Modeling the impacts of agricultural best management practices on runoff, sediment, and crop yield in an agriculture-pasture intensive watershed

Methodology for the assessment of poor-data water resources

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Evaluating the impacts of sustainable land management practices on water quality in an agricultural catchment in Lower Austria using SWAT

Managing agricultural watersheds in an environmentally friendly manner necessitate the strategic implementation of well-targeted sustainable land management (SLM) practices that limit soil and nonpoint source pollution losses and translocation. Watershed-scale SLM-scenario modeling has the potential to identify efficient and effective management strategies from the field to the integrated landscape level. In a case study targeting a 66-hectare watershed in Petzenkirchen, Lower Austria, the Soil and Water Assessment Tool (SWAT) was utilized to evaluate a variety of locally adoptable SLM practices. SWAT was calibrated and validated (monthly) at the catchment outlet for flow, sediment, nitrate-nitrogen (NO₃-N), ammonium nitrogen (NH₄-N), and mineralized phosphorus (PO₄-P) using SWATplusR. Considering the locally existing agricultural practices and socioeconomic and environmental factors of the research area, four conservation practices were evaluated: baseline scenario, contour farming (CF), winter cover crops (CC), and a combination of no-till and cover crops (NT + CC). The NT + CC SLM practice was found to be the most effective soil conservation practice in reducing soil loss by around 80%, whereas CF obtained the best results for decreasing the nutrient loads of NO₃-N and PO₄-P by 11% and 35%, respectively. The findings of this study imply that the setup SWAT model can serve the context-specific performance assessment and eventual promotion of SLM interventions that mitigate on-site land degradation and the consequential off-site environmental pollution resulting from agricultural nonpoint sources.

Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment

The quantitative prediction of hydrological components through hydrological models could serve as a basis for developing better land and water management policies. This study provides a comprehensive step by step modelling approach for a small agricultural watershed using the SWAT model. The watershed is situated in Petzenkirchen in the western part of Lower Austria and has total area of 66 hectares. At present, 87% of the catchment area is arable land, 5% is used as pasture, 6% is forested and 2% is paved. The calibration approach involves a sequential calibration of the model starting from surface runoff, and groundwater flow, followed by crop yields and then soil moisture, and finally total streamflow and sediment yields. Calibration and validation are carried out using the r-package SWATplusR. The impact of each calibration step on sediment yields and total streamflow is evaluated. The results of this approach are compared with those of the conventional model calibration approach, where all the parameters governing various hydrological processes are calibrated simultaneously. Results showed that the model was capable of successfully predicting surface runoff, groundwater flow, soil profile water content, total streamflow and sediment yields with Nash-Sutcliffe efficiency (NSE) of greater than 0.75. Crop yields were also well simulated with a percent bias (PBIAS) ranging from −17% to 14%. Surface runoff calibration had the highest impact on streamflow output, improving NSE from 0.39 to 0.77. The step-wise calibration approach performed better for streamflow prediction than the simultaneous calibration approach. The results of this study show that the step-wise calibration approach is more accurate, and provides a better representation of different hydrological components and processes than the simultaneous calibration approach.

Prioritization of Sub-Watersheds to Sediment Yield and Evaluation of Best Management Practices in Highland Ethiopia, Finchaa Catchment

Excessive soil loss and sediment yield in the highlands of Ethiopia are the primary factors that accelerate the decline of land productivity, water resources, operation and function of existing water infrastructure, as well as soil and water management practices. This study was conducted at Finchaa catchment in the Upper Blue Nile basin of Ethiopia to estimate the rate of soil erosion and sediment loss and prioritize the most sensitive sub-watersheds using the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated using the observed streamflow and sediment data. The average annual sediment yield (SY) in Finchaa catchment for the period 1990–2015 was 36.47 ton ha−1 yr−1 with the annual yield varying from negligible to about 107.2 ton ha−1 yr−1. Five sub-basins which account for about 24.83% of the area were predicted to suffer severely from soil erosion risks, with SY in excess of 50 ton ha−1 yr−1. Only 15.05% of the area within the tolerable rate of loss (below 11 ton ha−1yr−1) was considered as the least prioritized areas for maintenance of crop production. Despite the reasonable reduction of sediment yields by the management scenarios, the reduction by contour farming, slope terracing, zero free grazing and reforestation were still above the tolerable soil loss. Vegetative contour strips and soil bund were significant in reducing SY below the tolerable soil loss, which is equivalent to 63.9% and 64.8% reduction, respectively. In general, effective and sustainable soil erosion management requires not only prioritizations of the erosion hotspots but also prioritizations of the most effective management practices. We believe that the results provided new and updated insights that enable a proactive approach to preserve the soil and reduce land degradation risks that could allow resource regeneration.

