A Sensitivity Analysis of Impacts of Conservation Practices on Water Quality in L’Anguille River Watershed, Arkansas

Agricultural intensification has stimulated the economy in the Guayas River basin in Ecuador, but also affected several ecosystems. The increased use of pesticides poses a serious threat to the freshwater ecosystem, which urgently calls for an improved knowledge about the impact of pesticide practices in this study area. Several studies have shown that models can be appropriate tools to simulate pesticide dynamics in order to obtain this knowledge. This study tested the suitability of the Soil and Water Assessment Tool (SWAT) to simulate the dynamics of two different pesticides in the data scarce Guayas River basin. First, we set up, calibrated and validated the model using the streamflow data. Subsequently, we set up the model for the simulation of the selected pesticides (i.e., pendimethalin and fenpropimorph). While the hydrology was represented soundly by the model considering the data scare conditions, the simulation of the pesticides should be taken with care due to uncertainties behind essential drivers, e.g., application rates. Among the insights obtained from the pesticide simulations are the identification of critical zones for prioritisation, the dominant areas of pesticide sources and the impact of the different land uses. SWAT has been evaluated to be a suitable tool to investigate the impact of pesticide use under data scarcity in the Guayas River basin. The strengths of SWAT are its semi-distributed structure, availability of extensive online documentation, internal pesticide databases and user support while the limitations are high data requirements, time-intensive model development and challenging streamflow calibration. The results can also be helpful to design future water quality monitoring strategies. However, for future studies, we highly recommend extended monitoring of pesticide concentrations and sediment loads. Moreover, to substantially improve the model performance, the availability of better input data is needed such as higher resolution soil maps, more accurate pesticide application rate and actual land management programs. Provided that key suggestions for further improvement are considered, the model is valuable for applications in river ecosystem management of the Guayas River basin.

Section: Soil and Water Assessment Toolmentioning

confidence: 99%

Using the Soil and Water Assessment Tool to Simulate the Pesticide Dynamics in the Data Scarce Guayas River Basin, Ecuador

Cambien

Gobeyn

Nolivos

et al. 2020

“…First, besides quantifying the proportional contributions of soil water to vegetation and determining the RWU pattern, the isotope-based methods can be extended to partition ET (i.e., the ratio of E and T). The results obtained by the isotope-based models should be compared with the soil water balance models such as SIMDualKc [40] and SWAT [41]. Second, soil moisture is a critical factor of crop growth and transformation of water and energy in the soil-crop system.…”

Section: Scope For Further Research Arising From This Studymentioning

confidence: 99%

Seasonal Variations in Water Uptake Patterns of Winter Wheat under Different Irrigation and Fertilization Treatments

Song

2018

Irrigation and fertilization both affect the water cycle in agricultural ecosystems. It is difficult to quantify root water uptake (RWU) which varies with crop development and seasons. In this study, a Bayesian mixing model (MixSIAR) coupling with dual stable isotopes (D and 18 O) was used to quantify RWU patterns for winter wheat under different irrigation and fertilization treatments between 2014 and 2015 in Beijing, China. The main RWU depth during the greening-jointing, jointing-heading, heading-filling, and filling-harvest stages was 0-20 cm, 20-70 cm, 0-20 cm, and 20-70 cm, respectively, which showed water uptake proportions of 67.0%, 42.0%, 38.7%, and 34.9%, respectively. Significant differences in RWU patterns appeared between the 2014 and 2015 seasons. The main RWU depth increased gradually from 0-20 cm at the greening-jointing stage to 20-70 cm at the jointing-heading stage and 70-150 cm during the heading to harvest period in 2014. However, winter wheat primarily took up soil water from the 0-70 cm layer in 2015. The average water uptake proportion in the top layer (0-20 cm) in 2015 (42.6%) was remarkably higher than that in 2014 (28.7%). There was a significantly negative relationship (p < 0.01) between the water uptake proportion and the proportion of root length at the filling-harvest stage in 2014, while no significant correlation (p > 0.05) was found in 2015. Variable distributions of root characteristics and soil moisture induced by different irrigation and fertilization comprehensively affected the RWU profile, particularly under severe drought environments in 2015. Treatments with fertilization of 105 kg hm −2 N or irrigation of 20 mm during the greening-jointing stage significantly promoted water uptake contribution in the 70-150 cm (32.2%) and 150-200 cm (23.5%) layers at the jointing-heading stage in 2015, while other treatments had a shallow dominant RWU depth (0-20 cm). The planned wetting layer should be kept within the main RWU depth of 0-70 cm for improving irrigation water use efficiency.Since RWU generally does not lead to isotopic fractionation or the magnitude of fractionation is too low to be observable for the majority of plant species, stable isotopic analysis is a powerful and valuable tool to identify and quantify RWU via the measurements of δD and δ 18 O abundance in waters [7,8]. Widely used methods to determine RWU patterns include the IsoSource [9] and Bayesian mixing models (e.g., SIAR [10], MixSIR [11], and MixSIAR [12]). In comparison, the Bayesian mixing models are particularly efficient in the case of multiple layers for illustrating the interest of reaching the best vertical resolution and maximizing the number of identified potential sources [7]. The MixSIAR model incorporates advances in MixSIR and SIAR that considers multiple sources of uncertainty and provides potential proportions of source contributions [12]. The MixSIAR model has been employed successfully to determine the contributions of soil water at different layers to the water uptake of field crops and na...

“…Integrated ecological models can support environmental management and policy development in various ways. These models provide insights into water-related disease control [4], assessments of environmental impact of wastewater and combined sewer overflows [5], wastewater treatment selection [6] and improvement [7], ecosystem services analysis [8,9], effects of land use on water quality [10] and aquatic community composition [11], as well as ecological water quality [12], determination of habitat restoration projects [13,14], distribution prediction and control of invasive species [15], integration of ecological insights into flood mitigation [16], and flow control related to reservoir management [17]. Moreover, models can be used for exploration purposes [18] and trade-off analysis [19], and environmental impact assessment [20] to inspire stakeholders and to support decision-making for policy developers.…”

Section: Potential Applications Of Models In Water Managementmentioning

confidence: 99%

Advances in Ecological Water System Modeling: Integration and Leanification as a Basis for Application in Environmental Management

Goethals

Forio

2018

The art of applied modeling is determining an appropriate balance between integration of more processes and variables for the sake of increasing representativeness and reliability of the models, while also avoiding too long development and simulation times. The latter can be achieved via leanification, which can be based on reducing the number of variables and processes by focusing on key processes in the system and its management, but can be as well induced by using simplified methods for the description of relations among variables (such as regression and probabilistic methods) to, for instance, reduce the simulation time. In this way, integration and leanification can be combined and together contribute to models that are more relevant and convenient for use by water managers. In particular, it is crucial to find a good balance between the integration level of ecological processes answering environmental challenges in a relevant manner and costs for data collection and model development (and application).