Search Results (51)

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article

Santa María Bay–La Reforma (SMBLR), with its 58,300 ha is one of Mexico’s most extensive estuarine lagoon systems. It is made up of islands, estuaries, and mangrove areas, which provide a vital part of the habitat and refuge of a significant number of birds, fish, amphibians, reptiles, and mammals. The fishing of blue and brown shrimp, marine and estuarine fish, as well as the exploitation of crab and bivalve mollusks, represent an important economic value for the communities that live there and for the state of Sinaloa, Mexico. This state ranked second in fisheries production and first in aquaculture production by 2023. However, the biological richness of this ecosystem has historically been threatened by economic activities such as agriculture, livestock, and aquaculture that, via watersheds, translate into continuous inputs of nutrients and other pollutants. This has led to modifications to the system such as changes in the structure of pelagic and benthic communities, mainly in response to eutrophication. To understand the dynamics of nutrient inputs to the ecosystem, this work presents a comparative analysis of the system’s carrying capacity and the magnitude of the main economic activities from 2007 to 2019. We found that during each season of the year and its transitions, the system functions as a nitrogen and phosphorus sink, which is associated with autotrophic net ecosystem metabolism and nitrogen fixation processes. We suggest that while water residence times in SMBLR are short, these are strongly influenced by the high volumes of water and nutrient loads determined by the spatio-temporal variations in hydrological drainage from the basins of influence of the system. The discharge of agriculture and aquaculture drains into SMBLR are areas of concern due to the high amount of nutrients. Although SMBLR is mostly an autotrophic system, there are signs that the carrying capacity during some seasons has been exceeded, and adverse ecological and socioeconomic effects in the basin are evident. Full article

Blink detection is considered a useful indicator both for clinical conditions and drowsiness state. In this work, we propose and compare deep learning architectures for the task of detecting blinks in video frame sequences. The first step is the training and application of an eye detector that extracts the eye regions from each video frame. The cropped eye regions are organized as three-dimensional (3D) input with the third dimension spanning time of 300 ms. Two different 3D convolutional neural networks are utilized (a simple 3D CNN and 3D ResNet), as well as a 3D autoencoder combined with a classifier coupled to the latent space. Finally, we propose the usage of a frame prediction accumulator combined with morphological processing and watershed segmentation to detect blinks and determine their start and stop frame in previously unseen videos. The proposed framework was trained on ten (9) different participants and tested on five (8) different ones, with a total of 162,400 frames and 1172 blinks for each eye. The start and end frame of each blink in the dataset has been annotate by specialized ophthalmologist. Quantitative comparison with state-of-the-art blink detection methodologies provide favorable results for the proposed neural architectures coupled with the prediction accumulator, with the 3D ResNet being the best as well as the fastest performer. Full article

K-TIG welding offers the advantages of single-sided welding and double-sided formation, making it widely used for medium/thick-plate welding. The welding quality of K-TIG is closely linked to its penetration state. However, the assembly gap in medium/thick-plate workpieces can easily result in an unstable penetration state. In K-TIG welding, the geometric characteristics of the weld pool are closely related to the penetration state. Compared to arc voltage sensing and acoustic signal sensing, visual sensing is a method capable of obtaining the three-dimensional geometric features of the weld pool. To this end, a K-TIG weld pool three-dimensional monitoring algorithm based on a semantic segmentation network using a stereo vision system with a single High-Dynamic-Range (HDR) camera is proposed in this paper. In order to identify the assembly gap of medium/thick-plate workpieces, a gap width extraction algorithm based on the watershed method is proposed. Subsequently, a penetration state recognition model is constructed, taking the three-dimensional geometric features of the weld pool and the gap width as inputs, with the penetration state as the output. The relationship between the input features and the accuracy of penetration recognition is analyzed through feature ablation experiments. The findings reveal that gap width is the most critical feature influencing the accuracy of penetration recognition, while the area feature negatively affects this accuracy. After removing the area feature, the accuracy of the proposed penetration recognition model reaches 96.7%. Full article

