Agricultural Watersheds Research Articles

Future warming-induced phosphorus loss mitigated by land conversion and degradation

Under the context of global climate change, it is critical to understand future soil organic carbon (SOC) dynamics and the corresponding diffuse phosphorus pollution, and to explore their driving factors to facilitate sustainable soil and water management in agricultural watersheds. With the integrated application of the Denitrification-Decomposition (DNDC) and Soil and Water Assessment Tool (SWAT) models, we simulated the long-term changes in SOC and diffuse phosphorus loss during the 2011–2100 period in Naoli River watershed, which is a typical agricultural watershed in northeastern China. The results showed that approximately 14.71 % of the study area will be converted from natural land (i.e., forest, pasture, and wetland) into agricultural land, and 23.50 % of the uplands will be converted into paddy fields under future climate change conditions. Without additional carbon input, the SOC will decrease at an annual rate of 0.37 % from 24.48 g/kg in 2011 to 17.77 g/kg in 2100, indicating serious land degradation in the future. As a result, watershed diffuse phosphorus loss will increase in the first 40 years (from 2011 to 2050), but show a decreasing trend in the later 50 years (from 2051 to 2100). Overall, watershed diffuse phosphorus loss will decrease from 2.75 kg/ha to 2.24 kg/ha during the 2011–2100 period. According to the scenario comparison, the logarithmic mean Divisia index and structural equation model analysis indicated that climate-induced land-use changes was the dominant factor causing SOC changes and diffuse phosphorus pollution, rather than climate change alone. The expansion of paddy fields could mitigate diffuse phosphorus pollution in the future, and improving agronomic management, such as increasing carbon inputs and integrating water and fertilizer management, are suggested to be adopted to mitigate land degradation by increasing SOC sequestration.

Agricultural practices regulate the seasonality of groundwater-river nitrogen exchanges

Soil System Budgets (SSB) of nutrients are generally performed annually over arable land to infer their use efficiency and water pollution risk in highly exploited agricultural watersheds. They are seldom partitioned into seasonal budgets and matched with seasonal nutrient transport in adjacent river reaches. We calculated seasonal soil nitrogen (N) budgets in a Mincio River sub-basin (Italy), and we analyzed the dissolved inorganic N net export in the river reach draining such sub-basin. Our results show seasonal differences of SSB with N excess in winter and even more in spring, equilibrium among sources and sinks during autumn and N deficit during summer. Seasonal inorganic N loads transported by the river were not correlated with SSB as they peaked in late summer and were at their minimum in early spring. Fertilization uncoupled to significant uptake supports N excess in winter and spring, whereas crop uptake uncoupled to N inputs supports summer N deficit. Nitrification cannot explain nitrate accumulation in the river reach, suggesting alternative dynamics driving the local hydrology. Flood irrigation results in large soil nitrate solubilization, transport and in upward migration of the groundwater piezometric head during spring and summer periods. River water is likely replaced by nitrate-rich groundwater when the groundwater recharge exceeds a certain threshold coinciding with late summer. Irrigation is then interrupted and the piezometric head, together with nitrate exchange, decreases. This work suggests that a deep understanding of N dynamics in agricultural watersheds with flooding irrigation on permeable soils needs the reconstruction of the vertical pathways of nitrate and of river-groundwater interactions. Moreover, the partitioning of annual into seasonal N budgets and their combination with irrigation practices allows the identification of hot moments in N cycling. Agricultural practices minimizing nitrate excess, its mobility and the risk of surface and groundwater pollution are suggested for this area.

Climate factors affect N2O emissions by influencing the migration and transformation of nonpoint source nitrogen in an agricultural watershed

Precipitation can affect the residence time of nitrogen compounds, and temperature can influence nitrogen transformation in soil. Therefore, we hypothesized that climate factors can affect the emissions of N2O, an important greenhouse gas produced via nitrogen transformation, by influencing the migration and transformation of nonpoint source nitrogen in soil. To test this hypothesis and quantify the effect of climate factors on N2O emissions, the SWAT model and the modified SWAT-N2O coupler were used to study the effect of climate factors on the migration and transformation of nonpoint source nitrogen and N2O emissions in an agricultural watershed from 2009 to 2018. Temperature affected N2O emissions more significant than precipitation, and N2O emissions increased with temperature and reached a plateau when the average monthly temperature was 23.0 °C. The N2O emissions first increased rapidly with precipitation due to the increase in moisture. However, when the average monthly precipitation reached 78.8 mm, the N2O emissions began to decrease because the residence time of nitrogen compounds in soil were reduced due to fast removal via runoff, which inhibits N2O emissions. Under the context of climate change with three scenarios (RCP2.6, RCP4.5, RCP8.5), temperature would increase gradually while precipitation would not change significantly from 2021 to 2080, as a result, the changes would increase N2O emissions by 6.7%, 32.3%, and 70.7%, respectively. This study quantifies the feedback of N2O emissions to climate change in croplands, providing a scientific basis for climate change mitigation and agricultural management.

