Agricultural Watersheds Research Articles

Effects of source-sink landscape proximity on the spatial–temporal water quality from the perspective of cost distance in an agricultural watershed

Spatial patterns of source-sink landscapes play a critical role in assessing surface water quality. However, this relation may be different due to various proximity of source-sink landscapes. The effect of the source-sink landscape proximity on surface water quality remains poorly understood. The impact of source-sink landscape proximity on the spatial–temporal water quality were quantified based on field monitoring data (2013–2019) in an agricultural watershed. A new approach considering the cost distance of landscape types, slope, and vegetation on the transformation of nutrients from one point to water bodies was used to construct different buffer zones which reflected very high, high, moderate, low, very low proximities, while the subwatershed represented the very low proximity. Then the effects of the source-sink landscape proximity were assessed based on sub-watershed and other four buffers. The resutls showed that the TN and NO3−-N were the main non-point source pollution indices with significant spatial variation (p < 0.05). The water quality index (WQI) was higher during the dry season than during the wet season, indicating that the sampling sites also experienced seasonal variations. The multi-scale analysis demonstrated the significance of the source-sink landscape proximity in improving the quality of surface water. A stronger correlation between the ratios of sink landscape and the main water quality parameters (the TN and NO3−-N) was observed in the low proximity zone, suggestiong that the low proximity zone with moderate non-point source pollution risk was a critical region in water quality management. The results provide important insights into understanding the effects of the source-sink landscape proximity on water quality and how scientific and effective measures can be made to improve water quality.

Modeling lateral carbon fluxes for agroecosystems in the Mid-Atlantic region: Control factors and importance for carbon budget

Estimating lateral carbon fluxes in agroecosystems presents challenges due to intricate anthropogenic and biophysical interactions. We used a modeling technique to enhance our comprehension of the determinants influencing lateral carbon fluxes and their significance in agroecosystem carbon budgets. The SWAT-C model was refined by incorporating a dynamic dissolved inorganic carbon (DIC) module, enhancing our ability to accurately quantify total lateral carbon fluxes. This improved model was calibrated using observed data on riverine particulate organic carbon (POC) and dissolved organic carbon (DOC) fluxes, as well as net ecosystem exchange (NEE) data monitored by a flux tower situated in a representative agricultural watershed, the Tuckahoe Watershed (TW) of the Chesapeake Bay's coastal plain. We assessed the losses of POC, DOC, and DIC across five primary rotation types: C (continuous carbon), CS (corn-soybean), CSS (corn-soybean-soybean), CWS (corn-wheat-soybean), and CWSCS (corn-wheat-soybean-corn-soybean). Our study revealed notable variations in the average annual fluxes of POC (ranging between 152 and 198 kg ha−1), DOC (74–85 kg ha−1), and DIC (93–156 kg ha−1) across the five rotation types. The primary influencing factor for annual POC fluxes was identified as sediment yield. While both annual DOC and DIC fluxes displayed a marked correlation with surface runoff across all crop rotation schemes, soil respiration also significantly influenced annual DIC fluxes. Total lateral carbon fluxes (POC + DOC+DIC) constituted roughly 11 % of both net ecosystem production (NEP) and NEE, yet they represented a striking 95 % of net biome production (NBP) in the TW's agroecosystem. Grain yield carbon accounted for 80 % of both NEP and NEE and was nearly seven times that of NBP. Our findings suggest that introducing soybeans into cornfields tends to reduce NEP, NEE, and also NBP. Conversely, integrating winter wheat into the corn-soybean rotation significantly boosted NEP, NEE, and NBP values, with NBP even surpassing the levels in continuous corn cultivation.

