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Plastics have become an integral component in agricultural production as mulch films, nets, storage bins and in many other applications, but their widespread use has led to the accumulation of large quantities in soils. Rational use and reduction, collection, reuse, and innovative recycling are key measures to curb plastic pollution from agriculture. Plastics that cannot be collected after use must be biodegradable in an environmentally benign manner. Harmful plastic additives must be replaced with safer alternatives to reduce toxicity burdens and included in the ongoing negotiations surrounding the United Nations Plastics Treaty. Although full substitution of plastics is currently not possible without increasing the overall environmental footprint and jeopardizing food security, alternatives with smaller environmental impacts should be used and endorsed within a clear socio-economic framework. Better monitoring and reporting, technical innovation, education and training, and social and economic incentives are imperative to promote more sustainable use of plastics in agriculture.

Lake waters are a significant source of drinking water and contribute to the local economy (e.g. enabling irrigation, offering opportunities for tourism, waterways for transport, and meeting utility water demands); therefore, the ability to accurately forecast lake water levels is important. However, given the significant lack of research with respect to forecasting water levels in small lakes (i.e. 0.05 km2 < area < 10 km2), the present study sought to address this knowledge gap by testing a pair of hypotheses: (1) it is possible to forecast water levels in small surface lakes using artificial neural networks (ANN), and (2) better water-level forecasts will be obtained when the wavelet transform (WT) is used as an input data pre-processing tool. Based on an analysis of a case study in Lake Biskupinskie (1.16 km2) in Poland and based on a range of model performance statistics (e.g. mean absolute error, root mean square error, mean squared error, coefficient of determination, mean absolute percentage error), both hypotheses were confirmed for monthly forecasting of lake water levels. ANNs provided good forecasting results, and WT pre-processing of input data led to even better forecasts. Additionally, it was found that meteorological variables did not have a significant impact in forecasting water-level fluctuations. In light of the results and the limited scope of the present study, proposed future research directions and problems to be resolved are discussed.

Multivariate probability analysis of hydrological elements using copula functions can significantly improve the modeling of complex phenomena by considering several dependent variables simultaneously. The main objectives of this study were to: (i) develop a stand-alone and event-based rainfall-runoff (RR) model using the common bivariate copula functions (i.e. the BCRR model); (ii) improve the structure of the developed copula-based RR model by using a trivariate version of fully-nested Archimedean copulas (i.e. the FCRR model); and (iii) compare the performance of the developed copula-based RR models in an Iranian watershed. Results showed that both of the developed models had acceptable performance. However, the FCRR model outperformed the BCRR model and provided more reliable estimations, especially for lower joint probabilities. For example, when joint probabilities were increased from 0.5 to 0.8 for the peak discharge (qp) variable, the reliability criteria value increased from 0.0039 to 0.8000 in the FCRR model, but only from 0.0010 to 0.6400 in the BCRR model. This is likely because the FCRR considers more than one rainfall predictor, while the BCRR considers only one.

Baseflow, the sustained flow from groundwater, lakes, and snowmelt, is essential for maintaining surface water flow, particularly during droughts. Amid rising global water demands and climate change impacts, understanding baseflow dynamics is crucial for water resource management. This study offers new insights by assessing baseflow controls at finer temporal scales and examining their relationship with hydrological drought flows. We investigate how climatic factors influence seasonal baseflow in 7138 global catchments across five major climate regions. Our analysis identifies precipitation as the primary driver, affecting 58.3% of catchments, though its impact varies significantly across different climates. In temperate regions, precipitation dominates (61.9% of catchments), while in tropical regions, evaporative demand is the leading factor (47.3%). Snow fraction is particularly crucial in both snow-dominated (20.8%) and polar regions (48.5%). Negative baseflow trends generally emerge where the effects of evaporative demand or snow fraction outweigh those of precipitation. Specifically, in northern regions and the Rocky Mountains, where snow fraction predominantly controls baseflow changes, a negative trend is evident. Similarly, in tropical catchments, where evaporative demand drives baseflow changes, this also leads to a negative trend. Additionally, our findings indicate that baseflow changes are closely linked to hydrologic drought severity, with concurrent trends observed in 69% of catchments. These findings highlight the relationship between baseflow changes, the severity of hydrologic drought and shifts in precipitation, evaporative demand, and snow dynamics. This study provides crucial insights for sustainable water resource planning and climate change adaptation, emphasizing the importance of managing groundwater-fed river flows to mitigate drought impacts.

The widely scattered pattern of meteorological stations in large watersheds and remote locations, along with a need to estimate meteorological data for point sites or areas where little or no data have been recorded, has encouraged the development and implementation of spatial interpolation techniques. The various interpolation techniques featured in GIS software allow for the extraction of this new information from spatially distinct point data. Since no one interpolation method can be accurate in all regions, each method must be evaluated prior to each geographically distinct application. Many methods have been used for interpolating minimum temperature (Tmin), maximum temperature (Tmax) and precipitation data, and few have been used in the Zayandeh-Rud River basin, Iran, and no comparison of methods has ever been carried out in the area. The accuracies of six spatial interpolation methods [Inverse Distance Weighting, Natural Neighbor (NN), Regularized Spline, Tension Spline, Ordinary Kriging, Universal Kriging] were compared in this study simultaneously, and the best method for mapping monthly precipitation and temperature extremes was determined in a large semi-arid watershed with high temperature and rainfall variation. A cross-validation technique and long-term (1970–2014) average monthly Tmin, Tmax and precipitation data from meteorological stations within the basin were used to identify the best interpolation method for each variable dataset. For Tmin, Kriging (Gaussian) proved to be the most accurate interpolation method (MAE = 1.827 °C), whereas, for Tmax and precipitation the NN method performed best (MAE = 1.178 °C and 0.5241 mm, respectively). Accordingly, these variable-optimized interpolation methods were used to define spatial patterns of newly generated climatic maps.