Vertical distribution and influencing factors of deep soil organic carbon in a typical subtropical agricultural watershed

Soil organic carbon is one of the most commonly used indicators of soil health, as it plays a vital role in maintaining fertility and combating global warming. Understanding the vertical distribution and controlling factors of organic carbon in the entire regolith, rather than just the routinely defined upper 1 m portion of the soil, is crucial for assessing soil health in a holistic perspective. In this study, 21 boreholes in four different land uses were drilled from the land surface down to the bedrock in a typical subtropical agricultural watershed. The total organic carbon stock in the regolith ranged from 77.8 Mg C ha-1 to 311.8 Mg C ha-1 and the organic carbon content showed a progressive decline from land surface to bedrock. However, on average, only 19.0% of total organic carbon was stored at the depth 0–30 cm and 17.7% between 30 and 100 cm, whereas 63.3% was stored below 100 cm. Total organic carbon stock was significantly higher under paddy fields than under cropland, orchard or woodland in the upper 100 cm (p < 0.05) possibly due to straw incorporation, flooding of the paddy soils and their position on the lower slopes where eroded soil was deposited. However, there was no significant difference in total organic carbon stock below 200 cm (p > 0.05). According to the boosted regression tree analysis, soil texture outperformed the other edaphic factors and was the primary edaphic factor controlling TOC content of the different soils. The results show that there is a large carbon reservoir in the deep regolith. Land use strongly affects the distribution of carbon in the top 100 cm soil layers but has little effect on deep soil organic carbon. Deep TOC were closely linked to soil texture. This study highlights the importance of deep soil organic carbon for soil health and understanding the factors controlling its content for improved estimates of soil carbon storage.

Assessing the Effectiveness of Winter Cover Crops for Controlling Agricultural Nutrient Losses

ABSTRACTA nutrient loss reduction strategy is necessary to guide the efforts of improving water quality downstream of an agricultural watershed. In this study, the effectiveness of two winter cover crops, namely cereal rye and annual ryegrass, is explored as a loss reduction strategy in a watershed that ultimately drains into a water supply reservoir. Using a coupled optimization‐watershed model, optimal placements of the cover crops were identified that would result in the tradeoffs between nitrate‐N losses reduction and adoption levels. Analysis of the 10%, 25%, 50%, and 75% adoption levels extracted from the optimal tradeoffs showed that the cover crop placements would provide annual nitrate‐N loss reductions of 3.0%–3.7%, 7.8%–8.8%, 15%–17.5%, and 20.9%–24.3%, respectively. In addition, for the same adoption levels (i.e., 10%–75%), sediment (1.8%–17.7%), and total phosphorus losses (0.8%–8.6%) could be achieved. Results also indicate that implementing each cover crop on all croplands of the watershed could cause annual water yield reduction of at least 4.8%, with greater than 28% in the months of October and November. This could potentially be detrimental to the storage volume of the downstream reservoir, especially in drought years, if cover crops are adopted in most of the reservoir's drainage area. Evaluating water yield impacts, particularly in periods of low flows, is thus critical if cover crops are to be considered as best management practices in water supply watersheds.

Multi-spatial scale effects of multidimensional landscape pattern on stream water nitrogen pollution in a subtropical agricultural watershed

