Modelling of shallow water table dynamics using conceptual and physically based integrated surface water-groundwater hydrologic models

Abstract. Machine learning provides a great potential to model hydrological variables at a spatial resolution beyond the capabilities of traditional physically-based modelling. This study features an application of Random Forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at 50 m resolution over a 15,000 km2 large domain in Denmark. In Denmark, the shallow groundwater poses severe risks of groundwater induced flood events affecting both, urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at scales required for decision making, this study introduces a simple method to unify RF and physically-based modelling. Results from the national water resources model in Denmark (DK-model) at 500 m resolution are employed as covariate in the RF model. Thereby, RF ensures physical consistency at coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest waterbody was rated the most important covariate in the trained RF model followed by the DK-model. The validation test of the trained RF model was very satisfying with a mean absolute error of 79 cm and a coefficient of determination of 0.55. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterised with a depth less than 1 m during a typical wintertime minimum event. This study brings forward a novel method to assess the spatial patterns of covariate importance of the RF predictions which contributes to an increased interpretability of the RF model. Quantifying uncertainty of RF models is still rare for hydrological applications. Two approaches, namely Random Forests Regression Kriging (RFRK) and Quantile Regression Forests (QRF) were tested to estimate uncertainties related to the predicted groundwater levels. This study argues that the uncertainty sources captured by RFRK and QRF can be considered independent and hence, they can be combined to a total variance through simple uncertainty propagation.

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Modelling of shallow water table dynamics using conceptual and physically based integrated surface-water–groundwater hydrologic models

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2245-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2245-2260 ◽

Cited By ~ 3

Author(s):

Mohammad Bizhanimanzar ◽

Robert Leconte ◽

Mathieu Nuth

Keyword(s):

Shallow Water ◽

Groundwater Recharge ◽

Water Table ◽

Soil Column ◽

Three Dimensional ◽

Vertical Plane ◽

Dimensional Case ◽

Shallow Water Table ◽

Mike She ◽

Physically Based

Abstract. We present a new conceptual scheme of the interaction between unsaturated and saturated zones of the MOBIDIC (MOdello Bilancio Idrologico DIstributo e Continuo) hydrological model which is applicable to shallow water table conditions. First, MODFLOW was coupled to MOBIDIC as the physically based alternative to the conceptual groundwater component of the MOBIDIC–MODFLOW. Then, assuming a hydrostatic equilibrium moisture profile in the unsaturated zone, a dynamic specific yield that is dependent on the water table level was added to MOBIDIC–MODFLOW, and calculation of the groundwater recharge in MOBIDIC was revisited using a power-type equation based on the infiltration rate, soil moisture deficit, and a calibration parameter linked to the initial water table depth, soil type, and rainfall intensity. Using the water table fluctuation (WTF) method for a homogeneous soil column, the parameter of the proposed groundwater recharge equation was determined for four soil types, i.e. sand, loamy sand, sandy loam, and loam under a pulse of rain with different intensities. The fidelity of the introduced modifications in MOBIDIC–MODFLOW was assessed by comparison of the simulated water tables against those of MIKE SHE, a physically based integrated hydrological modelling system simulating surface and groundwater flow, in two numerical experiments: a two-dimensional case of a hypothetical watershed in a vertical plane (constant slope) under a 1 cm d−1 uniform rainfall rate and a quasi-real three-dimensional watershed under 1 month of a measured daily rainfall hyetograph. The comparative analysis confirmed that the simplified approach can mimic simple and complex groundwater systems with an acceptable level of accuracy. In addition, the computational efficiency of the proposed approach (MIKE SHE took 180 times longer to solve the three-dimensional case than the MOBIDIC–MODFLOW framework) demonstrates its applicability to real catchment case studies.

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Modelling of the shallow water table at high spatial resolution using random forests

Hydrology and Earth System Sciences ◽

10.5194/hess-23-4603-2019 ◽

2019 ◽

Vol 23 (11) ◽

pp. 4603-4619 ◽

Cited By ~ 5

Author(s):

Julian Koch ◽

Helen Berger ◽

Hans Jørgen Henriksen ◽

Torben Obel Sonnenborg

Keyword(s):

Shallow Water ◽

Water Table ◽

Spatial Resolution ◽

Random Forests ◽

Shallow Groundwater ◽

Coefficient Of Determination ◽

Shallow Water Table ◽

Simple Method ◽

Physically Based ◽

Physically Based Modelling

Abstract. Machine learning provides great potential for modelling hydrological variables at a spatial resolution beyond the capabilities of physically based modelling. This study features an application of random forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at a 50 m resolution over a 15 000 km2 domain in Denmark. In Denmark, the shallow groundwater poses severe risks with respect to groundwater-induced flood events, affecting both urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at the scales required for decision-making, this study introduces a simple method to unify RF and physically based modelling. Results from the national water resources model in Denmark (DK-model) at a 500 m resolution are employed as covariates in the RF model. Thus, RF ensures physical consistency at a coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest water body was rated as the most important covariate in the trained RF model followed by the DK-model. The evaluation test of the trained RF model was very satisfying with a mean absolute error of 76 cm and a coefficient of determination of 0.56. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterized as having a depth of less than 1 m during a typical wintertime minimum event. This study brings forward a novel method for assessing the spatial patterns of covariate importance of the RF predictions that contributes to an increased interpretability of the RF model. Quantifying the uncertainty of RF models is still rare for hydrological applications. Two approaches, namely random forests regression kriging (RFRK) and quantile regression forests (QRF), were tested to estimate uncertainties related to the predicted groundwater levels.