Assessing the Impact of BMPs on Water Quality and Quantity in a Flat Agricultural Watershed in Southern Ontario

Non-point source pollution poses a continuous threat to the quality of Great Lakes waters. To abate this problem, the Great Lakes Agricultural Stewardship Initiative (GLASI) was initiated in Ontario, Canada, with the primary aim of reducing phosphorus pollution. Therefore, a case-study analysis of the Wigle Creek watershed, one of the six priority watersheds under the GLASI program, was undertaken to evaluate the effectiveness of various existing and potential future Best Management Practices (BMPs) and to identify BMPs that might aid in mitigating the watershed’s contribution to phosphorus loads reaching Lake Erie. Given the watershed’s very flat topography, hydrological/nutrient modeling proved an extremely challenging exercise. The Soil and Water Assessment Tool (SWAT) model was used in this evaluation. Several digital elevation model (DEM) options were considered to accurately describe the watershed and represent flow conditions. A 30 m resolution DEM, implementing a modified burning in of streams based on ground truthing, was finally employed to develop the SWAT model’s drainage framework. The model was first calibrated for flow, sediment, and phosphorus loads. The calibrated model was used to evaluate the ability of potential BMPs (minimum tillage, no-till, retiring croplands into pasture, retiring croplands into forest, winter wheat cover crop, and vegetative filter strips) to reduce phosphorus loads compared to implemented practice. Converting all croplands into pasture or forest significantly decreased P loads reaching Lake Erie. Comparatively, a winter wheat cover crop had minimal effect on reducing phosphorus loading.

Exploring the Critical Zone Heterogeneity and the Hydrological Diversity Using an Integrated Ecohydrological Model in Three Contrasted Long‐Term Observatories

AbstractAn integrated ecohydrological modeling approach was deployed in three long‐term critical zone (CZ) observatories of the French CZ network (CZ Observatories—Application and Research) to better understand how the CZ heterogeneity modulates the water cycle within territories. Ecohydrological simulations with the physically based model EcH2O‐iso constrained by a wide range of observations crossing several disciplines (meteorology, hydrology, geomorphology, geophysics, soil sciences, and satellite imagery) are able to capture stream water discharges, evapotranspiration fluxes, and piezometric levels in the Naizin, Auradé, and Strengbach watersheds. In Naizin, an agricultural watershed in northwestern France with a schist bedrock underlying deep weathered materials (5–15 m) along gentle slopes, modeling results reveal a deep aquifer with a large total water storage (1,080–1,150 mm), an important fraction of inactive water storage (94%), and relatively long stream water transit times (0.5–2.5 years). In the Auradé watershed, representative of agricultural landscapes of the southwestern France developed on molasse, a relatively shallow regolith (1–8 m) is observed along hilly slopes. Simulations indicate a shallow aquifer with moderate total water storage (590–630 mm), an important fraction of inactive water storage (91%), and shorter stream water transit times (0.1–1.3 years). In the Strengbach watershed, typical of mid‐mountain forested landscapes developed on granite, CZ evolution implies a shallow regolith (1–5 m) along steep slopes. Modeling results infer a shallow aquifer with the smallest total water storage (475–575 mm), the shortest stream water transit times (0.1–0.7 years), but also the highest fraction of active water storage (18%).

Revealing the Extent of Pesticide Runoff to the Surface Water in Agricultural Watersheds

Pesticides are beneficial in protecting crops from pests and improving agricultural productivity; however, concerns on pesticide pollution in water have increased due to their indiscriminate use and lack of adequate regulations. Many studies have focused on the risks of pesticides considering the limited number and types of pesticide residues in crops and soils, and duration, and very few have focused on surface water throughout the year. Therefore, this study comprehensively identified 308 pesticides in surface water samples collected monthly over one year in the Saemangeum Basin, Korea. Both targeted and non-targeted analyses were used to identify 171 and 24 pesticides, respectively. Results highlight the extensive extent of pesticide contamination. Among the quantified pesticides, bromobutide and pretilachlor consistently exhibited high concentrations and risk levels, as indicated by their elevated risk quotient (RQ) values. Seasonal variations in pesticide concentrations revealed distinct patterns with intensified herbicide use during summer and increased insecticide concentrations during autumn. This study highlights the presence, distribution, and associated ecological risks of pesticides in surface waters, emphasizing the necessity of comprehensive monitoring and regulatory measures to protect aquatic ecosystems. The high RQ values identified for specific pesticides underscore the urgent need to implement effective strategies to mitigate these environmental risks.