Multidimensional (coupled land use, soil properties, and topography) landscape effects on stream water nitrogen (N) are complex and scale-dependent. However, studies that identify critical buffer zones that explain large variations in riverine N, and estimate specific thresholds of multidimensional landscape patterns at the class level, result in a sudden changes in riverine N pollution, are still limited. Here, a new multidimensional landscape metric that combined land use, soil properties, and topography effects was applied to various riparian buffer zones and sub-watershed scales, and their relationships to riverine N levels were investigated. We used stream water ammonium−N, nitrate−N, and total−N concentrations datasets, from 2010 to 2017, in the nine subtropical sub-watersheds in China. The results of model selection and model averaging in ordinary least squares regressions, indicated that the riparian buffer zone with widths of 400 m, had more pronounced influence on water NH4–N and TN levels than at other scales. Within the 400 m buffer zone, the key landscape metrics for NH4–N, NO3–N and TN concentrations in stream water were different, and explained up to 43.35%–76.55% (adjusted R2) of the total variation in river N levels. When ENN_MNClass17 below 39–56 m, PDClass8 above 4.63–6.55 n/km2, PLANDClass27 above 23–29%, and CONTIG_MNClass42 below 0.35–0.37% within the 400 m buffer zone, riverine NH4–N and TN would be abruptly increased. This study provided practical ideas for regulation regarding landscape management linked to watershed structure, and identified reference thresholds for multidimensional landscape metrics, which should help reduce riverine N pollution in subtropical China.

Implementing landscape connectivity with topographic filtering model: A simulation of suspended sediment delivery in an agricultural watershed.

The widespread availability of high-fidelity topography combined with advances in geospatial analysis offer the opportunity to reimagine approaches to the difficult problem of predicting sediment delivery from watersheds. Here we present a model that uses high-resolution topography to filter sediment sources to quantify sediment delivery to the watershed outlet. It is a reduced-complexity, top-down model that defines transfer functions-topographic filters-between spatially distributed sediment sources and spatially integrated sediment delivery. The goal of the model is to forecast changes in watershed suspended sediment delivery in response to spatially distributed changes in sediment source magnitude or delivery, whether a result of watershed drivers or intentional management actions. Such an application requires the context of a watershed model that accounts for all sediment sources, enforces sediment mass balance throughout the spatial domain, and accommodates sediment storage and delivery over time. The model is developed for a HUC-8 watershed with a flat upland dominated by corn-soybean agriculture and deeply incised valleys near the watershed outlet with large sediment contributions from near-channel sources. Topofilter computes delivery and storage of field-derived sediment according to its spatial and structural connectivity to the stream channel network; subsequently, delivery of both field- and near-channel-derived sediment along with floodplain storage are computed in the stream channel network to the watershed outlet. The model outputs provide a spatially rich representation of sediment delivery and storage on field and along the stream that is consistent with available independent information on sediment accumulations and fluxes. Rather than a single best-calibrated solution, Topofilter uses the Generalized Likelihood Uncertainty Estimate (GLUE) approach to develop many possible solutions with sediment delivery rates expressed as probability distributions across the watershed. The ensemble of simulation outputs provides a useful basis for estimating uncertainty in sediment delivery and the effectiveness of different landscape management allocation across a watershed.

Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS

The search and identification of organic contaminants in agricultural watersheds has become a crucial effort to better characterize watershed contamination by pesticides. The past decade has brought a more holistic view of watershed contamination via the deployment of powerful analytical strategies such as non-target and suspect screening analysis that can search more contaminants and their transformation products. However, suspect screening analysis remains broadly confined to known molecules, primarily due to the lack of analytical standards and suspect databases for unknowns such as pesticide transformation products. Here we developed a novel workflow by cross-comparing the results of various in silico prediction tools against literature data to create an enhanced database for suspect screening of pesticide transformation products. This workflow was applied on tebuconazole, used here as a model pesticide, and resulted in a suspect screening database counting 291 transformation products. The chromatographic retention times and tandem mass spectra were predicted for each of these compounds using 6 models based on multilinear regression and more complex machine-learning algorithms. This comprehensive approach to the investigation and identification of tebuconazole transformation products was retrospectively applied on environmental samples and found 6 transformation products identified for the first time in river water samples.

Applying the NWS’s Distributed Hydrologic Model to Short-Range Forecasting of Quickflow in the Mahantango Creek Watershed

Abstract Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE″ = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE″ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (&gt;90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture. Significance Statement Daily runoff forecasts can alert farmers to rainfall–runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1–3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

Potential Sources of Heavy Metals in Sediments of an Urban‒Agricultural Watershed and Relationship with Land Use Using a Statistical Approach

