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Suspended particulate matter (SPM) is an indispensable component of water environments. Its fate and transport involve various physical and biogeochemical cycles. This paper provides a comprehensive review of SPM dynamics by integrating insights from biogeochemical processes, spatiotemporal observation techniques, and numerical modeling approaches. It also explores methods for diagnosing SPM-mediated biogeochemical processes, such as the flocculation kinetics test and organic matter composition analysis. Advances in remote sensing, in situ monitoring, and high-resolution retrieval algorithms are discussed, highlighting their significance in detecting and quantifying SPM concentrations across varying spatial and temporal scales. Furthermore, this review examines integrated models that incorporate population balance equations on the basis of flocculation kinetics into multi-dimensional sediment transport models. The results from this study provide valuable insights into SPM dynamics, ultimately enhancing our knowledge of SPM behavior and transport in water environments. However, uncertainties remain due to limited field data on flocculation kinetics and the need for parameter optimization in numerical models. Addressing these gaps through enhanced fieldwork and model refinement will significantly improve our ability to predict and manage SPM dynamics, which is critical for sustainable aquatic ecosystem management in an era of rapid environmental change. [ABSTRACT FROM AUTHOR]

Estimating sediment yield in a river is a challenging task in the water resources field. Different methods are available for estimating sediment erosion and yield, but generally they are not spatially distributed in nature. This paper presents the application of the Revised Universal Soil Loss Equation (RUSLE) for estimating soil erosion and integrates it with spatially distributed Sediment Delivery Ratio (SDR) to calculate sediment yield in a Himalayan river. The study area is Kabeli sub-catchment, located upstream of the Koshi River Basin in the eastern part of Nepal. The Kabeli River is where numerous hydropower projects are envisaged, and sediment-related issues are of major concern. With the use of the RUSLE, the mean annual soil erosion is estimated at 35.96 tons/ha/yr. The estimated specific sediment yield (SSY) from the distributed SDR method is 6.74 tons/ha/yr, which is close to the observed SSY of 7.26 tons/ha/yr using the data records of ~8 years. Based on correlation analysis, the topographic factor ( L S ) is the most sensitive RUSLE parameter with respect to sediment erosion. The sloping areas near the river hillslope are particularly vulnerable to soil erosion. The results indicate that the approach employed in this study may be potentially applied in other catchments with similar physiographic characteristics for the estimation of sediment yield. [ABSTRACT FROM AUTHOR]

Studying the response of runoff to climate change and land use/cover change has guiding significance for watershed land planning, water resource planning, and ecological environment protection. Especially in the Yellow River Basin, which has a variable climate and fragile ecology, such research is more important. This article takes the Huangfuchuan River Basin (HFCRB) in the middle reaches of the Yellow River as the research area, and analyzes the impact of climate change scenarios and land use/cover change scenarios on runoff by constructing a SWAT model. Using CMIP6 GCMs to obtain future climate data and the CA–Markov model to predict future land use data, the two are coupled to estimate the future runoff process in the HFCRB, and the uncertainty of the estimated runoff is decomposed and quantified. The results were as follows: ① The SWAT model has good adaptability in the HFCRB. During the calibrated period and the validation period, R2 ≥ 0.84, NSE ≥ 0.8, and |PBIAS| ≤ 17.5%, all of which meet the model evaluation criteria. ② There is a negative correlation between temperature and runoff, and a positive correlation between precipitation and runoff. Runoff is more sensitive to temperature rise and precipitation increase. ③ The impact of land use types on runoff is in the order of cultivated land > grassland > forest land. ④ The variation range of runoff under the combined effects of future climate change and LUCC is between that of single climate change or LUCC scenarios. The increase in runoff under SSP126, SSP245, and SSP585 scenarios is 10.57%, 25.55%, and 31.28%, respectively. Precipitation is the main factor affecting the future runoff changes in the HFCRB. Model uncertainty is the main source of uncertainty in runoff prediction. [ABSTRACT FROM AUTHOR]

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15 k m from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands. [ABSTRACT FROM AUTHOR]

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors' findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources. [ABSTRACT FROM AUTHOR]

The theoretical paradigm of geographic automata systems (GAS) underpins a wide range of studies to represent dynamic complex geospatial phenomena. Specifically, cellular automata (CA) were used extensively over the past 40 years for geospatial applications, though primarily for modeling urban growth. Currently, the hyper-specialized and fragmented geospatial technology ecosystem supporting CA model implementation often necessitates programmed solutions or use of disconnected programs with graphical user interfaces (GUIs) separate from common geographic information systems (GIS) software. Therefore, the main goal of this study is to present a general-purpose, GIS-based CA modeling framework and extension for Esri's ArcGIS Pro software environment. The methodological approach centered around (1) developing generic functions for building binary or multi-class CA models to capture various spatiotemporal systems and (2) enabling end-to-end CA modeling projects that can incorporate built-in functionality available in ArcGIS Pro versions 3.1 and newer. Two case studies demonstrate the add-in capabilities to support geosimulation model-building activities and exploration of new hybrid models. This research contributes to advancing flexible, transparent spatiotemporal modeling tools within existing GIS software. The proposed approach addresses the lack of streamlined geospatial technologies capable of simulating numerous dynamic geospatial phenomena, exploring human and environmental processes, and examining possible futures with CA in research, decision making, or educational settings. [ABSTRACT FROM AUTHOR]

Datasets used in AI applications for human health require careful selection. In healthcare, machine learning (ML) models are fine-tuned to reduce errors, and our study focuses on minimizing errors by generating code snippets for cost-sensitive learning using water potability datasets. Water potability ensures safe drinking water through various scientific methods, with our approach using ML algorithms for prediction. We preprocess data with ChatGPT-generated code snippets and aim to demonstrate how zero-shot learning prompts in ChatGPT can produce reliable code snippets that cater to cost-sensitive learning. Our dataset is sourced from Kaggle. We compare model performance metrics of logistic regressors and gradient boosting classifiers without additional code fine-tuning to check the accuracy. Other classifier performance metrics are compared with results of the top 5 code authors on the Kaggle scoreboard. Cost-sensitive learning is crucial in domains like healthcare to prevent misclassifications with serious consequences, such as type II errors in water potability assessment. [ABSTRACT FROM AUTHOR]

