Editor’s Choice Articles

Future changes (2015–2100) in precipitation and meteorological droughts in Lake Urmia Basin were investigated using an average mean ensemble of eight general circulation models (GCMs) with high-resolution datasets in socioeconomic pathway scenarios (SSPs) from the Coupled Model Intercomparison Project (CMIP6). In order to project the drought, the standardized precipitation index (SPI) was calculated. Overall, the results revealed that precipitation in Lake Urmia Basin will decrease by 3.21% and 7.18% in the SSP1-2.6 and SSP5-8.5 scenarios, respectively. The results based on 6-month-timescale SPI indices projected more “Extremely dry” events in SSP5-8.5 scenarios. The frequency of “Extremely dry” months in SSP5-8.5 compared to SSP1-2.6 is expected to increase by 14, 7, 14, 10, 5, 14, and 7 months for the Mahabad, Maragheh, Saqez, Sarab, Tabriz, Takab, and Urmia stations, respectively. In contrast, the frequency of “Extremely wet” months will decline for all stations in Lake Urmia Basin. The results of this study provide useful insight for considering drought prevention measures to be implemented in advance for Lake Urmia Basin, which is currently experiencing various environmental issues. Full article

Outdoor recreation has grown rapidly in recent years, with an increasing preference for areas in good ecological condition. Since lakes represent some of the most important wetlands, providing a wide variety of ecosystem services, they have become a very popular destination. The present study aimed to assess the water quality of the largest artificial lake in Hungary (Kisköre Reservoir—Lake Tisza), and the role of ecological status in tourism development. Monthly water sampling from the basins of the lake (Tiszavalk, Poroszló, Sarud and Abádszalók basins) took place from April–November 2021 and in 2022. The majority of samples from the river section and from the lake are classified as Ca²⁺-HCO₃⁻ type or mixed Ca²⁺-Na⁺-HCO₃⁻ type. According to the results, the water quality of each basin is considered excellent or good. Rapid warming of the shallow water of the basins was detected during the summer months, resulting in different hydrochemical characteristics (pH, NH₄-N, NO₂-N, NO₃-N, PO₄-P, COD_cr BOI₅) compared to the river section. Differences in the plant nutrient and oxygen balance component groups have been revealed with hierarchical and two-step cluster analysis as well. The results demonstrated that the hydrochemical properties of the lake’s water are substantially influenced by the filling of the lake in spring from the River Tisza and the significant lowering (1.2 m) of the water level in the autumn each year, allowing the drainage of stagnant water, the removal of accumulated sediments and the oxidation of organic matter. The number of tourists on Lake Tisza has increased rapidly over the last decade, confirming that a wide range of ecosystem services have a significant attractive impact on waterfront activities and ecotourism. Full article

Concerns regarding plastic pollution, especially microplastics, have increased, as they can be present in different environmental compartments, including estuarine areas and saltmarshes. Although saltmarshes are highly vulnerable to different human activities and pressures, they have the ability to trap/retain contaminants in their vegetated sediments. However, there is still little information regarding the role of saltmarshes in microplastic retention. Thus, the present study aims to investigate the capability of an estuarine saltmarsh to trap microplastics by comparing microplastic concentrations in vegetated (saltmarsh) and non-vegetated sediments. Microplastic content from sediment (vegetated and non-vegetated) samples collected at different sampling sites in Lima River estuary was estimated using previously optimised extraction protocols, and the observed particles were then characterised accordingly to their size, colour, shape, and polymer (by FTIR). Water samples were also collected and analysed for their microplastics content to complement MPs characterisation within the estuarine area. Microplastics were detected in all sediment samples, with fibres being the most common type of microplastic found, followed by fragments/particles. Overall, vegetated sediments, especially those of saltmarsh species Juncus maritimus, presented a higher number of plastic items. These results indicated that microplastics tend to be trapped in vegetated sediments, supporting the fact that saltmarshes have a significant influence on the transport, distribution, and accumulation of MPs in estuarine areas. Full article

This article discusses the issues caused by traditional water resource development and utilization, as well as policy issues in China that have led to a water crisis. The article proposes a theoretical approach along with a quantitative accounting of water resources, in order to solve these problems. To improve the value accounting method for water resources, the study focuses on a unified accounting perspective of water quantity and quality, allowing for an evaluation of water use efficiency and quality. The study uses prefecture-level cities in Hubei Province as a case study and finds that the water use efficiency of these cities has constantly improved, while water quality has shown an annual improvement. Water resource assets, liabilities, and net assets have increased, but with fluctuations. The study shows differences in water resource assets, liabilities, and net assets in the eastern, central, and western regions of Hubei Province. The unified accounting perspective of water quantity and quality provides a new idea and method for the preparation of water resource balance sheets and will effectively improve the management level and efficiency of water resources. Full article

Improving the accuracy and stability of daily runoff prediction is crucial for effective water resource management and flood control. This study proposed a novel stacking ensemble learning model based on attention mechanism for the daily runoff prediction. The proposed model has a two-layer structure with the base model and the meta model. Three machine learning models, namely random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGB) are used as the base models. The attention mechanism is used as the meta model to integrate the output of the base model to obtain predictions. The proposed model is applied to predict the daily inflow to Fuchun River Reservoir in the Qiantang River basin. The results show that the proposed model outperforms the base models and other ensemble models in terms of prediction accuracy. Compared with the XGB and weighted averaging ensemble (WAE) models, the proposed model has a 10.22% and 8.54% increase in Nash–Sutcliffe efficiency (NSE), an 18.52% and 16.38% reduction in root mean square error (RMSE), a 28.17% and 18.66% reduction in mean absolute error (MAE), and a 4.54% and 4.19% increase in correlation coefficient (r). The proposed model significantly outperforms the base model and simple stacking model indicated by both the Friedman test and the Nemenyi test. Thus, the proposed model can produce reasonable and accurate prediction of the reservoir inflow, which is of great strategic significance and application value in formulating the rational allocation and optimal operation of water resources and improving the breadth and depth of hydrological forecasting integrated services. Full article

Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under different categories of machine learning methods, including support vector regression (SVR), extreme gradient boosting (XGBoost), and the long-short term memory neural network (LSTM), were assessed for simulating daily runoff over a mountainous river catchment. The performances with different input scenarios were compared. Additionally, the joint multifractal spectra (JMS) method was implemented to evaluate the simulation performances during wet and dry seasons. The results show that: (1) LSTM always obtained a higher accuracy than XGBoost and SVR; (2) the impacts of the input variables were different for different machine learning methods, such as antecedent streamflow for XGBoost and rainfall for LSTM; (3) XGBoost showed a relatively high performance during dry seasons, and the classification of wet and dry seasons improved the simulation performance, especially for LSTM during dry seasons; (4) the JMS analysis indicated the advantages of a hybrid model combined with LSTM trained with wet-season data and XGBoost trained with dry-season data. Full article