The quality of freshwater input from tributaries of the Western Mississippi Sound (WMSS) impacts the quality of coastal water. Hydrological and hydrodynamic models can be coupled to assess the impact of freshwater inflow from coastal watersheds. This study aims to compare the performance of a hydrodynamic model and a hydrological–hydrodynamic coupled model in detecting the effect of freshwater inflow from the coastal watersheds of the state of Mississippi into the WMSS. A hydrological model, the Soil and Water Assessment Tool (SWAT), and a hydrodynamic model, the visual Environmental Fluid Dynamics Code (vEFDC), were coupled to evaluate the difference between the hydrodynamical modelling approach, which employs an area-weighted approach to define flow and nutrient concentrations, and the more recent coupling model approach, which uses a hydrological model to determine the flow and nutrient load of the model. Furthermore, a nutrient load sensitivity analysis of the effect of freshwater inflow on water quality in the WMSS was conducted in addition to assessing the repercussions of tropical depressions. Hydrological assessments of the major tributaries watersheds of Saint Louis Bay (SLB) at the WMSS were performed using the SWAT model. After calibration/validation of the SWAT model, the streamflow output from the SWAT was incorporated into the vEFDC model. Finally, hydrodynamic simulation of the SWAT-vEFDC model was conducted, and water quality output was compared at different SLB locations. The salinity, dissolved oxygen, total nitrogen (TN), and total phosphorus (TP) were assessed by comparing the vEFDC and SWAT-vEFDC outputs. The results indicated that hydrological input from the SWAT alters the flow and nutrient concentration results as compared to an area-weighted approach. In addition, a major impact on the concentration of TN and TP occurred at the location where the freshwater flows into SLB. This impact diminishes further away from the point of freshwater inflow. Moreover, a 25% nutrient load variation did not demonstrate a difference in water quality at the WMSS besides TN and TP in a post-tropical depression scenario. Therefore, the SWAT-vEFDC coupled approach provided insights into evaluation of the area-weighted method, and of hydrological model output to the hydrodynamical model, the effect of freshwater inflow into coastal waters, and nutrient sensitivity analysis, which are important for integrated coastal ecosystems management. Full article

The hydrochemical characteristics of watersheds are influenced by many factors, with chemical weathering and human activities exerting the most substantial influence. Performing a quantitative evaluation of the factors contributing to the chemical weathering of rocks is of significant scientific importance. This research zeroes in on the Qingtang River basin to elaborate on the hydrochemical characteristic, explore the origins of ions, and quantify the influence of anthropogenic discharges amidst cation interferences, thus improving the accuracy of chemical weathering rate estimations. The samples encompassed surface water, groundwater, and water from dripping in karst caves. The findings indicate that human-induced alterations significantly influence hydrogeochemical dynamics, although chemical weathering of rocks in their natural state is the controlling factor. The mean contributions of cations from atmospheric deposition, human inputs, carbonate weathering, and silicate weathering were 17.56%, 21.05%, 51.77%, and 9.54%, respectively. The chemical weathering rate for carbonate rocks was 62.4 t·km⁻²·a⁻¹, which increased by 27.87% due to the influence of exogenous acids. The anthropogenic impact is predominantly evident in two aspects: (1) the alteration of hydrochemical processes within the watershed through direct input of ions, and (2) the acceleration of rock weathering rates in the watershed due to the exogenous acids. Full article