The process of estimating sediment load has been a daunting issue in hydraulics and the water resource field. Several methods exist for predicting the sediment load in a catchment or river, but the majority of these methods are empirical and depend on the specific location where they are used. Understanding the underlying mechanism of sediment generation and its transport in connection with precipitation, topography, and subsurface conditions to characterize its process is helpful for determining the sediment load in a river. For this purpose, we analyzed the daily suspended sediment data measured for 8 years at the headworks of the Kabeli A hydropower project in the Kabeli River, which originates from the Himalayan region. The analyses show that the suspended sediment concentration (SSC) varies in an orderly manner over time and asynchronously between seasons with respect to the river discharge. Clockwise hysteresis is observed in the yearly plots between the SSC and river discharge. The hysteresis becomes narrower when compared with the direct runoff obtained from a digital filtering algorithm and, even more so with the direct runoff from the hydrological model SWAT. The analysis shows that the sediment concentration is controlled not only by the total discharge in the river but also by the contribution of ground water to the river discharge, indicating that the total discharge alone cannot reflect the seasonal variation in SSC. It is inferred that the river is supply-limited and the hillslope is transport-limited with respect to sediment sources. The SWAT model suggests that the base flow contribution to the total river discharge is 78%. Here, we present a method for constructing the suspended sediment rating curve by comparing the direct runoff with the sediment concentration. The deduced sediment rating curve captures 84.51% of the total sediment load over the study period in the Kabeli River. This method may potentially be used in similar catchments with supply-limited rivers and transport-limited hillslopes. [ABSTRACT FROM AUTHOR]

This study applies the WaTEM/SEDEM model, a watershed-scale model based on the Universal Soil Loss Equation (USLE), to enhance sustainable watershed management by identifying high-erosion-risk areas for targeted mitigation at various scales. It focuses on identifying potential errors in using statewide or worldwide land use data layers derived from remote-sensing algorithms and inaccuracies in the spatial distribution of cropland and soil types resulting in misinterpretation of sediment yields. These model limitations emphasize the need for field validation and precise input data, particularly stream data, to improve the reliability of these models. The study examines hydrologic rainfall-runoff processes in Tennessee's 182 km2 Oostanaula catchment using various data sources, including the National Land Cover Database (NLCD), the European Space Agency WorldCover dataset (ESA), and manual field surveys. Three modeling scenarios were evaluated, with and without stream topology corrections, using the WaTEM/SEDEM model. It details the global data used, the methodology of the field survey, the simulation and validation of data, and the critical point identification. Significant discrepancies in long-term sediment transport predictions were found, depending on the land use data source. This study addresses watershed model validity and potential errors and recommendations for the use of globally available data. [ABSTRACT FROM AUTHOR]

The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers. [ABSTRACT FROM AUTHOR]

This study investigates soil loss erosion dynamics in the Nan River Basin, Thailand, focusing on the impact of land cover changes. Utilizing the Universal Soil Loss Equation (USLE) model, key factors, including rainfall erosivity, soil erodibility, topography, and land cover, are analyzed for the years 2001 to 2019. The findings reveal a substantial increase in human-induced soil erosion, emphasizing the pressing need for effective mitigation measures. Severity classification demonstrates shifting patterns, prompting targeted conservation strategies. The examination of land cover changes indicates significant alterations in the satellite image (MODIS), particularly an increase in Deciduous forest (~13.21%), Agriculture (~0.18%), and Paddy (~0.43%), and decrease in Evergreen Forest (~13.73%) and Water (~0.12%) cover types. Deciduous forest and Agriculture, associated with the highest soil loss rates, underscore the environmental consequences of specific land use practices. Notably, the increase in Deciduous forest and Agriculture significantly contributes to changes in soil loss rates, revealing the interconnectedness of land cover changes and soil erosion in ~18.05% and ~8.67%, respectively. This study contributes valuable insights for informed land management decisions and lays a foundation for future research in soil erosion dynamics. Additionally, the percentage increase in Agriculture corresponds to a notable rise in soil loss rates, underscoring the urgency for sustainable land use practices. [ABSTRACT FROM AUTHOR]

This review provides an overview of the progress made in remote sensing application for soil moisture, vegetation and inundation mapping in wetland environments. The main objective of the paper was to assess the link between soil moisture variations and vegetation characteristics in wetland remote sensing studies. To achieve this objective, relevant literature was gathered from established search engines, e.g. Science Direct and Web of Science, along with specific search strategies and key phrases. Three hundred and ninety-three journal articles on wetland remote sensing published between 1980 and 2023 were collected and subjected to a comprehensive analysis. The findings indicate that remote sensing of wetland moisture, vegetation and inundation has been increasing, from three published work in the 1980s to 22 in the 1990s, to 88 between 2001 and 2010, and to 278 between 2011 and 2023. Results showed that there has been an improvement in the application of remote sensing in mapping of wetland moisture, inundation and vegetation in Africa between 2015 and 2023. Despite wide application of remote sensing to map these aspects, very few studies (2.1%) have focused on establishing the relationship between them. The analysis indicated that the launch of new Sentinel-1 radar and Sentinel-2 optical sensors in addition to the Landsat series, along with a variety of analytical methods, provided a great opportunity for derivation of soil moisture and vegetation data which can be used to establish the soil moisture–vegetation nexus. [ABSTRACT FROM AUTHOR]

Reliable runoff modeling is essential for water resource allocation and management. However, a key uncertainty source is that the true precipitation field is difficult to measure, making reliable runoff modeling still challenging. To account for this uncertainty, this study developed a two-step approach combining ensemble average and cumulative distribution correction (i.e., EC) to incorporate information from the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model and multiple remotely sensed precipitation datasets. In the EC approach, firstly, the ensemble average is applied to construct transitional fluxes using the reproduced runoff information, which is yielded by applying various remotely sensed precipitation datasets to drive the GR4J model. Subsequently, the cumulative distribution correction is applied to enhance the transitional fluxes to model runoff. In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. Meanwhile, the EC method exhibited a more stable performance than the ensemble averaging method under different incorporation scenarios. However, the single precipitation dataset-based approaches tended to underestimate runoff (regression coefficients < 1), and there were similar errors between the calibration and validation stages. To further illustrate the effectiveness of the EC model, five watersheds (including the PW) of different hydrometeorological features were used to test the EC model and its benchmarks. The results show that both the EC model and the ensemble averaging had good transferability, but the EC model had better performance across all the test watersheds. Conversely, the single precipitation dataset-based approaches exhibited significant regional variations and, therefore, had low transferability. The current study concludes that the EC approach can be a robust alternative to model runoff and highlights the value of the incorporation of multiple precipitation datasets in runoff modeling. [ABSTRACT FROM AUTHOR]

Soil conservation (SC) plays a vital role in preventing soil erosion and ensuring ecological security. While current research on SC primarily focuses on historical spatiotemporal variations, there remains a dearth of sufficient simulation research exploring future development scenarios. In this study, simulations were applied to the source of Yellow River (SYR), a representative ecologically fragile area. Satellite remote sensing and product data, including precipitation, soil, land use/cover, DEM, and SPOT/VEGETATION NDVI, were utilized. The historical and future evolutionary trends of SC in the SYR were quantitatively assessed using the Revised Universal Soil Loss Equation (RUSLE) and trend analysis method, and the geographical detector was employed to explore the forces driving spatial differentiations in SC. The results demonstrated that: (1) 2000–2020, the spatial heterogeneity of SC in the SYR was characterized by the distribution of "gradually decreasing from Southeast to Northwest", demonstrated a trend of "increasing, decreasing, and then increasing". (2) Under the diverse development scenarios, the trend of SC change in the SYR was predominantly rising, and the natural change scenario (NCS) > ecological conservation scenario (ECS) > economic expansion scenario (EES). (3) Slope was the most important single driver affecting the spatiotemporal differentiation of SC, and the interaction of slope with average annual precipitation, and NDVI on the spatiotemporal heterogeneity of SC had the strongest explanatory ability. The results can serve as a scientific basis for regional SC and ecological protection and construction of the SYR. [ABSTRACT FROM AUTHOR]