This study verified pollution levels through evaluation of the Sediment Quality Guidelines (SQGs), pollution load index (PLI), and potential ecological risk index (PERI) by analyzing the concentrations of heavy metals in sediments of an urban‒agricultural watershed in the Yeongsan River basin, South Korea. Statistical analyses were performed to determine the relationships between pollution levels and land use, and potential sources of pollution were identified. For spatial distributions, Pb, Zn, Cu, Cd, and Hg concentrations were highest at mid-upstream, but As, Cr, and Ni concentrations were similar at most sites. The polluted sites, which showed the potential toxicity toward benthic organisms in comparison to SQGs, were most frequently observed at mid-upstream. Moreover, PLI and PERI evaluations also confirmed levels of high anthropogenic pollution and the potential ecological risk at mid-upstream. The mid-upstream sites with high heavy metal pollutions showed high correlations with urban land use, which showed the highest distribution, implying a close relationship with anthropogenic impacts such as high population density and industrial complexes. Statistical analyses also confirmed that high heavy metal concentrations in the mid-upstream were closely related to urban land use. These findings suggest that urban areas are highly likely to cause anthropogenic heavy metal pollution in sediments as point or non-point sources such as domestic sewage and industrial wastewater flow into rivers.

Total and dissolved phosphorus losses from agricultural headwater streams during extreme runoff events

Eutrophication continues to be a concerning global water quality issue. Managing and mitigating harmful algal blooms demands clear information on the conditions promoting large phosphorus losses from contributing watersheds. Of particular concern is the amount and form of phosphorus loading to receiving water bodies during extreme runoff events, which are expected to increase in frequency due to climate change. Five years (2015 to 2020) of water quantity and quality data from 11 agricultural watersheds in the lower Great Lakes basin were analyzed and used to model total and dissolved phosphorus losses. This study aimed to assess temporal dynamics in phosphorus concentrations and losses over runoff events covering a wide range of hydrologic conditions and to quantify their relative importance on annual phosphorus losses. Event concentration-discharge relationships for total and dissolved phosphorus were hysteretic and had contrasting dominant patterns across watersheds. The proportion of annual phosphorus losses during events was highly variable between watersheds, accounting for 47–94 %. Extreme events were particularly impactful: as few as three events per year were found to be responsible for nearly half of total phosphorus (20–50 %) and total dissolved phosphorus (14–44 %) losses. Variability in total and dissolved phosphorus losses and concentrations over a wide range of flow conditions suggests that event magnitude is an important control on the relative mobility of particulate and dissolved phosphorus fractions. This study showed that insights into nutrient dynamics and phosphorus budgets in the lower Great Lakes basin and agriculture dominated environments more broadly can be gained by assessing event nutrient losses with respect to flow conditions and patterns in concentration-discharge relationships.

Use of the WinTR-55 Hydrologic Model on Determination of Flood Peak Discharge: The Case of Kirklareli Vize Stream and Samsun Minoz Stream Watersheds

Climate change is the main parameter affecting water resources. This parameter will exacerbate hydrologic extreme events like drought and flood. Determination of possible peak flow in the agricultural watershed is important in terms of preventing crop losses. The materials and the methods suitable for agricultural watersheds (hydrology) were used in this study. The general aim of this study is to determine the success of estimation power of the Windows Technical Release-55 (WinTR-55) Model. In this study, the peak flows estimated by the WinTR-55 model using the data of the Kirklareli Vize and Samsun Minoz Stream watersheds were compared with the observed peak flows. The most successful estimation was for the 100-year return period with error 25% in the Vize stream watershed and was for the 10-year return period with error 2% in the Minoz Stream watershed. With the aid of the WinTR-55, which tends to predict larger peak flow rates, greater peak flow rates were estimated compared with observed peak flow for each return period. So, it was understood that WinTR-55 can be used for the prevention of flood damage in the Vize and Minoz Stream watersheds confidently. As a result, it is recommended that calculated peak flow in public institutions such as State Hydraulics Works (SHW) should made with the help of the WinTR-55 model.

An alternative to the Grain for Green Program for soil and water conservation in the upper Huaihe River basin, China

Study regionThe Xixian Watershed is an agricultural watershed in the upper Huaihe River basin, China, which suffers severe soil erosion. Study focusConverting the types of agricultural production without losing their agricultural purposes would be a choice for mitigating soil erosion compared to the Grain for Green Program (GGP). The Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model and the Soil and Water Assessment Tool (SWAT) model were used, and four land-use scenarios (S1: preference for dry land; S2: historical trend; S3: preference for paddy land; and S4: grain for green) were designed based on different preference in agricultural land and the GGP. The responses of runoff, sediment yields, and economic benefits were compared under each land-use scenario. New hydrological insights for the regionWe find that S1 increases runoff and sediment yields, S3 and S4 decrease runoff and sediment yields, and S2 slightly increases runoff and reduces sediment yields. Different types of agricultural land mainly affected runoff by redistributing surface and subsurface runoffs. Paddy land would increase subsurface runoff and decrease surface runoff and sediment yields, in contrast to dry land. The economic benefit could be increased under S3 and be opposite under other scenarios. Promoting paddy farming in a water-rich watershed can mitigate soil erosion, conserve groundwater, and gain more economic benefits simultaneously.

