Estimation of groundwater recharge from time series modeling of groundwater levels in non-linear systems

Author(s):  
Raoul Collenteur ◽  
Steffen Birk ◽  
Gernot Klammler ◽  
Mark Bakker

<p>Groundwater recharge remains a notoriously difficult flux to estimate, despite ongoing scientific efforts. In recent years, time series modeling using impulse response functions has gained popularity to simulate groundwater levels and is quickly becoming a common tool for hydrogeologists. Several approaches have been developed to estimate recharge from time series models for both linear and non-linear systems (e.g., [1], [2], and [3]). In this study, we introduce a novel approach to estimate groundwater recharge from observed groundwater levels in nonlinear systems (i.e., twice the precipitation does not necessarily lead to twice the recharge). We extend a time series model using impulse response functions with a non-linear unsaturated zone module that simulates recharge. The model parameters are estimated by fitting the simulated to the observed groundwater levels, with the groundwater recharge as an intermediate model result. </p><p>The method is tested on a time series of groundwater levels observed in Southeastern Austria (Wagna), where lysimeter data of seepage to the groundwater is available for model validation. The simulated groundwater recharge suggests an event-based recharge behavior, with most recharge occurring shortly after larger precipitation events. This finding agrees with the behavior observed in the lysimeter data. The estimated recharge fluxes show a high correlation with the observed seepage on time scales from years to months or weeks, while daily recharge rates show larger errors. Advantages of the method include limited data requirements (only precipitation, potential evapotranspiration, and groundwater time series are required) and the possibility to correct for other factors causing groundwater level fluctuations (e.g., pumping, river levels). This makes it possible to apply the method in locations where little system knowledge (e.g., soil profiles) is available.</p><p><strong>References:</strong><br>[1] Besbes, M. and De Marsily, G. (1984) From infiltration to recharge: use of a parametric transfer function, Journal of Hydrology.<br>[2] Peterson, T.J. and Fulton, S. (2019) Joint estimation of gross recharge, groundwater usage, and hydraulic properties within HydroSight, Groundwater.<br>[3] Obergfell, C., Bakker, M. and Maas, K. (2019) Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads, Groundwater.</p>

2014 ◽  
Vol 19 (4) ◽  
pp. 27-35
Author(s):  
Mariusz Sulima

Abstract This work presents a new DHT impulse response function based on the proposed nonlinear equation system obtained as a result of combining the DHT and IDHT equation systems. In the case of input time series with selected characteristics, the DHT results obtained using this impulse response function are characterised by a higher accuracy compared to the DHT results obtained based on the convolution using other known DHT impulse response functions. The results are also characterised by a higher accuracy than the DHT results obtained using the popular indirect DHT method based on discrete Fourier transform (DFT). Analysis of these example time series with selected characteristics was performed based on the signal-to-noise ratio.


Author(s):  
Mark A. Thoma ◽  
Wesley W. Wilson

Time series techniques—particularly impulse–response functions and variance decompositions—are used to characterize the short-run relationships between 17 variables in a vector autoregressive model designed to trace the short-run interconnections among variables affecting lockages on the Mississippi and Illinois Rivers. The model contains five categories of variables: lockages, barge rates, grain bids, rail rates, and rail deliveries. Variance decompositions are constructed that identify barge rates as the most important variable affecting lockages at both short and long horizons. Barge rates are, in turn, explained largely by lockages and rail rates, indicating two-way feedback or bidirectional causality between lockages and barge rates. Impulse–response functions are also examined. The variance decompositions indicate that barge rates are important in explaining lockages, and the impulse–response functions show how lockages and other variables respond to such shocks. In general, there is a substitution away from barge transportation and toward rail transportation when barge rates increase. The results are useful for illuminating the causal relationships among variables in the model and for understanding behavioral relationships present in the data and can be used to guide short- and long-run planning models. For example, many planning models assume that barge traffic does not respond significantly to changes in barge rates; however, results obtained here imply that barge traffic and rail deliveries do respond to such changes. This potentially important implication illustrates the usefulness of the time series techniques used.


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