Evaluation of future flood probability in agricultural reservoir watersheds using an integrated flood simulation system

In agricultural reservoir watersheds, the presence of various agricultural hydraulic structures combined with the influence of climate change necessitates effective flood risk analysis. This study aims to develop a flood risk evaluation technique for agricultural basins by incorporating the low temporal resolution of climate change scenarios. The approach utilizes flood probability as a key component and incorporates the Multiplicative Random Cascade model as a probability-based rainfall time decomposition model to capture the inherent uncertainty in climate change data. Reliability analysis with the limit state equation is employed for probability-based risk evaluation. Given that agricultural watersheds typically consist of agricultural reservoirs and farmland, the study develops a hydraulic and hydrological linkage model to comprehensively calculate flood probability, simulating from the agricultural reservoir in the upper side to the farmland located at the bottom edge of the watershed. The flood risk assessment is applied to the Yedang Reservoir watershed in Korea, using the Shared Socio-economic Pathways climate change scenario. Results indicate that flood probability is the highest in river levees during future precipitation events, converging to nearly 100% under 24-hour duration rainfall, irrespective of the rain frequency. Despite the hydraulic structures at the farmland being designed for a lower frequency of rain events, the average probability of flooding in the farmland have reached 50%. The agricultural reservoir exhibits a low risk of flooding even under climate change conditions. The proposed method not only enables the establishment of flood countermeasures but also facilitates the evaluation and prediction of future flood risk for each hydraulic structure located in rural areas affected by climate change.

Sustaining crop yield and water quality under climate change in intensively managed agricultural watersheds—the need for both adaptive and conservation measures

The projected near-future climate (2031–2059) of wetter springs and drier summers may negatively affect agricultural production in the US Midwest, mostly through reduced aeration of the root zone due to excess soil water and frequent loss of nutrients such as nitrate (NO3-N) and total phosphorus. Several agricultural adaptations—such as adding tile drains and increasing fertilizer rates—may be deployed to mitigate potential reductions in crop yield. However, these adaptations (generally driven by economic benefits) may have a severe impact on water quality, which is already under stress due to excess nutrient runoff from agricultural fields causing hypoxia in inland and coastal waters. Here, we evaluate the crop yield and water quality consequences of such adaptations under future climate with the Soil and Water Assessment Tool in a testbed watershed located in central Illinois. We show that additional tile drains and increased fertilizers can help achieve baseline (2003–2018) corn yields but with a nearly two-fold increase in riverine NO3-N yield affecting a major drinking water supply source. However, a shift to spring-only fertilizer application may not require additional fertilizer and reduces the increase in NO3-N loss to 1.25 times above the baseline. We also show that water quality may improve (better than baseline) with conservation measures such as cover crops and switchgrass. Our findings highlight the need to develop efficient climate change adaptation and conservation strategies for sustainable agriculture and water quality.

Spatiotemporal variability in the C-factor: An analysis using high resolution satellite imagery

Estimating the cover and management factor (C-factor) for Universal Soil Loss Equation (USLE) that varies spatially and temporally within a watershed is time-consuming and resource-intensive. The Normalized Difference Vegetation Index (NDVI) approach can offer a potential alternative for this process. The current study examines nine NDVI models to compare and evaluate their performance in estimating the C-factor values for an agricultural watershed in southwestern Ontario, Canada. Satellite imagery from 2013 to 2020 was used to analyze the models’ similarities and differences on a detailed spatial and temporal scale. The results showed different C-factor values for each model, reflecting that they were developed for different geographical areas and purposes. While the Karaburun model differed from all other models on an annual basis, a detailed combined analysis of different spatial and temporal scales revealed that it was similar to other models. Seasonal analysis was found to be adequate for the current study, as it reduced the resources required and provided an overall view of the vegetation situation. However, a detailed monthly analysis may be necessary when investigating a specific season. The current analysis found that the summer months of June, July, and August have similar trends when comparing different models for different land uses and individual months, which aligns with the seasonal analysis. In conclusion, the current study highlights the importance of incorporating spatial and temporal scales in hydrological modeling and provides valuable insight into the applicability of different NDVI models for estimating the C-factor for southwestern Ontario watersheds. These findings can help inform future research and aid in developing accurate models for estimating soil erosion in this region. The results also emphasize that the NDVI approach has the potential for estimating the USLE C-factor and improving the estimation of soil erosion from agricultural watersheds by incorporating a variable C-factor over time and space. However, further research is needed to validate each model and determine which model best suits the study area.

