northern part of the peninsula and shown serious effects of nitrate pollution in the groundwater. The present study aimed to investigate the groundwater quality in the southern areas of the peninsula for improved understanding of the water quality status for the entire peninsula. Groundwater samples were collected at 56 locations in Minami-Shimabara City from 28 July to 4 August 2021. The spatial distribution of water quality constituents was assessed by Piper-trilinear and Stiff diagrams for major ion concentrations. One agricultural area in the western parts exceeded Japanese recommended standards for water. According to the Piper-trilinear diagram, 44 sampling sites (78.6%) were classified as alkaline earth carbonate type, nine sites (16.1%) as alkaline earth non-carbonate type, and three sites (5.3%) as alkaline carbonate type. Stiff diagrams displayed Ca-HCO3 water type for most of the sites. Na-HCO3 and Mg-HCO3 types were found in coastal areas. Principal component analyses showed that the first component corresponded to dissolved constituents in groundwater and denitrification, the second effects of ion exchange and low nitrate pollution, and the third effects of severe nitrate pollution. Hierarchical cluster analysis was used to classify the groundwater into five groups. The first group included sites with relatively high nitrate concentration. The second group had relatively low ion concentration, distributed from center to eastern parts. The third group included intermediate ion concentration, distributed at lower altitudes along the coastal line. The fourth and fifth groups had a higher ion concentration, especially characterized by high sodium and bicarbonate concentration.

Soil degradation and low productivity are among the major agricultural problems facing farmers of the newly reclaimed agricultural area in the Nile Delta region, Egypt. High content of clay and silt characterizes the soil texture of all farms in the area, while farmers still rely on the traditional mole drainage (MD) system to reduce the salinity of the farm soil. We present a comparison of innovative geo-resistivity methods to evaluate mole drains and the salinity affected clay soils. Geoelectrical surveys were conducted on three newly reclaimed farms to image the subsurface soil drainage conditions and to evaluate the efficiency of using the traditional MD systems in these heavy clay environments. The surveys included measuring the natural spontaneous potential (SP), apparent resistivity gradient (RG), and electrical resistivity tomography (ERT). Integrating the results of the three methods reduced the ambiguous interpretation of the inverted ERT models and allowed us to determine the subsurface soil structure. The inverted ERT models were suitable for locating the buried MDs and delineating the upper surface of the undisturbed clay beds. The proximity of these layers to the topsoil reduces the role played by MDs in draining the soil in the first farm and prevents the growth of deep-rooted plants in the second farm. Time-lapse ERT measurements on the third farm revealed a defect in its drainage network where the slope of the clay beds opposes the main direction of the MDs. That has completely obstructed the drainage system of the farm and caused waterlogging. The presented geo-resistivity methods show that integrated models can be used to improve the assessment of in situ sub-surface drainage in clay-rich soils.

Groundwater contamination caused by the use of the aqueous film-forming foam (AFFF) containing per- and polyfluoroalkyl substances (PFAS) was investigated in southern Sweden. ΣPFAS concentrations in groundwater ranged between 20 and 20,000 ng L−1; PFAS composition was primarily represented by PFOS and PFHxS. The PFAS chain length was suggested to have an impact on the contaminant distribution and transport in the groundwater. PFAS profiling showed that the use of PFSAs- and PFCAs/FTSAs-based PFAS-AFFF can be a contributor to PFAS contamination of the drinking water source (groundwater). PFAS emission was connected to PFAS-AFFF use during the fire-training and fire-fighting equipment tests at the studied location. PFAS emission per individual fire training was (semi-quantitatively) estimated as [1.4 < 11.5 ± 5.7 < 43.7 kg] (n = 20,000). The annual emission estimates varied as [11 < 401 ± 233 < 1125 kg yr−1] (n = 1005) considering possible [2 < 35 ± 20 < 96] individual fire-training sessions per year.

