Dynamic evolution trend and driving mechanisms of water conservation in the Yellow River Basin, China.

Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach.

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha-1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0-30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Synergistic water quality and soil organic carbon sequestration benefits of winter cover crops.

Winter cover crops (WCCs) are promising best management practices for reducing nitrogen and sediment pollution and increasing soil organic carbon (SOC) sequestration in agricultural fields. Although previous watershed studies assessed water quality benefits of growing WCCs in the Chesapeake Bay watershed, the SOC sequestration impacts remain largely unknown. Here, we designed six WCC scenarios in the Tuckahoe Watershed (TW) to understand potential synergies or tradeoffs between multiple impacts of WCCs. Besides corroborating the nitrate reduction benefits of WCCs that have been reported in previous studies, our results also demonstrated comparable reduction in sediment. We also found that the six WCC scenarios can sequester 0.45-0.92 MgC ha-1 yr-1, with early-planted WCCs having more than 70% SOC sequestration benefits compared with their late-planted counterparts. With a linear extrapolation to all the cropland in Maryland, WCCs hold potential to contribute 2.1-4.4% toward Maryland's 2030 Greenhouse Gases reduction goal. Additionally, we showed that WCCs can noticeably increase evapotranspiration and decrease water yield and streamflow, potentially impacting aquatic ecosystem health and water supply. Overall, this study highlights the synergistic water quality and SOC sequestration benefits of WCCs in the Chesapeake Bay watershed. Meanwhile, sustainable adoption of WCCs into existing crop rotations will also require careful assessment of their impact on water availability.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow.

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of "observed" baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two "observed" data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %-34 % of the mean annual total streamflow (11.8 m3/s) and 8 %-37 % of the mean annual total nitrate load (14.3 kg·ha-1·year-1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Simulating runoff changes and evaluating under climate change using CMIP6 data and the optimal SWAT model: a case study.

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Predicting future impacts of climate and land use change on streamflow in the middle reaches of China's Yellow River.

With increasing temperatures, changing weather patterns and ongoing development, it is becoming increasingly important to clarify the evolution mechanism of future regional streamflow processes and their controlling factors. In this study, an integrated framework for watershed streamflow prediction based on a Global Climate Model (GCM), the Patch-generating Land Use Simulation model (PLUS), and the Soil and Water Assessment Tool (SWAT) was proposed in the middle Yellow River. The results indicate that, compared with the baseline period (1989-2018), levels of precipitation and maximum and minimum temperatures are expected to increase in the next 30 years, resulting in a warmer and wetter regional climate. Under various climate scenarios, the annual streamflow is projected to increase by 49.2-115.1%. The acreage of various land types may have tended to be saturated, and the main land types such as cropland, forest and grassland have little change (-6.6%-0.6%), so the impact on streamflow will be correspondingly reduced. Under various land use scenarios, the annual streamflow is projected to increase by 5.0%-7.3%. The annual average streamflow trends under the combined climate and land use scenarios are consistent with the climate change scenarios, while the mean values corresponding to the combined scenarios are higher than those of the single scenario. Findings show that climate change is the main driver influencing streamflow, with a contribution of 86.3%-95.1%. This study deepens understanding of the change pattern and influence mechanism of the streamflow process, which can provide a scientific basis for the development and refinement of regional ecological construction plans.

Limited Adipogenic Differentiation Potential of Human Dental Pulp Stem Cells Compared to Human Bone Marrow Stem Cells.

Bone marrow and teeth contain mesenchymal stem cells (MSCs) that could be used for cell-based regenerative therapies. MSCs from these two tissues represent heterogeneous cell populations with varying degrees of lineage commitment. Although human bone marrow stem cells (hBMSCs) and human dental pulp stem cells (hDPSCs) have been extensively studied, it is not yet fully defined if their adipogenic potential differs. Therefore, in this study, we compared the in vitro adipogenic differentiation potential of hDPSCs and hBMSCs. Both cell populations were cultured in adipogenic differentiation media, followed by specific lipid droplet staining to visualise cytodifferentiation. The in vitro differentiation assays were complemented with the expression of specific genes for adipogenesis and osteogenesis-dentinogenesis, as well as for genes involved in the Wnt and Notch signalling pathways. Our findings showed that hBMSCs formed adipocytes containing numerous and large lipid vesicles. In contrast to hBMSCs, hDPSCs did not acquire the typical adipocyte morphology and formed fewer lipid droplets of small size. Regarding the gene expression, cultured hBMSCs upregulated the expression of adipogenic-specific genes (e.g., PPARγ2, LPL, ADIPONECTIN). Furthermore, in these cells most Wnt pathway genes were downregulated, while the expression of NOTCH pathway genes (e.g., NOTCH1, NOTCH3, JAGGED1, HES5, HEY2) was upregulated. hDPSCs retained their osteogenic/dentinogenic molecular profile (e.g., RUNX2, ALP, COLIA1) and upregulated the WNT-specific genes but not the NOTCH pathway genes. Taken together, our in vitro findings demonstrate that hDPSCs are not entirely committed to the adipogenic fate, in contrast to the hBMSCs, which are more effective to fully differentiate into adipocytes.