A mini review on phytoremediation of fluoride-contaminated waters: a bibliometric analysis

Traditional techniques for removing fluoride (F−) from water are costly and complex, whereas phytoremediation is a less expensive method that can be adopted as an ecotechnological tool to control not only fluorine, but also other pollutants in urban and agricultural watersheds. Phytoremediation as a strategy for defluoridation is little used and still has a long way to go. Therefore, it is important to get a picture that describes scientific achievements and trends around this topic and support decision-making processes to move forward. Hence, the aim of this study was to collect bibliometric data on the use of phytoremediation to remove F−, from Scopus and Web of Science databases. Data collected considered the scenario of related publications regarding countries and researchers that publish the most, the chronological evolution of the publications, the co-occurrence of terms to point out overall focus and interests of researches and collaboration initiatives around the subject. India has the highest number of publications on F− phytoremediation, which can be explained by the high incidence of fluorosis in that country. The number of publications per year, although still small, has grown over the last 5 years. A cluster map showing the co-occurrence of terms indicated research focused on the effects of F− on plant metabolism, and the correlation of this contaminant when in the presence of heavy metals and with issues of sanitation. Different studies also seek species of accumulator plants, and investigate physical and chemical parameters of the environment that influence the absorption of the F− anion. New approaches to optimize the process and phytoremediation overall are on the rise like the use of phytohormones, growth-promoting bacteria, sorption processes and genetic manipulation.

Assessing hydrological and water quality parameters in the Hathmati Watershed using the SWAT (Soil and Water Assessment Tool)

Abstract The study is conducted on the Hathmati River, which is the principal tributary of the Sabarmati River, one of Gujarat's biggest rivers. Indicators of water quality such organic N, organic P, NO3, NH4, NO2, total N, and total P were compared between the observed values in the watershed and the streamflow models. For monthly streamflow and monthly nitrate during the calibration period of 1999–2012, and for 0.87 and 0.56 for the validation period of 2013–2020, respectively, the Nash–Sutcliffe efficiency was 0.89 and 0.79. The measured water quality data at three locations in the watershed were largely compatible with their corresponding modeled values, while the agreement varied by nutrients. Using the parameters used to forecast the hydrological and water quality responses in the watershed region, the Soil and Water Assessment Tool model faithfully duplicated the hydrological and water quality conditions in this agricultural watershed. The results of the modeling indicated that nutrients are one of the key determinants with regard to the quality of the water, in accordance with the current land use and management practices in the watershed. Management practices should be implemented in order to reduce the nutrient load and meet the watershed's requisite water quality standards.

Assessing the Impact of Climate and Land-Use Changes on the Hydrologic Cycle Using the SWAT Model in the Mun River Basin in Northeast Thailand

Climate change (CC) and land-use change (LUC) will alter a basin’s hydrological processes and water balance. Quantifying their significance is imperative in formulating appropriate countermeasures and management plans. This study assesses projected changes in hydrological variables under CC and LUC scenarios to provide multi-dimensional insight into water balance relevant to an agricultural watershed in Northeast Thailand. The soil and water assessment tool (SWAT) is utilized to simulate hydrological variables (evapotranspiration, soil moisture, surface runoff, and water yield) for the baseline (1981–2010) and the near-future (2021–2050) after calibrating the model. CC projections considering the CMIP6 model ensemble for the high-emission scenario (SSP5-8.5) show the annual rainfall may not change significantly (0.5% increase compared to baseline). However, the surface runoff will likely increase by 33% due to the projected increase in rainfall intensity and extremes. The increase in urban areas due to LUC for the business as usual (BAU) scenario is also expected to boost surface runoff by 38–87%. Similarly, the LUC scenario with forest expansion will increase evapotranspiration by up to 1.3%. While CC is anticipated to raise water yield by 11%, LUC may reduce it by 6%. Under the combined scenario, the yield is expected to increase by 8%, indicating CC as the dominating driver. The results show that although CC governs the runoff from the basin, land-use change will also impact flows at a monthly scale. Importantly, soil moisture in the future will decrease (8–9%) under both stressors, which will reverberate in the basin’s agricultural livelihood and socioeconomic settings unless the appropriate adaptation measures are implemented.