Xishan mining area in Taiyuan is a typical coal industry cluster with a variety of coal-related industrial sites such as coal mines, power plants and coking plants, which seriously pollute the native ecological environment. Study of the hydrochemical characteristics and pollution sources of groundwater in the area can contribute to the ecological protection and remediation of regional groundwater resources. In this study, we collected surface water and groundwater samples from the Xishan mining area and measured and analyzed hydrochemical and sulfur–oxygen isotopes. Results showed that 64.7% of groundwater in the study area exceeded the sulfate standard due to the influence of the coal industry, with some karst groundwater up to 2000 mg/L. In the runoff and discharge area of karst groundwater, the proportion of anthropogenic input of SO₄²⁻ increased, which led to the hydrochemical type of karst groundwater gradually changing from HCO₃-Ca·Mg (recharge area) to SO₄-Ca·Mg (discharge area). Results of sulfur–oxygen isotope tests indicated that the δ³⁴S_SO4 and δ₁₈O_SO4 values of samples were −10.01~24.42‰ and −4.90~12.40‰, respectively, and the sulfur–oxygen isotope values of some karst groundwater were close to the dissolved end of sulfide minerals, indicating their sulfate mainly came from the oxidation of pyrite. Sulfate sources in groundwater water were parsed using IsoSource model. Calculated results revealed that sulfate in pore groundwater mostly originated from pyrite oxidation, and karst groundwater in the recharge area was mainly influenced by atmosphere precipitation, while groundwater in the runoff and discharge areas were significantly affected by pyrite oxidation, accounting for up to 90% in some karst groundwater. Comparing the sulfur–oxygen isotope values of karst groundwater in 1989, 2016 and 2022, we found that the δ³⁴S_SO4 values in 2022 decreased significantly, which indicated the expansion of karst groundwater pollution in the Xishan mining area. This study highlights the pollution of regional groundwater by coal-related industrial agglomerations, and the groundwater pollution in the Xishan mining area requires urgent remediation and restoration. Full article

Despite being relatively rare, Mediterranean tropical-like cyclones, also known as Medicanes, induce significant impacts on coastal Mediterranean areas. Under climate change, it is possible that these effects will increase in frequency and severity. Currently, there is only a broad understanding of the types and mechanisms of these impacts. This work studied Medicane Ianos (September 2020) and its effects on the Ionian Islands, in Greece, by developing a database of distinct impact elements based on field surveys and public records. Through this archive, the study explored the range of Ianos’ impacts to develop a systematic categorization. Results showed different types of effects induced on the natural and the built environment that can be grouped into 3 categories and 39 sub-categories in inland and coastal areas, indicating an extensive diversity of impacts, ranging from flooding and geomorphic effects to damages in various facilities, vehicles and infrastructure. The systematic description of the typology of Medicanes’ effects presented in this study is a contribution to a better understanding of their consequences as means to improve our ability to prepare for, respond to, and recover from them, a necessary stepping stone in improving the overall preparedness of both the general public and relevant authorities. Full article

Medium-range streamflow forecasts largely depend on the accuracy of meteorological forecasts. Due to large errors in precipitation forecasts, most streamflow forecasts based on deep learning rely only on historical data. Here, we apply a cascade Long Short-Term Memory (LSTM) model to forecast daily streamflow over 49 watersheds in the Yangtze River basin for up to 15 days. The first layer of the cascade LSTM model uses atmospheric circulation factors to predict future precipitation, and the second layer uses forecast precipitation to predict streamflow. The results show that the default LSTM model provides skillful streamflow forecasts over most watersheds. At the lead times of 1, 7, and 15 days, the streamflow Kling–Gupta efficiency (KGE) of 78%, 30%, and 20% watersheds are greater than 0.5, respectively. Its performance improves with the increase in drainage area. After implementing the cascade LSTM model, 61–88% of the watersheds show increased KGE at different leads, and the increase is more obvious at longer leads. Using cascade LSTM with perfect future precipitation shows further improvement, especially over small watersheds. In general, cascade LSTM modeling is a good attempt for streamflow forecasts over the Yangtze River, and it has a potential to connect with dynamical meteorological forecasts. Full article

Droughts are serious natural disasters that adversely affect water resources, agriculture, the economy, and the environment. Reconstructing historical drought records is necessary to assess the impact of droughts and their evolution and has become a top priority to support and improve sustainable water management decisions. In this study, we used Shanxi Province as the research area, and meteorological data from the early years of Guangxu in the Qing Dynasty were reconstructed using historical rain and snow records. The Variable Infiltration Capacity (VIC) model is driven by the reconstruction of historical meteorological data. The study area’s monthly runoff and soil water sequence from 1875 to 1879 were simulated, and the hydrology and soil of the ancient historical period were reproduced in the absence of data. The results show the following: (1) The idea of reconstructing hydrological parameters using historical data is feasible and the VIC model can be used to study drought characteristics under specific scenarios. (2) The proportions of areas with runoff depths less than 10 mm throughout Shanxi from 1875 to 1879 were 55%, 48%, 58%, 19%, and 30%. The annual runoff depth in each region from 1875 to 1877 was less than 60 mm. The hydrological drought from 1875 to 1877 was very serious, and the area covered by the drought was relatively large. (3) The annual average soil water content of various regions was stable between 150 and 510 mm from 1875 to 1879. The soil water content had no apparent interannual variation. The area with soil water content less than 180 mm accounted for ratios as high as 31%. This research provides new ideas for ancient drought research and a scientific basis for regional drought prevention, mitigation, and water resources management, and ensures the orderly progress of agricultural production activities. Full article

This study compares the performance of three different neural network models to estimate daily streamflow in a watershed under a natural flow regime. Based on existing and public tools, different types of NN models were developed, namely, multi-layer perceptron, long short-term memory, and convolutional neural network. Precipitation was either considered an input variable on its own or combined with air temperature as another input variable. Different periods of accumulation, average, and/or delay were considered. The models’ structures were optimized and automatically showed that CNN performed best, reaching, for example, a Nash–Sutcliffe efficiency of 0.86 and a root mean square error of 4.2 m³ s⁻¹. This solution considers a 1D convolutional layer and a dense layer as the input and output layers, respectively. Between those layers, two 1D convolutional layers are considered. As input variables, the best performance was reached when the accumulated precipitation values were 1 to 5, and 10 days and delayed by 1 to 7 days. Full article

Soil heterogeneities can impact hillslope hydropedological processes (e.g., portioning between infiltration and runoff), creating a need for in-depth knowledge of processes governing water dynamics and redistribution. The presented study was conducted at the SUPREHILL Critical Zone Observatory (CZO) (hillslope vineyard) in 2021. A combination of field investigation (soil sampling and monitoring campaign) and numerical modeling with hydrological simulator HYDRUS-1D was used to explore the water dynamics in conjunction with data from a sensor network (soil water content (SWC) and soil-water potential (SWP) sensors), along the hillslope (hilltop, backslope, and footslope). Soil hydraulic properties (SHP) were estimated based on (i) pedotransfer functions (PTFs), (ii) undisturbed soil cores, and (iii) sensor network data, and tested in HYDRUS. Additionally, a model ensemble mean from HYDRUS simulations was calculated with PTFs. The highest agreement of simulated with observed SWC for 40 cm soil depth was found with the combination of laboratory and field data, with the lowest average MAE, RMSE and MAPE (0.02, 0.02, and 5.34%, respectively), and highest average R² (0.93), while at 80 cm soil depth, PTF model ensemble performed better (MAE = 0.03, RMSE = 0.03, MAPE = 7.55%, R² = 0.81) than other datasets. Field observations indicated that heterogeneity and spatial variability regarding soil parameters were present at the site. Over the hillslope, SWC acted in a heterogeneous manner, which was most pronounced during soil rewetting. Model results suggested that the incorporation of field data expands model performance and that the PTF model ensemble is a feasible option in the absence of laboratory data. Full article

