scholarly journals Does the weighting of climate simulations result in a more reasonable quantification of hydrological impacts?

2019 ◽  
Author(s):  
Hui-Min Wang ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
Shenglian Guo ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
Impact Studies ◽  
Hydrological Responses ◽  
Hydrological Impacts ◽  
Climate Simulations ◽  
The Impact ◽  
Equal Weighting

Abstract. With the increase in the number of available global climate models (GCMs), pragmatic questions come up when using them to quantify the climate change impacts on hydrology: Is it necessary to weight GCM outputs in the impact studies, and if so, how to weight them? Some weighting methods have been proposed based on the performances of GCM simulations with respect to reproducing the observed climate. However, the process from climate variables to hydrological responses is nonlinear, and thus the assigned weights based on their performances in climate simulations may not be translated to hydrological responses. Assigning weights to GCM outputs based on their ability to represent hydrological simulations is more straightforward. Accordingly, the present study assigns weights to GCM simulations based on their ability to reproduce hydrological characteristics and investigates their influence on the quantification of hydrological impacts. Specifically, eight weighting schemes are used to determine the weights of GCM simulations based on streamflow series simulated by a lumped hydrological model using raw or bias-corrected GCM outputs. The impacts of weighting GCM simulations are investigated in terms of reproducing the observed hydrological regimes for the reference period (1970–1999) and quantifying the uncertainty of hydrological changes for the future period (2070–2099). The results show that when using raw GCM outputs with no bias correction, streamflow-based weights better represent the mean hydrograph and reduce the bias of annual streamflow. However, when applying bias correction to GCM simulations before driving the hydrological model, the climate simulations become rather close to the observed climate, so that compared to equal weighting, the streamflow-based weights do not bring significant differences in the multi-model ensemble mean and uncertainty of hydrological impacts. Since bias correction has been an indispensable procedure in hydrological impact studies, the equal weighting method may still be a viable and conservative choice for the studies of hydrological climate change impacts.

scholarly journals Does the weighting of climate simulations result in a better quantification of hydrological impacts?

2019 ◽  
Vol 23 (10) ◽  
pp. 4033-4050 ◽  
Author(s):  
Hui-Min Wang ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
Shenglian Guo ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Hydrological Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Impact Studies ◽  
Hydrological Responses ◽  
Hydrological Impacts ◽  
Climate Simulations ◽  
The Impact

Abstract. With the increase in the number of available global climate models (GCMs), pragmatic questions come up in using them to quantify climate change impacts on hydrology: is it necessary to unequally weight GCM outputs in the impact studies, and if so, how should they be weighted? Some weighting methods have been proposed based on the performances of GCM simulations with respect to reproducing the observed climate. However, the process from climate variables to hydrological responses is nonlinear, and thus the assigned weights based on performances of GCMs in climate simulations may not be correctly translated to hydrological responses. Assigning weights to GCM outputs based on their ability to represent hydrological simulations is more straightforward. Accordingly, the present study assigns weights to GCM simulations based on their ability to reproduce hydrological characteristics and investigates their influences on the quantification of hydrological impacts. Specifically, eight weighting schemes are used to determine the weights of GCM simulations based on streamflow series simulated by a lumped hydrological model using raw or bias-corrected GCM outputs. The impacts of weighting GCM simulations are investigated in terms of reproducing the observed hydrological regimes for the reference period (1970–1999) and quantifying the uncertainty of hydrological changes for the future period (2070–2099). The results show that when using raw GCM outputs to simulate streamflows, streamflow-based weights have a better performance in reproducing observed mean hydrograph than climate-variable-based weights. However, when bias correction is applied to GCM simulations before driving the hydrological model, the streamflow-based unequal weights do not bring significant differences in the multi-model ensemble mean and uncertainty of hydrological impacts, since bias-corrected climate simulations become rather close to observations. Thus, it is likely that using bias correction and equal weighting is viable and sufficient for hydrological impact studies.

