Overcoming the challenges of using a rainfall–runoff model to estimate the impacts of groundwater extraction on low flows in an ephemeral stream

2013 ◽  
Vol 45 (1) ◽  
pp. 58-72 ◽  
Author(s):  
K. M. Ivkovic ◽  
B. F. W. Croke ◽  
R. A. Kelly
Keyword(s):  
River System ◽  
Groundwater Extraction ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Water Accounting ◽  
River Reach ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Modelling Approach

Simple modelling approaches such as a spatially lumped, rainfall–runoff model offer a number of advantages in the management of water resources including the relative ease with which groundwater and surface water accounts can be evaluated at the river-reach scale in data-poor areas. However, rainfall–runoff models are generally not well suited for use in ephemeral river systems because of their inability to simulate abrupt transitions from flow to no-flow periods and the highly non-linear rainfall–runoff relationships that exist in low yielding catchments. This paper discusses some of the challenges of using a rainfall–runoff model to assess the impacts of groundwater extraction on low flows within an ephemeral river system and demonstrates how these challenges were overcome during the development of the IHACRES_GW (Identification of Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data – with Ground Water store) model. Details on the model algorithms, calibration, validation and objective function fits are provided. The performance of the IHACRES_GW model in Cox's Creek (Namoi Valley, Australia), and 13 additional areas investigated, suggests that this simple modelling approach may be of considerable utility for water accounting, especially when attempting to evaluate the impacts of groundwater extraction on low flows in similar systems.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2015 ◽  
Vol 12 (6) ◽  
pp. 5389-5426 ◽  
Author(s):  
S. Almeida ◽  
N. Le Vine ◽  
N. McIntyre ◽  
T. Wagener ◽  
W. Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 10 (1) ◽  
pp. 449-485 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Low Flows ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. In a three-part paper we assess the performance of runoff predictions in ungauged basins in a comparative way. While Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation of hydrographs and hydrological extremes through a literature review, in this paper we assess prediction of a range of runoff signatures for a consistent dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-Kriging, a geostatistical interpolation method that accounts for the river network hierarchy. From the runoff timeseries, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrograph. The predictive performance is assessed by the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation mode. Results of the comparative assessment show that the geostatistical approach (Top-Kriging) generally outperforms the regionalised rainfall–runoff model. The predictive performance increases with catchment area for both methods and all signatures, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows is the most difficult to capture followed by the low flows. The relative predictive performance of the signatures depends on the selected performance measures. It is therefore essential to report performance in a consistent way by more than one performance measure.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2016 ◽  
Vol 20 (2) ◽  
pp. 887-901 ◽  
Author(s):  
Susana Almeida ◽  
Nataliya Le Vine ◽  
Neil McIntyre ◽  
Thorsten Wagener ◽  
Wouter Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall–runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall–runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.

A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data

2020 ◽  
Vol 591 ◽  
pp. 125129 ◽  
Author(s):  
Julien Lerat ◽  
Mark Thyer ◽  
David McInerney ◽  
Dmitri Kavetski ◽  
Fitsum Woldemeskel ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Robust Approach ◽  
Monthly Streamflow ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Step wise Multi-criteria Performance Evaluation of Rainfall-Runoff Models using WETSPRO

1970 ◽  
Vol 7 (1) ◽  
pp. 18-29 ◽  
Author(s):  
PC Shakti ◽  
NK Shrestha ◽  
P Gurung
Keyword(s):  
Time Series ◽  
Performance Evaluation ◽  
Assessment Tool ◽  
Model Performance ◽  
Rainfall Runoff ◽  
Peak Flows ◽  
Low Flows ◽  
Flow Components ◽  
Rainfall Runoff Model ◽  
Runoff Model

This paper illustrates a methodology to evaluate model’s performance of rainfall runoff model using a tool called WETSPRO (Water Engineering Time Series PROcessing tool). Simulated results of physically based semidistributed model - SWAT (Soil and Water Assessment Tool) for Kliene Nete watershed (581 km2), Belgium are considered in this study. Paper presents a series of sequential time series processing tasks to be performed to evaluate model’s performance thoroughly. The problem of serial dependence and heteroscedasticity is addressed and model performance evaluation on different flow components (peak flows, low flows and volume) and flow volume is carried. Performance evaluation of both flow components on their extremes is also performed. Two most commonly used goodness-fit-statistics (Mean Square Error – MSE and Nash Sutcliff Efficiency − NSE) are used with number of complementary graphical plots for evaluation propose. Results indicated model’s robust performance on peak flows although base flows are slightly underestimated especially for lower return periods. Cumulative flow volumes tend to be overestimated. Based upon the study, some recommendations are summarized to enhance model’s ability to simulate the flows events. Keywords: Rainfall runoff model; SWAT; WETSPRO; Kliene Nete; peak flows; low flows. DOI: http://dx.doi.org/10.3126/jhm.v7i1.5613 JHM 2010; 7(1): 18-29

scholarly journals Constraining a lumped rainfall-runoff model with piezometry to improve low-flow simulation

2021 ◽  
Author(s):  
Antoine Pelletier ◽  
Vakzen Andréassian
Keyword(s):  
Flow Simulation ◽  
Daily Rainfall ◽  
Climatic Conditions ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Low Flows ◽  
Sample Test ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Most lumped hydrological models are focused on the rainfall-runoff relationship, since climatic conditions are the driving force of the hydrological behaviour of a catchment. Many hydrological models, like the ones used by the French national PREMHYCE platform, only take climatic variables as inputs – daily rainfall and potential evaporation – to simulate and forecast low-flows. Yet, a hydrological drought is generally a medium- to long-term phenomenon, which is the consequence of long records of dry climatic conditions. Daily lumped hydrological models often struggle to integrate these records to reproduce catchment memory.</p><p>In many French catchments, it was observed that this memory of past hydroclimatic conditions is well represented in piezometric signals that are broadly available over the national territory. Indeed, aquifers, especially the large ones, do store water on the long, feeding rivers during droughts: aquifers are not only <em>water carriers</em> – the etymology for the word <em>aquifer </em>– they are also <em>memory carriers</em>. A dataset of 108 catchments, each of them being associated with one or several piezometers, was used to investigate whether the GR6J daily lumped rainfall-runoff model could be constrained by piezometric time series to improve low-flow simulations. We found that a particular state of the model, the exponential store, is particularly well correlated with piezometry in most studied catchments.</p><p>In order to get a univocal relationship between the exponential store and piezometry, a multi-objective calibration approach was implemented, optimising both (i) flow simulation with a criterion focused on low-flows and (ii) affine correspondence between the exponential store level and piezometry. For that purpose, a new parameter was added to the model. The modified calibration was then evaluated through a split-sample test and the performance in simulating particular drought events. The calibrated store-piezometry relationship can now be used for data assimilation to improve low-flow forecasting.</p>

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