In this study, a new fuzzy methodology for a multi-objective optimization of reservoir Water Quality Monitoring Stations (WQMS) was developed, based on Transinformation Entropy (TE), the IRanian Water Quality Index (IRWQI), and fuzzy social choice considering uncertainties. The approach was utilized in the Karkheh Dam reservoir in Iran. The objective functions were: 1) minimizing costs, 2) minimizing redundant information and uncertainties, and 3) maximizing the spatial coverage of the network. A CE-QUAL-W2 model was used for the simulation of water quality variables. The IRWQI was computed to reveal a complete picture of the reservoir water quality. The TE quantities were calculated for each pair of potential stations. The TE values were plotted against the spatial distances among potential WQMS to obtain the TE–Distance (TE–D) curve, and minimize redundant information among stations, while providing coverage of the entire network. A multi-objective Genetic Algorithm (NSGA-II) was applied to obtain Pareto-optimal solutions taking stakeholder preference into account. The most preferred solution was then obtained using fuzzy social choice approaches to achieve a consensus. The fuzziness embedded in the decision-making procedure, the uncertainty in the value of mutual information, and the uncertainty in identifying the optimal distance among WQMS were also investigated. Results indicated that the three fuzzy social choice approaches (Borda Count, Minimax, and Approval Voting) led to the same number of optimized WQMS in each fuzzy alpha-cut. Based on the fuzzy linguistic quantifiers method, the number of optimized WQMS was increased.

The Upper Indus Basin (UIB), situated in the Himalaya–Karakoram–Hindukush (HKH) mountain ranges, is the major contributor to the supply of water for irrigation in Pakistan. Improved management of downstream water resources requires studying and comparing spatiotemporal changes in the snow cover and hydrological behavior of the river basins located in the HKH region. This study explored in detail the recent changes that have occurred in the Gilgit River basin (12,656 km 2 ; western sub-basin of UIB), which is characterized by a mean catchment elevation of 4250 m above sea level (m ASL). The basin’s snow cover was monitored through the snow products provided by the MODIS satellite sensor, while analysis of its hydrological regime was supported by hydrological and climatic data recorded at different altitudes. The Gilgit basin findings were compared to those previously obtained for the lower-altitude Astore basin (mean catchment elevation = 4100 m ASL) and the higher-altitude Hunza basin (mean catchment elevation = 4650 m ASL). These three catchments were selected because of their different glacier coverage, contrasting area distribution at high altitudes and significant impact on the Upper Indus River flow. Almost 7, 5 and 33 % of the area of the Gilgit, Astore and Hunza basins, respectively, are situated above 5000 m ASL, and approximately 8, 6 and 25 %, respectively, are covered by glaciers. The UIB region was found to follow a stable or slightly increasing trend in snow coverage and had a discharge dominated by snow and glacier melt in its western (Hindukush–Karakoram), southern (Western-Himalaya) and northern (Central-Karakoram) sub-basins.

Freeze–thaw processes shape ecosystems, hydrology, and infrastructure across northern high latitudes. Here we use satellite-based observations from 1979–2021 across 47 northern hemisphere ecoregions to examine changes in the number of frozen land-surface days per year. We find widespread declines, with 70% of ecoregions showing significant reductions, primarily linked to rising air temperatures and thinning snowpacks. Causal analysis demonstrates that air temperature and snow depth exert consistent controls on the number of frozen days. A trend-informed assessment based on historical observations suggests a potential average loss of more than 30 frozen days per year by the end of the century, with the steepest decreases in Alaska, northern Canada, northern Europe, and eastern Russia. Scenario-based analysis indicates that each 1 °C increase in air temperature reduces frozen days by ~6-days, while each 1 cm decrease in snow depth leads to a ~ 3-day reduction. These shifts carry major ecological and socio-economic implications. Rising air temperatures and thinning snowpacks result in a significantly declining number of frozen days per year across 70% of northern hemisphere ecoregions, according to analysis of satellite-based freeze-thaw data.

This study developed a hydrodynamic reduced-order model (ROM) to regenerate surface currents in Gorgan Bay, Iran. The developed ROM was based on linking a three-dimensional hydrodynamic model, MIKE3-FM, with a data reduction technique, proper orthogonal decomposition (POD). The MIKE3-FM model was first run to simulate surface currents in the bay under a real wind scenario for two years starting July 1, 2010. Thereafter, time and space steps of 6 hours and 500 m, respectively, were chosen to capture 2,920 snapshots of the simulated surface currents using the MIKE3-FM model on 1,937 grids in the bay. The snapshots were then used as input for the POD model to develop the ROM. By applying the POD on the snapshots, necessary spatial and temporal components of surface currents used to develop the ROM were calculated. Having spatial and temporal terms, two ROMs for regeneration of surface currents U and V in two directions x and y, respectively, were developed. Analysis of ROM results revealed they accurately regenerated surface currents using only the first ten modes (among 2,920 modes). Comparison of MIKE3-FM and ROMs developed by the first ten modes revealed there were only negligible differences between their results when they simulated and regenerated, respectively, U and V, in the bay.

We are pleased to learn that the hydrodynamic reduced-order model (ROM) for the regeneration of surface currents in Gorgan Bay, Iran, presented in our research article, entitled ‘A ROM for the regeneration of surface currents in Gorgan Bay, Iran’ and published in the Journal of Hydroinformatics in 2018 (Kheirabadi et al. 2018), is of interest to the readers of this journal.