Most hydrological and water resources researchers prioritize the development of an accurate sediment prediction model. Several conventional techniques have failed to accurately predict suspended sediment. Because of the intricacy, non-stationarity, and nonlinearity of sediment movement behavior in streams and rivers, many techniques fall short. Over the last several years, there have been meaningful theoretical improvements in the understanding of machine learning approaches, vis a vis strategy for the implementation of their processes and uses of the technique for practical and hydrological issues. To produce the desired output, machine learning models and other algorithms have been employed to predict complicated nonlinear connections and patterns of huge input parameters. This paper examines a few key works of the literature on sediment transport prediction while focusing on a variety of machine learning applications. Sediment transport models aided by machine learning have attracted a growing number of researchers in recent years. As a result, they must gain in-depth knowledge of their theory and modeling methodologies. Furthermore, this chapter includes an overview of the machine learning technique and other developing hybrid models that have produced promising outcomes. This overview also includes various examples of successful machine learning applications in sediment prediction. [ABSTRACT FROM AUTHOR]

Marsh wetlands are significant ecosystems located between land and water bodies which can both protect species diversity and provide habitats. Changes in the hydrological situation of marsh wetlands as a result of climate change and human activities have led to the degradation of wetland landscapes. Taking the Honghe National Nature Reserve (HNNR) in Heilongjiang Province, China, as an example, this paper gathered information on the reserve's wetland landscape before and after dam construction. The information was obtained using field survey data and a random forest classification method based on Landsat data powered by the Google Earth Engine (GEE) cloud platform. Then, on the basis of the water level data and the digital elevation model, the wetland landscape dynamics of HNNR under three water level conditions were simulated. The findings were as follows: (1) From 1998 to 2008, the area of marsh and meadow had a downward trend, while the area of forest, farmland and water showed a gradual and upward trend; a marked rise in the area of marsh and a continued rise in the area of forest, farmland and water, and a sharp decline in the area of meadow during 2008 to 2018 was observed. (2) There was a significant increase in the area of marsh under the 20 and 40 cm water level simulation scenarios, with a decrease in the number of patches, and an increase in the aggregation index with rising water levels; in contrast, when the water level rose to 60 cm, the area of marsh and the number of patches decreased, but the aggregation index continued to increase. (3) The correlation between wetland landscape and the water level was a nonlinear one. The area of marsh increased and then decreased with increasing water level, reaching a maximum at the 40 cm water level; therefore, 40 cm was the optimal water level regulation scenario. Hydrological processes are the most fundamental ecological processes in marsh wetlands. Understanding the scientific pattern of the spatial pattern characteristics of species as a function of water level environment is important for scientifically guiding the restoration of marsh vegetation. [ABSTRACT FROM AUTHOR]

Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field. [ABSTRACT FROM AUTHOR]

Much land has been cleared of indigenous forest for pastoral agriculture worldwide. In New Zealand, the clearance of indigenous forest on hill country has resulted in high food production, but waterways have become turbid, with high nutrient and E. coli concentrations. A range of on-farm mitigations are available, but it is unclear how they should be applied catchment-wide. We have developed a catchment-scale model that integrates economics with ecosystem services to find a better balance between agriculture and nature. In the upper Wairua catchment, Northland, if three actions are prioritised—(1) keeping stock out of streams, (2) constructing flood retention bunds in first-order catchments, and (3) planting trees on highly erodible land—then sediment loads, E. coli levels, and flooding are significantly reduced. Implementing these actions would cost approximately 10% of catchment net revenue, so it is feasible with a combination of regulation and subsidy. Many catchments in New Zealand are primarily pastoral agriculture, as in other countries (in North and South America, Australasia, and the United Kingdom), and would benefit from the analysis presented here to guide development along sustainable pathways. While pastoral agriculture typically stresses waterways, with increased sedimentation and freshwater contaminants, much can be done to mitigate these effects with improved farm and riparian management. [ABSTRACT FROM AUTHOR]

Establishing an optimal operation model of water resources is a crucial mean to promote the social and economic development in the Jiaodong area, where water resources are seriously deficient. Constraints of the optimal operation model mainly include water balance constraint, discharge capacity constraint, and constraint on the full utilization of operating water. For water transfer projects that have been in operation for decades, the parameters of these constraints, such as Discharge Capacity (DC), Water Conveyance Efficiency (WCE), Evapotranspiration (E), and Water Supply Volume (WSV), have changed from their original design values, which in turn affect the results of the operation model. In order to address the uncertainties caused by corresponding parameters, according to the characteristics of each parameter, an Interval-Chance Constrained Programming (ICCP) model for the Jiaodong Water Transfer Project was proposed. Interval Programming (IP) and Chance-Constrained Programming (CCP) were used to optimize the parameters of constraints involving WCE, DC, E, and WSV. Then, the Sobol method was used to analyze the sensitivity of each parameter to the operating objective function. The results reveal that (1) total water shortage ratio decreased by [14.82%, 17.26%], [14.81%, 17.25%], and [14.82%, 17.26%], respectively, under the incoming water condition of 50%, 75%, and 95%, indicating that ICCP model can adequately consider complex uncertainties and effectively alleviate water shortage; (2) WCE and DC are important parameters for optimal operation model of water resources, and therefore, channels should be regularly maintained to ensure that WCE and DC would not reduce; (3) Decision variables in this study are in the form of intervals, which are more reasonable because they provide more decision-making options to managers. [ABSTRACT FROM AUTHOR]

The suspended sediment (SS) load provides valuable insights into soil loss magnitude, requiring comprehensive monitoring of streamflow (Q) and SS concentrations (SSC) across various hydrological conditions. The primary aim of this paper was to quantify SS loads in two mountainous river basins: the Carapelle (506 km2) and the Celone (72 km2) located in Apulia (Southeast Italy) where different monitoring strategies were adopted (i.e., continuous and discrete). The specific objective was to develop sediment rating curves to address gaps in the SSC time series. An optical probe was used to continuously monitor the SSC during 2007 to 2011 in a river section of the Carapelle river, while Q was measured with the ultrasonic method. A comprehensive dataset comprising continuous Q measurements and discrete SSC measurements was systematically acquired for the Celone river over the period of 2010 to 2011. Distinct sediment rating curves were formulated for three specific subsets of data delineated by discernible hydrological conditions (i.e., high, normal, and low flow) and SSCs were computed for the missing daily records. The annual specific sediment load exhibited a range of 2.4 to 6.06 t ha−1 yr−1 for the Celone river, while the Carapelle river displayed a range of 0.9 to 7.45 t ha−1 yr−1. A significant majority of the SS load was transported during high-flow conditions, accounting for over 80% of the total load. In contrast, during low-flow conditions, the SS load constituted less than 1% of the total load. The findings of this study highlight the significance of the hydrological regime as a critical factor influencing sediment transport in mountainous Mediterranean rivers. Furthermore, it demonstrates that the duration of the sampling period, along with its specific characteristics, such as dry or wet conditions, can have a substantial impact on the accurate quantification of the sediment load. [ABSTRACT FROM AUTHOR]