The United Nations Member States created a common roadmap for sustainability and development in 2015. The UN-SDGs are included in the 2030 Plan as an immediate call to action from all nations in the form of global partnerships. To date, a handful of countries have achieved substantial progress toward the targets. The climate–water–energy–food nexus is being advocated as a conceptual method for achieving sustainable development. According to research, frameworks for adopting nexus thinking have not been the best solution to clearly or sufficiently include thoughts on sustainability. Therefore, there is much room for other solutions; these are in the form of newer Fourth Industrial Revolution digital technologies, as well as transitioning from a linear economy to a circular economy. In this paper, we come to understand these two models and their linkages between climate, water, energy, and food; their application and challenges, and, finally, the effects on the UN-SDGs. It was found that both circular economy and newer Fourth Industrial Revolution digital technologies can positively support the nexus as well as directly address the UN-SDGs, specifically SDGs 7, 8, 9, 11, 12, and 13. Full article

The aim of the present study was to provide new knowledge regarding the effect of non-expansive inert material addition on anionic pollutant removal efficiency in Fe⁰-H₂O system. Non-disturbed batch experiments and continuous-flow-through column tests were conducted using Cr^VI as a redox–active contaminant in three different systems: “Fe⁰ + sand”, “Fe⁰ only” and ”sand only”. Both experimental procedures have the advantage that formation of (hydr)oxide layers on Fe⁰ is not altered, which makes them appropriate proxies for real Fe⁰-based filter technologies. Batch experiments carried out at pH 6.5 showed a slight improvement of Cr^VI removal in a 20% Fe⁰ system, compared to 50, 80 and 100% Fe⁰ systems. Column tests conducted at pH 6.5 supported results of batch experiments, revealing highest Cr^VI removal efficiencies for “Fe⁰ + sand” systems with lowest Fe⁰ ratio. However, the positive effect of sand co-presence decreases with increasing pH from 6.5 to 7.1. Scanning electron microscopy—energy dispersive angle X-ray spectrometry and X-ray diffraction spectroscopy employed for the characterization of Fe⁰ before and after experiments indicated that the higher the volumetric ratio of sand in “Fe⁰ + sand” system, the more intense the corrosion processes affecting the Fe⁰ grains. Results presented herein indicate the capacity of sand at sustaining the efficiency of Cr^VI removal in Fe⁰-H₂O system. The outcomes of the present study suggest that a volumetric ratio Fe⁰:sand = 1:3 could assure not only the long-term permeability of Fe⁰-based filters, but also enhanced removal efficiency of Cr^VI from contaminated water. Full article

The main objective of this study was to assess the usefulness of exhausted activated carbon-based filter cartridges for the removal of organic pollutants from aqueous solutions using the example of two model pollutants: synthetic dyes with different particle sizes, i.e., methylene blue (MB) and malachite green (MG). In order to determine the organic dyes’ adsorption mechanism, the effects of phase contact time, initial dye concentration, pH, and temperature of the system were investigated. Langmuir and Freundlich isotherm models were employed to analyze the experimental data. Additionally, all adsorbents were characterized in terms of the ash content, type of porous structure, presence of surface functional groups, pH value, and iodine adsorption number—which is one of the quality control parameters of activated carbons. Adsorption tests have shown that carbonaceous materials from bottle filters and filter jugs can be successfully used for the removal of organic dyes from the liquid phase. The maximum sorption capacity of this type of adsorbent towards methylene blue was 333.06 mg/g, while in the case of malachite green it was 308.75 mg/g. For all carbonaceous materials, a better fit to the experimental data was achieved with a Langmuir isotherm than a Freundlich one. It has also been shown that the efficiency of MB and MG adsorption from aqueous solutions decreases with increasing temperature of the system—the best results were obtained at 25 °C. A better fit of the kinetics data was achieved using the pseudo-second order model. Full article

Salinity tolerance is a determinant of a narrow or wide distribution range of organisms. Crayfishes are important key species in many aquatic environments so require a better understanding of their ability to live in different saline regimes. We identified all alien crayfish and examined their habitats (freshwater and/or saline) and origins to test whether these factors predict their dispersal. We used contingency tables populated with raw frequency data with χ2—tests and assessed statistical significance at α of 0.05. We identified 21 alien crayfishes and we found that alien crayfish species were disproportionately freshwater (71%), with significantly lower proportions of euryhaline crayfishes inhabiting freshwater to saline environments (29%). Alien crayfishes also significantly disproportionally originate from America (67% of these taxa) when compared to all ‘other’ grouped regions (33%). In total, 36% of American crayfishes represent euryhaline species inhabiting freshwater to saline habitats against only 14% of crayfishes from all “other” grouped regions. This suggests that binomial euryhalinity/origin can help understand the potential of spread. We discussed obtained results with known experimental data on salinity tolerance, osmoregulation, growth, and reproduction of American alien crayfish. The paper will help in the management of crayfish spread. Full article

Recalcitrant groundwater contamination is a common problem at hazardous waste sites worldwide. Groundwater contamination persists despite decades of remediation efforts at many sites because contaminants sorbed or dissolved within low-conductivity zones can back diffuse into high-conductivity zones, and therefore act as a continuing source of contamination to flowing groundwater. A review of the available literature on remediation of plume persistence due to back diffusion was conducted, and four sites were selected as case studies. Remediation at the sites included pump and treat, enhanced bioremediation, and thermal treatment. Our review highlights that a relatively small number of sites have been studied in sufficient detail to fully evaluate remediation of back diffusion; however, three general conclusions can be made based on the review. First, it is difficult to assess the significance of back diffusion without sufficient data to distinguish between multiple factors contributing to contaminant rebound and plume persistence. Second, high-resolution vertical samples are decidedly valuable for back diffusion assessment but are generally lacking in post-treatment assessments. Third, complete contaminant mass removal from back diffusion sources may not always be possible. Partial contaminant mass removal may nonetheless have potential benefits, similar to partial mass removal from primary DNAPL source zones. Full article