Identification of priority management areas for non-point source pollution based on critical source areas in an agricultural watershed of Northeast China

Identification of critical source areas (CSAs) for non-point source (NPS) pollution is of great significance for environment governance and prevention. However, the CSAs are generally characterized as great spatial dispersion, and spatially heterogeneous precipitation has a great influence on the spatial distribution of nutrient yields. Therefore, we identify the CSAs for nutrient yields in an agricultural watershed of Northeast China at hydrological response units (HRUs) scale based on the Soil and Water Assessment Tool (SWAT), assess the impacts of spatially heterogeneity of precipitation on the identification of the CSAs, analyze the sensitivity of nutrient yields to precipitation by scenarios analysis method, and further identify priority management areas (PMAs) that have poor ability to retain nutrients. The results showed that the CSAs for nutrient yields identified by uniform precipitation showed greater fluctuation range and coverage area than actual precipitation; the major prevention areas of total nitrogen (TN) yield were mainly distributed in regions nearby main stem of lower reaches, while that of total phosphorus (TP) yield were mostly located in urban area nearby outlet of the watershed; the identification of the PMAs significantly decreased the CSAs for TN yield, whereas that for TP yield was no significant difference with the CSAs. This study could provide scientific guidance for the NPS pollution governance and prevention.

Residential and agricultural soils dominate soil organic matter loss in a typical agricultural watershed of subtropical China

Soil organic matter (SOM) loss in agricultural watersheds driven by hydrology threatens the aquatic environment, and increases the risk of greenhouse gas emissions from aquatic systems, and will be detrimental to both the green development of agriculture and the achievement of the national “carbon peaking and carbon neutrality” goal of China. Consequently, we explored the use of δ13C, δ15N, and the carbon to nitrogen ratio (C/N) as end-members and combined this with a Bayesian stable-isotope mixed model to quantitatively estimate the contribution of typical land-use types (i.e., forest, rice, vegetable, tea, and residential soils) to sedimentary organic matter in a typical agricultural watershed in subtropical China. The results indicated that there was significant spatial variation of δ13C, δ15N, and the C/N in the target sediment mixture and its sources. The range of sediment δ13C (−26.2‰ to −23.8‰) and δ15N (3.9–7.9‰) were within the ranges of the sources, with δ13C and δ15N ranges of − 29.3‰ to − 20‰ and − 1.2‰ to 9‰, respectively. The average C/N of sediment was 11.1 ± 0.52, which was significantly higher than all source samples. The spatial distribution of δ13C and δ15N indicated that SOM loss in this catchment may primarily be attributed to vegetable and residential soils. According to the qualitative identification from dual fingerprint mark composite graphs of the C/N and δ15N, as well as δ13C and δ15N, the sedimentary SOM in the Tuojia watershed was mainly derived from sewage. Furthermore, Bayesian stable-isotope mixed model analysis indicated that residential soil was the main contributor to the SOM loss in the watershed, with a contribution rate of 75.8 %, followed by agricultural land (rice and vegetable soils) with a combined input of 12 %. The findings highlight that anthropogenic life and agricultural production have a vital impact on the carbon and nitrogen cycle in this agricultural catchment, and improving the rural habitation environment may play a key role in the control of SOM loss in subtropical agricultural watersheds.