The increasing prevalence of wicked problems, such as climate change, requires a transformation in education that equips students with the skills, competencies and knowledge to address these complex challenges. Wicked problems are characterised by their incomplete, contradictory, and ever-changing requirements, rendering them difficult to resolve due to intricate interdependencies. Nexus thinking offers a valuable approach to these problems, as it emphasises the interconnectedness of various systems, fostering a more comprehensive understanding of the challenges at hand. In this paper, we propose the use of Climate, Land, Energy, and Water (CLEWs) modelling as an innovative pedagogical strategy tool to cultivate nexus thinking among students. Building upon the pioneering CLEWs pedagogical work of Shivakumar et al., in their 'Introduction to CLEWs' Open Learn course, we demonstrate how this approach can be utilised in a Higher Education (HE) setting in the form of a Masters' module for geography students. [ABSTRACT FROM AUTHOR]

Soil erosion and sediment transport have significant consequences, including decreased agricultural production, water quality degradation, and modification to stream channels. Understanding these processes and their interactions with contributing factors is crucial for assessing the environmental impacts of erosion. The primary objective of this review is to identify a suitable soil erosion and sediment transport model for catchment-scale application. The study considers various model selection processes, including model capability and the spatial and temporal domains for assessing spatiotemporal distributions. The review acknowledges the limitations, uncertainties, and unrealistic assumptions associated with soil erosion and sediment transport models. Models are usually developed with a particular objective, which demands an assessment of capabilities, spatial, and temporal applicability, and catchment-scale applicability. Distributed models are often preferred for catchment-scale applications, as they can adequately account for spatial variations in erosion potential and sediment yield, aiding in the evaluation of erosion-contributing elements and planning erosion control measures. Based on the findings of this study, the authors encourage utilizing models (such as Soil and Water Assessment Tool (SWAT) or Automated Geospatial Watershed Assessment Tool (AGWA)) that can forecast net erosion as a function of sediment output for catchment erosion and sediment yield modeling. This review helps researchers and practitioners involved in erosion and sediment modeling by guiding the selection of an appropriate model type based on specific modeling purposes and basin scale. By choosing appropriate models, the accuracy and effectiveness of sediment yield estimation and erosion control measures can be improved. [ABSTRACT FROM AUTHOR]

Bedrock river width is an essential geometric parameter relevant to understanding flood hazards and gauging station rating curves, and is critical to stream power incision models and many other landscape evolution models. Obtaining bedrock river width measurements, however, typically requires extensive field campaigns that take place in rugged and steep topography where river access is often physically challenging. Although prior work has turned to measuring channel width from satellite imagery, these data present a snapshot in time, are typically limited to rivers ≥ 10–30 m wide due to the image resolution, and are physically restricted to areas devoid of vegetation. For these reasons, we are generally data limited, and the factors impacting bedrock channel width remain poorly understood. Due to these limitations, researchers often turn to assumptions of width‐scaling relationships with drainage area or discharge to estimate bedrock channel width. Here we present a new method of obtaining bedrock channel width at a desired river discharge through the incorporation of a high‐resolution bare‐earth digital elevation model (DEM) using MATLAB Topotoolbox and the HEC‐RAS river analysis system. We validate this method by comparing modeled results to US Geological Survey (USGS) field measurements at existing gauging stations, as well as field channel measurements. We show that this method can capture general characteristics of discharge rating curves and predict field‐measured channel widths within uncertainty. As high‐resolution DEMs become more available across the United States through the USGS three‐dimensional elevation program (3DEP), the future utility of this method is notable. Through developing and validating a streamlined, open‐source, and freely available workflow of channel width extraction, we hope this method can be applied to future research to improve the quantity of channel width measurements and improve our understanding of bedrock channels. [ABSTRACT FROM AUTHOR]

Landmines and explosive remnants of war are still present in the Western Balkans and remain a deadly legacy of the hostilities at the end of the 20th century. Over the years, several incidents have occurred in Bosnia and Herzegovina, in Serbia, and in Croatia where intact ordnance has caused injuries and fatalities. Floods, torrential flows, and gravitational mass movements pose a particular threat. Landmines and explosive remnants of war are mobilized and displaced into previously uncontaminated areas. We first discuss the historical and technical background of this hazardous situation. We then show which hydro-morphological processes are responsible for the mobilization and displacement. We then illustrate how a prediction of the likely contaminated areas can be obtained. We show that the problem can only be tackled using a stochastic-deterministic model. However, for the eventual development of risk-hazard maps, preliminary work using laboratory experiments and field surveys is required. The article, therefore, proposes a novel approach to the problem in an international research project. The aim would be to produce risk-hazard maps that can be used by elected decision-makers, administrative authorities, and emergency personnel in affected municipalities. [ABSTRACT FROM AUTHOR]

Intensifying agricultural activity associated with rapid population growth in rural western Uganda exerts immense pressure on natural resources, threatening not only soil fertility in the uplands but also water quality of the region's many small crater lakes. To assess the relative risk of excess sediment and nutrient loading to individual lakes due to (inter) rill erosion within the catchments, we used the revised universal soil loss equation (RUSLE) and sediment delivery distributed model (SEDD) to estimate soil loss and sedimentation in 75 crater-lake catchments with diverse types and intensities of land use, including 17 catchments situated partly or entirely in national parks. We found that variation in potential soil loss (Ap) among all studied catchments was strongly related to differences in mean slope within each catchment. We also found substantial seasonal variation in vegetation cover, and thus, estimated actual soil loss (Am), on both cultivated land and protected savanna grassland, whereas the vegetation cover of protected semi-deciduous tropical forest was seasonally stable. Lacking detailed field data to validate model output, we used the ratio between estimated actual soil loss (Am) and potential soil loss (Ap) to evaluate the relative influences of land-use intensity and type, as well as the impact of protective measures. Our results showed that due to their characteristically steep slopes (21% on average), all crater catchments were highly susceptible to soil loss, and because most of them were small (203 ha on average), a large portion of the eroded material was transported to and deposited in the lakes. Given the strong dependence of the local population on these crater lakes as source of water and fish protein, and on the surrounding land for crop production, increased effort by environmental planners and managers is required to safeguard or restore the long-term availability of these natural resources. Avoiding bare soil conditions by restoring natural vegetation or employing agricultural techniques that provide high vegetation cover throughout the year are likely to result in considerable improvements. [ABSTRACT FROM AUTHOR]