Climate change (CC) directly affects crops’ growth stages or level of maturity, solar radiation, humidity, temperature, and wind speed, and thus crop evapotranspiration (ET_c). Increased crop ET_c shifts the fraction of discharge from groundwater aquifers, while long-term shifts in discharge can change the groundwater level and, subsequently, aquifer storage. The long-term effect of CC on the groundwater flow under different values of ET_c was assessed for the Nile Delta aquifer (NDA) in Egypt. To quantify such impacts, numerical modeling using MODFLOW was set up to simulate the groundwater flow and differences in groundwater levels in the long term in the years 2030, 2050, and 2070. The model was initially calibrated against the hydraulic conductivity of the aquifer layers of the groundwater levels in the year 2008 from 60 observation wells throughout the study area. Then, it was validated with the current groundwater levels using an independent set of data (23 points), obtaining a very good agreement between the calculated and observed heads. The results showed that the combination of solar radiation, vapor pressure deficit, and humidity (H) are the best variables for predicting ET_c in Nile Delta zones (north, middle, and south). ET_c among the whole Nile Delta will increase by 11.2, 15.0, and 19.0% for the years 2030, 2050, and 2070, respectively. Zone budget analysis revealed that the increase of ET_c will decrease the inflow and the groundwater head difference (GWHD). Recharge of the aquifer will be decreased by 19.74, 27.16, and 36.84% in 2030, 2050, and 2070, respectively. The GWHD will record 0.95 m, 1.05 m, and 1.40 m in 2030, 2050, and 2070, respectively when considering the increase of ET_c. This reduction will lead to a slight decline in the storage of the Nile Delta groundwater aquifer. Our findings support the decision of the designers and the policymakers to guarantee a long-term sustainable management plan of the groundwater for the NDA and deltas with similar climate conditions. Full article

Microplastic (MP) pollution in Antarctica is a hot topic that has gained increasing attention in recent years. However, information regarding MP pollution in Antarctic fishes is currently very limited. The present study provides the first evidence of the occurrence and characteristics of MPs in species from five families of the order Perciformes, from the Amundsen Sea (AS) and Ross Sea (RS), Antarctica. MP abundances within the order Perciformes were at a medium level on a global scale, but were higher than those reported in other Antarctic organisms. The detection rate and abundance of MPs in the order Perciformes from the RS (50% and 1.286 items individual⁻¹) were both higher than those from the AS (36% and 1.227 items individual⁻¹). Moreover, the major composition and size of MPs were, respectively, polyacrylamide (PAM) and 100–200 μm in the RS, but rayon and 500–1000 μm in the AS. These differences may be attributed to the different onshore scientific research stations, wastewater treatment facilities, marine activities, ocean currents, and local gyres in the two sea areas. Among the five fish families, members of the Artedidraconidae ingested the smallest MPs and the highest proportion of PAM, which is probably associated with their habitat and degradation effect of unique gut microbiome. The higher hazard index of MPs in fish from the RS is due to the presence of PAM and epoxy resin, which may also have far-reaching health implications for other Antarctic organisms and humans through food web transmission. Overall, long-term monitoring of MP pollution in Antarctic fish and their surrounding marine environment is highly desirable. Full article

Low-impact development (LID) is increasingly used to reduce stormwater’s quality and quantity impacts associated with climate change and increased urbanization. However, due to the significant variations in their efficiencies and site-specific requirements, an optimal combination of different LIDs is required to benefit from their full potential. In this article, the multi-objective genetic algorithm (MOGA) was coupled with the stormwater management model (SWMM) to identify both hydrological and cost-effective LIDs combinations within a large urban watershed. MOGA iteratively optimizes the types, sizes, and locations of different LIDs using a combined cost- and runoff-related objective function under both past and future stormwater conditions. The infiltration trench (IT), rain barrel (RB), rain gardens (RG), bioretention (BR), and permeable pavement were used as potential LIDs since they are common in our study area—the city of Renton, WA, USA. The city is currently adapting different LIDs to mitigate the recent increase in stormwater system failures and flooding. The results from our study showed that the optimum combination of LIDs in the city could reduce the peak flow and total runoff volume by up to 62.25% and 80% for past storms and by13% and 29% for future storms, respectively. The findings and methodologies presented in this study are expected to contribute to the ongoing efforts to improve the performance of large-scale implementations of LIDs. Full article

Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R² > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas. Full article

In the present study, a simple-to-use method is proposed for a preliminary prediction of the occurrence of shallow landslides (generally, with a thickness of 1–2 m) due to rainfall. This method can be used when a water table forms within the slope or the existing groundwater level rises due to rain infiltration, resulting in an increase in the pore water pressure. A relationship is also provided to establish when these conditions occur and the method can consequently be used. The proposed method combines a simplified solution to evaluate the change in pore water pressure within the slope due to infiltration, with the simple scheme of infinite slope to calculate a critical value of the pore water pressure that determines the incipient failure condition of the slope. In this way, a threshold curve can be also determined to readily assess whether a rainfall event with expected intensity and duration is capable of causing a slope failure at a given depth, where the initial pore water pressure is known. The method is completely analytical and only requires a few parameters as input data, which in addition can be obtained from conventional tests. A well-documented case study is considered to show how the method can be used for routine applications. Full article

Accurately determining the maximum designed water discharges of dams is extremely important, considering the economic costs of carrying out these types of hydrotechnical works and the possible disastrous consequences resulting from their incorrect design. This article describes and applies probability distributions used in hydrology, with some recommended by Romanian legislation standard NP 129-2011. The methods for estimating the parameters presented in this article, as well as the establishment of directions for correlating the normative with international regulations, resulting from the research on many rivers with different characteristics, conducted within the Faculty of Hydrotechnics, were completed with specialized computer applications for applying the normative. In this article, two case studies reflecting this research are presented. The verification of the proposed recommendations, on rivers with hydrographic basins with different physiographic characteristics, confirmed the opportunity to implement rigorous and simple criteria. The presentation of the quantile form of some distributions (especially Pearson III) and of the expressions of moments (central and raw) of high order, as well as the presentation of the frequency factors of each analyzed distribution necessary to calculate the confidence interval, constitute novelties, thus facilitating the ease of use of these distributions. Full article

Soil salinization is a problem that arouses the world’s attention. Soil salinity is an important limitation for agriculture production in coastal area. Phosphorus is a very important nutrient element in the process of plant growth, and its effectiveness affects plant growth to a great extent. In this study, soil available phosphorus and its component in Hedley phosphorus classification were found to be affected by soil salinity in coastal areas of Jiangsu Province. Several key environmental factors changed under the saline environment of the coastal areas, such as soil salinity, soil pH, and soil alkaline phosphatase activity. These environmental factors were significantly correlated with soil available phosphorus. Results showed that there were significant correlations between soil salinity and other environmental factors, and soil salinity and alkaline phosphatase activity were the main influencing factors of soil available phosphorus in this study. Significant positive correlation was found between alkaline phosphatase activity and soil salt content, and soil salinity was considered as the most important impact factor for soil available phosphorus as it affected the surrounding environment, and the soil alkaline phosphatase could be considered as the direct influencing factor for soil available phosphorus. Analysis between the soil alkaline phosphatase activity and phosphorus component showed that soil alkaline phosphatase activity could increase the proportion of active inorganic phosphorus and medium active inorganic phosphorus in soil phosphorus pool, which explained the effect of soil alkaline phosphatase activity on soil available phosphorus. Full article