In recent years, the analysis of abrupt and non-abrupt changes in precipitation has received much attention due to the importance of climate change-related issues (e.g., extreme climate events). In this study, we used a novel segmentation algorithm, DBEST (Detecting Breakpoints and Estimating Segments in Trend), to analyze the greatest changes in precipitation using a monthly pixel-based satellite precipitation dataset (TRMM 3B43) at three different scales: (i) global, (ii) continental, and (iii) climate zone, during the 1998–2019 period. We found significant breakpoints, 14.1%, both in the form of abrupt and non-abrupt changes, in the global scale precipitation at the 0.05 significance level. Most of the abrupt changes were observed near the Equator in the Pacific Ocean and Asian continent, relative to the rest of the globe. Most detected breakpoints occurred during the 1998–1999 and 2009–2011 periods on the global scale. The average precipitation change for the detected breakpoint was ±100 mm, with some regions reaching ±3000 mm. For instance, most portions of northern Africa and Asia experienced major changes of approximately +100 mm. In contrast, most of the South Pacific and South Atlantic Ocean experienced changes of −100 mm during the studied period. Our findings indicated that the larger areas of Africa (23.9%), Asia (22.9%), and Australia (15.4%) experienced significant precipitation breakpoints compared to North America (11.6%), South America (9.3%), Europe (8.3%), and Oceania (9.6%). Furthermore, we found that the majority of detected significant breakpoints occurred in the arid (31.6%) and polar (24.1%) climate zones, while the least significant breakpoints were found for snow-covered (11.5%), equatorial (7.5%), and warm temperate (7.7%) climate zones. Positive breakpoints’ temporal coverage in the arid (54.0%) and equatorial (51.9%) climates were more than those in other climates zones. Here, the findings indicated that large areas of Africa and Asia experienced significant changes in precipitation (−250 to +250 mm). Compared to the average state (trend during a specific period), the greatest changes in precipitation were more abrupt and unpredictable, which might impose a severe threat to the ecology, environment, and natural resources.

Abstract Precipitation varies spatio‐temporally in amount, intensity, and frequency. Although, much research has been conducted on analyzing precipitation patterns and variation at the global scale, trend types have still not received much attention. This study developed a new polynomial‐based model for detecting nonlinear and linear trends in a satellite precipitation product (TRMM 3B43) for the 1998–2017 period at a near‐global scale. We used an automated trend classification method that detects significant trends and classifies them into linear and nonlinear (cubic, quadratic, and concealed) trend types in satellite‐based precipitation at near‐global, continental, and climate zone scales. We found that 12.3% of pixel‐based precipitation time series across the globe have significant trend at 0.05 significance level (50% positive and 50% negative trends). In all continents except Asia, decreasing trends were found to cover larger areas than corresponding increasing trends. Regarding climate zone and precipitation trend change, our results indicate that a linear trend is dominant in the warm temperate (77.7%) and equatorial climates (80.4%) while the least linear change was detected in the polar climate (68.9%). The combined results of continental and climate zone scales indicate significant increasing trends in Asia and arid climate over the last 20 yr. Furthermore, positive trends were found to be more significant at the continental scale, particularly, in Asia relative to the climate zone scale. Linear change in precipitation (80%) was the most dominant trend observed as opposed to nonlinear (quadratic [11%] and cubic [9%]) trend types at the global scale.

Spurred by the degraded forest in the 2022 Chongli Winter Olympic Games area, the Chinese government initiated a national program for mountain forest rehabilitation. We developed a method to assess the quality of mountain forests using an index system composed of stand structure, site conditions, and landscape aesthetics at three criteria levels. The method involves index weights determined by the analytical hierarchy process (AHP) and entropy method. The results show that landscape aesthetics was the most important measure for the criterion layer. Slope aspect and naturalness were the most and second-most important indices, respectively, for the alternative layer. The quality of the mountain forest in the Chongli area was divided into four grades. The area had 7.8% with high quality, 46.7% with medium quality, 36.6% with low quality, and 8.9% with inferior quality. In total 76.6% of the damaged forest were distributed on sloping and steep sloping ground at 1700 to 2050 m altitude, and Betula platyphylla Sukaczev and Larix gmelinii var. principis-rupprechtii (Mayr) Pilg. were the predominating trees. The damaged forest was divided into over-dense, over-sparse, degraded, inappropriate tree species, and inferior landscape forest. For different types of damaged forest, corresponding modification measures were proposed. The methods developed in this study can be used for rehabilitation projects to improve the quality of degraded forests in mountainous temperate areas.