Water supply and demand management (WSDM) is essential for developing sustainable cities and societies. WSDM is only effective when tackled from the perspective of a holistic system understanding that considers social, environmental, hydrological, and economic (SEHEc) sub-systems. System dynamics modeling (SDM) is recommended by water resource researchers as it models the biophysical and socio-economic systems simultaneously. This study presents a comprehensive literature review of SDM applications in sustainable WSDM. The reviewed articles were methodologically analyzed considering SEHEc sub-systems and the type of modeling approach used. This study revealed that problem conceptualization using the causal loop diagram (CLD) was performed in only 58% of the studies. Moreover, 70% of the reviewed articles used the stock flow diagram (SFD) to perform a quantitative system analysis. Furthermore, stakeholder engagement plays a significant role in understanding the core issues and divergent views and needs of users, but it was incorporated by only 36% of the studies. Although climate change significantly affects water management strategies, only 51% of the reviewed articles considered it. Although the scenario analysis is supported by simulation models, they further require the optimization models to yield optimal key parameter values. One noticeable finding is that only 12% of the articles used quantitative models to complement SDM for the decision-making process. The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). The key findings for the sustainable development of water resources included the per capita water reduction, water conservation through public awareness campaigns, the use of treated wastewater, the adoption of efficient irrigation practices including drip irrigation, the cultivation of low-water-consuming crops in water-stressed regions, and regulations to control the overexploitation of groundwater. In conclusion, it is established that SDM is an effective tool for devising strategies that enable sustainable water supply and demand management. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the extent and severity of water erosion in the Toudgha river catchment in the Central High Atlas of Morocco using two different erosion models, the Erosion Potential Model (EPM) and the Priority Actions Programme/Regional Activity Centre (PAP/RAC) model. From the modeling results, the catchment was affected by varying degrees of erosion, ranging from "very slight" to "excessive", with different locations identified under each model. The very high erosion areas were located in the extreme northwest of the catchment area for both of the applied models, covering 9.65% (according to PAP/RAC) and 8.56% (EPM) of the total area primarily driven by factors such as intense rainfall events, limited vegetation cover, high soil erodibility due to low organic matter content and coarser soil texture, and human activities such as overgrazing and land use changes, which exacerbate the effects of these natural factors on water erosion in these semi-arid areas. The study's findings suggest that erosion is a significant concern in these environmental areas and provide valuable information for designing effective erosion control measures and guiding soil and environmental management practices. Both models effectively simulated the erosion phenomenon and provided useful tools for soil and environmental management. The EPM model can be used to design effective erosion control measures, while the PAP/RAC model can be used to develop a comprehensive strategy for the sustainable management of the catchment area. These results have implications for the implementation of effective erosion control measures in mountainous watersheds and highlight the need for further research in this area. [ABSTRACT FROM AUTHOR]

The health of soil-sediment-water ecosystems is under pressure from economic activities and a changing climate. This decreases health and hampers the service provision capacity of these ecosystems and thus impacts human well-being. Protecting and where feasible restoring of ecosystem health has currently become the key European environmental policy objective and for this it is needed to take an entire system approach and engage stakeholders. 'Entire' means that soil, sediment and water are regarded as closely interlinked environmental matrices that need to be managed by taking a 'river (or mountain) to sea' perspective, crossing spatial, discipline, political and cultural boundaries. This paper presents a conceptual model to support that purpose. Essentially, the conceptual model presents an approach for ecosystem-based management aimed to achieve healthy ecosystems, i.e. soil-sediment-water ecosystems that have the continued capacity to support ecosystem services to the benefit of their users. The model proposes a cyclic (iterative, learning-by-doing) approach and integrates soil-sediment-water, ecosystems, ecosystem services, users (stakeholders), pressures, information, management strategy and program of measures as building blocks. To successfully apply the model, it is above all needed to take an entrepreneurial approach, i.e. leave comfort zones, take an adventurous road, learn together to manage together, be adaptive and consider other than only command-and-control solutions. Furthermore, authorities should become facilitative leaders to engage users in co-creation of an ecosystem-based management strategy. Real live and place-based experimenting with multiple stakeholders, such as in the Living Labs and Lighthouses that are proposed in the EU soil mission, may provide an ideal instrument for such application, i.e. where the conceptual model can be used and support the achievement of European environmental policy objectives. [ABSTRACT FROM AUTHOR]

Forecasting of sediment load (SL) is essential for reservoir operations, design of water resource structures, risk management, water resource planning and for preventing natural disasters in the river basin systems. Direct measurement of SL is difficult, labour intensive, and expensive. The development of an accurate and reliable model for forecasting the SL is required. Sediment transport is highly non-linear and is influenced by a variety of factors. Forecasting of the SL using various conventional methods is not highly accurate because of the association of various complex phenomena. In this study, major key factors such as rock type (RT), relief (R), rainfall (RF), water discharge (WD), temperature (T), catchment area (CA), and SL are recognized in developing the one-step-ahead SL forecasting model in the Mahanadi River (MR), which is among India's largest rivers. Artificial neural networks (ANN) in conjunction with multi-objective genetic algorithm (ANN-MOGA)-based forecasting models were developed for forecasting the SL in the MR. The ANN-MOGA model was employed to optimize the two competing objective functions (bias and error variance) with simultaneous optimization of all associated ANN parameters. The performances of the proposed novel model were finally compared to other existing methods to verify the forecasting capability of the model. The ANN-MOGA model improved the performance by 12.81% and 10.19% compared to traditional AR and MAR regression models, respectively. The results suggested that hybrid ANN-MOGA models outperform traditional autoregressive and multivariate autoregressive forecasting models. Overall, hybrid ANN-MOGA intelligent techniques are recommended for the forecasting of SL in rivers because of their relatively better performance as compared to other existing models and simplicity of application. [ABSTRACT FROM AUTHOR]

Featured Application: This work applies machine learning to enhance the prediction of deforestation related to oil palm. This research can be used for decision makers trying to foresee and manage deforestation caused by palm oil production. Providing information about deforestation prediction can help users to make appropriate decisions about where and when they can establish new plantations to ensure a sustainable oil palm production. Deforestation is recognized as an issue that has negative effects on the ecosystem. Predicting deforestation and defining the causes of deforestation is an important process that could help monitor and prevent deforestation. Deforestation prediction has been boosted by recent advances in geospatial technologies and applications, especially remote sensing technologies and machine learning techniques. This paper highlights the issue of predicting deforestation related to oil palm, which has not been focused on in existing research studies. The paper proposes an approach that aims to enhance the prediction of deforestation related to oil palm plantations and palm oil production. The proposed approach is based on a conceptual framework and an assessment of a set of criteria related to such deforestation. The criteria are assessed and validated based on a sensitivity analysis. The framework is based on machine learning and image processing techniques. It consists of three main steps, which are data preparation, model training, and validation. The framework is implemented in a case study in the Aceh province of Indonesia to show the feasibility of our proposed approach in predicting deforestation related to oil palm. The implementation of the proposed approach shows an acceptable accuracy for predicting deforestation. [ABSTRACT FROM AUTHOR]