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region

The 2015 “Paris Agreement” aims to limit the global average temperature rise to significantly less than 2 °C, preferably within 1.5 °C above pre-industrial levels. A multitude of studies have focused on evaluating how different sectors respond to such levels of warming. Nonetheless, most of these studies fail to provide a clear roadmap to mitigate these impacts. A case in point is the anticipated decline in corn and soybean yields and increased phosphorus (P) and nitrogen (N) discharge into water bodies, a trend linked to past agricultural practices and climate change. In this research, we employ a novel assessment of how existing management practices under 1.5 °C and 2 °C global warming (GW) scenarios can affect nutrient availability in time and space as well as crop yield in a typical agricultural watershed in the Mid-Atlantic Region, specifically the Upper Maurice River Watershed (UMRW) in New Jersey.Using the Soil and Water Assessment Tool (SWAT) with multiple Global Climate Model (GCM) projections, we found that compared to 1.5 °C, a 2 °C GW scenario would exacerbate runoff, leading to amplified nutrient leaching. These losses decrease nutrient availability during the crop growing season. Moreover, a mismatch between the timing of fertilizer application and crop nutrient absorption caused nutrient-related stress. This nutrient and anticipated temperature stress resulted in a more significant decrease in crop yields under the 2 °C GW scenario than the 1.5 °C scenario. We have designed a management scenario to reduce future nutrient losses while increasing crop yields. The strategy involves altering the timing of planting/harvesting and the fertilizer application rate in response to a warming climate. This approach is projected to increase corn and soybean yields by +39 % (+21 %) and +2 % (+17 %), respectively, under the 1.5 °C (2.0 °C) GW scenario for the RCP-4.5 pathway. Simultaneously, it is expected to decrease the N and P loads at 1.5 °C (2.0 °C) GW. Comparable projections are also observed under the RCP-8.5 pathway.

Modeling Land Use and Management Practices Impacts on Soil Organic Carbon Loss in an Agricultural Watershed in the Mid-Atlantic Region

Measuring organic carbon (OC) losses from soils presents a challenge because of the intricate interplay of human-induced and biophysical processes. This study employs SWAT-C to simulate particulate OC (POC) and dissolved OC (DOC) losses from the Upper Maurice Watershed in the Mid-Atlantic Region. Simulation outcomes reveal that surface runoff was the primary contributor to the total DOC load (65%), followed by lateral flow (30%), and then groundwater (5%). Meanwhile, POC load was linked to erosion processes induced by surface runoff. Our findings indicate that agricultural land-use types exhibited the highest annual average DOC and POC loads. Forests and grasslands displayed intermediate loads, while barren land had the lowest load. Concerning seasonal fluctuations, agricultural land-use types exhibited distinct DOC and POC load patterns when compared to forest and grassland types, indicating the dominant role of management practices in determining soil OC (SOC) losses. Additional modeling of management practices’ impact on SOC budgets indicates maximal SOC sequestration with full irrigation, no-till (NT), and full fertilization. In contrast, the largest SOC depletion arises from combining conservation tillage (CT) and no fertilization, irrespective of irrigation. This study shows that SWAT-C can be used to simulate land use and management impacts on SOC dynamics.

Riverine nitrogen source and yield in urban systems

Although human reshaping of the nitrogen (N) cycle is well established, contributions of individual N sources to riverine and coastal eutrophication are less certain. Urban N fluxes are potentially substantial, particularly from sewer overflows. Results from four longitudinal surveys in rivers in and around the city of Pittsburgh, Pennsylvania, were used to characterize N chemistry and isotopic composition and were compared with LOADEST‐model‐derived total N (TN) flux budgets from three urban areas along the Ohio River (Pittsburgh, Pennsylvania; Cincinnati, Ohio; and Louisville, Kentucky). Triple nitrate isotopes reveal that riverine nitrate in the Pittsburgh region is dominated by wastewater inputs despite high atmospheric deposition rates. Our budget estimates demonstrate that the magnitude of urban N yields is comparable to yields reported for agricultural watersheds and that these high urban N yields cannot consist of permitted, point‐source discharges alone. Our results reveal that nonpoint sources in urban systems represent an important but overlooked source of TN to overall riverine budgets.

