Watershed Scale Research Articles

Russian olive distribution and invasion dynamics along the Powder River, Montana and Wyoming, USA

The invasive shrub, Russian olive (Elaeagnus augustifolia), is widely established within riparian areas across North America and eastern Europe. Limited information on its distribution and invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: (1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, United States) as of 2020 with field data and remote sensing; and (2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. Field data showed Russian olive has reached near equal canopy cover (18.3%) to native Plains cottonwood (Populus deltoides; 19.1%) and has a broader distribution. At the watershed scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial imagery, and spectral variables from Sentinel-2 MultiSpectral Instrument using a random forest model (RMSE = 15.42, R2 = 0.64). A statistical model linking the resulting Russian olive percent cover detection map to environmental variables for the entire watershed indicated Russian olive cover increased with flow accumulation and decreased with elevation, and was associated with poorer soil types. We attribute the success of Russian olive to its broad habitat suitability combined with changing hydrologic conditions favoring it over natives. The maps of Russian olive cover along the Powder River and its main tributaries in northern Wyoming and southern Montana revealed regions of the watershed with high and low cover, which can guide landscape-scale management prioritization. This study provides a repeatable Russian olive detection method due to the use of Sentinel-2 imagery that is available worldwide and provides insight into Russian olive’s ecological relationships and success with relevance for management across areas with similar environmental conditions.

Size-dependent of phosphorus loss and migration driven by rainfall: Evidences from observation and stochastic simulation

A deep understanding of the loss and migration of various sizes of phosphorus (P) is crucial for the effective management of nutrients in agricultural watersheds. In this study, three P species including large-particle phosphorus (LPP, >1 μm), colloidal phosphorus (CP, 1 nm–1 μm), and dissolved phosphorus (DP, <1 nm), were observed and simulated. Then the size-dependent of nonpoint source P (NPS-P) process was discussed at both field and watershed scales. Finally, a stochastic rainfall algorithm was developed to investigate different P species driven by rainfall. The results indicated the losses of LPP, CP, and DP under different land use patterns differ markedly, whereas paddy fields are hotspots of CP loss. Sloping farmlands typically exhibit greater losses of DP and LPP, while CP account for 13 %–38 % of total P losses. At the watershed scale, the contribution of CP riverine inputs ranged from 13 % to 40 % due to its mobility and special feature. Compared with LPP, CP has an extended half-decay distance and is more capable of being transported to water bodies. Heavy rainfall significantly increases the contribution of CP to NPS-P. At rainfall levels of 300 mm, the contribution of CP riverine inputs from paddy fields can be up to 47.4 %. The findings provide novel insights into future agricultural nutrient management and NPS-P control from the perspective of the size effect.

Understanding the effects of afforestation on water quantity and quality at watershed scale by considering the influences of tree species and local moisture recycling

Forest restoration emerges as a sustainable practice to counteract biodiversity loss and enhance ecosystem services such as carbon sequestration and water quality improvement. However, more research is necessary on the hydrological effects of forest restoration under different strategies such as tree species options. In this study, we investigate how afforestation with longleaf pine (Pinus palustris) and loblolly pine (Pinus taeda L.) may affect the full hydrologic cycles including precipitation (P) and water quality across two large watersheds: the Alabama-Coosa-Tallapoosa (ACT) and Tombigbee-Black Warrior (TBW) river basins in the southeast United States. To capture the impacts of afforestation on precipitation, we leveraged the Soil and Water Assessment Tool (SWAT) model and a local moisture recycling ratio (LMR) dataset to establish a relationship between model-simulated evapotranspiration (ET) and LMR. Longleaf pine and loblolly pine have contrasting forest structure characteristics that affect model parameters and hydrological responses. Results showed that afforestation with longleaf pine increased mean annual ET by 3 % (25 mm/year) and 6 % (48 mm/year) across the ACT and TBW watersheds, respectively. As a result, mean annual streamflow decreased by 3.3 % (18 mm/year) and 1.6 % (11 mm/year) in the ACT and TBW watersheds, respectively. In contrast, afforestation with loblolly pine led to larger increases in mean annual ET of 17 % (131 mm/year)and 10 % (79 mm/year) in affected areas in the ACT and TBW watersheds, respectively. As a result, mean annual streamflow decreased by 5.2 % (29 mm/year) and 2.8 % (19 mm/year) at the watershed level in the ACT and TBW watersheds, respectively. Overall, the afforestation scenarios led to decreases in watershed-scale sediment and nutrient exports, especially under longleaf pine afforestation. Large-scale afforestation had negligible effects on precipitation via local moisture recycling at the watershed scale. Our study indicates that the choices of tree species and forest structure are important to water yield in the southeastern U.S. and that moisture recycling has minor influences on the local water cycle of the study region.