Modeling the fate and transport of E. coli pathogens in the Tano River Basin of Ghana under climate change and socioeconomic scenarios.

Surface water contamination by fecal matter threatens human health due to human and biological processes within a watershed, making socioeconomic development crucial for predicting and improving microbiological water quality. Consequently, climate change alters climatic parameters that affect flow regimes and the movement and fate of microorganisms. This study assessed the fate and transport of microbial Escherichia coli (E. coli) concentrations and their sources in the Tano River Basin in Ghana. Additionally, the study predicted future E. coli concentrations using climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC)'s most recent representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). Scenario_1 featured planned urbanization, enhanced manure and wastewater treatment, moderate population, livestock density growth, and climate change. Scenario_2 involved higher population growth, minimal improvements in wastewater management, zero manure treatment, higher livestock population, urbanization, and substantial climate change. Calibration and validation using E. coli data from June 2022 to April 2023 showed good agreement with observed concentrations (R2, 0.75 and 0.89; NSE, 0.69 and 0.68; PBIAS, 3.4 and 1.9, respectively). The measured and modeled E. coli concentrations were high, with the highest recording at 2.39 log cfu/100 ml during the rainy season. The study finds that the main causes of E. coli concentrations (44%) are point sources, primarily from human feces and livestock manure, followed by upstream pollution (34%) and non-point sources (22%). Non-point sources became the predominant contributors during periods of maximum discharge due to runoff from land and the dilution of point sources. Again Scenario_1 E. coli dropped to 68% and 97% of reference point levels by the 2050s and 2100s, respectively. E. coli concentrations decrease even more with subsequent treatment, such as tertiary treatment, manure treatment, or both. The scenario analysis demonstrates the potential for E. coli reduction through wastewater and manure treatment, driven by socioeconomic and climate change scenarios.

Mutual impact of salinity and climate change on crop production water footprint in a semi-arid agricultural watershed: Application of SWAT-MODFLOW-Salt.

Sustainable agriculture in intensively irrigated watersheds, particularly those in arid and semi-arid regions, requires enhanced management practices to maintain crop production, which depends on climate, available water resources, soil conditions, irrigation practices, and crop type. Among these factors, soil salinity and climate change are significant challenges to agricultural productivity. To investigate the long-term influence of salinity and climate change on crop production from 1999 to 2100 in irrigated semi-arid regions, we apply the water footprint (WF) concept using the hydro-chemical watershed model SWAT-MODFLOW-Salt, driven by five General Circulation Models (GCMs) and two climate scenarios (RCP4.5 and RCP8.5), to a 732 km2 irrigated stream-aquifer system within the Lower Arkansas River Valley (LARV), Colorado, USA. The study focused on calculating the green (WFgreen), blue (WFblue), and total (WFtotal) crop production WFs for 29 crops in the region, with and without including salinity effect on crop yield. Results reveal that during the baseline period (1999-2009), the total annual average WFgreen, WFblue, and WFtotal increased by 7.6 %, 4.4 %, and 6.5 %, respectively, under salinity stress, as crops experienced reductions of up to 4.6 %, 1.6 %, and 2.3 % in green, blue, and total crop yield. The mutual impact of salinity and the worst-case climate model (IPSL_CM5A_MR) under the higher emission scenario (RCP8.5) led to a 3.3 %, 1.9 %, and 3 % increase in green, blue, and total crop production WFs. Furthermore, the study highlighted that the proportion of green, blue, and total crop production WFs in the LARV exceeded the world average. This discrepancy was attributed to various factors, including different spatial and temporal crop distribution, irrigation practices, soil types, and climate conditions. Notably, salinity stress affected green crop yield and green WF more significantly than blue crop yield and blue WF across all GCM models. This finding underscores the importance of prioritizing management practices to control salinity-associated challenges within the region.

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment.

