Assessing uncertainties in climate change impacts on runoff in Western Mediterranean basins

Ruelland, Denis; Hublart, P.; Tramblay, Yves

doi:10.5194/piahs-371-75-2015

Cited by 19 publications

(17 citation statements)

References 20 publications

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“…The DSST methodology represents the most frequently used method for the diagnosis of model stability and the described evaluation of cross‐calibration and validation over contrasted periods. Furthermore, it provides an approach for temporal transposability of the model parameters over climate‐contrasted periods (Ruelland et al, ; Thirel et al, ).…”

Section: Methodsmentioning

confidence: 99%

Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments

Rau

Bourrel

Labat

et al. 2018

Hydrological Processes

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In a context of water scarcity in Peruvian Pacific catchments as a crucial issue for Peru, added to the paucity of data availability, we propose a methodology that provides new perspectives for freshwater availability estimation as a base reference for unimpaired conditions. Under those considerations, a regional discharge of 709 m3/s to the Pacific Ocean is estimated with a significant increasing trend of about 43 m3/s per decade over the 1970–2010 period. To represent the multidecadal behaviour of freshwater runoff along the region, a regional runoff analysis is proposed based on hydrological modelling at annual and monthly time step for unimpaired conditions over the whole 1970–2010 period. Differential Split‐Sample Tests are used to assess the hydrological modelling robustness of the GR1A and GR2M conceptual lumped models, showing a satisfactory transposability from dry to wet years inside the thresholds defined for Nash–Sutcliffe and bias criteria. This allowed relating physical catchment characteristics with calibrated and validated model parameters, thus offering a regional perspective for dryland conditions in the study area (e.g., the anticlockwise hysteresis relationship found for seasonal precipitation–runoff relationship) as well as the impacts of climate variability and catchment characteristics.

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Section: Methodsmentioning

confidence: 99%

Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments

Rau

Bourrel

Labat

et al. 2018

Hydrological Processes

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“…Another limitation of this study is that only different calibration strategies were taken into account in the parameter estimation. It would be more comprehensive to calibrate parameters over different periods, such as wet and dry climate periods [82], as well as by a multi-objective calibration approach [83]. In addition, Seiller et al, [77] showed that the diagnosis of the impacts of climate change on water resources is very much affected by the objective functions used for hydrological model calibration.…”

Section: Discussionmentioning

confidence: 99%

Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin

Xiang

Chen

et al. 2019

Water

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The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment.

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“…Finally, although it is commonly acknowledged that the uncertainty resulting from climate modeling (GCMs, gas emission scenarios and downscaling methods) is highest in a context of climate change (e.g., Wilby and Harris, 2006;Arnell, 2011;Teng et al, 2012), it should be noted that the uncertainty stemming from hydrological modeling may also be high. Several authors (e.g., Benke et al, 2008;Brigode et al, 2013;Hublart et al, 2015;Ruelland et al, 2015) showed that the choice of the hydrological model (structural uncertainty) and its parameterization (parameter uncertainty) could cause significant variability in runoff simulations. Consequently, further analyses of the applicability of the model parameters in a nonstationary context and with different calibration criteria are needed before the model is used in future climate conditions.…”

Section: Discussionmentioning

confidence: 99%

“…3). Many studies have demonstrated the ability of the model to perform well under a wide range of hydro-climatic conditions (e.g., Perrin et al, 2003;Vaze et al, 2010;Coron et al, 2012) and notably in the Mediterranean region (e.g., Tramblay et al, 2013;Fabre et al, 2015;Ruelland et al, 2015). This model relies on precipitation (P) and potential evapotranspiration (PE) and is based on a production function that determines the effective precipitation (the fraction of the precipitation involved in runoff) that supplies the production reservoir and on a routing function based on a unit hydrograph.…”

Section: Hydrological Modelmentioning

confidence: 99%

“…Preliminary studies (Tramblay et al, 2013;Fabre et al, 2015;Ruelland et al, 2015) provided daily hydro-climatic data (precipitation, temperature and streamflow) over a common 20-year period (1986-2005), thus making it possible to compare the basins. Climate data for the Herault basin were extracted from the SAFRAN 8 × 8 km meteorological analysis system (Vidal et al, 2010) and observed runoff was provided by the French ministry of ecology and sustainable development from their Banque Hydro database (MEDDE, 2010).…”

Section: Hydro-climatic Datamentioning

confidence: 99%

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Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

Grouillet

Ruelland

Ayar

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydrometeorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.

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Assessing uncertainties in climate change impacts on runoff in Western Mediterranean basins

Cited by 19 publications

References 20 publications

Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments

Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments

Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

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