Statistical Interpolation of Groundwater Hydrographs

Peterson, T. J.; Western, Andrew W.

doi:10.1029/2017wr021838

Cited by 23 publications

(13 citation statements)

References 25 publications

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“…Hydrosight applies the time series modeling approach of Peterson and Western () to generate a groundwater hydrograph based on climatic forcing (rainfall and potential evapotranspiration as described in section ). In a subsequent step, the resulting groundwater hydrograph is made to perfectly honor each observation using the interpolation methods of Peterson and Western (). Outlier observations are identified and removed as per Peterson et al ().…”

Section: Methodsmentioning

confidence: 99%

Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections

Fowler

Knoben

Peel

et al. 2020

Water Resources Research

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Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual "bucket" rainfall-runoff models are unable to replicate multiyear trends exhibited by natural systems during the "Millennium Drought" in south-east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13-year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model "buckets" are already "emptying" on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall-runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall-runoff relationships in the context of a changing climate.Plain Language Summary It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13-year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used "bucket" models. The models do not show the long, slow drying up-they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow.

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Section: Methodsmentioning

confidence: 99%

Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections

Fowler

Knoben

Peel

et al. 2020

Water Resources Research

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“…The selection of the most appropriate predictor station is not a trivial task (Giustarini et al, 2016;Harvey et al, 2012;Peterson and Western, 2018). Here, the most similar station to the target station is chosen as predictor.…”

Section: Predictor Station Selection For Real Gaps Reconstructionmentioning

confidence: 99%

“…They range from simple 4 linear models to complex deterministic or stochastic techniques. The most common approaches include the simple nearest neighbor method by data transfer (Bárdossy and Pegram, 2014;Giustarini et al, 2016), interpolation techniques (Hughes and Smakhtin, 1996;Pappas et al, 2014;Peterson and Western, 2018;Piazza et al, 2015;Rees, 2008;Teegavarapu, 2014), autoregressive models (Bennis et al, 1997;Tencaliec et al, 2015), simple and multiple regressions (Dumedah and Coulibaly, 2011;Hirsch, 1979;1982;Miaou, 1990;Woodhouse et al, 2006), classification and regression trees (Giustarini et al, 2016;Sidibe et al, 2018), recession methods (Gyau-Boakye and Schultz, 1994), recursive models (Lambert, 1969), nonlinear and storage models (Coulibaly and Baldwin, 2005;Dawdy and O'Donnell, 1965), satellite data applications (Papadakis et al, 1993), dynamic state-space models (Amisigo and Van De Giesen, 2005;Berendrecht and van Geer, 2016), and various forms of artificial neural networks (Coulibaly and Evora, 2007;Dastorani et al, 2010;Elshorbagy et al, 2002;Khalil et al, 2001;Panu et al, 2000;Tfwala et al, 2013) among others (Bárdossy and Pegram, 2014;Dumedah and Coulibaly, 2011;Gyau-Boakye and Schultz, 1994;Harvey et al, 2012;Sidibe et al, 2018). Different studies provided a review of these methods…”

Section: Introductionmentioning

confidence: 99%

“…Different studies provided a review of these methods (Gyau-Boakye and Schultz, 1994;Harvey et al, 2012;Kottegoda and Elgy, 1977;Lepot et al, 2017;Marwala, 2009;Pigott, 2001), which all have their limitations depending on the application (Amisigo and Van De Giesen, 2005;Mwale et al, 2012;Rees, 2008). For instance, the nearest neighbor method brings discontinuity in the time series (Gnauck, 2004;Lepot et al, 2017;Peterson and Western, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings

Dembélé

Oriani

Tumbulto³

et al. 2019

Journal of Hydrology

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Complete hydrological time series are necessary for water resources management and modeling.This can be challenging in data scarce environments where data gaps are ubiquitous. In many applications, repetitive gaps can have unfortunate consequences including ineffective model calibration, unreliable timing of peak flows, and biased statistics. Here, Direct Sampling (DS) is used as a non-parametric stochastic method for infilling gaps in daily streamflow records. A thorough gap-filling framework including the selection of predictor stations and the optimization of the DS parameters is developed and applied to data collected in the Volta River basin, West Africa. Various synthetic missing data scenarios are developed to assess the performance of the method, followed by a real-case application to the existing gaps in the flow record. The contribution of this study includes the assessment of the method for different climatic zones and hydrological regimes and for different upstream-downstream relations among the gauging stations used for gap filling. Tested in various missing data conditions, the method allows a precise and reliable simulation of the missing data by using the data patterns available in other stations as predictor variables. The developed gap-filling framework is transferable to other hydrological applications, and it is promising for environmental modeling.

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“…Next, time series analysis can be applied to detect, quantify, and resolve many of these issues. If time series analysis results in a good fit, it can be used to detect errors and identify outliers (e.g., Peterson et al ), to fill gaps (e.g., Peterson and Western ), or to extend series towards the past or future.…”

Section: Introductionmentioning

confidence: 99%

Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model

Bakker¹,

Schaars²

2019

Groundwater

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Time series analysis is a data‐driven approach to analyze time series of heads measured in an observation well. Time series models are commonly much simpler and give much better fits than regular groundwater models. Time series analysis with response functions gives insight into why heads vary, while such insight is difficult to gain with black box models out of the artificial intelligence world. An important application is to quantify the contributions to the head variation of different stresses on the aquifer, such as rainfall and evaporation, pumping, and surface water levels. Time series analysis may be applied to answer many groundwater questions without the need for a regular groundwater model, such as what is the drawdown caused by a pumping station? Or, how long will it take before groundwater levels recover after a period of drought? Even when a regular groundwater model is needed to solve a groundwater problem, time series analysis can be of great value. It can be used to clean up the data, identify the major stresses on the aquifer, determine the most important processes that affect flow in the aquifer, and give an indication of the fit that can be expected. In addition, it can be used to determine calibration targets for steady‐state models, and it can provide several alternative calibration methods for transient models. In summary, the overarching message of this paper is that it would be wise to do time series analysis for any application that uses measured groundwater heads.

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Statistical Interpolation of Groundwater Hydrographs

Cited by 23 publications

References 25 publications

Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections

Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections

Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings

Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model

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