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

Near future flash flood prediction in an arid region under climate change.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one. Subsequently, we assessed four CMIP6 scenarios for future rainfall analysis. Next, different Machine Learning (ML) algorithms were employed through H2O-AutoML to identify the best model for downscaling future rainfall predictions. Forty distribution functions were then fitted to the future daily rainfall, and the best-fit model was selected to project future Intensity-Duration-Frequency (IDF) curves. Finally, the Soil and Water Assessment Tool (SWAT) model was utilized with sub-daily time steps to make accurate flash flood predictions in the study area. The findings reveal that IITM-ESM is the most effective among GCM models. Additionally, the application of stacked ensemble ML model proved to be the most reliable in downscaling future rainfall. Furthermore, this study highlighted that floods entering urbanized areas could reach 20.33 and 20.70 m³/s under pessimistic scenarios during rainfall events with 100 and 200-year return periods, respectively. This hierarchical comprehensive approach provides reliable results by utilizing the most effective model at each step, offering in-depth insight into future flash flood prediction.

An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat.

Harmful algal blooms (HABs) problem in Lake Erie has become critical recently-primarily triggered by phosphorus losses from cropland in the Maumee River watershed (major crops of corn/soybeans/wheat). Implementing agricultural best management practices (BMPs) is crucial to reduce excess nutrient loadings. Nutrient management is the management of nutrient applications for crop production that maximizes nutrient use efficiencies and minimizes nutrient losses. However, an integrated watershed-scale tool is needed for cost-effectively applying nutrient management practices for corn/soybeans/wheat considering the 4Rs (Right nutrient source, Right rate, Right time, and Right place of nutrient applications). In this study, by combining an improved Soil and Water Assessment Tool (SWAT) for nutrient management (SWAT-NM) and an improved BMP Cost Evaluation Tool (BMP-COST) for economic evaluations of nutrient management (BMP-COST-NM) considering the 4Rs for corn/soybeans/wheat, an integrated tool SWAT-COST-NM was created. SWAT-COST-NM was demonstrated in the AXL watershed (a typical agricultural area in the Maumee River watershed). The impacts of single nutrient management practices (single-NM, which separately changed the rate, place, time, or nutrient source of fertilizer applications) and combined-NM practices (multiple single-NM practices combined as one nutrient management practice) for corn/soybeans/wheat were evaluated. Tradeoffs in yearly net costs, crop yields, and March-July/yearly nutrient losses (Total Phosphorus-TP, Dissolved Reactive Phosphorus-DRP, and Total Nitrogen-TN) existed. Nutrient management did not necessarily lead to sufficient increases in crop yields to generate extra revenues that match or exceed the additional costs of the activities (compared to existing practices). One of the combined-NM practices could simultaneously reduce March-July TP, DRP, and TN losses by 5.89%, 8.19%, and 8.23%, respectively, while increasing crop yields with additional income (0.50 $/ha/yr of cropped area). SWAT-COST-NM, which can quantify various factors and tradeoffs when evaluating the impacts of nutrient management practices for corn/soybeans/wheat, can assist decision-makers in cost-effectively applying nutrient management practices considering the 4Rs.

Stakeholder-based management of ecosystem services in agricultural areas: Integrating the leader-follower game and a conflict resolution model.

Given the substantial effects of agricultural practices on the environment, this paper introduces a novel stakeholder-based framework for assessing the ecosystem services (ESs) provided by agricultural areas. Ecosystem services include essential functions such as water supply, food production, carbon storage, soil erosion control, and habitat support. In addition to ESs, water footprint is also taken into account to evaluate the impacts of agricultural activities on water resources. Some of the mentioned ESs are assessed using the Soil and Water Assessment Tool (SWAT). Then, by extending and combining the Conflict Resolution Model with the Composition of Probabilistic Preferences (CRMCPP) method and the leader-follower game (LFG), while considering the hierarchical structure of decision-makers, the best scenario for enhancing the ESs is selected. The Zarrinehroud River Basin (ZRB) in Iran has been chosen as a case study to evaluate the performance of the proposed framework, as this basin is vital for supplying water to Lake Urmia, the largest hypersaline lake in the Middle East. In this paper, 16 Water and Environmental Resources Management (WERM) scenarios have been defined according to the Urmia Lake Restoration National Committee (ULRNC) projects. Then, the mentioned ESs have been evaluated under different WERM scenarios. Ultimately, by utilizing the CRMCPP-LFG method and taking into account the hierarchical structure of decision-makers, we can identify the optimal WERM scenario. The criteria for making this decision include various factors, such as ecosystem services and the costs involved in implementing the WERM scenarios. In the selected scenario, the average water inflow into Lake Urmia is projected to rise to 1329 million cubic meters per year, which is 6.3% more than the average inflow in the current condition. Key initiatives in this scenario include reducing cultivated areas, altering irrigation methods, changing crop patterns, and incorporating water-efficient plant species.