Optimal design of two-dimensional water trading considering hybrid “three waters”-government participation for an agricultural watershed

Optimal design of two-dimensional water trading considering hybrid “three waters”-government participation for an agricultural watershed

A century of nitrogen dynamics in agricultural watersheds of Denmark

Intensive agriculture has been linked to increased nitrogen loads and adverse effects on downstream aquatic ecosystems. Sustained large net nitrogen surpluses have been shown in several contexts to form legacies in soil or waters, which delay the effects of reduction measures. In this study, detailed land use and agricultural statistics were used to reconstruct the annual nitrogen surpluses in three agriculture-dominated watersheds of Denmark (600–2700 km2) with well-drained loamy soils. These surpluses and long-term hydrological records were used as inputs to the process model ELEMeNT to quantify the nitrogen stores and fluxes for 1920–2020. A multi-objective calibration using timeseries of river nitrate loads, as well as other non-conventional data sources, allowed to explore the potential of these different data to constrain the nitrogen cycling model. We found the flux-weighted nitrate concentrations in the root zone percolate below croplands, a dataset not commonly used in calibrating watershed models, to be critical in reducing parameter uncertainty. Groundwater nitrate legacies built up in all three studied watersheds during 1950–1990 corresponding to ∼2% of the surplus (or ∼1 kg N ha yr−1) before they went down at a similar rate during 1990–2015. Over the same periods active soil nitrogen legacies first accumulated by approximately 10% of the surplus (∼5 kg N ha yr−1), before undergoing a commensurate reduction. Both legacies appear to have been the drivers of hysteresis in the diffuse load at the catchments’ outlet and hindrances to reaching water quality goals. Results indicate that the low cropland surpluses enforced during 2008–2015 had a larger impact on the diffuse river loads than the European Union’s untargeted grass set-aside policy of 1993–2008. Collectively, the measures of 1990–2015 are estimated to have reset the diffuse load regimes of the watersheds back to the situation prevailing in the 1960s.

Agricultural activity on the Mun River basin: insight from spatial distribution and sources of dissolved rare earth elements in northeast Thailand.

Rare earth elements (REE) are emerging pollutants of concern, impacted by intensive fertilizer use and discharge of human and animal waste into agricultural watersheds. However, the natural values and potential anthropogenic enrichment of REE in aqueous systems of the agricultural basins remain poorly understood. This study investigated the spatial variation of dissolved REE in a predominantly agricultural river (Mun River) in northeast Thailand. Dissolved ΣREE concentrations in the Mun River ranged from 5.08 to 272.91 ng/L, with the highest concentrations observed in the middle reaches where agricultural fertilizers and wastewater increased dissolved REE concentrations. The PAAS-normalized patterns and dissolved Eu anomaly jointly reveal that the dissolved ΣREE mainly originated from local rocks and agricultural fertilizers. The dissolved REE in the Mun River is characteristic of a depleted light REE relative to heavy REE, slightly negative Ce anomaly, positive Eu anomaly, and positive Gd anomaly in a punctate distribution. The correlation analysis of (La/Yb)N with fluvial pH and HCO3- indicates that the water environment characteristics of the Mun River control dissolved REE fractionation. The Ce anomaly is associated with the oxidation environment, whereas the Eu anomaly is linked to the lithologic inheritance. Positive punctate Gd anomalies are influenced by human-caused wastewater discharge and applying fertilizers, raising Gd concentrations beyond natural background levels. This study has suggested that the geochemical characteristics of dissolved REE are affected by agricultural disturbances, and future environmental research on dissolved REE is essential to clarifying the impacts of REE on agriculture, the environment, and human health.