Multi-stakeholder participation processes in watershed management face challenges due to limited monitoring and baseline data, resulting in a lack of awareness among stakeholders about the current state of the watershed. This knowledge gap often leads to conflicts of interest, wherein the broader impacts of individual decisions are overlooked. To overcome these limitations, this paper explores the design and implementation of a Serious Game (SG) aimed at coproducing a watershed management plan at the basin scale within the specific context of the Campoalegre River basin in Colombia. By providing an interactive platform, the SG facilitates collaboration between local actors, who may be unfamiliar with existing watershed plans, and decision-makers. The goal is to create a participatory space where stakeholders can comprehend the watershed management plan structure and prioritize actions based on various climatic, social, and economic conditions. Following the application of the SG, stakeholders demonstrated an improved understanding of the basin, fostering increased participation, open debate, and the proposal of actions. These outcomes serve as valuable inputs for the implementation of water management planning policies, showcasing the potential of SGs in bridging knowledge gaps, and fostering effective multi-stakeholder engagement. Full article

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R²) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds. Full article

Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality. However, little is known about the relationship between headwater wetlands’ nutrient function (i.e., nutrient load removal (R_L) and removal efficiency (E_R)) and their physical characteristics. Here, we investigate this relationship for 44 headwater wetlands located within the Upper Fish River watershed (UFRW) in coastal Alabama. To accomplish this objective, we apply the process-based watershed model SWAT (Soil and Water Assessment Tool) to generate flow and nutrient loadings to each study wetland and subsequently quantify the wetland-level nutrient removal efficiencies using the process-based wetland model WetQual. Results show that the calculated removal efficiencies of the headwater wetlands in the UFRW are 75–84% and 27–35% for nitrate (${\mathrm{N}\mathrm{O}}_3^{-}$) and phosphate ${(\mathrm{P}\mathrm{O}}_4^{+})$, respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated ${\mathrm{P}\mathrm{O}}_4^{+}$E_R shows a significant decreasing trend with increased input loadings. The relationship between ${\mathrm{N}\mathrm{O}}_3^{-}$E_R and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (p > 0.05), while for ${\mathrm{P}\mathrm{O}}_4^{+}$, the correlation is positive and statistically significant (p < 0.05). On the other hand, flashiness (flow pulsing) and baseflow index (fraction of inflow that is coming from baseflow) have a strong effect on ${\mathrm{N}\mathrm{O}}_3^{-}$ removal but not on ${\mathrm{P}\mathrm{O}}_4^{+}$ removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major ${\mathrm{N}\mathrm{O}}_3^{-}$ removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for ${\mathrm{P}\mathrm{O}}_4^{+}$ removal. Additionally, the computed nutrient E_R is higher during the driest year of the simulated period compared to during the wettest year. Our findings are in line with global-level studies and offer new insights into wetland physical characteristics affecting nutrient removal efficiency and the importance of headwater wetlands in mitigating water quality deterioration in coastal areas. The regression relationships for ${\mathrm{N}\mathrm{O}}_3^{-}$ and ${\mathrm{P}\mathrm{O}}_4^{+}$ load removals in the selected 44 wetlands are then used to extrapolate nutrient load removals to 348 unmodeled non-riverine and non-riparian wetlands in the UFRW (41% of UFRW drains to them). Results show that these wetlands remove 51–61% of the ${\mathrm{N}\mathrm{O}}_3^{-}$ and 5–10% of the ${\mathrm{P}\mathrm{O}}_4^{+}$ loading they receive from their respective drainage areas. Due to geographical proximity and physiographic similarity, these results can be scaled up to the coastal plains of Alabama and Northwest Florida. Full article

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights. Full article

The profound impact of soil temperature (T_S) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses T_S datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R²) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R² of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R² of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time. Full article