Land use and urbanization indirectly control riverine CH4 and CO2 emissions by altering nutrient input

Urban rivers are recognized as significant sources of methane (CH4) and carbon dioxide (CO2) emissions. Despite this, the influence of land use and urbanization on carbon emissions across rural-urban rivers at the watershed scale has been insufficiently explored. This study utilized in-situ surveys of the Liao River in northern China to investigate the spatial and temporal variations of CH4 and CO2 emissions and their relationship with urbanization and its potential controlling factors. The findings revealed that CH4 emissions peaked in fall, whereas CO2 emissions were highest in summer. The average fluxes of CH4 and CO2 at the water-gas interface were 1387.22 ± 2474.98 µmol·m−2·d−1 and 52.78 ± 54.44 mmol·m−2·d−1, respectively. Water quality parameters accounted for 80.49 % of the total variation in CH4 and CO2 concentrations and fluxes. Structural equation modeling indicated that TN, TP, DTC, and conductivity had direct effects on riverine CH4 and CO2 emissions, with standardized direct effects of 0.50 and 0.49, respectively. Nutrient input emerged as the primary driver, increasing CH4 and CO2 concentrations and fluxes, particularly in urban-adjacent river sections likely receiving higher nutrient loads. This study underscores that land use and urbanization indirectly influence riverine CH4 and CO2 emissions by modifying nutrient inputs. Effective land use management and nutrient input control are recommended strategies to mitigate riverine CH4 and CO2 emissions.

Investigating Mountain Watershed Headwater‐To‐Groundwater Connections, Water Sources, and Storage Selection Behavior With Dynamic‐Flux Particle Tracking

AbstractClimate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed‐scale approaches to understand water source, storage, and exit flux connections using a dynamic‐flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow‐derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt‐derived streamflow preferentially discharges older water from snow‐derived storage. Dynamic‐flux particle tracking and new approaches presented here enable novel model‐tracer comparisons in large‐scale watersheds to better understand watershed behavior in a changing climate.

Potential of Gridded Precipitation Estimate Products for Hydrological Modelling: The Case of Small Watersheds in Côte d'Ivoire

Rainfall information measured on the ground is not sufficiently representative the watershed scale in Côte d'Ivoire due to a low density of the rain gauge network. Many watersheds are ungauged or present a high rate of missing data on flow record since 1990s. The use of gridded precipitation estimate products is an alternative to remedy this lack of information. Therefore, the aim of this study was to evaluate the performance of CHIRPS, GPCC, MSWEP, PERSIANN-CDR, PERSIANN-CCS-CDR, TAMSAT, WFDE5-CRU, and WFDE5-CRU-GPCC precipitation estimates for hydrological modelling in Côte d'Ivoire over the period from 1984 to 2004. A continuous-based flow simulation was made for 14 small watersheds using GR4J lumped model. The methodology was based on a global analysis to identify the products that gave the best scores in both calibration and validation over the selected watersheds. KGE2 was the major metric considered along with NSE, VE, PBIAS, and ME. The performance of these gridded precipitation estimate products was then evaluated according to the different climatic zones of Côte d'Ivoire. The results showed that WFDE5-CRU-GPCC performed well in all climatic zones with KGE2 &gt; 0.6 for at least the half watersheds for each climatic zone. CHIRPS and PERSIANN-CDR exhibited the best performance in the transitional tropical and mountainous zones (KGE2mean &gt; 0,7), whereas WFDE5-CRU was good, mainly in the mountainous zone with KGE2 &gt; 0,7 for 2 watersheds out of 3.

Impacts of Land Use on Runoff and Sediment Dynamics in Tropical Watersheds: A Case Study in Bogowonto Upper Watershed

Land use changes in tropical regions have increased, leading to rising environmental stress in Java, Indonesia. Food shortages have driven land conversion and expansion, which increases peak flows during the rainy season and reduces water storage in the dry season, heightening flood risks. Research on integrated catchment hydrology is crucial. This study examines the relationship between land use, runoff, and sediment in the Bogowonto Upper Watershed using SWAT hydrological modeling. The SWAT model helps understand hydrological processes at the watershed scale and the impact of land use changes on runoff and sediment dynamics. The sensitivity of SWAT model parameters varies in the Bogowonto Upper Watershed. Runoff sensitivity analysis indicates a +62% increase with a 50% change in CN value, showing high sensitivity. A 50% change in vegetation cover results in a +50% model output, indicating moderate sensitivity. Slope, Ksat (saturated hydraulic conductivity), and bulk density are fairly sensitive, while AWC is slightly sensitive. For sediment, a 50% increase in CN value results in a +47% change, and a 50% increase in vegetation cover leads to a +58% model output, showing moderate sensitivity. The model, run from 2014-2019, shows excellent accuracy with NSE of 0.82, RRMSE of 0.43, R² of 0.83, and PBIAS of 9.8%.