scholarly journals Transferability of climate simulation uncertainty to hydrological climate change impacts

2018 ◽  
Author(s):  
Hui-Min Wang ◽  
Jie Chen ◽  
Alex J. Cannon ◽  
Chong-Yu Xu ◽  
Hua Chen
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Climate Simulation ◽  
Climate Variables ◽  
Total Uncertainty ◽  
Impact Studies ◽  
Hydrological Impacts ◽  
Climate Simulations ◽  
Hydrological Variables

Abstract. Increasing number of climate models are being produced to cover the uncertainty, which makes it infeasible to use all of them in climate change impact studies. In order to thoughtfully select subsets of climate simulations from a large ensemble, several envelope-based methods have been proposed. The subsets are expected to cover a similar uncertainty envelope as the full ensemble in terms of climate variables. However, it is not a given that the uncertainty in hydrological impacts will be similarly well represented. Therefore, this study investigates the transferability of climate uncertainty related to the choice of climate simulations to hydrological impacts. Two envelope-based selection methods, K-means clustering and Katsavounidis–Kuo–Zhang (KKZ) method, are used to select subsets from an ensemble of 50 climate simulations over two watersheds with very different climates using 31 precipitation and temperature variables. Transferability is evaluated by comparing uncertainty coverage between climate variables and 17 hydrological variables simulated by a hydrological model. The importance of properly choosing climate variables in selecting subsets is investigated by including and excluding temperature variables. Results show that KKZ performs better than K-means at selecting subsets of climate simulations for hydrological impacts, and the uncertainty coverage of climate variables is similar to that of hydrological variables. The subset of first 10 simulations covers over 85 % of total uncertainty. As expected, temperature variables are important for the snow-related watershed, but less important for the rainfall-driven watershed. Overall, envelope-based selection of around 10 climate simulations, based on climate variables that characterize the physical processes controlling hydrology of the watershed, is recommended for hydrological impact studies.

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

scholarly journals High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation in Morocco

2013 ◽  
Vol 10 (5) ◽  
pp. 5687-5737 ◽  
Author(s):  
Y. Tramblay ◽  
D. Ruelland ◽  
S. Somot ◽  
R. Bouaicha ◽  
E. Servat
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Impact Studies ◽  
Hydrological Impact ◽  
Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of these high-resolution simulations. Different approaches are compared to analyze the climate change impacts on the hydrology of a catchment located in North Morocco, using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 km and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30% to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15% and −19% and for temperature between +1.28°C and +1.87°C.

scholarly journals Ensemble bias correction of climate simulations: preserving internal variability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pradeebane Vaittinada Ayar ◽  
Mathieu Vrac ◽  
Alain Mailhot
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Internal Variability ◽  
Ensemble Member ◽  
Climate Change Signal ◽  
Changing Climate ◽  
Uncertainty Model ◽  
Climate Simulations

AbstractClimate simulations often need to be adjusted (i.e., corrected) before any climate change impacts studies. However usual bias correction approaches do not differentiate the bias from the different uncertainties of the climate simulations: scenario uncertainty, model uncertainty and internal variability. In particular, in the case of a multi-run ensemble of simulations (i.e., multiple runs of one model), correcting, as usual, each member separately, would mix up the model biases with its internal variability. In this study, two ensemble bias correction approaches preserving the internal variability of the initial ensemble are proposed. These “Ensemble bias correction” (EnsBC) approaches are assessed and compared to the approach where each ensemble member is corrected separately, using precipitation and temperature series at two locations in North America from a multi-member regional climate ensemble. The preservation of the internal variability is assessed in terms of monthly mean and hourly quantiles. Besides, the preservation of the internal variability in a changing climate is evaluated. Results show that, contrary to the usual approach, the proposed ensemble bias correction approaches adequately preserve the internal variability even in changing climate. Moreover, the climate change signal given by the original ensemble is also conserved by both approaches.