The negative changes in the hydrological cycle are increasing due to climate change and urbanization, resulting in deterioration of water quality and environmental issues. Although Low-Impact Development (LID) techniques studies have been conducted to solve this problem, the spatial distribution of LID facilities and sewer types has received less attention. In this study, it is proposed to analyze the effects of sewer type, the spatial distribution of LID facilities, and LID type on runoff and water quality using the Storm Water Management Model and to identify effective ways of improving the hydrological cycle and Non-Point Source (NPS) pollution associated with urbanization. As a result of the runoff reduction analysis, 68% of the rainfall was discharged at the outlet for separate sewers, 79% for combined sewers without storage tank, and 49% for combined sewers with storage tank. The LID scenario results showed the distributed LID application method has higher reduction efficiency of runoff and NPS pollution than the intensive application method. Moreover, intensive application of LID in downstream areas resulted in higher runoff reduction efficiency than the application of LID in upstream areas. It will be used not only in the hydrological cycle plan but also in NPS pollution management. Full article

The global increase in potato production and yield is expected to lead to increased irrigation needs and this has prompted concerns with respect to the sustainability of irrigation water sources, such as groundwater. The magnitude, and inter- and intra-annual variation, of the crop water requirements and irrigation needs for potato production together with their impact on aquifer storage in a temperate humid region (Prince Edward Island, Canada) were estimated by using long-term (i.e., 2010–2019) daily soil water content (SWC). The amount of supplemental irrigation required for the minimal irrigation scenario (SWC = 70% of field capacity; 0.7 FC) was relatively small (i.e., 17.0 mm); however, this increased significantly, to 85.2 and 189.6 mm, for the moderate (SWC = 0.8 FC) and extensive (SWC = 0.9 FC) irrigation scenarios, respectively. The water supply requirement for the growing season (GS) increased to 154.9 and 344.7 mm for a moderately efficient irrigation system (55% efficiency) for the SWC = 0.8 FC and SWC = 0.9 FC irrigation scenarios, respectively. Depending on the efficiency and the areal extent of the irrigation system, the irrigation water supply requirement can approach or exceed both the GS and annual groundwater recharge. The methodology developed in this research has been translated into a free online tool (SWIB—Soil Water Stress, Irrigation Requirement and Water Balance), which can be applied to other areas or crops where an estimation of soil water deficit and irrigation requirement is sought. Full article

The literature on irrigated agriculture is primarily concerned with irrigation techniques, irrigation water-use efficiency, and crop yields. How human and non-human agents co-shape(d) irrigation landscapes through their activities and how these actions impact long-term developments are less well studied. In this study, we aim to (1) explore interactions between human and non-human agents in an irrigation system; (2) model the realistic operation of an irrigation system in an agent-based model environment, and; (3) study how short-term irrigation management actions create long-term irrigation system patterns. An agent-based model (ABM) was used to build our Irrigation-Related Agent-Based Model (IRABM). We implemented various scenarios, combining different irrigation control methods (time versus water demand), different river discharges, varied gate capacities, and several water allocation strategies. These scenarios result in different yields, which we analyse on the levels of individual farmer, canal, and system. Demand control gives better yields under conditions of sufficient water availability, whereas time control copes better with water deficiency. As expected, barley (Hordeum vulgare, Poaceae) yields generally increase when irrigation time and/or river discharge increase. The effect of gate capacity is visible with yields not changing linearly with changing gate capacities, but showing threshold behaviour. With the findings and analysis, we conclude that IRABM provides a new perspective on modelling the human-water system, as non-human model agents can create the dynamics that realistic irrigation systems show as well. Moreover, this type of modelling approach has a large potential to be theoretically and empirically used to explore the interactions between irrigation-related agents and understand how these interactions create water and yields patterns. Furthermore, the developed user-interface model allows non-technical stakeholders to participate and play a role in modelling work. Full article

The quantification of antibiotics (ABs) in sediments is an analytical challenge, but at the same time, it is indispensable to understand the fate of ABs in aquatic systems such as rivers. The aim of this study was to develop a comprehensive method to determine 19 ABs classified as macrolides, sulfonamides, fluoroquinolones, tetracyclines, clindamycin and trimethoprim in river sediments, using a combination of pressurized liquid extraction and solid phase extraction with the separation and detection with liquid chromatography coupled with mass spectrometry. Our results showed that the physical-chemical properties (e.g., log(Kow) value) of the analytes affected the extraction efficiency. Therefore, we propose to order ABs based on their log(Kow) values instead of traditional classification (macrolides, sulfonamides etc.) to select a suitable extraction solvent. ABs with log(Kow) values below zero (mainly fluoroquinolones and tetracyclines) were difficult to extract with all of the tested protocols compared to ABs with a log(Kow) larger than zero. After comparing different extraction protocols for ABs from solid and sediments, we concluded that recoveries in the range of 0.8 to 64.8% could be achieved for ABs with a log(Kow) value larger than zero using a mixture of acetonitrile and 50 mM phosphoric acid (50/50, v/v) in two extraction cycles at 100 °C. Full article

The Mediterranean region is one of the most responsive areas to climate change and was identified as a major “hot-spot” based on global climate change analyses. This study provides insight into local climate changes in the Mediterranean region under the scope of the InTheMED project, which is part of the PRIMA programme. Precipitation and temperature were analyzed in an historical period and until the end of this century for five pilot sites, located between the two shores of the Mediterranean region. We used an ensemble of 17 Regional Climate Models, developed in the framework of the EURO-CORDEX initiative, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Over the historical period, the temperature presents upward trends, which are statistically significant for some sites, while precipitation does not show significant tendencies. These trends will be maintained in the future as predicted by the climate models projections: all models indicate a progressive and robust warming in all study areas and moderate change in total annual precipitation, but some seasonal variations are identified. Future changes in droughts events over the Mediterranean region were studied considering the maximum duration of the heat waves, their peak temperature, and the number of consecutive dry days. All pilot sites are expected to increase the maximum duration of heat waves and their peak temperature. Furthermore, the maximum number of consecutive dry days is expected to increase for most of the study areas. Full article

The sulphurous mineral waters of ‘Entre-os-Rios’, which is sited in NW Portugal, are famous for their long history as thermal baths dating back at least to the mid-sixteenth century. Because of the singularity of its water composition, especially the highest sulphur content, the mineral waters of ‘Entre-os-Rios’ are one of the most important sulphurous waters in Portugal. Despite these mineral waters having a protection perimeter buffer zone to avoid water contamination, there are potentially damaging installations (e.g., fuel station) in the closed protection buffer zone that, according to existing law, are not permitted within the protection perimeters, which defeats the purpose of their delineation. A vulnerability map was created using geographic information system (GIS) tools based on multi-criteria analysis, combining thematic maps and parameters of the DRASTIC index, for evaluating the risk of contamination in the protection area. The results showed that within the perimeter, there was a low risk of pollution. The alluvium-covered terrain was vulnerable to moderate contamination, but it was far from the catchment point. Areas of minimal risk corresponded to locations where the granitic massif had not been significantly weathered. The map enables information collection for a better definition of local resource structures and planning, namely, for restricted areas emplacement where some activities should not be allowed (e.g., agriculture and water prospection), given its influence on the confined granitic aquifer. Full article

