The suite of lumped GR hydrological models in an R package

Coron, Laurent; Thirel, Guillaume; Delaigue, Olivier; Perrin, Charles; Andréassian, Vazken

doi:10.1016/j.envsoft.2017.05.002

Cited by 224 publications

(184 citation statements)

References 17 publications

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“…GR4J has been shown to reliably simulate the hydrology of a diverse set of catchments (Perrin et al, 2003) including temporal transition between wet and dry periods (Broderick et al, 2016), and for the generation of ESP forecasts (e.g. Pagano et al, 2010 (Coron et al, 2016(Coron et al, , 2017 with the inbuilt calibration optimisation algorithm based on a steepest descent local search procedure and default parameter ranges. The modified Kling-Gupta efficiency (KGEmod, Gupta et al, 2009, Kling et al, 2012 applied to root squared transformed flows KGEmod[sqrt] was used as the objective function for automatic fitting, thus placing weight on mid-range flows, rather than high or low flows.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

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Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decisionmaking in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in 10 the absence of skilful future meteorological predictions, instead using Initial Hydrological Conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1-day to 12-months initialised at the first of each month over a 50-year 15 hindcast period from 1965-2015. Results show ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialisation month, and individual catchment location and storage properties. UK-wide mean ESP skill decays exponentially as a function of lead time with mean squared error skill scores across the year of 0.839, 0.303, and 0.179 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialisation months. For lead times up to 1-20 month, ESP skill was higher than average when initialised in summer and lower in winter, whereas for longer seasonal and annual lead times skill was highest when initialised in autumn and winter months and lowest in April. ESP is most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment Baseflow Index (BFI) and ESP skill was very strong ( = 0.896 at 1-month lead time). This is in contrast to the more highly responsive catchments in the north and west which are generally not skilful 25 at seasonal lead times. Overall, this work provides a scientifically defensible justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK and creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Hydrological Modellingmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…A lumped conceptual daily rainfall-runoff model-GR4J (Perrin et al, 2003)-is used to simulate daily streamflow forced with different input options of climate data (observed, raw, and bias-corrected climate time series for historical and future periods). The daily GR4J model is a simple and popular model and has been used successfully in many catchments over the world (Coron et al, 2017;Pagano et al, 2010;Perrin et al, 2003;Teng et al, 2015). The model requires daily time series of precipitation and potential evapotranspiration as an input and only four parameters to define its structure.…”

Section: Runoff Simulationsmentioning

confidence: 99%

Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach

Nguyen

Mehrotra

Sharma

2020

Water Resources Research

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Raw simulations of global or regional climate models are rarely used in catchment scale hydrological impact assessment or subsequent reservoir storage change assessment studies. Keeping this in mind, this study uses a frequency bias correction alternative for multiple variables to evaluate the impact of climate change on reservoir storage reliability, resilience, and vulnerability across Australia. The bias‐corrected time series of daily rainfall and temperature are used as inputs to a hydrological model to derive flows and assess change to reservoir storage attributes. A total of six fifth phase of the Coupled Model Intercomparison Project climate models dynamically downscaled using the Conformal Cubic Atmospheric Model are used. Streamflow data of 222 high‐quality catchments in near‐natural conditions across Australia are used and change ascertained. The results for the historical climate show that the multivariate frequency bias correction approach outperforms the traditional quantile matching alternative in representing the runoff characteristics related to reservoir storage. For the future climate, the results suggest decrease in the annual mean runoff for most catchments. The proposed approach leads to a smaller decrease in the standard deviation of annual runoff and a reduction in the water supply capability, as indicated by a reduction in reliability and resilience and an increase in vulnerability, to meet the demand in comparison to both raw and quantile matching‐based climate simulations across for most catchments. Overall, a reduction in water supply capability to meet a given demand in the future for most regional climate models and catchments is projected.

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“…This model is a simple GR-type model [80,81] with a single parameter to calibrate. It is composed of a production reservoir whose capacity is related to hydrogeology, a routing reservoir with a uniform capacity and a 2-h unit hydrograph [46].…”

Section: Process-based Methodsmentioning

confidence: 99%

Comparison of Flood Frequency Analysis Methods for Ungauged Catchments in France

Odry¹,

Arnaud²

2017

Geosciences

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Abstract:The objective of flood frequency analysis (FFA) is to associate flood intensity with a probability of exceedance. Many methods are currently employed for this, ranging from statistical distribution fitting to simulation approaches. In many cases the site of interest is actually ungauged, and a regionalisation scheme has to be associated with the FFA method, leading to a multiplication of the number of possible methods available. This paper presents the results of a wide-range comparison of FFA methods from statistical and simulation families associated with different regionalisation schemes based on regression, or spatial or physical proximity. The methods are applied to a set of 1535 French catchments, and a k-fold cross-validation procedure is used to consider the ungauged configuration. The results suggest that FFA from the statistical family largely relies on the regionalisation step, whereas the simulation-based method is more stable regarding regionalisation. This conclusion emphasises the difficulty of the regionalisation process. The results are also contrasted depending on the type of climate: the Mediterranean catchments tend to aggravate the differences between the methods.

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The suite of lumped GR hydrological models in an R package

Cited by 224 publications

References 17 publications

Benchmarking Ensemble Streamflow Prediction skill in the UK

Benchmarking Ensemble Streamflow Prediction skill in the UK

Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach

Comparison of Flood Frequency Analysis Methods for Ungauged Catchments in France

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