Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

Peterson, T. J.; Fulton, Simon

doi:10.1111/gwat.12946

Cited by 16 publications

(25 citation statements)

References 28 publications

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“…Recognizing the importance of evaporation in their model setup, they constrained the parameter estimation by including the correct simulation of the seasonal behavior in the objective function. Peterson and Fulton (2019) used a nonlinear TFN model that includes a soil moisture module to estimate recharge (Peterson and Western, 2014). To obtain reasonable estimates of recharge, the model was constrained by comparing the modeled evaporation to the expected actual evaporation obtained using the Budyko curve.…”

Section: Introductionmentioning

confidence: 99%

Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data

Collenteur

Bakker

Klammler³

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The estimation of groundwater recharge is of paramount importance to assess the sustainability of groundwater use in aquifers around the world. Estimation of the recharge flux, however, remains notoriously difficult. In this study the application of nonlinear transfer function noise (TFN) models using impulse response functions is explored to simulate groundwater levels and estimate groundwater recharge. A nonlinear root zone model that simulates recharge is developed and implemented in a TFN model and is compared to a more commonly used linear recharge model. An additional novel aspect of this study is the use of an autoregressive–moving-average noise model so that the remaining noise fulfills the statistical conditions to reliably estimate parameter uncertainties and compute the confidence intervals of the recharge estimates. The models are calibrated on groundwater-level data observed at the Wagna hydrological research station in the southeastern part of Austria. The nonlinear model improves the simulation of groundwater levels compared to the linear model. The annual recharge rates estimated with the nonlinear model are comparable to the average seepage rates observed with two lysimeters. The recharges estimates from the nonlinear model are also in reasonably good agreement with the lysimeter data at the smaller timescale of recharge per 10 d. This is an improvement over previous studies that used comparable methods but only reported annual recharge rates. The presented framework requires limited input data (precipitation, potential evaporation, and groundwater levels) and can easily be extended to support applications in different hydrogeological settings than those presented here.

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Section: Introductionmentioning

confidence: 99%

Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data

Collenteur

Bakker

Klammler³

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…For annual recharge rates it was found that the non-linear model provides good estimates with relatively small deviations from the recharge measured with the lysimeters, while the linear models shows significantly larger deviations and a structural overestimation of annual recharge rates. The non-linear model also provided reasonable estimates for recharge summed over 10-day periods, suggesting that this model may be used to obtain recharge estimates at smaller time scales than reported so far (e.g., Obergfell et al, 2019;Peterson and Fulton, 2019). Using detailed information from the lysimeters present https://doi.org/10.5194/hess-2020-392 Preprint.…”

Section: Discussionmentioning

confidence: 71%

“…A response function that accounts for this could then be used (e.g., the four-parameter response function), but the estimated flux R should then be interpreted as drainage from the root zone to the groundwater and not as recharge occurring at the water table. Peterson and Fulton (2019) suggested that the flux could be "averaged over a period greater than the time lag" to provide an estimate of gross recharge in this case. This approach was applied here for the presentation of the annual recharge rates, where also the recharge estimates from the linear model were considered.…”

Section: Hydrogeological Settingmentioning

confidence: 99%

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Estimating groundwater recharge from groundwater levels using non-linear transfer function noise models and comparison to lysimeter data

Collenteur¹,

Bakker²,

Klammler³

et al. 2020

Preprint

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Abstract. The application of non-linear transfer function noise (TFN) models using impulse response functions is explored to estimate groundwater recharge and simulate groundwater levels. A non-linear root zone model that simulates recharge is developed and implemented in a TFN model, and is compared to a more commonly used linear recharge model. An additional novel aspect of this study is the use of an autoregressive-moving average noise model so that the remaining noise fulfills the statistical conditions to reliably estimate parameter uncertainties and compute the confidence intervals of the recharge estimates. The models are calibrated on groundwater level data observed at the Wagna hydrological research station in the southeastern part of Austria. The non-linear model improves the simulation of groundwater levels compared to the linear model. The annual recharge rates estimated with the non-linear model are comparable to the average seepage rates observed with two lysimeters. The recharges estimates from the non-linear model are also in reasonably good agreement with the lysimeter data at the smaller time scale of recharge per 10 days. This is an improvement over the results from previous studies that used comparable methods, but only reported annual recharge rates. The presented framework requires limited input data (precipitation, potential evaporation, and groundwater levels) and can easily be extended to support applications in different hydrogeological settings than those presented here.