Microplastic pollution has been widely studied as a global issue due to increased plastic usage and its effect on human and aquatic life. Microplastics originate from domestic and industrial activities. Wastewater treatment plants (WWTPs) play an important role in removing a significant amount of microplastics; otherwise, they end up in bioaccumulation. This study provides knowledge about the characteristics of microplastics, removal efficiency, and the correlation between wastewater quality and microplastic concentrations from three different WWTPs that differ in the type of biological and advanced wastewater treatment techniques that are believed to play an important role in microplastic removal. Microplastics of different types, such as fragments, fibers, and beads, are identified by using an optical microscope before and after the treatment process at each stage to assess the effect of different treatment techniques. In the screening unit and primary clarifier unit, WWTP-B shows the highest removal efficiency with 74.76% due to a distribution flow system installed before the primary clarifier to ensure a constant flow of wastewater. WWTP-B uses a bioreactor consisting of a filter plate coated with activated carbon (BSTS II) that can enhance the adaptability and adhesion of microorganisms and showed that 91.04% of the microplastic was removed. Furthermore, only WWTP-A and WWTP-B were applied coagulation, followed by the disc filter; they showed significant results in microplastic removal, compared to WWTP-C, which only used a disc filter. In conclusion, from all WWTP, WWTP-B shows good treatment series for removing microplastic in wastewater; however, WWTP-B showed a high rate of microplastic removal; unfortunately, large amounts of microplastics are still released into rivers. Full article

Fish are indicative species with a relatively balanced ecosystem. Underwater target fish detection is of great significance to fishery resource investigations. Traditional investigation methods cannot meet the increasing requirements of environmental protection and investigation, and the existing target detection technology has few studies on the dynamic identification of underwater fish and small targets. To reduce environmental disturbances and solve the problems of many fish, dense, mutual occlusion and difficult detection of small targets, an improved CME-YOLOv5 network is proposed to detect fish in dense groups and small targets. First, the coordinate attention (CA) mechanism and cross-stage partial networks with 3 convolutions (C3) structure are fused into the C3CA module to replace the C3 module of the backbone in you only look once (YOLOv5) to improve the extraction of target feature information and detection accuracy. Second, the three detection layers are expanded to four, which enhances the model’s ability to capture information in different dimensions and improves detection performance. Finally, the efficient intersection over union (EIOU) loss function is used instead of the generalized intersection over union (GIOU) loss function to optimize the convergence rate and location accuracy. Based on the actual image data and a small number of datasets obtained online, the experimental results showed that the mean average precision ([email protected]) of the proposed algorithm reached 94.9%, which is 4.4 percentage points higher than that of the YOLOv5 algorithm, and the number of fish and small target detection performances was 24.6% higher. The results show that our proposed algorithm exhibits good detection performance when applied to densely spaced fish and small targets and can be used as an alternative or supplemental method for fishery resource investigation. Full article

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies. Full article

The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u₂) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d⁻¹, R² = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u_2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE < 0.385 mm d⁻¹, R² ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo. Full article

The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction. Full article

The many hydrodynamic implications associated with the geomorphological evolution of braided rivers are still not profoundly examined in both experimental and numerical analyses, due to the generation of three-dimensional turbulence structures around sediment bars. In this experimental research, the 3D velocity fields were measured through an acoustic Doppler velocimeter during flume-scale laboratory experimental runs over an emerging sand bar model, to reproduce the hydrodynamic conditions of real braided rivers, and the 3D Turbulent Kinetic Energy (TKE) components were analyzed and discussed here in detail. Given the three-dimensionality of the examined water flow in the proximity of the experimental bar, the statistical analysis of the octagonal bursting events was applied to analyze and discuss the different flume-scale 3D turbulence structures. The main novelty of this study is the proposal of the 3D Hole Size (3DHS) analysis, used for separating the extreme events observed in the experimental runs from the low-intensity events. Full article

Groundwater irrigation has a critical role in the sustainability of arable farming in many developing countries including Pakistan. Groundwater irrigation is generally practiced to supplement surface water supplies in Pakistan. Nevertheless, uninterrupted and extensive use of groundwater irrigation has raised several concerns about its sustainability and resultant environmental implications. Due to the scarcity of groundwater and heterogeneity in farmers’ resources, informal groundwater markets have emerged in Pakistan, where farmers trade water using a contractual system. Yet, the effects of these markets on agricultural productivity and equity remain largely unknown. This paper aims to analyze the impact of participation in the groundwater market on farmland utilization, cropping patterns, water access, and income. We analyze these impacts using primary data collected from 360 farmers in three different zones of the country’s largest province. The farmers were categorized as buyers, sellers, and self-users of water. Results indicate that participation in water markets increased agricultural land utilization, evinced by a higher cropping intensity among participants. A horizontal and vertical equity analysis of water markets shows that although large farmers have better access to groundwater irrigation, water market participation improves equity to water access. Based on income inequality measures such as the Gini coefficient and the Lorenz curve, water market participation also improves farmer incomes regardless of farm size. Propensity score matching revealed that wheat yield and income among water-market participants went up by approximately 150 kg and PKR 4503 per acre compared with non-participants. Groundwater market participants’ higher crop productivity and income level suggest that water markets need a thorough revisit in terms of policy focus and institutional support to ensure sustainable rural development. Full article

More frequent and more intense heat waves and greater drought stress will occur in the future climate environment. Short-term extreme heat and drought stress often occur simultaneously after winter wheat anthesis, which has become the major constraint threatening future wheat yield. In this study, short-term heat, drought and their combination stress were applied to wheat plants after anthesis, and all wheat plants were restored to the outdoor normal temperature and full watering after stress treatment. The aim of the current study was to evaluate the role of nitrogen (N) in modulating the effects of post-anthesis short-term heat, drought and their combination stress on photosynthesis, N metabolism-related enzymes, the accumulation of N and protein and growth, as well as on the yield and water (WUE) and N use efficiency (NUE) of wheat after stress treatment. The results showed that compared with low N application (N1), medium application (N2) enhanced the activities of nitrate reductase (NR) and glutamine synthase (GS) in grains under post-anthesis heat and drought stress alone, which provided a basis for the accumulation of N and protein in grains at the later stage of growth. Under post-anthesis individual stresses, N2 or high application (N3) increased the leaf photosynthetic rate (A_n), PSII photochemical efficiency and instantaneous WUE compared with N1, whereas these parameters were usually significantly improved by N1 application under post-anthesis combined stress. The positive effect of increased A_n by N application on growth was well represented in a higher green leaf area, aboveground dry mass and plant height, and the variation in A_n can be explained more accurately by the N content per unit leaf area. Short-term heat, drought and combined stress after anthesis resulted in a pronounced decrease in yield by reducing grain number per spike and thousand kernel weight. The reduction in NUE under combined stress was higher than that under individual heat and drought stress. Compared with N1, N2 or N3 application significantly prevented the decrease in yield and NUE caused by post-anthesis heat and drought stress alone. However, N1 application was conducive to improving the productivity, WUE and NUE of wheat when exposed to post-anthesis combined stress. The current data indicated that under short-term individual heat and drought stress after anthesis, appropriately increasing N application effectively improved the growth and physiological activity of wheat compared with N1, alleviating the reduction in yield, WUE and NUE. However, under combined stress conditions, reducing N application (N1) may be a suitable strategy to compensate for the decrease in yield, WUE and NUE. Full article