Assessment of the impact of land use in an agricultural catchment area on water quality of lowland rivers

In several countries around the world, agricultural land area exceeds 70% (Uruguay 82.6%, Kazakhstan 80.4%, Turkmenistan 72.0%, Great Britain 71.7%, Ukraine 71.6% and others). This poses a serious risk of dissipating nitrates into the aquatic environment in agricultural catchments. The aim of this study was to assess the impact of land use on water quality parameters in an agricultural catchment area. It was decided to select for analysis the catchment of the Orla River (river length of 88 km, catchment area of 1,546 km²). The catchment area is predominantly agricultural in character and its entire area has been declared as an agricultural nitrate vulnerable zone (NVZ). A total of 27 survey sites were selected on the main watercourse and its tributaries. Analyses were conducted in the years 2010-2012 to determine physical and chemical parameters of water (pH reaction, conductivity, dissolved oxygen, total nitrogen, organic nitrogen, ammonia nitrogen, nitrates, total and reactive phosphorus) as well as six macrophyte metrics of ecological status assessment (MIR, IBMR, RMNI, MTR, TIM, RI). The average values of most physico-chemical parameters of water quality repeatedly exceeded limits of good ecological status, both in the Orla River and its tributaries. As many as 18 survey sites were classified as moderate ecological status, five sites as poor and only four as good ecological status. The results indicate the impact of land use in the catchment on water conductivity. Differences were observed in the concentrations of biotic components in the main watercourse and its tributaries, and in water quality in the southern part of the catchment in relation to the rest of the study area. This is probably connected with a greater share of forests and surface waters in that area.

Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

In the Texas High Plains (THP), diminishing irrigation well-capacities, and increasing costs of energy and equipment associated with groundwater extraction and application are contributing factors to a transition from irrigated to dryland agriculture. The primary goal of this modeling exercise was to investigate whether and to what extent hypothetical changes in factors putatively associated with soil health would affect dryland cotton (Gossypium hirsutum L.) yields. The factors selected were drainage, surface runoff, soil water holding capacity, soil organic carbon (SOC) and albedo. As a first analysis to evaluate these factors, we used the CROPGRO-Cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model. Specifically, we evaluated the effects of reduced surface runoff, increased soil water holding capacity, and SOC, doubling of the soil albedo through stubble mulching, and of soil drainage by enhancing infiltration with no-tillage/cover crops on yield by adjusting related soil properties. In our analysis, we used mean yields simulated with soil properties of a Pullman clay loam soil at Halfway, TX on the THP as baseline, which were compared to values obtained with the adjusted factors using weather data from 2005 to 2019. Simulated mean yield increased by 27% when the soil water holding capacity was increased by 25 mm, 7% when the runoff curve number was decreased from 73 to 60, 16% when soil albedo was increased from 0.2 to 0.4, and by 58% when the soil drainage factor (fraction day−1) was doubled from 0.2. No significant statistical change in simulated mean yield was calculated when SOC was increased by 1%. Further, effects of a 50 mm pre-plant irrigation were also assessed, simulating limited irrigation in the transition to dryland agriculture that resulted in a statistically insignificant 12% increase in seed-cotton yield. Simultaneous implementation of the four statistically significant individual scenarios (increased water holding capacity, infiltration, albedo, and drainage) resulted in the highest increase (93%) in mean seed-cotton yield. An economic and risk analysis of simulated yields under different scenarios indicated that these factors could reduce revenue risk for dryland cotton producers, with most of the effect from soil drainage improvements.