Today, in developing countries, the low surface water distribution efficiency and the lack of supplying water needs of farmers by surface water resources are compensated by excessive aquifer water withdrawal. This mismanagement has caused a sharp drop in the groundwater level in many countries. On the other hand, climate change and drought have intensified the pressure on water resources. This study aims to evaluate novel strategies for developing surface water distribution systems for stress reduction of the Najafabad aquifer in Isfahan, central plateau of Iran. The performance of several strategies for agricultural water distribution and delivery, such as hydro-mechanical operating system, manual-based operating system, and centralized automatic operating system, was evaluated in this study. In the first step, two indices, i.e., water distribution adequacy and dependability, were obtained using a flow hydraulic simulation model. Then, the water distribution adequacy map and amount of reduction in the water withdrawal of existing wells were determined for each strategy. Finally, using the MODFLOW groundwater simulation model, the changes in groundwater levels due to the normal and drought scenarios (15 and 30%) were extracted during five years for each strategy. The findings for the normal scenario showed that the centralized automatic operating system strategy had the most significant impact on agricultural water management in the surface water distribution system with a 30% increase in agricultural water distribution adequacy index compared to the current situation. This strategy increased the groundwater level by 11.6 m and closed 35% of the groundwater wells. In this scenario, the hydro-mechanical operating system strategy had the weakest performance by increasing the aquifer level by only 1.31 m. In the 15% and 30% drought scenarios, the centralized automatic operating system strategy exerted the best performance among other strategies by increasing the aquifer water level by 10.18 and 9.4 m, respectively, compared to the current situation. Finally, the results showed that the spatial segmentation of the aquifer exerted better efficiency and better monitoring in the more susceptible regions.

Heavy metal (HM) accumulation in soil and plants can occur when water contaminated with HMs is used as a source of irrigation (El-Salam Canal, Egypt). In this study, the effect of watering potato crop in sandy soil from a polluted water source under flood irrigation (FI), sprinkler irrigation (SI), and surface drip irrigation (DI) on the transport of the HMs copper (Cu), manganese (Mn), lead (Pb), and zinc (Zn) in the root zone was experimentally investigated. HM concentrations in potato plant parts was also determined. The field experiments were conducted in a completely randomized block with three replicates for each irrigation method by using nine field lysimeters. Soil and plant samples were collected at the end of the growing season to determine their HM content. The results showed that regardless of irrigation method, irrigation with HM contaminated water raised HM concentrations in both soil and potato plants. DI produced the highest concentrations of most HMs (Cu, Mn, and Pb) in the upper soil layer (0–40 cm) and highest Cu, Pb, and Zn concentrations in plant tubers as well. Maximum Zn concentration in the upper soil layer and maximum Mn concentration in plant tubers occurred under SI. The maximum concentrations of Cu, Mn, Pb, and Zn in both the upper soil layer and plant tubers were 12.0, 140.0, 11.6 and 67.9 mg/kg and 6.3, 9.4, 2.3 and 23.9 mg/kg, respectively. However, FI produced the highest concentrations in the deep soil layer (40–60 cm) and the least concentration of HMs in plant tubers. These concentrations were 18.8, 203.8, 13.3 and 70 mg/kg and 4.0, 6.0, 0.6 and 17.1 mg/kg in soil and plant tubers for Cu, Mn, Pb, and Zn, respectively. The maximum concentrations of HMs in soil and potato plants were lower than the maximum permissible limits. Therefore, El-Salam Canal water appears not to be harmful in the short term. However, as shown in the results, HM accumulation depends on irrigation technique; thus, more studies are needed to determine harmful effects in the long term.

