Groundwater recharge modelling in a large scale basin: an example using the SWAT hydrologic model

Abstract. Most of the current large scale hydrological models do not contain a physically-based groundwater flow component. The main difficulties in large-scale groundwater modeling include the efficient representation of unsaturated zone flow, the characterization of dynamic groundwater-surface water interaction and the numerical stability while preserving complex physical processes and high resolution. To address these problems, we propose a highly-scalable coupled hydrologic and groundwater model (mHM#OGS) based on the integration of two open-source modeling codes: the mesoscale hydrologic Model (mHM) and the finite element simulator OpenGeoSys (OGS). mHM#OGS is coupled using a boundary condition-based coupling scheme that dynamically links the surface and subsurface parts. Nested time stepping allows smaller time steps for typically faster surface runoff routing in mHM and larger time steps for slower subsurface flow in OGS. mHM#OGS features the coupling interface which can transfer the groundwater recharge and river baseflow rate between mHM and OpenGeoSys. Verification of the coupled model was conducted using the time-series of observed streamflow and groundwater levels. Moreover, we force the transient model using groundwater recharge in two scenarios: (1) spatially variable recharge based on the mHM simulations, and (2) spatially homogeneous groundwater recharge. The modeling result in first scenario has a slightly higher correlation with groundwater head time-series, which further validates the plausibility of spatial groundwater recharge distribution calculated by mHM in the mesocale. The statistical analysis of model predictions shows a promising prediction ability of the model. The offline coupling method implemented here can reproduce reasonable groundwater head time series while keep a desired level of detail in the subsurface model structure with little surplus in computational cost. Our exemplary calculations show that the coupled model mHM#OGS can be a valuable tool to assess the effects of variability in land surface heterogeneity, meteorological, topographical forces and geological zonation on the groundwater flow dynamics.

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Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Hydrogeology Journal ◽

10.1007/s10040-021-02377-1 ◽

2021 ◽

Author(s):

Daniel Bittner ◽

Beatrice Richieri ◽

Gabriele Chiogna

Keyword(s):

Groundwater Recharge ◽

Temporal Variations ◽

Hydrologic Model ◽

Time Dependent ◽

Model Parameters ◽

Lumped Parameter ◽

Hydrodynamic Processes ◽

Input Model ◽

Karst Model ◽

Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-21-117-2017 ◽

2017 ◽

Vol 21 (1) ◽

pp. 117-132 ◽

Cited By ~ 15

Author(s):

Jannis M. Hoch ◽

Arjen V. Haag ◽

Arthur van Dam ◽

Hessel C. Winsemius ◽

Ludovicus P. H. van Beek ◽

...

Keyword(s):

Spatial Resolution ◽

Hydrodynamic Model ◽

Large Scale ◽

Hydrologic Model ◽

Flood Events ◽

Coupled Models ◽

Validation Parameters ◽

Directional Coupling ◽

Inundation Extent ◽

The Impact

Abstract. Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.

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Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

10.5194/egusphere-egu21-8782 ◽

2021 ◽

Author(s):

Moctar Dembélé ◽

Bettina Schaefli ◽

Grégoire Mariéthoz

Keyword(s):

West Africa ◽

Large Scale ◽

Hydrological Model ◽

Meteorological Data ◽

Model Performance ◽

Hydrologic Model ◽

Hydrological Modelling ◽

Climate Change Scenarios ◽

Depth Analysis ◽

Input Dataset

The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Demb&#233;l&#233; et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.Demb&#233;l&#233;, M., Schaefli, B., van de Giesen, N., & Mari&#233;thoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020

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Improving runoff estimates from regional climate models: a performance analysis in Spain

Hydrology and Earth System Sciences ◽

10.5194/hess-16-1709-2012 ◽

2012 ◽

Vol 16 (6) ◽

pp. 1709-1723 ◽

Cited By ~ 18

Author(s):

D. González-Zeas ◽

L. Garrote ◽

A. Iglesias ◽

A. Sordo-Ward

Keyword(s):

Time Series ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Aridity Index ◽

Regional Climate Models ◽

Hydrologic Model ◽

Direct Runoff ◽

Interpolation Methods ◽

Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

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A multi-compartment hydrologic model to estimate groundwater recharge in an alluvial-karst system

Arabian Journal of Geosciences ◽

10.1007/s12517-015-2084-0 ◽

2016 ◽

Vol 9 (3) ◽

Cited By ~ 5

Author(s):

Ata Joodavi ◽

Mohammad Zare ◽

Ezzat Raeisi ◽

Mohammad Bagher Ahmadi

Keyword(s):

Groundwater Recharge ◽

Hydrologic Model ◽

Karst System

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Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0021.1 ◽

2019 ◽

Vol 20 (11) ◽

pp. 2203-2214 ◽

Cited By ~ 1

Author(s):

Hoang Tran ◽

Phu Nguyen ◽

Mohammed Ombadi ◽

Kuolin Hsu ◽

Soroosh Sorooshian ◽

...

Keyword(s):

Optical Sensors ◽

Large Scale ◽

Dimensional Space ◽

Three Dimensional ◽

Skill Score ◽

Hydrologic Model ◽

Probability Of Detection ◽

Landsat 8 ◽

Inundation Maps ◽

Flood Maps

Abstract Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.