A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico

Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.

Multi-habitat distribution and coalescence of resistomes at the watershed scale based on metagenomics

The characteristics of the resistome distribution in rivers have been extensively studied. However, the distribution patterns of resistomes in multiple habitats and contributions of upstream habitats to the resistome profile in water bodies remains unclear. The current study explored the distribution and coalescence of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and mobile genetic elements (MGEs) in four habitats (including water bodies, sediments, biofilms, and riparian soils) within the Shichuan River watershed. The results revealed significant variations in the abundances and diversity of resistomes across the four habitats and two seasons. Assembly processes of resistomes were predominated by stochastic processes in summer but deterministic processes in winter. The main source of the resistome in summer water bodies was the movement of genes from upstream water bodies. However, the main sources of resistome in downstream water bodies in winter were the movement of resistomes in upstream sediments and the input of external pollution. The physicochemical properties of winter water bodies significantly influenced the movement of the resistomes across habitats. The current study elucidated the multi-habitat distribution pattern and migration mechanism of the resistome in the river system, providing new insights for effectively monitoring and controlling bacterial resistance.

Incentive mechanism for allocating wastewater discharge responsibility based on cooperative game theory

Clarifying responsibility for wastewater discharges and enhancing reasonable allocation of wastewater emission permits are critical for controlling wastewater discharge in the context global sustainable development plans. The traditional wastewater allocation approach has several drawbacks, such as sub-regional "free-riding," imbalanced regional development and demand, and unfair allocation mechanism. This study developed a model based on cooperative game theory to allocate responsibility for wastewater discharge in China's Tuojiang River Basin. In this model, the level of impact of a watershed sub-region on the water environment is determined, according to which the wastewater discharge responsibility is assigned. The stronger the impact of upstream sub-regions on downstream wastewater discharge, the greater the responsibility for wastewater discharge. Furthermore, by implementing the wastewater cooperation model, sub-regions can monitor and incentivize each other, through which wastewater discharges can be reduced by 16.38%, compared to the baseline mechanism and overall discharges can be reduced by 5007.99 tons. This study provides recommendations for appropriate management authorities to improve the allocation of wastewater discharge responsibility at the watershed scale.

Using net anthropogenic nutrient inputs at fine spatial scales benefits decision‐making in watersheds to protect water quality

Abstract Excessive nutrient loadings from various human activities on land to inland and coastal waters result in water quality degradation through cultural eutrophication. Accurately identifying and quantifying the main nitrogen and phosphorus sources on land is essential to implement proper intervention strategies to reduce loadings in order to preserve water quality for the long term. The Net Anthropogenic Nitrogen/Phosphorus Inputs (NANI/NAPI) mass balance approach has successfully been used to quantify nutrients added on land to sustain human activities and predict the fraction exported to water. However, the rich detail of information used to generate the mass balance is typically merged and upscaled to whole watersheds, averaging out highly heterogeneous patterns on land, which would be key to identify regions that disproportionately load nutrients to receiving waters. Here we present NANI and NAPI at increasingly finer scales (watershed, county, and municipal) in eight catchments of the Saint‐Lawrence Basin in Québec, Canada, and compare input changes between two different decades (1981 and 2021). In 2021, NANI ranged from 828 to 6602 kg km−2 at the watershed scale whereas the range at the municipal one was much greater from near 0 to 27,413 kg km−2, highlighting the importance of using the municipal scale to pinpoint specific regions of high inputs. We then show how partitioning individual NANI components (the same could be done for NAPI) could reveal the dominant input type among urbanisation, crop‐intensive, or livestock‐intensive agriculture, which can help decision makers choose the most effective intervention strategy based on dominant input type. Finally, we discuss how information derived over time can further be used to enhance understanding around mitigation strategies. Policy implications. The use of the detailed components of NANI and NAPI at the finest scale possible provides relevant quantitative information and acts as an effective communication tool between scientists and decision makers. We suggest that the use of this approach can better enable targeted nutrient reduction strategies based on scientific evidence to protect water quality.

A random forest machine learning model to detect fluvial hazards

AbstractFluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.

Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale

Sequential Gaussian simulation for mapping the spatial variability of saturated soil hydraulic conductivity at watershed scale

Soil loss estimation using RUSLE model: Comparison of conventional and digital soil data at watershed scale in central Iran

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.

