Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

Defterdarović, Jasmina; Filipović, Lana; Kranjčec, Filip; Ondrašek, Gabrijel; Kikić, Diana; Novosel, Alen; Mustać, Ivan; Krevh, Vedran; Magdić, Ivan; Rubinić, Vedran; Bogunović, Igor; Dugan, Ivan; Čopec, Krešimir; He, Hailong; Filipović, Vilim

doi:10.3390/su13126688

Cited by 7 publications

(6 citation statements)

References 73 publications

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“…Other studies showed similar results, in which nitrogen was the main predictor of nitrates (Oehler and Elliott 2011). Nitrogen leaches when transformed into nitrate form, and the main issue then with nitrates is their mobility in the soil and the fact that they can persist in SW and GW (Defterdarović et al 2021). Agricultural activity is the main source of nitrogen in the watershed (Dorado-Guerra et al 2021); therefore, DP is the most probable cause for the higher nitrate probabilities and the increase of the nitrate concentrations in the river.…”

Section: Use Of Extrinsic Features Of Surface Water Bodies and Their ...mentioning

confidence: 55%

“…Its main source in Europe is diffuse pollution (Grinsven et al 2015, Alcon et al 2022, whereby nitrogen leaches when transformed into nitrate form. The main issue, then, with nitrates is their mobility in soil, and the fact that they can persist in surface water (SW) and groundwater (GW) (Defterdarović et al 2021), contributing to poor water quality and eutrophication (Pang et al 2022). Currently, the ecological status of more than half the water bodies in the EU is assessed as poor (Poikane et al 2019), contrary to the requirements of the Water Framework Directive (WFD) and Nitrates Directive (Directive 91/676/EEC).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning models to predict nitrate concentration in a river basin

Dorado-Guerra

Corzo

Paredes-Arquiola

et al. 2022

Environ. Res. Commun.

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Aquifer-stream interactions affect the water quality in Mediterranean areas; therefore, the coupling of surface water and groundwater models is generally used to solve water-planning and pollution problems in river basins. However, their use is limited because model inputs and outputs are not spatially and temporally linked, and the data update and fitting are laborious tasks. Machine learning models have shown great potential in water quality simulation, as they can identify the statistical relationship between input and output data without the explicit requirement of knowing the physical processes. This allows the ecological, hydrological, and environmental variables that influence water quality to be analyzed with a holistic approach. In this research, feature selection (FS) methods and algorithms of artificial intelligence— random forest (RF) and eXtreme Gradient Boosting (XGBoost) trees—are used to simulate nitrate concentration and determine the main drivers related to nitrate pollution in Mediterranean streams. The developed models included 19 inputs and sampling of nitrate concentration in 159 surface water quality-gauging stations as explanatory variables. The models were trained on 70 percent data, with 30 percent used to validate the predictions. Results showed that the combination of FS method with local knowledge about the dataset is the best option to improve the model’s performance, while RF and XGBoost simulate the nitrate concentration with high performance (r=0.93 and r=0.92, respectively). The final ranking, based on the relative importance of the variables in the RF and XGBoost models, showed that, regarding nitrogen and phosphorus concentration, the location explained 87 percent of the nitrate variability. RF and XGBoost predicted nitrate concentration in surface water with high accuracy without using conditions or parameters of entry, and enabled the observation of different relationships between drivers. Thus, it is possible to identify and delimit zones with a spatial risk of pollution and approaches to implementing solutions.

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Section: Use Of Extrinsic Features Of Surface Water Bodies and Their ...mentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Machine learning models to predict nitrate concentration in a river basin

Dorado-Guerra

Corzo

Paredes-Arquiola

et al. 2022

Environ. Res. Commun.

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“…A more pronounced water movement was observed at the footslope owing to the slight differences in the bulk density and total porosity, which can lead to differences in the water flow, i.e. macropores will cause increased water movement [35], or it could even be a result of preferential flows [8,11,48]. Furthermore, the surface runoff was more pronounced at the hilltop, resulting in a higher water volume at footslope positions.…”

Section: Determination Of the Water Flow Using Lysimeters And Sensorsmentioning

confidence: 99%

“…The topsoil had a better soil structure in general with a higher C org content, lower bulk density, and higher porosity, resulting in more aerated conditions [35]. However, according to Figure 5, at the hilltop, the soil texture changes to a heavier silty clay loam and silty clay in the deeper layers (50-100 cm).…”

Section: Physical Chemical and Hydraulic Characteristicsmentioning

confidence: 99%

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Defterdarović,

Filipović,

Ondrašek

et al. 2024

Land

Self Cite

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Major losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources.

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“…The HYDRUS (2D/3D) model [35] has been widely used in the analysis of water, heat and solute transport in variably saturated porous media in agricultural systems [36][37][38][39][40]. In this study, we developed a simple NLSB treatment for a post-construction earth embankment using casing-wells to backfill NaCl-laden soil, which acts as a barrier against subterranean termites.…”

Section: Introductionmentioning

confidence: 99%

Barrier Longevity of NaCl-Laden Soil against Subterranean Termites in an Earth Embankment

Liu

Pan

2021

Sustainability

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Subterranean termite-induced damage to earth embankments in agricultural systems occurs globally. NaCl-laden soil barriers (NLSBs) are an environmentally sustainable termite control method, and have exhibited good potential in preventing termite-related tunneling damage in Zhejiang Province, China. The persistence of the NaCl concentration in NLSBs is a key characteristic for the long-term prevention of subterranean termite infestations. This study is a scientific attempt to estimate the field efficacy and barrier longevity of NLSBs in reservoir embankments based on the Richards equation and the convection–dispersion equation using HYDRUS (2D/3D). The observed and simulated NaCl concentrations at the end of a 1915-day simulation were compared. The results indicated that the proposed model performed well and can effectively characterize the water flow and salt transport in NLSBs. The salt desalination rate of the NLSB in the upstream slope was higher than that in the downstream slope, both of which were significantly higher than that at the embankment axis. Regardless of the type of embankment (homogeneous or core-wall), the barrier longevity of NaCl-laden soil against subterranean termites can reach 50 years with an optimized NaCl/soil ratio in different parts of the embankment.

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Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

Cited by 7 publications

References 73 publications

Machine learning models to predict nitrate concentration in a river basin

Machine learning models to predict nitrate concentration in a river basin

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Barrier Longevity of NaCl-Laden Soil against Subterranean Termites in an Earth Embankment

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