The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the streamflow, as one of the main components of the hydrological cycle, is a primary subject. In this study, a statistical method, Fuzzy C-means clustering, was used to find the periodicity in the daily discharge time series, whereas autoregressive moving average, ARMA, was used in modeling every cluster. Dividing the daily stream flow time series into smaller groups based on their similar statistical behavior by using a statistical method for analyzing and a combination of Fuzzy C-means clustering and ARMA modeling is the innovation of this study. We draw on the daily discharge data of four different river stations in Hesse state in Germany. The collected data cover 18 years, from 2000 to 2017. Root mean square error (RMSE) was used to evaluate the accuracy. The results revealed that the performance of ARMA in four stations for predicting every cluster was reliable. In addition, it must be highlighted that by clustering the daily stream flow time series into smaller groups, forecasting different days of the year will be possible. [ABSTRACT FROM AUTHOR]

Soil loss caused by erosion has destroyed landscapes, as well as depositing sterile material on fertile lands and rivers, clogged waterways and accelerated flash floods, declined the populations of fish and other species, and diminish soil fertility. In some places, erosion has also destroyed buildings, caused mudflow, create new landforms, displaced people, and slowed down the economy of the affected community by destroying roads and homes. Erosion is aggravated by climate change and anthropogenic factors such as deforestation, overgrazing, inappropriate methods of tillage, and unsustainable agricultural practices. In this study, remote sensing (RS) and geographic information (GIS) data and tools were used to model erosion and estimate soil loss in the catchment area of Koromi-Federe in Jos East, Plateau State Nigeria which is our study area. Soil loss estimation was performed using the revised universal soil loss equation (RUSLE) model and was computed by substituting the corresponding values of each factor inherent in the equation (rainfall erosivity, soil erodibility, slope steepness and slope length, cover management, and conservation practices) using RS and GIS tools. Soil data was obtained from the study area and analyzed in the laboratory, rainfall data, land cover, digital elevation model (DEM), as well as the management practice of the study area were the parameters computed in spatial analyst tool using map algebra based on RUSLE. The soil loss generated was classified into four classes and the results revealed 95.27% of the catchment with a tolerable loss of less than 10 t/h−1/y−1. At 3.6%, a low or minimal loss of 10–20 t/h−1/y−1, at 1.03% there exists a moderate loss of 20–50 t/h−1/y−1, while there was and critical or high loss of >50 t/h−1/y−1 at 0.12% of the catchment. The result showed that critical soil loss in the catchment area is exacerbated by the influence of the slope length and steepness, and the amount of rainfall received. This poses great concern with annual rainfall projected to increase up to 12% in West Africa. However, our sensitivity analysis revealed that it can be reduced with the effect of vegetated cover and management practices. This is an important finding as it can guide sustainability practices to control erosion and the loss of valuable lands in the region, especially now under climate change. [ABSTRACT FROM AUTHOR]

Climate change is an important driver of soil erosion and sediment delivery to water bodies. We use observation data from 193 locations in the Elbe River basin as well as spatially distributed erosion rates and sediment delivery simulated in the WaTEM/SEDEM to identify current erosion hotspots and to assess the impact of climate change on future erosion and sediment delivery. We further quantified the uncertainty of the modelling approach by using an ensemble of 21 combinations of global and regional climate models, different emission scenarios and stochastic erosion modelling. Erosion rates are highest on hilly arable land in the central part of the basin as well as in the northeast of Bohemia. Despite considerable differences between climate models and emission scenarios and considerable uncertainties of the erosion model, a future increase in soil erosion and sediment delivery is highly likely. Using the median of climate models and behavioral erosion models, this increase can be up to 14% higher in the far future (2071–2100) than in the reference period (1971–2000) using RCP 8.5. The increase is highest in the Czech part of the basin. [ABSTRACT FROM AUTHOR]

Groundwater degradation is a major issue on an increasingly hot and thirsty planet. The problem is critical in drylands, where recharge rates are low and groundwater is the only reliable resource in a context of water scarcity and stress. Aquifer depletion and contamination is a process of desertification. Land Degradation Neutrality is regarded as the main initiative to tackle land degradation and desertification. It is embedded in target 15.3 of the Sustainable Development Goals and focused on preventing these dynamics. Within this framework, we present an approach to assess risks of degradation and desertification in coastal basins with aquifers threatened by seawater intrusion. The approach utilizes an integrated system dynamics model representing the main relationships between the aquifer and an intensively irrigated area (greenhouses) driven by short- and medium-term profitability. The study area is located in a semi-arid region in Southern Spain, the Gualchos stream basin, which contains the Castell de Ferro aquifer. We found that the risk of salinization of the aquifer is 73%, while there is a 70% risk that the system would increases its demand for surface water in the future, and the chance of doubling the current demand is almost 50%. If the current system of reservoirs in the area were not able to satisfy such an increase in demand because of climate change, the basin would be at a serious risk of desertification. [ABSTRACT FROM AUTHOR]

This paper studies the possibility of using the WaTEM/SEDEM model to assess sediment yield from the catchment area within the Lena River catchment. The study was carried out based on a comparison of predicted data and measured data of the suspended sediment yield at the gauging stations of the state monitoring network of Russia. The study was performed within two areas, with plain and mountainous relief. The first site is located within the catchment area of the river Chara with an area of 4150 km2. The second site rests on the catchment area of the Lena River between the Tabaginskiy and Kangalassky capes near Yakutsk city. The catchment area of this site is 15,740 km2. The values of sediment yield from the "Yakutsk" catchment area are in much better agreement with the values of the measured sediment yield values than in the "Chara" catchment area. The predicted sediment yield from the study area remained almost unchanged from the period 1986–2019 and amounted to 3.5 t/km2, while the suspended sediment yield in the Lena at the Tabaga gauging station slightly increased from 7 to 9.45 t/km2 per year. [ABSTRACT FROM AUTHOR]

Erosion is a natural process, but it can be accelerated by anthropogenic activities. Two of the predominant types of human-induced erosion are related to agricultural and construction activities. Of the two, construction-induced erosion is more severe because of the simultaneous removal of the land cover, disturbance of the soil, and eventual compaction of the soil by heavy machinery. Eroded materials released from bridge construction sites can alter the sediment regime and geomorphological conditions of receiving streams and may have short- and long-term impacts on aquatic habitats. Several models have been developed to estimate the total amount of soil erosion and sediment yield; however, no predictive model is available to quantify the potential release of sediment during the construction of bridges or to predict the quantity, size fraction, and accumulation depths for the extent of the measurable downstream effect. A GIS-based predictive sediment toolkit is developed to estimate the overland erosion and to determine the potential depositional area and suspended sediment concentration downstream of bridges. The performance of the GIS toolkit in estimating soil erosion was assessed using field data collected from the Wilson Creek bridge construction site in McKenney, Texas, U.S., and it was concluded that it predicted the overland erosion rate and sediment yield within the ranges observed in the field. [ABSTRACT FROM AUTHOR]

