Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?

International audienceThis paper investigates the uncertainty of hydrological predictions due to rainfall-runoff model parameters in the context of climate change impact studies. Two sources of uncertainty were considered: (i) the dependence of the optimal parameter set on the climate characteristics of the calibration period and (ii) the use of several posterior parameter sets over a given calibration period. The first source of uncertainty often refers to the lack of model robustness, while the second one refers to parameter uncertainty estimation based on Bayesian inference. Two rainfall-runoff models were tested on 89 catchments in northern and central France. The two sources of uncertainty were assessed in the past observed period and in future climate conditions. The results show that, given the evaluation approach followed here, the lack of robustness was the major source of variability in streamflow projections in future climate conditions for the two models tested. The hydrological projections generated by an ensemble of posterior parameter sets are close to those associated with the optimal set. Therefore, it seems that greater effort should be invested in improving the robustness of models for climate change impact studies, especially by developing more suitable model structures and proposing calibration procedures that increase their robustness

Pitfalls of space-time trading when parametrizing a land use dependent hydrological model

Evaporation is a function of both climate conditions and other environmental conditions, including land use. In the context of large-scale environmental changes, understanding the relative impacts of each driver of past and future evaporation changes is necessary for land and water planning. While climate change impacts on evaporation can be estimated straightforwardly by original Budyko formulations, including the role of land use within these formulations remains an open question. In this paper, we collected an extensive set of 5026 worldwide catchments to parametrize a land use dependent Budyko-type formulation. By trading space for time, we then assess the potentialities of the proposed formulation in predicting the impacts of land use changes on the evaporation changes. Results show a clear modulation of land use on evapotranspiration, suggesting larger and lower evaporation rates over croplands and urban areas respectively. The proposed formulation was able to reasonably predict the magnitude of the decrease of the evaporative ratio on urbanizing catchments, but fails to efficiently predict the hydrological impacts of vegetated land use conversions, both in terms of direction and magnitude of changes. This suggests either the proposed formulation is too crude, or the underlying hypotheses of space-time trading are not valid

Pitfalls of space-time trading when parametrizing a land use dependent hydrological model

Evaporation is a function of both climate conditions and other environmental conditions, including land use. In the context of large-scale environmental changes, understanding the relative impacts of each driver of past and future evaporation changes is necessary for land and water planning. While climate change impacts on evaporation can be estimated straightforwardly by original Budyko formulations, including the role of land use within these formulations remains an open question. In this paper, we collected an extensive set of 5026 worldwide catchments to parametrize a land use dependent Budyko-type formulation. By trading space for time, we then assess the potentialities of the proposed formulation in predicting the impacts of land use changes on the evaporation changes. Results show a clear modulation of land use on evapotranspiration, suggesting larger and lower evaporation rates over croplands and urban areas respectively. The proposed formulation was able to reasonably predict the magnitude of the decrease of the evaporative ratio on urbanizing catchments, but fails to efficiently predict the hydrological impacts of vegetated land use conversions, both in terms of direction and magnitude of changes. This suggests either the proposed formulation is too crude, or the underlying hypotheses of space-time trading are not valid

Physical modelling to remove hydrological effects at local and regional scale: application to the 100-m hydrostatic inclinometer in Sainte-Croix-aux-Mines (France)

International audienceNew inclinometers devoted to hydrological studies were set up in the Vosges Mountains (France). Two orthogonal 100-meter base hydrostatic inclinometers were installed in December 2004 as well as a hydrometeorological monitoring system for the 100-km² hydrological unit around the inclinometer. As inclinometers are very sensitive to environmental influences, this observatory is a test site to confront hydrological modelling and geodetic observations. Physical modelling to remove hydrological effects without calibrating on geodetic data is tested on these instruments. Specifically, two deformation processes are most important: fluid pressure variations in nearby hydraulically active fractures and surface loading at regional scale

Adaptación de un método de optimización multiobjetivo para modelos de pronóstico de inundaciones

Los métodos de optimización multi-objetivo tienen diversas aplicacionesen la ingeniería. En particular, en la hidrología son una herramienta de gran valor para encontrar un conjunto de soluciones de compromiso para dos objetivos hidrológicos cualesquiera en conflicto. Por su parte, los modelos de pronóstico hidrológico se emplean durante un evento lluvioso con el fin de predecir el caudal que se producirá en una sección de un río con un cierto tiempo de anticipación (horizonte de pronóstico).Como información de entrada suelen utilizar la lluvia y el caudal de la sección de interés, registrados en tiempo real. Sin embargo, durante un evento de gran magnitud, algunos sistemas de pronóstico experimentan una degradación en la calidad de los caudales observados en tiempo real, y en ciertos casos se produce incluso pérdida de dicha información. Cuando la cadena de transmisión de datos hidrometeorológicos (redpluviométrica e hidrométrica) a tiempo real falla, el desempeño del modelo puede decaer drásticamente respecto del esperado en un caso real. En particular, si lainformación a tiempo real del último caudal observado se pierde (daño en la estación hidrométrica, o en la red de comunicación, etc.) y no puede ser asimilado, el modelo hidrológico corre en una situación típica de simulación, ejercicio para el cual otro modelo (o el mismo, pero parametrizado de otro modo) podría conducir a resultados más confiables. Por esta razón, un método de optimización multi-objetivo puede ser empleado para encontrar soluciones de compromiso entre ambos criterios. En este trabajo fue llevada a cabo una optimización multi-objetivo del modelo GR4P, mediante el uso del algoritmo MOCOM-UA. Se observó que el método era incapaz de encontrar en todos los experimentos de manera robusta el mismo frente de Pareto, por lo quefueron llevadas a cabo un conjunto de modificaciones al método con el fin de mejorar la robustez. Finalmente, fue posible obtener una versión adaptada del algoritmo MOCOMUA con la cual se llega en los sucesivos experimentos a frentes de Pareto bien semejantes entre sí. Los trabajos fueron realizados sobre una cuenca pequeña de la Bretaña francesa

Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds

International audienceWe introduce a new diagnosis tool that is well suited to analyzing simulation results over large samples of watersheds. It consists of a modification of the classical Taylor diagram to simultaneously visualize several error components (based on bias, standard deviation or squared errors) that are commonly used in efficiency criteria (such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE)) to evaluate hydrological model performance. We propose a methodological framework that explicitly links the graphical and numerical evaluation approaches, and show how they can be usefully combined to visually interpret numerical experiments conducted on large datasets. The approach is illustrated using results obtained by testing two rainfall-runoff models on a sample of 2050 watersheds from 8 countries and calibrated with two alternative objective functions (NSE and KGE). The assessment tool clearly highlights well-documented problems related to the use of the NSE for the calibration of rainfall-runoff models, which arise due to interactions between the ratio of simulated to observed standard deviations and the correlation coefficient. We also illustrate the negative impacts of classical mathematical transformations (square root) applied to streamflow when employing NSE and KGE as metrics for model calibration