Modelling water discharge and nitrogen loads from drained agricultural land at field and watershed scale

This thesis examines water discharge and NO₃-N loads from drained agricultural land in southern Sweden by modelling at field and watershed scale. In the first stage of the work, the ability of DRAINMOD to simulate outflow in subsurface drains and that of DRAINMOD-N II to simulate NO₃-N loads in these drains was evaluated in field experiments. In addition, the ROSETTA pedotransfer model was used to estimate soil hydraulic properties required by DRAINMOD. In the second stage, DRAINMOD was integrated with Arc Hydro in a GIS framework (Arc Hydro-DRAINMOD) to simulate the hydrological response of an artificially drained watershed. DRAINMOD-N II and a temperature-dependent NO₃-N removal equation were also included in Arc Hydro-DRAINMOD to predict NO₃-N loading. Arc Hydro-DRAINMOD used a distributed modelling approach to aggregate the results of field-scale simulations, where the Arc Hydro data model described the drainage patterns in the watershed and connected the model simulations from fields through the stream network to the watershed outlet. GLUE methodology was applied to estimate uncertainties in the framework inputs. At field scale, monthly values of drain outflows simulated by DRAINMOD and NO₃-N loads simulated by DRAINMOD-N II showed good agreement with observed values. Good agreement was also found between observed and DRAINMOD-simulated drainage rates when ROSETTA-estimated Ks values were used as inputs in DRAINMOD. At watershed scale, temporal trend and magnitude of monthly measured discharge and NO₃-N loads were well predicted by Arc Hydro-DRAINMOD, which included uncertainty estimation using GLUE methodology. Sensitivity analysis showed that NO₃-N loads from the stream baseflow and N removal in the stream network processes had the most sensitive parameters. These results demonstrate the potential of DRAINMOD/DRAINMOD-N II and Arc Hydro-DRAINMOD for simulating hydrological and N processes in drained agricultural land at field and watershed scale. These models can contribute to improve water use efficiency in watersheds and to evaluate best management practices for preventing surface water and groundwater pollution

Simulation of flow and water quality from tile drains at the watershed and field scale

Simulation models such as the Soil and Water Assessment Tool (SWAT) have become widely used in determining the water quality impacts of various management practices. Ensuring that the algorithms accurately represent the processes simulated has become an important goal. Tile drainage is a standard practice in the Midwest, US in order to reduce risk of yield loss due to excess water. Multiple tile drainage and water table algorithms have been available in the SWAT model between the initial SWAT release and revision 638 used in this study. Testing of those algorithms is often limited. Furthermore, algorithms in the current version have not been tested using small scale measured tile discharge. To better represent the hydrologic processes related to subsurface drainage, four modifications were made to the SWAT model subsurface hydrology routines in order to increase the physical basis of these algorithms. First, percolation through the soil profile was altered to be based on Darcy’s Law and the Buckingham-Darcy Law. Second, the restrictive layer of the soil profile was redefined to be the bottom of the soil profile and an additional variable was added to control the seepage through the restrictive layer. Third, the water table height algorithm, which was based on an algorithm applicable at only one site, was redefined to be within the lowest unsaturated layer. Lastly, the lag through the tile drains, which caused an unrealistic delay under default conditions was removed and flow is delayed by only the drainage coefficient. These changes were evaluated at the experimental tile drained field at the Southeast Purdue Agricultural Center (SEPAC). The model was developed with a single hydrologic response unit (HRU) and calibrated for both tile flow and nitrate. The modifications improved the performance of SWAT for water table and tile flow predictions, although the nitrate was more severely under-predicted. The modifications were tested on a small watershed located in Central Ohio monitored by the USDA-ARS. Each tile output in this watershed was monitored allowing for each tile to be individually modeled and analyzed with SWAT. This watershed was also calibrated for tile flow and nitrate. Here again, the modifications showed an improvement for tile flow but a reduction in performance for nitrate. Phosphorus was also looked at but not calibrated for, and an extreme under-prediction issue was observed. These modifications improved the physical basis or simplified the process representation in the SWAT model, and showed improvement to the tile flow model predictions. The model should be further tested and further developments, specifically for nitrogen and phosphorus, should continue

Predicting Field Water Balance, Crop Yield, and the Economics of Drainage Under Various Cropping Systems Using Drainmod