The Pasur River is a vital reservoir of surface water in the Sundarbon area in Bangladesh. Mongla seaport is located on the bank of this river. Many industries and other commercial sectors situated in this port area are discharging waste into the river without proper treatment. For this reason, geospatial analysis and mapping of water pollutant distribution were performed to assess the physicochemical and toxicological situation in the study area. We used different water quality indices such as Metal Index (MI), Comprehensive Pollution Index (CPI), and Weighted Arithmetic Water Quality Index Method (WQI) to improve the understanding of pollution distribution and processes determining the quality of river water. Multivariate statistical methods were used to evaluate loads and sources of pollutants in the Pasur River system. The results indicate that the sources of contaminants are both geogenic and anthropogenic, including untreated or poorly treated wastewater from industries and urban domestic waste discharge. The concentration range of total suspended solid (TSS), chloride, iron (Fe), and manganese (Mn) were from 363.2 to 1482.7, 108.2 to 708.93, 1.13 to 2.75, and 0.19 to 1.41 mg/L, respectively, significantly exceeding the health-based guideline of WHO and Bangladeshi standards. The high Fe and Mn contents are contributions from geogenic and anthropogenic sources such as industrial waste and construction activities. The average pH value was 8.73, higher than the WHO and Bangladeshi standard limit. WQI (ranging from 391 to 1336), CPI (6.71 to 23.1), and MI (7.23 to 23.3) were very high and greatly exceeded standard limits indicating that the Pasur River water is highly polluted. The results of this study can be used as a first reference work for developing a surface water quality monitoring system and guide decisionmakers for priorities regarding wastewater treatment.

We assess the sustainability of rural electrification in Manica Province, Mozambique, focusing on different alternatives for mini-grid and off-grid power supply. The qualitative assessment considers four dimensions of sustainability, namely environmental, socio-cultural, economic, and institutional. We argue that small-scale hydropower is the most sustainable alternative for off-grid or mini-grid solutions in rural Manica Province with good possibilities to scale up this to the major parts of rural Mozambique. The investigation shows that social acceptance for small-scale hydropower is high. Environmental sustainability of small-scale hydropower is higher than for PV systems. To speed up the electrification process, efficient rural electrification has to connect policy to local scale and institutional strengthening. The legislation needs to be improved, and there is a need for better institutional coordination for hydropower mini-grids’ regulation. Along this line, a national framework to support small and independent power producers is necessary.

In view of climate change, increasing soil salinity is expected worldwide. It is therefore important to improve prediction ability of plant salinity effects. For this purpose, brackish/saline irrigation water from two areas in central and coastal Tunisia was sampled. The water samples were classified as C3 (EC: 2.01–2.24 dS m−1) and C4 (EC: 3.46–7.00 dS m−1), indicating that the water was questionable and not suitable for irrigation, respectively. The water samples were tested for their genotoxic potential and growth effects on Vicia faba seedlings. Results showed a decrease in mitotic index (MI) and, consequently, growth parameters concomitant to the appearance of micronucleus (MCN) and chromosome aberrations when the water salinity increased. Salt ion concentration had striking influence on genome stability and growth parameters. Pearson correlation underlined the negative connection between most ions in the water inappropriate for irrigation (C4) and MI as well as growth parameters. MI was strongly influenced by Mg2+, Na+, Cl−, and to a less degree Ca2+, K+, and SO42−. Growth parameters were moderately to weakly affected by K+ and Ca2+, respectively. Re-garding MCN, a very strong positive correlation was found for MCN and K+. Despite its short-term application, the Vicia-MCN Test showed a real ability to predict toxicity induced by salt ions confirming that is has a relevant role in hazard identification and risk assessment of salinity effects.

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

Increasing land use pressure is a primary force for degradation of agricultural areas. The drivers for these pressures are initiated by a series of interconnected processes. This study presents a novel methodology to analyze drivers of changing land use pressure and the effects on society and landscape. The focus was on characterizing these drivers and relate them to land use statistics obtained from geospatial data from the important semiarid Merguellil Wadi between 1976 and 2016. Cause-and-effect relationships between different drivers of land use change were analyzed using the DPSIR approach. Results show that during the 40-year period cultivated land increased and wetland areas decreased substantially. Drivers for change were pressure from economic development, cultivation practices, and hydro-agricultural techniques. This leads to stress on water and soil resulting in soil erosion, poverty increase, and rural exodus. We show that hydro-agricultural techniques adapted to the semiarid climate, allocation of land property rights, resource allocation, and improved marketing of agricultural products can help rural residents to diversify their economy, and thus better preserve the fragile semiarid landscape. Results of this study can be used to ensure sustainable management of water and soil resources in areas with similar climate and socio-economic conditions.