Evaluation of the Hg Contamination from Gold Mining in French Guiana at the Watershed Scale Using Hg Isotopic Composition in River Sediments

Evaluation of the Hg Contamination from Gold Mining in French Guiana at the Watershed Scale Using Hg Isotopic Composition in River Sediments

Identifying the Optimal Layout of Low-Impact Development Measures at an Urban Watershed Scale Using a Multi-Objective Decision-Making Framework

Identifying the Optimal Layout of Low-Impact Development Measures at an Urban Watershed Scale Using a Multi-Objective Decision-Making Framework

Water quality management in a tropical karstic system influenced by land use in Chiapas, Mexico

Urbanization and agricultural activities are increasingly threatening karstic systems and the water resources they provide, which are crucial for the livelihood of rural communities. Over the past decade, the Río Grande de Comitán-Lagos de Montebello (RGC-LM) watershed in Chiapas, Mexico, has experienced significant deterioration in water quality. The objective of this study was to analyze the water quality dynamics within the RGC-LM watershed and their potential interaction with land use change. We conducted assessments of the physicochemical and microbiological parameters across the lotic and lentic systems within the watershed over eight monitoring campaigns (from 2013 to 2020). Cluster Analysis (CA) and Principal Component Analysis (PCA) statistical approaches were performed to identify the main drivers of surface water quality variability. This approach aims to enhance water quality assessments within environmental management strategies, with the goal of promoting sustainable land-use practices aimed at protecting and improving water resources within the watershed. Our findings indicate that there is a substantial difference between the upper and lower watershed water quality. The good water quality conditions observed in the higher altitude lakes (mountain lakes) can be attributed to favorable topographical features, specific land use patterns, and effective conservation measures. In contrast, the lotic system in the middle watershed and the lentic water bodies located at lower altitudes (plain lakes) exhibit signs of water quality degradation due to inefficient wastewater treatment and agricultural runoff. This study highlights the importance of implementing adequate environmental management strategies to address these water quality challenges effectively. Given the pressing issue of water quality degradation in karstic environments, we emphasize on the need for increased monitoring frequency of both physicochemical and microbiological parameters to capture seasonal variations and to further understand the vulnerability of water resources in karstic environments. Future assessment of nutrient concentrations, pesticide levels, and blue-green algae populations will also be crucial for evaluating the trophic state at a watershed scale.

Computationally efficient Watershed-Scale hydrological Modeling: Integrating HYDRUS-1D and KINEROS2 for coupled Surface-Subsurface analysis

Watershed flow processes consist of partitioning, movement, storage, and redistribution of water fluxes in space and time. However, the integrated modeling of these processes is challenging due to computational burden, extensive data requirements, and/or reliance on simplifying assumptions. This study introduces a novel and computationally efficient modeling framework that leverages two state-of-the-art process-based models: HYDRUS-1D (H1D) for unsaturated flow and KINEROS2 (K2) for overland flow. The framework extends a hillslope-scale coupled H1D-K2 model to simulate watershed-scale processes, where H1D replaces the three-parameter Parlange’s infiltration equation in the event-based K2 model. Boundary condition switching is employed to account for surface ponding and water exchange between the two model domains. The structure of the coupled watershed-scale H1D-K2 model consists of a cascade of connected rectangular planes, channel elements, and 1D soil profiles to simulate 1D overland flow, infiltration, unsaturated zone flow, and recharge. Computational efficiency relative to HYDRUS-2D is achieved through a dynamic time-stepping approach and dimensionality reduction. The watershed scale model improved the computational time by 14.3% and 47% compared to the corresponding Hillslope scale H1D-K2 and HYDRUS-2D, respectively. The performance and efficiency of the new watershed model are demonstrated using benchmark watershed and/or hillslope simulations. Calibrated hydrographs and water balance components using Walnut Gulch Experimental Watershed data, showed excellent agreement with observed data with a Nash-Sutcliffe coefficient (NSC) greator than 0.8 and Pearson correlation coefficient (R2) greater than 0.92.

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash-Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation-based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Impacts on water quality of producing biogas on pig farms as a function of the associated agricultural practices

The aim of this study was to assess positive or negative impacts of anaerobic digestion (AD) on water quality using a systemic approach. To this end, we used the agro-hydrological model Topography-based Nitrogen Transfer and Transformation (TNT2), a spatially explicit model that simulates nitrogen and water flows at the watershed scale on a daily time step. Four scenarios were constructed and analyzed: a baseline before the introduction of AD (S0), AD with adjusted fertilization (S1), AD with unadjusted fertilization (S2), and agroecological AD (S3). The results showed that, when spreading practices were similar and an equivalent amount of effective nitrogen was applied, digested pig slurry generally had a predicted amount of nitrate leaching similar to that of undigested pig slurry. In addition, replacing catch crops with energy cover crops had little impact on water quality. Scenario S3 was the most favorable one for water quality and biogas production, but not for soil organic nitrogen storage and food and feed production. This study’s strength is its systemic approach, which considered both environmental and agronomic aspects to assess the scenarios.