LiDAR based hydro-conditioned hydrological modeling for enhancing precise conservation practice placement in agricultural watersheds

LiDAR based hydro-conditioned hydrological modeling for enhancing precise conservation practice placement in agricultural watersheds

Spatio-temporal variations of rainfall erosivity, correlation of climatic indices and influence on human activities in the Huaihe River Basin, China

Rainfall erosivity (RE) is an important factor in the soil erosion process, which cannot be altered by human intervention alone. The Huaihe River Basin (HRB) is a large agricultural watershed, suffering severe water erosion, investigating the spatio-temporal variation of RE is essential for local soil erosion prevention. The linear regression, Yue-Pilon method, and the Hurst exponent were used in analyzing the spatio-temporal variation within the HRB during 1960–2018. The slip correlation analysis and F-test were applied to obtain the correlation between climatic indices [the Arctic Oscillation (AO), the Southern Oscillation (SOI), the North Pacific Index (NPI), and the Niño 4SST index (SST)] and RE in the HRB. Results revealed that the annual average RE were 4280, 5061, 4068, 4886, and 4089 MJ mm hm−2 h−1 within the HRB, upper-HRB, middle-HRB, lower-HRB, and the Yishusi River Watershed, respectively. The annual average RE will increase within the upper-HRB and lower-HRB and decrease within the middle-HRB and the Yishusi River Watershed in the future. The seasonal average RE was ranked as summer > autumn = spring > winter in the HRB. The spatiotemporal difference was significant in the HRB, and different sub-regions exhibited a different trend in seasonal RE, except for the winter RE that increased significantly in all sub-regions (p < 0.05). The highest monthly RE occurred in July, with RE during May–September accounting for approximately 87% of the annual RE in the HRB. The AO and NPI had significant correlations with RE (p < 0.05) both on the annual and monthly scales with different lag times. The monthly AO, SOI, SST, and NPI were significantly correlated with RE in the long term with different lag times in different months (p < 0.05). These findings could provide potential predictive factors for RE prediction and help prevent soil erosion within the HRB.

Can slope adjusted Curve Number models compensate runoff underestimation in steep watersheds?: A study over experimental plots in India

The Natural Resources Conservation Service - Curve Number (NRCS-CN) model was originally developed to simulate runoff from a given storm on small agricultural watersheds having mild terrain slope (≤5%), but it has been applied in diverse field conditions and even much beyond its intended scope. The use of published CN table without slope adjustment and with a fixed initial abstraction ratio (λ = 0.2) has resulted in severe underestimation of runoff. In this study, the effect of slope on CN and λ was investigated using the measured rainfall-runoff data from small experimental plots in India. The investigation corroborated the requirement of a slope-adjusted CN equation suited for Indian hydro-climatic conditions, so three slope-adjusted CN models (CN2θ) having λ decaying with slope (θ) were proposed to improve the surface runoff prediction. The proposed models were calibrated and validated using 390 and 127 rainfall-runoff events, respectively, from 39 plots having different land-uses and slopes varying from 5% to 16%. The performance of new models was evaluated using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Normalized Root Mean Square Error (nRMSE), Mean Bias Error (mBIAS), and 1:1 plot. The proposed models exhibited superior performance statistics to existing CN adjustment models for both λ = 0.2 and 0.03. Also, the use of proposed CN2θ significantly improved the values of NSE, R2, nRMSE, mBIAS and regression slope from −0.37, 0.33, 0.960, 10.72 mm and 0.335 to 0.53, 0.61, 0.562, −0.40 mm and 0.865, respectively, as compared to handbook CN values without any slope adjustment.

Assessment of influencing factors on non-point source pollution critical source areas in an agricultural watershed

Critical Source Areas (CSAs) are areas that contribute disproportionate high levels of non-point source (NPS) pollution to receiving waters, and their occurrence is the result of the complex interaction between the factors related to the sources and transport processes of NPS pollution. A systematic understanding of how these influencing factors affect CSAs is essential for successful watershed management. In this study, we applied a statistical data mining technique boosted regression tree model to quantify the contribution of eight influencing factors (soil type, slope, elevation, RUSLE LS factor, RUSLE K factor, runoff, fertilizer application rate and land use) on two types of CSAs (TN-CSAs and TP-CSAs), as well as the marginal effects and potential thresholds of influencing factors on the occurrence of CSAs. Results show that land use (37.35%, 25.03%), fertilizer application (36.93%, 57.83%) and soil type (17.59%, 13.70%) have higher importance in determining the occurrence of TN-CSAs and TP-CSAs; and the incidence of TN-CSAs is positively correlated with most factors before the threshold for each influencing factor, after which the marginal effect largely leveled off or dropped slightly; TP-CSAs have essentially the same characteristics as TN-CSAs, but TP-CSAs are more likely to occur in areas with an annual runoff of around 244.92 mm. In addition, this study discussed the application of machine learning techniques in predicting CSAs under climate change without physical-based models, as well as a preliminary watershed management planning for NPS pollution control in the study watershed. These results provided important information for nutrient management regulations.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.