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

Study region: Karkheh Dam Reservoir (KDR) located in western Iran. Study focus: To date, there has been no research in the KDR that investigates the interconnections among thermal stratification (ThS), water quality, and nutrients, based on field measurements. This study explored the temporal trend of ThS in the KDR and its interrelationship with water quality parameters based on data measured from 2005–2006. New hydrological insights for the region: The results showed that a noticeable ThS in the KDR starts in late April and continues until early December. The strongest ThS occurs during late summer when the water temperature difference between the surface and bottom layers in the reservoir exceeds 18 °C. As a result of external forces that generally intensify in December, vertical water circulation occurs, and by January and February there is a minimal temperature gradient between the surface and bottom layers. During ThS, dissolved oxygen (DO) is strongly confined by the metalimnion and does not penetrate into the hypolimnion. However, even during late December to February, there is a large difference between DO concentration in the surface and bottom layers, which indicates limited mixing. Ammonium increasing and nitrate decreasing with depth was observed, likely due to denitrification (in the bottom layers) and nitrification (in the surface layers), respectively. The results of the present study provide new information on the spatio-temporal variation of water quality in large reservoirs, which is important for stakeholders with concerns related to lake and reservoir eutrophication and water quality issues. Keywords: Ammonium, Stratification, Reservoir, Water quality, Overturn

Climate change and direct anthropogenic impact are recognized as two major factors affecting catchment runoff. This study investigated the separate effect of each of these factors for runoff from the important Tunisian Merguellil catchment. For this purpose, more than forty years of hydrological data were used. The methodology was based on hydrological characterization, NDVI index to monitor land use dynamics, and the Budyko approach to specify origin of change. The results show that hydrological change is much more important upstream than downstream. The last three decades display a 40% reduction in runoff. This is associated with the direct influence of humans, who are responsible for about 78% of the variation in flow. It appears that climate change contributes to less than about 22%. The combination of increased cultivated land and decreased annual rainfall is the main reason for reduced catchment runoff. Consequently, these effects threaten the sustainable runoff, water in reservoirs, and future water supply in general. Ultimately, the available runoff remains an important parameter and a key indicator to guide the choices of decision-makers and practitioners in current and future climatic conditions. This contributes to supporting sustainable management of remaining water resources.

This paper presents a novel framework comprising analytical, hydrological, and remote sensing techniques to separate the impacts of climate variation and regional human activities on streamflow changes in the Karkheh River basin (KRB) of western Iran. To investigate the type of streamflow changes, the recently developed DBEST algorithm was used to provide a better view of the underlying reasons. The Budyko method and the HBV model were used to investigate the decreasing streamflow, and DBEST detected a non-abrupt change in the streamflow trend, indicating the impacts of human activity in the region. Remote sensing analysis confirmed this finding by distinguishing land-use change in the region. The algorithm found an abrupt change in precipitation, reflecting the impacts of climate variation on streamflow. The final assessment showed that the observed streamflow reduction is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19% of the total basin area), reduction of forests (from 11 to 3%), and decreasing annual precipitation has substantially reduced the streamflow rate in the basin. The developed framework can be implemented in other regions to thoroughly investigate human vs. climate impacts on the hydrological cycle, particularly where data availability is a challenge.