Assessment of the spatial and temporal distribution of surface water balance (SWB) components for a river basin having complex topography with limited observed data is challenging. This paper focused on estimating the spatial-temporal variability of long-term average monthly, seasonal, and annual SWB components using the modified Water Energy Transfer between Soil, Plants, and Atmosphere in Steady State (WetSpass-M) physically based hydrological model in Ethiopia's data-scarce Omo River basin. Further, the model output was used for estimating the spatial variation of the average monthly crop water deficit. A sensitivity analysis was performed on the global and local parameters of the model for analysing the relative variation in the SWB components. It was found that actual evapotranspiration (AET) and interception are insensitive to average rainfall intensity while surface runoff is highly sensitive. The variation of SWB under different combinations of land-use/land cover (LU/LC) and soil type indicates that SWB types such as surface runoff, AET, and interception are influenced more by LU/LC than by soil type. [ABSTRACT FROM AUTHOR]

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An approach of reporting long-term trends in groundwater extraction and baseflow impacts in the Murray-Darling Basin (MDB) in south-eastern Australia was developed and tested. The principal aim of the framework was to provide early warning of any potential adverse impacts from groundwater extraction on environmental releases of surface water for baseflow, support adaptive management of these impacts, and highlight those areas which may benefit from conjunctive water management. The analysis showed that there is no current decadal trend in the annual aggregate groundwater extraction volumes or stream impact across the non-Victorian MDB, with much of the interannual variability being related to rainfall. Despite this, increasing volumes of environmental releases of water for baseflows in some river valleys are being required to replace the stream depletion caused by historical patterns of groundwater extraction established before 2003. Two valleys were identified for which there may be insufficient surface water storage to release water to substitute stream losses to groundwater and still support ecosystems during dry periods. The increasing trend in extraction since 2003 in one of the units has significantly increased the risk in that valley. The reporting framework was shown to be effective for alluvial groundwater systems connected to regulated rivers. [ABSTRACT FROM AUTHOR]

Hebei Province of China is facing a severe water resource shortage, making it urgent to formulate economical and effective water conservation policies. However, few studies have focused on analyzing the resource and economic impacts of a water policy. This study developed an improved computable general equilibrium (CGE) model with an extended water resources module as a policy analysis tool. The extended water resources module includes different water resources as commodities and water sectors, and introduces a substitution mechanism among the water resources. Policy scenarios containing different policy types and policy objects were established, including water price, technology (tech) improvement, structure adjustment, and water reuse policies in primary, secondary, and tertiary sectors. The impact on the water resource and economy of the scenarios was analyzed using the CGE model. The recommended policies include: an agricultural technology improvement policy that decreases groundwater usage by 240 hm3; an industrial technology improvement policy under which water usage per 10,000 CNY of industrial added value decreases by 13%; an industrial water reuse policy that increases unconventional water usage by 20%; and a structure adjustment policy to increase the proportion of the tertiary sector. The study provides an analysis tool for simulating and evaluating a water resource policy. [ABSTRACT FROM AUTHOR]

The Australian Sustainability Development Goals (SDGs) Summit in 2018 attracted much-needed national attention towards environmental goals and targets compared with other aspects of sustainability. Road infrastructure is the backbone of modern society and plays a crucial role in accomplishing a targeted balance between these aspects of sustainability and achieving the SDGs. This article presents an integrated sustainability performance assessment methodology that acts as a decision support tool. A series of two conceptual modelling techniques—drivers—pressure—state—impact—response (DPSIR) and system dynamics (SD)—is employed, with the cause-and-effect relationships of the sustainability indicators developed utilising the DPSIR framework, and a quantitative analysis carried out through a subsequent SD model. The end result is the generation of a Sustainability Performance Index (SPI) for road infrastructure created by analysing the SD model and DPSIR index layer relationship. The benefits and applicability of the proposed methodology are validated through case study analysis. The overall aim is to determine restricting factors and response strategies influencing road infrastructure and transport sustainability performance during the operation and maintenance phase. Thus, a significant contribution is made through the proposed methodology for assessing factors influencing the long-term achievement of the SDGs. [ABSTRACT FROM AUTHOR]

To investigate relevant processes as well as to predict the possible impact of soil erosion, many soil erosion modelling tools have been developed. The most productive development of process-based models took place at the end of the 20th century. Since then, the methods available to observe and measure soil erosion features as well as methods to inter- and extrapolate such data have undergone rapid development, e.g., photogrammetry, light detection and ranging (LiDAR) and sediment tracing are now readily available methods, which can be applied by a broader community with lower effort. This review takes 13 process-based soil erosion models and different assessment techniques into account. It shows where and how such methods were already implemented in soil erosion modelling approaches. Several areas were found in which the models miss the capability to fully implement the information, which can be drawn from the now-available observation and data preparation methods. So far, most process-based models are not capable of implementing cross-scale erosional processes and can only in parts profit from the available resolution on a temporal and spatial scale. We conclude that the models' process description, adaptability to scale, parameterization, and calibration need further development. The main challenge is to enhance the models, so they are able to simulate soil erosion processes as complex as they need to be. Thanks to the progress made in data acquisition techniques, achieving this aim is closer than ever, if models are able to reap the benefit. [ABSTRACT FROM AUTHOR]

Systems models of the Food–Water–Energy (FWE) nexus face a conceptual difficulty: the systematic integration of local stakeholder perspectives into a coherent framework for analysis. We present a novel procedure to co-produce and systematize the real-life complexity of stakeholder knowledge and forge it into a clear-cut set of challenges. These are clustered into the Pressure–State–Response (PSIR) framework, which ultimately guides the development of a conceptual systems model closely attuned to the needs of local stakeholders. We apply this approach to the case of the emerging megacity Pune and the Bhima basin in India. Through stakeholder workshops, involving 75 resource users and experts, we identified 22 individual challenges. They include exogenous pressures, such as climate change and urbanization, and endogenous pressures, such as agricultural groundwater over-abstraction and land use change. These pressures alter the Bhima basin's system state, characterized by inefficient water and energy supply systems and regional scarcity. The consequent impacts on society encompass the inadequate provision with food, water, and energy and livelihood challenges for farmers in the basin. An evaluation of policy responses within the conceptual systems model shows the complex cause–effect interactions between nexus subsystems. One single response action, such as the promotion of solar farming, can affect multiple challenges. The resulting concise picture of the regional FWE system serves resource users, policymakers, and researchers to evaluate long-term policies within the context of the urban FWE system. While the presented results are specific to the case study, the approach can be transferred to any other FWE nexus system. [ABSTRACT FROM AUTHOR]