Analysis of mitochondrial biogenesis and protein localization by genetic screens and automated imaging.

Budding yeast is a laboratory model of a simple eukaryotic cell. Its compact genome is very easy to edit. This allowed to create systematic collections (libraries) of yeast strains where every gene is either perturbed or tagged. Here we review how such collections were used to study mitochondrial biology by doing genetic screens. First, we introduce the principles of yeast genome editing and the basics of its life cycle that are useful for genetic experiments. Then we overview what yeast strain collections were created over the past years. We also describe the creation and the usage of the new generation of SWAP-Tag (SWAT) collections that allow to create custom libraries. We outline the principles of changing the genetic background of whole collections in parallel, and the basics of synthetic genetic array (SGA) approach. Then we review the discoveries that were made using different types of genetic screens focusing on general mitochondrial functions, proteome, and protein targeting pathways. The development of new collections and screening techniques will continue to bring valuable insight into the function of mitochondria and other organelles.

Survey on the resolution and accuracy of input data validity for SWAT-based hydrological models.

This review was conducted to highlight the most influential factors and specify the trends reducing uncertainty and increasing the accuracy of soil and water assessment tool (SWAT)-based hydrological models. Although the resolution of input data on the results of SWAT-based hydrological models has been extensively determined. There is still a gap in providing comprehensive review framework to be emerged for identifying the impact of the data resolution and accuracy. The factors taken into consideration in this study were the impact of digital elevation model (DEM) resolution, soil data resolution, land use and land cover (LULC) resolution, and the impact of weather data resolution. Identifying the best DEM resolution depends on the watershed response and hydrological processes. However, for sediment yield estimation, more attention should be paid to the accuracy of soil data. Furthermore, the impact of LULC resolution on the accuracy of streamflow is still not sufficiently understood, whereas fine resolution is required for an accurate simulation of the sediment yield. Sub-daily precipitation data is essential for an accurate estimation of streamflow. Despite the fact that climate forecast system reanalysis (CFSR) and tropical rainfall measuring mission (TRMM) are the most widely used climate products, climate hazards group infrared precipitation with station data (CHIRPS) produces an adequate estimation for streamflow when there is insufficient gauged data. However, other aspects have not been deeply taken into consideration, including the interactive and complementary impacts of these factors. Thus, more attention and focus should be given to these issues. This review and evaluation can be a significant guide for selecting the suitable input data to implement efficient SWAT-based watershed models.

Identifying and assessing pond best management practice under future climate scenarios.

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040-2060) and late (2079-2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26-46%, and 107-150% respectively by the late century compared to the baseline period (2002-2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44-46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

Patterns of nitrate load variability under surface water-groundwater interactions in agriculturally intensive valley watersheds.

Nitrate pollution is a significant environmental issue closely related to human activities, complicated hydrological interactions and nitrate fate in the valley watershed strongly affects nitrate load in hydrological systems. In this study, a nitrate reactive transport model by coupling SWAT-MODFLOW-RT3D between surface water and groundwater interactions at the watershed scale was developed, which was used to reproduce the interaction between surface water and groundwater in the basin from 2016 to 2019 and to reveal the nitrogen transformation process and the evolving trend of nitrate load within the hydrological system of the valley watershed. The results showed that the basin exhibited groundwater recharge to surface water in 2016-2019, particularly in the northwestern and northeastern mountainous regions of the valley watershed and the southern Beishan Reservoir vicinity. Groundwater recharge to surface water declined by 20.17 % from 2016 to 2019 due to precipitation. Nitrate loads in the hydrologic system of the watershed are primarily derived from human activities (including fertilizer application from agricultural activities and residential wastewater discharges) and the nitrogen cycle. Nitrate loads in surface water declined 16.05 % from 2016 to 2019. Nitrate levels are higher in agricultural farming and residential areas on the eastern and northern sides of the watershed. Additionally, hydrological interactions are usually accompanied by material accumulation and environmental changes. Nitrate levels tend to rise with converging water flows, a process that becomes more pronounced during precipitation events and cropping seasons in agriculturally intensive valley watersheds. However, environmental changes alter nitrogen transformation processes. Nitrogen fixation, nitrification, and ammonification intensify nitrogen inputs during river pooling, enhancing nitrogen cycling fluxes and elevating nitrate loads. These processes are further enhanced during groundwater recharge to surface water, leading to evaluated nitrate load. Enhanced denitrification, dissimilatory nitrate reduction to ammonium (DNRA), anaerobic ammonia oxidation, and assimilation promote the nitrogen export from the system and reduce the nitrate load during surface water recharge to groundwater.