Root distribution during rice cultivation is a governing factor that considerably affects soil water content (SWC) and root water uptake (RWU). In this study, the effects of activating root growth (using growth function) and assigning a constant average root depth (no growth during simulation) on SWC and RWU for rice cultivation under four deficit drip irrigation treatments (T90, T80, T70, and T60) were compared in the HYDRUS-2D/3D model version 3.03. A secondary objective was to investigate the effect of applied deficit irrigation treatments on grain yield, irrigation water use efficiency (IWUE), and growth traits of rice. The simulated DI system was designed to reflect a representative field experiment implemented in El-Fayoum Governorate, Egypt, during two successive seasons during 2017 and 2018. The deficit treatments (T90, T80, T70, and T60) used in the current study represent scenarios at which the first irrigation event was applied when the pre-irrigation average SWC within the upper 60 cm of soil depth was equal to 90%, 80%, 70%, and 60% of plant-available water, respectively. Simulation results showed that as water deficiency increased, SWC in the simulation domain decreased, and thereby, RWU decreased. The average SWC within the root zone during rice-growing season under different deficit treatments was slightly higher when activating root growth function than when considering constant average root depth. Cumulative RWU fluxes for the case of no growth were slightly higher than for the case of root growth function for T90, T80, and T70 accounting for 1289.50, 1179.30, and 1073.10 cm2, respectively. Average SWC during the growth season (24 h after the first irrigation event, mid-season, and 24 h after the last irrigation event) between the two cases of root growth was strongly correlated for T90, T80, T70, and T60, where r2 equaled 0.918, 0.902, 0.892, and 0.876, respectively. ANOVA test showed that there was no significant difference for SWC between treatments for the case of assigning root growth function while the difference in SWC among treatments was significant for the case of the constant average root depth, where p-values equaled 0.0893 and 0.0433, respectively. Experimental results showed that as water deficiency decreased, IWUE increased. IWUE equaled 1.65, 1.58, 1.31, and 1.21 kg m−3 for T90, T80, T70, and T60, respectively. Moreover, higher grain yield and growth traits of rice (plant height, tillers number plant−1, panicles length, panicle weight, and grain number panicles−1) were obtained corresponding to T90 as compared with other treatments. Activating the root growth module in HYDRUS simulations can lead to more precise simulation results for specific dates within different growth stages. Therefore, the root growth module is a powerful tool for accurately investigating the change in SWC during simulation. Users of older versions of HYDRUS-2D/3D (version 2.05 and earlier) should consider the limitations of these versions for irrigation scheduling.

Climate change's effect on sea surface temperature (SST) at the regional scale vary due to driving forces that include potential changes in ocean circulation and internal climate variability, ice cover, thermal stability, and ocean mixing layer depth. For a better understanding of future effects, it is important to analyze historical changes in SST at regional scales and test prediction techniques. In this study, the variation in SST across the Persian Gulf and Gulf of Oman (PG&GO) during the past four decades was analyzed and predicted to the end of 21st century using a proper orthogonal decomposition (POD) model. As input, daily optimum interpolation SST anomaly (DOISSTA) data, available from the National Oceanic and Atmospheric Administration of the United States, were used. Descriptive analyses and POD results demonstrated a gradually increasing trend in DOISSTA in the PG&GO over the past four decades. The spatial distribution of DOISSTA indicated: (1) that shallow parts of the Persian Gulf have experienced minimum and maximum values of DOISSTA and (2) high variability in DOISSTA in shallow parts of the Persian Gulf, including some parts of southern and northwestern coasts. Prediction of future SST using the POD model revealed the highest warming during summer in the entire PG&GO by 2100 and the lowest warming during fall and winter in the Persian Gulf and Gulf of Oman, respectively. The model indicated that monthly SST in the Persian Gulf may increase by up to 4.3 °C in August by the turn of the century. Similarly, mean annual changes in SST across the PG&GO may increase by about 2.2 °C by 2100.

Nitrate pollution in groundwater is a serious problem in Shimabara Peninsula, Nagasaki, Japan. A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is important for health and environment. In this study, groundwater samples were collected at 12 residential and 57 municipal water supply wells and springs in July and August 2018. Nitrate (NO3−N) concentration at eight sampling sites (12%) exceeded Japanese drinking water standard for NO3 + NO2−N (10 mg L−1). The highest nitrate concentration was 19.9 mg L−1. Polluted groundwater is distributed in northeastern, northwestern, and southwestern areas, where land is used for intensive agriculture. Correlation analysis suggests that nitrate sources are agricultural fertilizers and livestock waste. Dominant groundwater chemistry is (Ca+Mg)−HCO3 or (Ca+Mg)−(SO4+NO3) type. Groundwater with higher nitrate concentration is of (Ca+Mg)−(SO4+NO3) type, indicating nitrate pollution affecting water chemistry. Principal component analysis extracted two important factors controlling water chemistry. The first principal component explained dissolved ions through water–rock interaction and agricultural activities. The second principal component explained cation exchange and dominant agricultural effects from fertilizers. Hierarchical cluster analysis classified groundwater into four groups. One of these is related to the dissolution of major ions. The other three represent nitrate pollution.