NGHIÊN CỨU MỐI TƯƠNG QUAN GIỮA DIỆN TÍCH SỬ DỤNG ĐẤT NÔNG NGHIỆP ĐẾN TẢI LƯỢNG CÁC CHẤT DINH DƯỠNG TRONG MÔI TRƯỜNG NƯỚC TRÊN LƯU VỰC SÔNG CẢ

The purpose of this research is to determine the relationship between the land area in the agricultural watershed and the nutrient load in the Ca River basin. Eight sampling sites in the Ca River basin were chosen for sampling in two phases during the dry season (5/2021) and the rainy season (September-2021). The results show that the measured water flow ranges from 27.7 m3 /s to 206.1 m3 /s in the dry season, with the highest reaching 2696 m3 /s and the lowest at 395 m3 /s in the rainy season. The load results of nutrients TN and, TP show a close relationship with flow and topography. The area of agricultural land in the basins influences nutrient load, which follows two distinct trends, increasing steadily in the northwest side river group and fluctuating in the southeast side river group. The land use process has a more obvious impact on the load during the dry season, whereas natural factors change the variation of nutrient load flows during the rainy season.

Assessing the watershed-scale effects of tillage management on surface runoff and sediment loss using a Curve Number-precipitation relationship based approach

Poor soil management limits the soil functions of water infiltration and storage and enhances water and soil losses. Adoption of improved soil management practices is thus vital to protecting agricultural soils amid recurring droughts and declining water availability. Although the benefits of adopting soil health promoting practices such as no-tillage (NT) have been extensively investigated at field scale, studies quantifying the watershed-scale impacts of such practices are lacking. This study aims to address this gap by quantifying soil and water conservation benefits of adopting NT over conventional tillage (CT) on all croplands in the Brushy Creek Watershed in the Texas Blackland Prairies using the Agricultural Policy/Environmental eXtender (APEX) model. Most hydrologic and water quality models often fall short in accurately simulating the effects of management practices since they are not capable of simulating changes in soil and hydrologic properties that result from changes in soil management. This study proposed a novel framework to translate field-scale rainfall and runoff relationships into a watershed-scale parameterization tool that can be used for any agricultural watershed where measured rainfall and runoff data are available. Curve Number–Precipitation (CN-P) relationships were first defined based on field observations of rainfall and runoff from paired CT and NT fields. The CT and NT APEX models were then trained to follow the defined CN-P relationships to reflect the associated changes in hydrologic properties. Changing management from CT to NT on all croplands in the study watershed (i.e., in 35% of the watershed area) resulted in a 25% (38 mm year−1) reduction in average annual runoff and a 57% (1.13 t ha -1year−1) reduction in average annual sediment loss at the outlet of the watershed. The reduction in sediment loss from individual subareas with NT management was found to be sensitive to subarea physical properties such as slope and channel depth to width ratio, but the reduction in runoff was not sensitive to those properties. Results from this study are useful to natural resource conservationists and watershed managers in effectively implementing improved soil management practices.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater. Previous studies have applied regression and classification ML algorithms to predict nutrient concentrations in surface water and/or groundwater, or to categorize an output variable using a limited number of input variables. However, there have been no studies that examined the application of different ML classification algorithms in agricultural settings to classify various output variables using a wide range of input variables. In this study, twenty-four ML classification algorithms were implemented on a dataset from three locations within the Upper Parkhill watershed, an agricultural watershed in southern Ontario, Canada. Nutrient concentrations in surface water were classified using geochemical and physical water parameters of surface water and groundwater (e.g., pH), climate and field conditions as the input variables. The performance of these algorithms was evaluated using four evaluation metrics (e.g., classification accuracy) to identify the optimal algorithm for classifying the output variables. Ensemble bagged trees was found to be the optimal ML algorithm for classifying nitrate concentration in surface water (accuracy of 90.9%), while the weighted KNN was the most appropriate algorithm for categorizing the total phosphorus concentration (accuracy of 87%). The ensemble subspace discriminant algorithm gave the highest overall classification accuracy for the concentration of soluble reactive phosphorus and total dissolved phosphorus in surface water with an accuracy of 79.2% and 77.9%, respectively. This study exemplifies that ML algorithms can be used to signify exceedance of recommended concentrations of nutrients in surface waters in agricultural watersheds. Results are useful for decision makers to develop nutrient management strategies.