Assessment of sediment yield and accumulation in reservoir: The case of Gibe One Reservoir, Southwestern Ethiopia.

Soil erosion and sediment buildup are the factors that speed up the decline in capacity and function of reservoirs, agricultural products, and water resources. In order to simulate sediment and runoff and map high sediment-yielding sub-basins in the Gibe Gojeb catchment in southwest Ethiopia, this study used the Soil and Water Assessment Tool (SWAT) model. Using data on sediment and river flow, calibration and validation were carried out. Between 2003 and 2016, the catchment produced an average annual sediment loading of 62.5 tons ha-1 yr-1, with loading fluctuations ranging from 0.2 to 108.4 tons ha-1 yr-1. The acceptable sediment yield threshold value ranges from 12.3 to 108.4 tons ha-1 yr-1 for 56 sub-basins, and from 0.2 to 10 tons ha-1 yr-1 for 5 sub-basins. The most significant sub-basins with very high to extremely severe sediment yields were sub-basins 1 to 30, 32 to 44, 47, 48, 50, 51, and 53 to 61. After thirteen years of operation, the yearly amount of 58,802 tons of sediment transferred from the catchment and deposited into Gibe One reservoir has decreased the capacity by 5.7 %. The accumulation of sediment in a reservoir has an impact on its functionality, power production, and capacity, affecting the safety of dams and the environment. The study's findings enhanced our comprehension of sediment accumulation in reservoirs and furnished us with the necessary information regarding reservoir safety, integrated soil, and water management.

How atmospheric deposition contribute to watershed heavy metals contamination in coastal watersheds in China: A case study of Laizhou Bay.

Atmospheric deposition is a significant source of heavy metal (HM) pollution. In order to understand the migration and transformation process of atmospheric HMs within the watershed and quantify the amount transported offshore by rivers, the Soil and Water Assessment Tool (SWAT) was developed to trace the migration of HMs from atmospheric deposition. The model simulates HMs in three forms: dissolved, adsorbed, and granular. It quantifies the movements of Cd, Cr, Cu, Hg, Pb, and Zn from atmospheric deposition into the sea via rivers in five coastal watersheds in East China and analyzes the effects of meteorological factors, vegetation cover, and slope on non-point pollution of these metals by Spearman correlation analysis. The results showed that the annual flux of HMs from atmospheric deposition to the sea through rivers accounted for 5 %-69 % of the total rivers flux. Among meteorological factors, precipitation demonstrated the strongest correlation with the monthly loads of HMs entering rivers from atmospheric deposition. Additionally, HMs loads entering rivers from atmospheric deposition were more closely related to vegetation cover than topographic slope. This model provides a new approach to distinguishing the flux of atmospheric HMs entering offshore waters through rivers. The findings will deepen our understanding of the migration and transformation of HMs from atmospheric deposition, enhance the ability to control offshore HMs pollution, and reduce the ecological risks associated by HMs.

Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland.

Modeling nitrate fate and transport in water sources is an essential component of predictive water quality management. Both mechanistic and data-driven models are currently in use. Mechanistic models, such as SWAT, simulate daily nitrate loads based on the results of simulating water flow. Data-driven models allow one to simulate nitrate loads and water flow independently. Performance of SWAT and deep learning model was evaluated in cases when deep learning model is used in (a) independent simulations of flow series and nitrate concentration series, and (b) in both flow rate and concentration simulations to obtain nitrate load values. The data were collected at the Tuckahoe Creek watershed in Maryland, United States. The data-driven deep learning model was built using long-short-term-memory (LSTM) and three-dimensional convolutional networks (3D Convolutional Networks) to simulate flow rate and nitrate concentration using weather data and imagery to derive leaf area index according to land use. Models were calibrated with data over training period 2014-2017 and validated with data over testing period. SWAT Nash-Sutcliffe efficiency (NSE) was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load rate over training and testing periods, respectively. Three data-driven modeling scenarios were implemented: (1) using the observed flow rate and simulated nitrate concentration, (2) using the simulated flow rate and observed nitrate concentration, and (3) using the simulated flow rate and nitrate concentration. The deep learning model performed better than SWAT in all three scenarios with NSE from 0.49 to 0.58 for training and from 0.28 to 0.80 for testing periods with scenario 1 showing the best results. The difference in performance was most pronounced in fall and winter seasons. The deep learning modeling can be an efficient alternative to mechanistic watershed-scale water quality models provided the regular high-frequency data collection is implemented.