Lumped hydrological models are catchment-scale representations of the transformation of precipitation into discharge. They are widely-used tools for real-time flow forecasting, flood design and ...climate change impact assessment, and they are often used for training and educational purposes. This article presents an R-package, airGR, to facilitate the implementation of the GR lumped hydrological models (including GR4J) and a snow-accumulation and melt model. The package allows users to calibrate and run hourly to annual models on catchment sets and to analyse their outputs. While the core of the models is implemented in Fortran, the user can manage the input/output data within R. A number of options and plotting functions are proposed to ease automate tests and analyses of the results. The codes are flexible enough to include external models, other calibration routines or efficiency criteria. To illustrate the features of airGR, we present one application example for a French mountainous catchment.
This paper investigates the actual extrapolation capacity of three hydrological models in differing climate conditions. We propose a general testing framework, in which we perform series of ...split‐sample tests, testing all possible combinations of calibration‐validation periods using a 10 year sliding window. This methodology, which we have called the generalized split‐sample test (GSST), provides insights into the model's transposability over time under various climatic conditions. The three conceptual rainfall‐runoff models yielded similar results over a set of 216 catchments in southeast Australia. First, we assessed the model's efficiency in validation using a criterion combining the root‐mean‐square error and bias. A relation was found between this efficiency and the changes in mean rainfall (P) but not with changes in mean potential evapotranspiration (PE) or air temperature (T). Second, we focused on average runoff volumes and found that simulation biases are greatly affected by changes in P. Calibration over a wetter (drier) climate than the validation climate leads to an overestimation (underestimation) of the mean simulated runoff. We observed different magnitudes of these models deficiencies depending on the catchment considered. Results indicate that the transfer of model parameters in time may introduce a significant level of errors in simulations, meaning increased uncertainty in the various practical applications of these models (flow simulation, forecasting, design, reservoir management, climate change impact assessments, etc.). Testing model robustness with respect to this issue should help better quantify these uncertainties.
Key Points
Proposing methods to analyse model robustness in contrasted climate conditions
Quantifying robustness losses due to climate change in parameters transfer
Estimating homogeneity of results over a large number of catchments
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge ...in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.
This study deals with the Rimbaud catchment, a sub-catchment within the Réal Collobrier hydrological observatory in southeastern France, managed by Irstea since 1966. This observatory suffered a ...wildfire in 1990. Because of the dense network of streamgauges and raingauges available, this site provides a unique opportunity to test and compare two types of analysis, one based on paired catchments and the other a rainfall–runoff model, used to assess the hydrological impact of forest fire. In the present case, more than 20 years of pre-fire and post-fire data are available. We compare the ability of the two approaches to detect gradual changes at a daily time step. This case study illustrates how natural climatic variability (here a long drought which preceded the wildfire) can make the identification of hydrological changes extremely difficult.
In semidistributed hydrological modeling, sequential calibration usually refers to the calibration of a model by considering not only the flows observed at the outlet of a catchment but also the ...different gauging points inside the catchment from upstream to downstream. While sequential calibration aims to optimize the performance at these interior gauged points, we show that it generally fails to improve performance at ungauged points. In this paper, we propose a regularization approach for the sequential calibration of semidistributed hydrological models. It consists in adding a priori information on optimal parameter sets for each modeling unit of the semidistributed model. Calibration iterations are then performed by jointly maximizing simulation performance and minimizing drifts from the a priori parameter sets. The combination of these two sources of information is handled by a parameter k to which the method is quite sensitive. The method is applied to 1,305 catchments in France over 30 years. The leave‐one‐out validation shows that, at locations considered as ungauged, model simulations are significantly improved (over all the catchments, the median KGE criterion is increased from 0.75 to 0.83 and the first quartile from 0.35 to 0.66), while model performance at gauged points is not significantly impacted by the use of the regularization approach. Small catchments benefit most from this calibration strategy. These performances are, however, very similar to the performances obtained with a lumped model based on similar conceptualization.
Key Points
Sequential calibration without regularization yields low performance in small ungauged catchments
Sequential calibration constrained by regularization strategy helps improving model regionalization
Regularization benefits from a priori parameter values of neighboring catchments
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