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The long-term performance of Soil Aquifer Treatment (SAT) for effluent reuse

Water Science & Technology Water Supply ◽

10.2166/ws.2003.0068 ◽

2003 ◽

Vol 3 (4) ◽

pp. 239-246 ◽

Cited By ~ 11

Author(s):

E. Idelovitch ◽

N. Icekson-Tal ◽

O. Avraham ◽

M. Michail

Keyword(s):

Groundwater Recharge ◽

Large Scale ◽

Soil Layer ◽

Chemical Precipitation ◽

Quality Data ◽

Soil Aquifer Treatment ◽

Ammonia Adsorption ◽

Water Quality Data ◽

Aquifer System ◽

Effluent Reuse

An innovative scheme of groundwater recharge for wastewater effluent reuse has been practiced on a large scale in the Dan Region Project in Israel since 1977. The system, referred to as SAT (for Soil Aquifer Treatment), provides advanced treatment prior to effluent reuse for unrestricted irrigation. A major study recently carried out consisted of a comprehensive analysis of the water quality data available in the recharged effluent (before SAT), as well as in observation wells and recovery wells (after SAT). The results obtained with respect to suspended solids, organics and nutrients (nitrogen compounds and phosphorus) are presented and discussed. The main processes occurring in the soil-aquifer system, which are responsible for the removal of the above contaminants are filtration through the upper soil layer, organic matter biodegradation and adsorption, ammonia adsorption and biological nitirification-denitrification, and chemical precipitation and adsorption of phosphorus. The findings of the study have provided valuable information on the above processes and their interaction, and have demonstrated that the SAT system should be considered an attractive method for effluent reuse in areas where hydrogeological conditions are suitable for groundwater recharge via spreading basins.

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Evaluation of aquifer parameters at regional scale by spectral analysis of discharge time series

10.5194/egusphere-egu2020-10502 ◽

2020 ◽

Author(s):

Mariaines Di Dato ◽

Rohini Kumar ◽

Estanislao Pujades ◽

Timo Houben ◽

Sabine Attinger

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Characteristic Time ◽

Large Scale ◽

Multiple Scales ◽

Regional Scale ◽

Hydrologic Model ◽

Aquifer Parameters ◽

Basin Scale ◽

Linear Reservoir

River stream is the result of several complex processes operating at basin scale. Therefore, the river catchment can be conceptualized as a series of interlinked compartments, which are characterized by their own response time to a rainfall event. Each compartment generates a flow component, such as the direct runoff, the interflow and the baseflow. The latter, typically generating from groundwater, is the slower portion of stream flow and plays a key role in studying the hydrological droughts.In many catchment or large-scale hydrologic models, the groundwater dynamics are typically described by a linear reservoir model, which depends on the state of the reservoir and the parameter, known as recession coefficient or characteristic time. The characteristic time can be considered as the time needed until an aquifer reacts to a certain perturbation. So far, the characteristic time has been estimated by analyzing the slope of the recession (discharge) curve. However, as this method assumes that the recharge is zero within the basin, it may lead to inaccurate estimate when such a hypothesis is not fulfilled in reality.The present work proposes to infer the characteristic time by using a stochastic approach based on spectral analysis. The catchment aquifer can be viewed as a filter, which modifies an input signal (e.g., rainfall or recharge) into an output signal (e.g., the baseflow or the hydraulic head). Since the transfer function, namely the ratio between the spectrum of baseflow and the spectrum of recharge, is dependent on the aquifer characteristics, it can be used to infer the aquifer parameters. In particular, the characteristic time is evaluated by fitting the spectrum and the variance of the measured baseflow with the analytical stochastic solutions for the linear reservoir. We compare six different methods for hydrograph separation, thereby highlighting a systematic uncertainty in determining the characteristic time due to the choice of filter used. To reduce the uncertainty in fitting, we will use the mesoscale Hydrological Model (mHM) (Samaniego et al., 2010; Kumar et al., 2013) to generate realistic time series for recharge. We apply the spectral analysis method to several river basins in Germany, with the goal to define a regionalization rule for characteristic time.&#160;References:<ul><li>Samaniego L., R. Kumar, S. Attinger (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327.</li> <li>Kumar, R., L. Samaniego, and S. Attinger (2013): Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, doi:10.1029/2012WR012195</li> </ul>

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V2Karst V1.0: A parsimonious large-scale integrated vegetation-recharge model to simulate the impact of climate and land cover change in karst regions

10.5194/gmd-2017-315 ◽

2018 ◽

Cited By ~ 1

Author(s):

Fanny Sarrazin ◽

Andreas Hartmann ◽

Francesca Pianosi ◽

Thorsten Wagener

Keyword(s):

Land Cover ◽

Groundwater Recharge ◽

Land Cover Change ◽

Large Scale ◽

Synthetic Data ◽

Temporal Distribution ◽

Soil Parameters ◽

Large Scale Assessment ◽

Karst Areas ◽

The Impact

Abstract. Karst aquifers are an important source of drinking water in many regions of the world. Karst areas are highly permeable and produce large amounts of groundwater recharge, while surface runoff is typically negligible. As a result, recharge in these systems may have a different sensitivity to climate and land cover changes compared to other less permeable systems. However, little effort has been directed toward assessing the impact of climate and land cover change in karst areas at large-scales. In this study, we address this gap by (1) introducing the first large-scale hydrological model including an explicit representation of both karst and land cover properties, and by (2) analysing the model's recharge production behaviour. To achieve these points, we first improve the evapotranspiration estimation of a previous large-scale karst recharge model (VarKarst). The new model (V2Karst V1.0) includes a parsimonious representation of relevant ET processes for climate and land cover change impact studies. We demonstrate the plausibility of V2Karst simulations at carbonate rock FLUXNET sites using soft rules and global sensitivity analysis. Then, we use virtual experiments with synthetic data to assess the sensitivity of simulated recharge to precipitation characteristics and land cover. Results reveal how both vegetation and soil parameters control the model behaviour, and they suggest that simulated recharge is sensitive to both precipitation (overall amount and temporal distribution) and land cover. Large-scale assessment of future karst groundwater recharge should therefore consider the combined impact of changes in land cover and precipitation properties, if it is to produce realistic projections of future change impacts.

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