The lack of valuable baseline information about groundwater availability hinders the robust decision-making process of water management in humid, arid, and semi-arid climate regions of the world. In sustainable groundwater management, identifying the spatiotemporal and extrapolative monitoring of potential zone is crucial. Thus, the present study focused on determining potential aquifer recharge zones using geospatial techniques for proxy data of the Gilgel Gibe catchment, Ethiopia. Proxy data are site information derived from satellite imageries or conventional sources that are operated as a layer attribute in the geographical information system (GIS) to identify groundwater occurrence. First, GIS and analytical hierarchy process (AHP) were applied to analyze ten groundwater recharge controlling factors: slope, lithology, topographic position index lineament density, rainfall, soil, elevation, land use/cover, topographic wetness index, and drainage density. Each layer was given relative rank priority depending on the predictive implication of groundwater potentiality. Next, the normalized weight of thematic layers was evaluated using a multi-criteria decision analysis AHP algorithm with a pairwise comparison matrix based on aquifer infiltration relative significance. Lithology, rainfall, and land use/cover were dominant factors covering a weight of 50%. The computed consistency ratio (CR = 0.092, less than 10%) and consistency index (CI = 0.1371) revealed the reliability of input proxy layers’ in the analysis. Then, a GIS-based weighted overlay analysis was performed to delineate very high, high, moderate, low, and very low potential aquifer zones. The delineated map ensures very high (29%), high (25%), moderate (28%), low (13%), and very low (5%) of the total area. According to validation, most of the inventory wells are located in very high (57%), high (32), and moderate (12%) zones. The validation results realized that the method affords substantial results supportive of sustainable development and groundwater exploitation. Therefore, this study could be a vigorous input to enhance development programs to alleviate water scarcity in the study area. Full article

Nowadays, in the management of water distribution networks (WDNs), particular attention is paid to digital transition and the improvement of the energy efficiency of these systems. New technologies have been developed in the recent years and their implementation can be crucial to achieve a sustainable level of water networks, namely, in water and energy losses. In particular, Digital Twins (DT) represents a very innovative technology, which relies on the integration of virtual network models, optimization algorithms, real time data collection, and smart actuators information with Geographic Information System (GIS) data. This research defines a new methodology for an efficient application of DT expertise within water distribution networks. Assuming a DMA of a real water distribution network as a case study, it was demonstrated that a fast detection of leakage along with an optimal setting of pressure control valves by means of DT together with an optimization procedure can ensure up to 28% of water savings, contributing to significantly increase the efficiency of the whole system. Full article

Algal blooms have various effects on drinking water supply systems; thus, proper monitoring is essential. Traditional visual identification using a microscope is a time-consuming method and requires extensive labor. Recently, advanced machine learning algorithms have been increasingly applied for the development of object detection models. The You-Only-Look-Once (YOLO) model is a novel machine learning algorithm used for object detection; it has been continuously improved in newer versions, and a tiny version of each standard model presented. The tiny versions applied a less complicated architecture using a smaller number of convolutional layers to enable faster object detection than the standard version. This study compared the applicability of the YOLO models for algal image detection from a practical aspect in terms of classification accuracy and inference time. Therefore, automated algal cell detection models were developed using YOLO v3 and YOLO v4, in which a tiny version of each model was also applied. The cell images of 30 algal genera were used for training and testing the models. The model performances were compared using the mean average precision (mAP). The mAP values of the four models were 40.9, 88.8, 84.4, and 89.8 for YOLO v3, YOLO v3-tiny, YOLO v4, and YOLO v4-tiny, respectively, demonstrating that YOLO v4 is more precise than YOLO v3. The tiny version models presented noticeably higher model accuracy than the standard models, allowing up to ten times faster object detection time. These results demonstrate the practical advantage of tiny version models for the application of object detection with a limited number of object classes. Full article

Sluice gates are common hydraulic structures for controlling and regulating flow in open channels. This study investigates five models’ performance in distinguishing conditions of flow regimes, estimating the discharge coefficient ($C_d$) and flow rate. Experiments were conducted for different gate openings, flow rates, upstream and downstream conditions. New equation forms and methods are proposed to determine $C_d$ for energy–momentum considering losses (EML) and HEC-RAS models. For distinguishing the flow regimes, results indicated a reasonable performance for energy–momentum (EM), EML, and Swamee’s models. For flow rate and discharge coefficient performance of EM, EML, and Henry’s models in free flow and for EM and EML in submerged flow were reasonable. The effects of physical scale on models were investigated. There were concerns about the generality and accuracy of Swamee’s model. Scaling effects were observed on loss factor k in EML. A new equation and method were proposed to calibrate k that improved the EML model’s accuracy. This study facilitates the application and analysis of the studied models for the design or calibration of sluice gates and where the flow in open channels needs to be controlled or measured using sluice gates such as irrigation channels or water delivery channels of small run-of-river hydropower plants. Full article

Routine maintenance of fishing vessels and wastewater discharges are primary sources of heavy metals in fishing ports. Sediment pollution assessment is necessary in fishing port management, including sediment dredging and disposal, sewage treatment facility construction, and pollution source control. In this study, sediment heavy metal contents in Qianzhen Fishing Port, the largest pelagic fishery port in Taiwan, were investigated to assess the contamination levels and related potential ecological risks using multiple sediment pollution indices. Normalization methods were applied to identify the potential sources of heavy metals in fishing port sediments. Results showed that Cu, Zn, Pb, and Cr contents in the sediments of the inner fishing port (averages of 276, 742, 113, and 221 mg/kg, respectively) were 3–5 times greater compared to those along the port entrance and outside, indicating the strong impacts of anthropogenic pollution (EF_Cu: 5.6–12.5; EF_Zn: 2.8–4.3; EF_Pb: 2.4–5.4; EF_Cr: 1.1–3.2). Copper pollution was more severe, with high maxima contamination factor (CF_Cu: 15.1–24.8), probably contributed by copper-based antifouling paints used in fishing vessels. The sediments in the inner fishing port are categorized as having considerable ecological risk and toxicity (mERMq: 0.61–0.91; ΣTU: 7.5–11.7) that can potentially cause adverse effects on benthic organisms. Qianzhen Fishing Port sediments can be characterized as high Cu/Fe and Pb/Fe, moderate Zn/Fe, and high total grease content, indicating that the potential sources of heavy metals are primarily antifouling paints and oil spills from the fishing vessels. This study provides valuable data for pollution control, remediation, and environmental management of fishing ports. Full article