The GeoWEPP model has estimated water and soil losses caused by erosion at the watershed level in different parts of the world. However, this model was developed and its parameters have been adjusted for temperate climates, which are different from tropical climates such as those found in Brazil. Our study evaluated the performance of the GeoWEPP model in estimating soil erosion in three micro-watersheds in the Cerrado (i.e., savannah) of southeastern Mato Grosso state, Brazil. Major land uses modeled were soybean and corn cultivation, traditional pasture, and native vegetation. Input parameters for the GeoWEPP model involved climate, soil, land use and management, and topography. GeoWEPP was calibrated with input parameters for soil erodibility specified as interrill and rill soil erosion, soil critical shear stress, and saturated hydraulic conductivity obtained experimentally and estimated by internal routine equations of the GeoWEPP model. Soil losses observed in micro-watersheds with agriculture, pasture, and native vegetation were 0.11, 0.06, and 0.10 metric tons per hectare per year, respectively. GeoWEPP best modeled soil erosion for native vegetation and pasture, while over-estimating that for crops. Surface runoff was best modeled for crops versus native vegetation and pasture. The GeoWEPP model performed better when using soil erodibility input parameters. Full article

Purpose: Understanding the main factors affecting the environmental impacts of milk production and consumption along the value chain is key towards reducing these impacts. This paper aims to present detailed spatialized distributions of impacts associated with milk production and consumption across the United States (U.S.), accounting for locations of both feed and on-farm activities, as well as variations in impact intensity. Using a Life Cycle Analysis (LCA) approach, focus is given to impacts related to (a) water consumption, (b) eutrophication of marine and freshwater, (c) land use, (d) human toxicity and ecotoxicity, and (e) greenhouse gases. Methods: Drawing on data representing regional agricultural practices, feed production is modelled for 50 states and 18 main watersheds and linked to regions of milk production in a spatialized matrix-based approach to yield milk produced at farm gate. Milk processing, distribution, retail, and consumption are then modelled at a national level, accounting for retail and consumer losses. Custom characterization factors are developed for freshwater and marine eutrophication in the U.S. context. Results and discussion: In the overall life cycle, up to 30% of the impact per kg milk consumed is due to milk losses that occur during the retail and consumption phases (i.e., after production), emphasizing the importance of differentiating between farm gate and consumer estimates. Water scarcity is the impact category with the highest spatial variability. Watersheds in the western part of the U.S. are the dominant contributors to the total water consumed, with 80% of water scarcity impacts driven by only 40% of the total milk production. Freshwater eutrophication also has strong spatial variation, with high persistence of emitted phosphorus in Midwest and Great Lakes area, but high freshwater eutrophication impacts associated with extant phosphorus concentration above 100 µg/L in the California, Missouri, and Upper Mississippi water basins. Overall, normalized impacts of fluid milk consumption represent 0.25% to 0.8% of the annual average impact of a person living in the U.S. As milk at farm gate is used for fluid milk and other dairy products, the production of milk at farm gate represents 0.5% to 3% of this annual impact. Dominant contributions to human health impacts are from fine particulate matter and from climate change, whereas ecosystem impacts of milk are mostly due to land use and water consumption. Conclusion: This study provides a systematic, national perspective on the environmental impacts of milk production and consumption in the United States, showing high spatial variation in inputs, farm practices, and impacts. Full article

Groundwater nitrate contamination is a significant concern in agricultural watersheds worldwide with it becoming a more pervasive problem in the last three decades. Models are great tools that are used to identify the sources and spatial patterns of nitrate contamination of groundwater due to agricultural activities. This Systematic Review (SR) seeks to provide a comprehensive overview of different models used to estimate nitrate contamination of groundwater due to agricultural activities. We described different types of models available in the field of modeling groundwater nitrate contamination, the models used, the input requirements of different models, and the evaluation metrics used. Out of all the models reviewed, stand-alone process-based models are predominantly used for modeling nitrate contamination, followed by integrated models, with HYDRUS and LEACHM models being the two most commonly used process-based models worldwide. Most models are evaluated using the statistical metric Root Mean Square Error (RMSE) followed by the correlation coefficient (r). This study provides the current basis for model selection in modeling nitrate contamination of groundwater due to agricultural activities. In addition, it also provides a clear and concise picture of the state of the art and implications to the scientific community doing groundwater quality modeling studies. Full article

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT. Full article