Subsurface drainage received considerable attention during the recent few years in South Dakota. While subsurface drainage is a widely accepted water management practice for increasing crop yield, research implicated tile drainage in surface and groundwater quality problems. Conservation practices such as crop rotation and controlled drainage may decrease tile flows and improve water quality. A two-year (2014-2015) subsurface drainage study was conducted at South Dakota State University Southeast Research Farm (SERF) near Beresford, South Dakota to evaluate the effectiveness of selected conservation practices in reducing drainage volume and nitrate losses. Six experimental plots, under corn-soybean rotation, divided into drained and undrained plots, were monitored for baseline data (i.e. drainage discharge, water table depth, infiltration, bulk density, and rainfall) collection. DRAINMOD was used with the baseline data to quantify the long-term hydrologic impacts of subsurface tile drainage on field water balance for different drainage conditions (conventional drainage, controlled drainage, and undrained condition) and cropping practices. Long-term simulations for 12-year period (2004-2015) were conducted to predict annual and monthly water balance, crop yield response under conventional drainage, controlled drainage, and undrained conditions for continuous corn, corn-soybean, soybean-corn, corn-wheat, wheat-corn, soybean-wheat, and wheat-soybean cropping practices. Average annual subsurface drainage results for continuous corn, corn-soybean, soybean-corn, corn-wheat, soybean-wheat, wheat-corn, and wheat-soybean cropping practices under controlled drainage showed drainage volume reduction of 28%, 24%, 24%, 52%, 37%, 54%, and 40%, respectively, compared to conventional drainage. Similarly, average annual surface runoff results for continuous corn, corn-soybean, soybean-corn, and wheat-soybean rotation under conventional drainage indicated runoff volume reduction of 72%, 75%, 71%, and 76%, respectively, compared to undrained conditions, and under controlled drainage runoff volume reductions for same cropping practices were 65%, 68%, 65%, and 66%, respectively, compared to undrained conditions. Average monthly water balance showed high ET water loss during the month of May to August and high drainage water loss during month of May and June. Drainage volume reduction of 57.0% was observed in June for wheat-corn rotation under controlled drainage compared to conventional drainage. Likewise, surface runoff volume reduction of 86.7%, and 70.0% in conventional drainage and 86.6% and 63.3% in controlled drainage for May and June was observed in soybean-corn rotation compared to undrained conditions. Predicted relative crop yield percentage showed high yield in soybean-corn, and corn-soybean rotation under conventional drainage and controlled drainage compared to all other cropping practices. Relative crop yield for soybean-corn and corn-soybean under conventional drainage was 81.6% and 80.9%, respectively, and under controlled drainage, relative yield was 81.8% and 81.7%, respectively. Crop relative yield results indicated better yield for soybean-corn followed by corn-soybean production under both conventional and controlled drainage compared to undrained conditions but economic analysis results showed better net annual return form soybean-corn rotation under controlled drainage compared to all other cropping practices in controlled drainage, conventional drainage, and undrained conditions

Managing Water Quantity and Quality with Subsurface Drainage in Eastern South Dakota

Moisture extremes (excess and deficit) impact crop loss and water quality. Due to excessively wet springs and dry summers, crop damage can occur within the same county or field within the same year. To determine the magnitude of this problem in eastern South Dakota, indemnified crop insurance data for drought and excessive moisture claims were assessed for the years 1991-2020 for the occurrence of both excess moisture and drought in four counties in eastern South Dakota. Results show that there is greater than a 70% chance of the moisture extremes happening in the same year, making subsurface drainage, controlled drainage, and irrigation viable options for mitigating the damages. To determine the number of times controlled drainage could have had an impact on soil moisture, a DRAINMOD simulation was analyzed for the years 1950-2012. The results showed on dry and average years, when controlled drainage has potential for the greatest impact on soil moisture, 20 of 47 years had potential to retain soil moisture in the soil profile. In addition to challenges related to water quantity, water quality can be impacted by tile drainage systems. To assess the amount of nitrate-nitrogen entering surface water, 23 tile outlets were monitored weekly for nitrate concentration and flow depth in the tile outlet pipe. The results showed of 352 samples taken (mean 12.4 mg L-1 nitrate-N), 195 samples were above and 157 were below the drinking water standard of 10 mg L-1, with the majority of samples taken at a depth less than 0.15 of the tile diameter, indicating a low flow year

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Drainage models: an evaluation of their applicability for the design of drainage systems in arid regions

Only 5%–10% of irrigated lands in least developed countries (LDCs) are currently drained. Although drainage simulation models (DSMs) are used to evaluate alternative designs, it is unclear which drainage model is suitable for LDCs' arid and semi-arid regions. This study evaluates selected DSMs (ADAPT, RZWQM2, DRAINMOD, EPIC, HYDRUS-1D, WaSim and SWAP) and critically assesses their applicability to arid and semi-arid areas. Also, establish and apply selection criteria based on the availability of data in LDCs with Libya as a case study, and identify the most suitable model for application in Libya. DRAINMOD had the highest overall score, and alternative methods to predict missing input parameters for DRAINMOD are discussed. Evaluating the feasibility of using predicted input parameters for DSMs to design drainage systems in LDCs would help farmers, planners and decision-makers to reduce the overall cost of drainage system and, also, make DRAINMOD a more accessible tool to evaluate different drainage designs

Subsurface Drainage in Iowa and the Water Quality Benefits and Problem

It is estimated that there are approximately 3.6 million ha of land with artificial subsurface drainage in Iowa, with 2.4 million ha of that within the 3000 organized drainage districts (total land area of the state is 14.6 million ha). This drainage has made otherwise wet soils very productive. Much of this drainage was installed early last century and is reaching the end of its service life. One challenge will be the repair/replacement of these drainage systems. Because subsurface drainage short circuits some infiltrating water back to surface water resources, there is also a water quality challenge. Research has shown that during rainfall-runoff events, the presence of artificial subsurface drainage generally delays and reduces the volume of surface runoff. Therefore, total losses of sediment, phosphorus, ammonium-nitrogen, pesticides, and micro-organisms are decreased with subsurface drainage. However, nitrate-nitrogen leaching is increased with subsurface drainage water, and has been implicated as a major factor relative to hypoxia in the Gulf of Mexico. Research has identified several factors relative to soils, weather, and management (cropping, tillage, chemical application practices, and drainage parameters) that influence the nitrate-nitrogen leaching problem. This will be discussed along with implications for possible changes in the drainage systems and land management that may be needed to sustain production while reducing nitrate-nitrogen losses