Urban flooding poses risks to the safety of drivers and pedestrians, and damages infrastructures and lifelines. It is important to accommodate cities and local agencies with enhanced rapid flood detection skills and tools to better understand how much flooding a region may experience at a certain period of time. This results in flood management orders being announced in a timely manner, allowing residents and drivers to preemptively avoid flooded areas. This research combines information received from ground observed data derived from road closure reports from the police department, with remotely sensed satellite imagery to develop and train machine-learning models for flood detection for the City of San Diego, CA, USA. For this purpose, flooding information are extracted from Sentinel 1 satellite imagery and fed into various supervised and unsupervised machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classifier (MLC), to detect flooded pixels in images and evaluate the performance of these ML models. Moreover, a new unsupervised machine learning framework is developed which works based on the change detection (CD) approach and combines the Otsu algorithm, fuzzy rules, and iso-clustering methods for urban flood detection. Results from the performance evaluation of RF, SVM, MLC and CD models show 0.53, 0.85, 0.75 and 0.81 precision measures, 0.9, 0.85, 0.85 and 0.9 for recall values, 0.67, 0.85, 0.79 and 0.85 for the F1-score, and 0.69, 0.87, 0.83 and 0.87 for the accuracy measure, respectively, for each model. In conclusion, the new unsupervised flood image classification and detection method offers better performance with the least required data and computational time for enhanced rapid flood mapping. This systematic approach will be potentially useful for other cities at risk of urban flooding, and hopefully for detecting nuisance floods, by using satellite images and reducing the flood risk of transportation design and urban infrastructure planning. Full article

The precipitation phase (i.e., rain and snow) is important for the global hydrologic cycle and climate system. The objective of this study is to evaluate the precipitation-phase partitioning capabilities of remote sensing and reanalysis modeling methods on the global scale. Specifically, observation data from the National Centers for Environmental Prediction (NCEP) Automated Data Processing (ADP), from 2000 to 2007, are used to evaluate the rain–snow discrimination accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth-generation reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5). The results show that: (1) the ERA5 performs better than the IMERG at distinguishing rainfall and snowfall events, overall. (2) The ERA5 has high accuracy in all continents except for South America, while the IMERG performs well only in Antarctica and North America. (3) Compared with the IMERG, the ERA5 can more effectively capture snowfall events at high latitudes but shows worse performance at mid-low latitude regions. Both the IMERG and ERA5 have lower accuracy for rain–snow partitioning under heavy precipitation. Overall, the results of this study provide references for the application and improvement of global rain–snow partitioning products. Full article

To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations. Full article

Exploring alternative freshwater resources other than those surrounding the Nile is critical to disperse Egypt’s population to other uninhabited desert areas. This study aims to locate groundwater potential zones (GWPZs) in the water-scarce desert between the Qina and Safga-Bir Queh regions to build groundwater wells, thereby attracting and supporting people’s demand for water, food, and urban development. Multi-criteria evaluation (MCE) and analytical hierarchical process (AHP) techniques based on remote sensing (RS) and Geographic Information System (GIS) were used to map GWPZs. The outcome of the GWPZs map was divided into six different classes. High and very-high aquifer recharge potentials were localized in the middle and western parts, spanning 19.3% and 17% (16.4% and 15.7%) by MCE (AHP). Low and very low aquifer recharge potentials were distributed randomly in the eastern part over an area of 29% and 14.3% (26.9% and 6.1%) by MCE (AHP). Validation has been undertaken between the collected Total Dissolved Solid (TDS) and with the calculated GWPZs, indicating that the highest and lowest TDS concentrations of most aquifers are correlated with low to very low and high to very high aquifer potential, respectively. The study is promising and can be applied anywhere with similar setups for groundwater prospect and management. Full article

Although machine learning (ML) techniques are increasingly used in rainfall-runoff models, most of them are based on one-dimensional datasets. In this study, a rainfall-runoff model with deep learning algorithms (CNN-LSTM) was proposed to compute runoff in the watershed based on two-dimensional rainfall radar maps directly. The model explored a convolutional neural network (CNN) to process two-dimensional rainfall maps and long short-term memory (LSTM) to process one-dimensional output data from the CNN and the upstream runoff in order to calculate the flow of the downstream runoff. In addition, the Elbe River basin in Sachsen, Germany, was selected as the study area, and the high-water periods of 2006, 2011, and 2013, and the low-water periods of 2015 and 2018 were used as the study periods. Via the fivefold validation, we found that the Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE) fluctuated from 0.46 to 0.97 and from 0.47 to 0.92 for the high-water period, where the optimal fold achieved 0.97 and 0.92, respectively. For the low-water period, the NSE and KGE ranged from 0.63 to 0.86 and from 0.68 to 0.93, where the optimal fold achieved 0.86 and 0.93, respectively. Our results demonstrate that CNN-LSTM would be useful for estimating water availability and flood alerts for river basin management. Full article

Understanding salinity and fertilizer interaction is of great importance to improve crop production and fertilizer use efficiency in saline areas. To evaluate the interactive effects of different soil salinity levels and nitrogen (N) applications rates on the sunflower photosynthetic characteristics of N uptake and N use efficiency, a two-year field experiment was conducted in Hetao Irrigation District, China. The experiment consisted of three initial salinity (IS) levels expressed as the electrical conductivity of a saturated soil extract (EC_e) (S0: 1.72–2.61 dS/m; S1: 4.73–5.90 dS/m; S2: 6.85–9.04 dS/m) and four N rates (45, 90, 135, and 180 kg/ha), referred as N0–N3, respectively. The results indicated that the net photosynthetic rate (Pn) of sunflowers treated with S0 and S1 levels both had a significant decrease in the bud stage, and then reached their maximum at anthesis. However, during the crop cycle, the Pn at S2 level only had small fluctuations and still remained at a high level (>40 μmol CO₂/(m² s)) at the early mature stage. When increasing IS levels from S0 to S1, the plant N uptake (PNU) under the same N rates were only decreased by less than 10% at maturity, whereas the decline was expanded to 17.2–45.7% from S1 to S2. Additionally, though applying the N2 rate could not increase sunflower PNU at the S0 and S1 levels, its N use efficiency was better than those under N3. Meanwhile, at the S2 level, the application of the N0 rate produced a higher N productive efficiency (NPE) and N uptake efficiency (NUPE) than the other N rates. Therefore, our study proposed recommended rates of N fertilizer (S0 and S1: 135 kg/ha, S2: 45 kg/ha) for sunflowers under different saline conditions. Full article

Antibiotics are the most common pharmaceutical compounds, and they have been extensively used for the prevention and treatment of bacterial diseases for more than 50 years. However, merely a small fraction of antibiotics is metabolized in the body, while the rest is discharged into the environment through excretion, which can cause potential ecological problems and human health risks. In this study, the elimination of seventeen antibiotics from real livestock wastewater effluents was investigated by UV/TiO₂ advanced oxidation process. The effect of process parameters, such as TiO₂ loadings, solution pHs, and antibiotic concentrations, on the efficiency of the UV/TiO₂ process was assessed. The degradation efficiency was affected by the solution pH, and higher removal efficiency was observed at pH 5.8 and 9.9, while the catalyst loading had no significant effect on the degradation efficiency at these experimental conditions. UV photolysis showed a good removal efficiency of the antibiotics. However, the highest removal efficiency was shown by the UV/photocatalyst system due to their synergistic effects. The results showed that more than 90% of antibiotics were removed by UV/TiO₂ system during the 60 min illumination, while the corresponding TOC and COD removal was only 10 and 13%, respectively. The results of the current study indicated that UV/TiO₂ advanced oxidation process is a promising method for the elimination of various types of antibiotics from real livestock wastewater effluents. Full article