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“…(2) Guided global search of the solution space using Shuffled Complex Evolution (SCE, ) resulting in a single model run with the highest error metric value achievable. Due to inherent randomness in search routines like SCE, it is a common practice to repeat the search multiple times (Peterson & Fulton, 2019;. Here, each modeling example was repeated 10 times for each error metric.…”

Section: Experiments Designmentioning

confidence: 99%

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

Khatami

2020

Preprint

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Catchment models are conventionally evaluated in terms of their response surface or likelihood surface constructed from model runs using different sets of model parameters. Model evaluation methods are mainly based upon the concept of the equifinality of model structures or parameter sets. The operational definition of equifinality is that multiple model structures/parameters are equally capable of producing acceptable simulations of catchment processes such as runoff. Examining various aspects of this convention, in this thesis I demonstrate their shortcomings and introduce improvements including new approaches and insights for evaluating catchment models as multiple working hypotheses (MWH). First (Chapter 2), arguing that there is more to equifinality than just model structures/parameters, I propose a theoretical framework to conceptualise various facets of equifinality, based on a meta-synthesis of a broad range of literature across geosciences, system theory, and philosophy of science. I distinguish between process-equifinality (equifinality within the real-world systems/processes) and model-equifinality (equifinality within models of real-world systems), explain various aspects of each of these two facets, and discuss their implications for hypothesis testing and modelling of hydrological systems under uncertainty. Second (Chapter 3), building up on this theoretical framework, I propose that characterising model-equifinality based on model internal fluxes — instead of model parameters which is the current approach to account for model-equifinality — provides valuable insights for evaluating catchment models. I developed a new method for model evaluation — called flux mapping — based on the equifinality of runoff generating fluxes of large ensembles of catchment model simulations (1 million model runs for each catchment). Evaluating the model behaviour within the flux space is a powerful approach, beyond the convention, to formulate testable hypotheses for runoff generation processes at the catchment scale. Third (Chapter 4), I further explore the dependency of the flux map of a catchment model upon the choice of model structure and parameterisation, error metric, and data information content. I compare two catchment models (SIMHYD and SACRAMENTO) across 221 Australian catchments (known as Hydrologic Reference Stations, HRS) using multiple error metrics. I particularly demonstrate the fundamental shortcomings of two widely used error metrics — i.e. Nash–Sutcliffe efficiency and Willmott’s refined index of agreement — in model evaluation. I develop the skill score version of Kling–Gupta efficiency (KGEss), and argue it is a more reliable error metric that the other metrics. I also compare two strategies of random sampling (Latin Hypercube Sampling) and guided search (Shuffled Complex Evolution) for model parameterisation, and discuss their implications in evaluating catchment models as MWH. Finally (Chapter 5), I explore how catchment characteristics (physiographic, climatic, and streamflow response characteristics) control the flux map of catchment models (i.e. runoff generation hypotheses). To this end, I formulate runoff generating hypotheses from a large ensemble of SIMHYD simulations (1 million model runs in each catchment). These hypotheses are based on the internal runoff fluxes of SIMHYD — namely infiltration excess overland flow, interflow and saturation excess overland flow, and baseflow — which represent runoff generation at catchment scale. I examine the dependency of these hypotheses on 22 different catchment attributes across 186 of the HRS catchments with acceptable model performance and sufficient parameter sampling. The model performance of each simulation is evaluated using KGEss metric benchmarked against the catchment-specific calendar day average observed flow model, which is more informative than the conventional benchmark of average overall observed flow. I identify catchment attributes that control the degree of equifinality of model runoff fluxes. Higher degree of flux equifinality implies larger uncertainties associated with the representation of runoff processes at catchment scale, and hence pose a greater challenge for reliable and realistic simulation and prediction of streamflow. The findings of this chapter provides insights into the functional connectivity of catchment attributes and the internal dynamics of model runoff fluxes.

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Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

Cited by 16 publications

References 28 publications

Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data

Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data

Estimating groundwater recharge from groundwater levels using non-linear transfer function noise models and comparison to lysimeter data

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

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