Human activities have caused a significant change in the function and services that ecosystems have provided to society since historical times. In mountainous landscapes, the regulation of services such as water quality or erosion control has been impacted by land use and land cover (LULC) changes, especially the loss and fragmentation of forest patches. In this work, we develop a Remote Sensing (RS)-based modelling approach to identify areas for the implementation of nature-based solutions (NBS) (i.e., natural forest conservation and restoration) that allow reducing the vulnerability of aquatic ecosystems to siltation in mountainous regions. We used time series Landsat 5TM, 7ETM+, 8OLI and Sentinel 2A/2B MSI (S2) imagery to map forest dynamics and wetland distribution in Picos de Europa National Park (Cantabrian Mountains, northern Spain). We fed RS-based models with detailed in situ information based on photo-interpretation and fieldwork completed from 2017 to 2021. We estimated a forest cover increase rate of 2 ha/year comparing current and past LULC maps against external validation data. We applied this forest gain to a scenario generator model to derive a 30-year future LULC map that defines the potential forest extent for the study area in 2049. We then modelled the distribution of wetlands to identify the areas with the greatest potential for moisture accumulation. We used an S2 mosaic and topography-derived data such as the slope and topographic wetness index (TWI), which indicate terrain water accumulation. Overall accuracy scores reached values of 86% for LULC classification and 61% for wetland mapping. At the same time, we obtained the potential erosion using the NetMap software to identify potential sediment production, transport and deposition areas. Finally, forest dynamics, wetland distribution and potential erosion were combined in a multi-criteria analysis aiming to reduce the amount of sediment reaching selected wetlands. We achieved this by identifying the most suitable locations for the conservation and restoration of natural forests on slopes and in riparian areas, which may reduce the risk of soil erosion and maximise sediment filtering, respectively. The results show a network pattern for forest management that would allow for controlling erosion effects across space and time at three levels: one, by reducing the load that originates upslope in the absence of forest cover; two, by intersecting runoff at watercourses related to sediment transport; and three, by a lack of former barriers, by trapping erosion near to the receiving wetland systems, main river axes and contributing streams. In conclusion, the proposed methodology, which could be transferred to other mountain regions, allows to optimise investment for erosion prevention and wetland conservation by using only very specific areas of the landscape for habitat management (e.g., for NBS implementation). [ABSTRACT FROM AUTHOR]

Sediment removed in the detachment process is transported by overland flow. Previous experimental and field works studied that sediment transport is influenced by hydraulic properties of flow, physical properties of soil, and surface characteristics. Several equations in predicting sediment transport have been developed from previous research. The objective of this paper was to establish the selected parameters that contribute to the sediment transport capacity in overland flow conditions under different rainfall pattern conditions and to evaluate their significance. The establishment of independent variables was performed using the dimensional analysis approach that is Buckingham's π theorem. The final results obtained are a series of independent parameters; the Reynolds number ( R e) , dimensionless rainfall parameter i L ν , hydraulic characteristics Q L ν that related to the dependent parameters; and dimensionless sediment transport q s ρ v . The relationship indicates that 63.6% to 72.44% of the variance in the independent parameters is in relation to the dependent parameter. From the iteration method, the estimation of constant and regression coefficient values is presented in the form of the general formula for linear and nonlinear model equations. The linear and nonlinear model equations have the highest model accuracy of 93.1% and 81.5%, respectively. However, the nonlinear model equation has the higher discrepancy ratio of 54.9%. [ABSTRACT FROM AUTHOR]

Numerical simulation of sediment transport in alluvial rivers often becomes untenable due to the absence of requisite data. Over the years, artificial neural network (ANN) models have been successfully employed for tackling such scenarios. However, the effect of input and target datasets on the simulation of sediment transport using ANN has not yet been adequately addressed in the literature so far. To study the effect of input-target dataset on the performance of ANN models, the present study employs seven input-target datasets for the development of several ANN models and assessment of their performance. The limited study carried out reveals that there are multiple ANN models – having different combinations of training algorithms, transfer functions and input/target datasets – that can reliably be employed to estimate bed elevation changes in alluvial rivers. [ABSTRACT FROM AUTHOR]

Due to the high rainfall erosivity and highly erodible soils, water erosion is severe in Brazil. Soil and ecosystem degradation occurs when erosion exceeds on‐ and off‐site soil loss tolerances, with significant socioeconomic and environmental impacts. In the last 50 years, the Brazilian Cerrado had 53% of its original vegetation converted to agriculture and pastureland. Although erosion plot studies exist in the region, the data are fragmented and unexplored, hindering the development of soil conservation policies. The objective of the present research was to compile, systematize, and statistically analyze the existing erosion plot data in the Brazilian Cerrado, correlating the observed results with different environmental and management factors, and with the corresponding soil loss tolerances. Twenty runoff plot datasets of the Brazilian Cerrado, encompassing 5 states, 10 sites, 108 plots, and 360 plot·years were compiled and thoroughly analyzed. Mean annual rainfall, runoff, and soil loss were 1443.5 mm year−1, 83.1 mm year−1, and 8.9 Mg ha−1 year−1, respectively. After the data were normalized with respect to plot length, steepness, and climate, runoff and soil loss were found to be significantly higher in soils with impermeable horizons and in land uses without permanent soil cover (p < 0.05). Erosion under permanently covered plots was below the on‐ and off‐site soil loss tolerances. A power equation provided the best fit between plot runoff and soil loss (R2 = 0.71; p < 0.05), indicating that runoff volume, easier to estimate, could be used as a proxy for upslope erosion. Although erosion plot data cannot be extrapolated to the whole landscape, the research results provide useful elements for the development of sound conservation policies in the Cerrado and in other similar savannas of the world. [ABSTRACT FROM AUTHOR]

Computational Fluid Dynamics (CFD) represents a useful tool to study natural currents in the rivers and estuaries with erosive materials; therefore, it is always in the keen interest for scientists to further study and advance it, especially when numerical model has the advantages compared to actual laboratory experiment in terms of cost, time, and restrictions on conditions of the physical models and field collections. The present study deals with the hydro-morphological investigation and numerical modeling of a group of vertically stationed parallel groynes using FLOW-3D commercial software. To validate the results of the FLOW-3D simulation, it has been compared to the experiments from literature. Besides, a SSIIM 2.0 software has also been employed to compare with some of the FLOW-3D results. It was found that the accuracy of the FLOW-3D model influenced by the approach Froude number and the critical velocity ratio (Uavg/Ucr). Even though it underestimated the measured scour depth (due to complex and intense vortices, which reduce the accuracy of the numerical models), but general results from the model have reproduced the measured data well. [ABSTRACT FROM AUTHOR]