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Modelling of shallow water table dynamics using conceptual and physically based integrated surface water-groundwater hydrologic models

10.5194/hess-2018-359 ◽

2018 ◽

Author(s):

Mohammad Bizhanimanzar ◽

Robert Leconte ◽

Mathieu Nuth

Keyword(s):

Shallow Water ◽

Groundwater Recharge ◽

Water Table ◽

Sandy Loam ◽

Soil Column ◽

Vertical Plane ◽

Specific Yield ◽

Shallow Water Table ◽

Mike She ◽

Physically Based

Abstract. We present a new conceptual scheme of the interaction between unsaturated and saturated zones of the MOBIDIC (Modello Bilancio Idrologico DIstributo e Continuo) hydrological model which is applicable to shallow water table conditions. First, a hydrostatic equilibrium moisture profile was assumed for simulating changes in water table levels. This resulted in a water table based expression of specific yield, which was included in the coupled MOBIDIC-MODFLOW modelling framework for capturing shallow water tables fluctuations. Second, the groundwater recharge was defined using a power type equation based on infiltration rate, soil moisture deficit and a calibration parameter linked to initial water table level, soil type and rainfall intensity. Using the Water Table Fluctuation (WTF) method, the water table rise for a homogeneous soil column under a pulse of rain with different intensities (up to 30 mm/day) the parameter of the proposed groundwater recharge equation was determined for four soil types i.e., sand, loamy sand, sandy loam and loam. The simulated water table levels were compared against those simulated by MIKE-SHE, a physically based integrated hydrological modelling system simulating surface and groundwater flow. Two numerical experiments were carried out: a two-dimensional case of a hypothetical watershed in a vertical plane (constant slope) under a 1 cm/day uniform rainfall rate, and a quasi-real three dimensional watershed under one month of measured daily rainfall hyetograph. The comparative analysis confirmed that the simplified approach can mimic simple and complex groundwater systems with an acceptable level of accuracy. In addition, the computational efficiency of the proposed approach (MIKE-SHE took 180 times longer to solve the 3D case than the MOBIDIC-MODFLOW framework) demonstrates its applicability to real catchment case studies.

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Implementation of shallow water table effects on pesticide runoff mitigation efficiency by vegetative filter strips within SWAN-VFSMOD

10.5194/egusphere-egu21-7829 ◽

2021 ◽

Author(s):

Robin Sur ◽

Rafael Muñoz-Carpena ◽

Stefan Reichenberger ◽

Klaus Hammel ◽

Horatio Meyer ◽

...

Keyword(s):

Surface Water ◽

Shallow Water ◽

Water Table ◽

Shallow Water Table ◽

Vegetative Filter Strips ◽

Worst Case ◽

Filter Strips ◽

Pesticide Runoff ◽

Selection Of

Quantitative mitigation of pesticides entering surface water using vegetative filter strips (VFS) is currently available within the regulatory SWAN tool for EU FOCUS STEP 4 simulations. For the VFSMOD model option, field estimates of surface runoff, sediment and pesticide loads simulated with the model PRZM are routed through the VFS where VFSMOD estimates the reductions of total inflow (dQ), eroded sediment (dE) and pesticide (dP) loads before the remaining runoff enters the waterbody. The reduced runoff is handed over to the TOXSWA aquatic model to calculate predicted environmental concentrations in surface water (PECsw). Brown et al. (2012) proposed VFSMOD parametrization rules including the selection of VFS soils and other characteristics for use in the FOCUS R1 to R4 (Rx) SWAN scenarios. The rules apply to free-draining soils, described in VFSMOD by the Green-Ampt model extended for unsteady rainfall. However, in some EU regions, the presence of a seasonal shallow water table (sWT) is common. In these cases, the VFS efficiency can be limited, depending on water table depth (WTD) and soil type. VFSMOD incorporates a sWT mechanistic infiltration component that has proven successful to predict sWT effects in VFS experiments. This component requires soil hydraulic characteristics, described by e.g. the Mualem-van Genuchten (MvG) equations.The main objective of this study is to identify Rx representative VFS soils to study the effects of sWT on pesticide mitigation for a combination of illustrative storms and pesticides, as well as on PECsw from long-term SWAN simulations.The selection and testing of the Rx VFS soils seeks to reflect a 90th-percentile worst case in space of dP. The multicriteria adopted in the soil selection evaluate not only dP, but also the percentile of important soil parameters for noWT (Ks, Sav) and sWT infiltration conditions (fillable pore volume fpv). The framework consisted of 4 steps: (a) soil spatial soil database analysis for VFS Rx mitigation scenarios; (b) selection of VFS candidate soils; (c) analysis of effects of sWT and sorption on dP for individual storm events; (d) Effect of sWT on long-term STEP 4 SWAN VFS mitigation simulations. For (a), representative soil profiles and area coverage for each of the EU Rx were obtained by combining the latest EU JRC soil profile databases SPADE2 and SPADE14. Each multilayer soil was aggregated into single-layer depth-weighted profiles, and MvG parameters were estimated using HYPRES pedotransfer functions (PTF). Water table depths (WTD) were set at equilibrium with TOXSWA median surface water level, and Sav and fpv were calculated by numerical integration from MvG characteristics. For (b), 10644 VFSMOD simulations were run for all combinations of soils, T=1 and 10 yr storms, high/low Koc pesticides, and sWT/noWT conditions. Candidate Rx VFS soils were selected for the most conservative case (low Koc=100 Kg/L pesticide, T=10 yr storm) and noWT to achieve the target spatial 90th percentile worst case of pesticide load reduction by the VFS.&#160;The implementation of the new sWT VFS mitigation component provides a more realistic description of pesticide reduction in accordance with STEP 4 EU FOCUS objectives.

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