The 2016 Kumamoto earthquake had a significant impact on groundwater levels and quality. In some areas, the groundwater level increased significantly due to the release of groundwater from upstream mountainous regions. Conversely, the groundwater level in other areas greatly decreased due to the creation of new fracture networks by the earthquake. There were also significant changes in certain groundwater quality variables. In this study, we used clustering based SOM (self-organizing maps) analysis to improve the understanding of earthquake effects on groundwater quality. We were especially interested in effects on groundwater used for drinking purposes and in nitrate concentration. For this purpose, we studied groundwater nitrate (NO3− + NO2−–N) concentrations for the period 2012–2017. Nitrate concentration changes were classified into seven typical SOM clusters. The clusters were distributed in three representative geographical regions: a high concentration region (>4 mg/L), a low concentration region (

Vegetation is an important component of the terrestrial ecosystem that plays an essential role in the exchange of water and energy in climate and biogeochemical cycles. This study investigated the spatiotemporal variation of normalized difference vegetation index (NDVI) in northern China using the GIMMS-MODIS NDVI during 1982–2018. We explored the dominant drivers of NDVI change using regression analyses. Results show that the regional average NDVI for northern China increased at a rate of 0.001 year−1. NDVI improved and degraded area corresponded to 36.1% and 9.7% of the total investigated area, respectively. Climate drivers were responsible for NDVI change in 46.2% of the study area, and the regional average NDVI trend in the region where the dominant drivers were temperature (T), precipitation (P), and the combination of precipitation and temperature (P&T), increased at a rate of 0.0028, 0.0027, and 0.0056 year−1, respectively. We conclude that P has positive dominant effects on NDVI in the subregion VIAiia, VIAiic, VIAiib, VIAib of temperate grassland region, and VIIBiia of temperate desert region in northern China. T has positive dominant effects on NDVI in the alpine vegetation region of Qinghai Tibet Plateau. NDVI is negatively dominated by T in the subregion VIIBiib, VIIBib, VIIAi, and VIIBi of temperate desert regions. Human activities affect NDVI directly by reforestation, especially in Shaanxi, Shanxi, and Hebei provinces.

Urban flooding is a growing concern in Northern Europe. While all countries in this region invest substantial resources into urban flood risk management, all property owners have unequal opportunity to have their flood risk managed. This paper presents the notion of equity in the urban flood risk management, focusing on urban flood exposure and compensation after sewage surcharge on the 31 August 2014 cloudburst over Malmö in Sweden. All damage claims and the procedures assessing them were analysed based on the type of sewer system (combined or separated) that affected the properties. The results demonstrated considerable systemic inequity in both flood exposure and damage compensation. The owners of properties connected to combined systems were four times more likely to make a damage claim, while having their damage claims approved just over half as often, compared with owners of properties connected to separated sewage systems. Considering the multifaceted nature of both inequities and their possible resolution, current praxis is not sufficient for the management of future urban drainage systems; not only concerning changing climatic conditions but also concerning social conditions.

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

Central Asia is an increasingly important strategic geopolitical region. During the latest decades, the region has often been identified as close to potential conflict regarding water usage. This includes the sharing of water from the Syr Darya and the Amu Darya in the Aral Sea Basin. The Aral Sea disaster has exposed a complex picture of water needs and potential political conflict. Rapid population increase together with climate change impacts are likely to further aggravate the short- and long-term future precarious situation for water management in the region. This Special Issue focuses on present and future water management issues in Central Asia in view of future climate changes and how these will affect socioeconomic development. Central Asia is, in general, water rich; however, exercising efficient and fair water management will be important in view of future population increase and climate change. At the same time, water and natural resource development is a cornerstone in all the Central Asian republics. Especially, water resources are, to a great extent, shared between all five republics. A common ground for water-sharing is, therefore, of utmost importance.