scholarly journals High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco

2013 ◽  
Vol 17 (10) ◽  
pp. 3721-3739 ◽  
Author(s):  
Y. Tramblay ◽  
D. Ruelland ◽  
S. Somot ◽  
R. Bouaicha ◽  
E. Servat
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Hydrological Model ◽  
Climate Model ◽  
Regional Climate ◽  
Impact Studies ◽  
Hydrological Impact ◽  
Split Sample

Abstract. In the framework of the international CORDEX program, new regional climate model (RCM) simulations at high spatial resolutions are becoming available for the Mediterranean region (Med-CORDEX initiative). This study provides the first evaluation for hydrological impact studies of one of these high-resolution simulations in a 1800 km2 catchment located in North Morocco. Different approaches are compared to analyze the climate change impacts on the hydrology of this catchment using a high-resolution RCM (ALADIN-Climate) from the Med-CORDEX initiative at two different spatial resolutions (50 and 12 km) and for two different Radiative Concentration Pathway scenarios (RCP4.5 and RCP8.5). The main issues addressed in the present study are: (i) what is the impact of increased RCM resolution on present-climate hydrological simulations and on future projections? (ii) Are the bias-correction of the RCM model and the parameters of the hydrological model stationary and transferable to different climatic conditions? (iii) What is the climate and hydrological change signal based on the new Radiative Concentration Pathways scenarios (RCP4.5 and RCP8.5)? Results indicate that high resolution simulations at 12 km better reproduce the seasonal patterns, the seasonal distributions and the extreme events of precipitation. The parameters of the hydrological model, calibrated to reproduce runoff at the monthly time step over the 1984–2010 period, do not show a strong variability between dry and wet calibration periods in a differential split-sample test. However the bias correction of precipitation by quantile-matching does not give satisfactory results in validation using the same differential split-sample testing method. Therefore a quantile-perturbation method that does not rely on any stationarity assumption and produces ensembles of perturbed series of precipitation was introduced. The climate change signal under scenarios 4.5 and 8.5 indicates a decrease of respectively −30 to −57% in surface runoff for the mid-term (2041–2062), when for the same period the projections for precipitation are ranging between −15 and −19% and for temperature between +1.3 and +1.9 °C.

Between high mountain processes and urban needs: implementing lake regulations into a hydrological model to anticipate climate change impacts

2021 ◽  
Author(s):  
Tobias Wechsler ◽  
Andreas Inderwildi ◽  
Bettina Schaefli ◽  
Massimiliano Zappa
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
Anthropogenic Effects ◽  
Lake Ecosystem ◽  
High Mountain ◽  
Climate Change Scenarios ◽  
Lake Levels ◽  
Daily Streamflow ◽  
The Impact

<p>From snow-covered peaks to urban heat islands, this gradient, in its most concentrated form, is the essence of Alpine regions; it spans not only diverse ecosystems, but also diverse demands on water resources. Continuing climate change modifies the water supply and accentuates the pressure from competing water uses. Large Alpine lakes play hereby a key role, for water resource and natural hazard management, but surprisingly, are often only crudely modelled in available climate change impact studies on hydrology. Indeed, regulation of Alpine lake outlets, where daily specifications for lake level and outflow are defined, are the crux to bringing together diverse stakeholders. Ideally, a common regulation is agreed upon with an annual pattern that both corresponds to natural fluctuations and respects the different needs of the lake ecosystem, its immediate environment and upstream and downstream interests, such as fishery, shipping, energy production, nature conservation and the mitigation of high and low extremes. Surprisingly, a key question that remains open to date is how to incorporate these anthropogenic effects into a hydrological model?</p><p>To estimate climate change impacts, daily streamflow through this century was calculated with the hydrological model PREVAH, using 39 climate model chains in transient simulation from the new Swiss Climate Change Scenarios CH2018, corresponding to the three different CO<sub>2</sub> emission scenarios RCP2.6, RCP4.5 and RCP8.5. PREVAH is based on a 200×200 m grid resolution and consists of several model components covering the hydrological cycle: interception, evapotranspiration, snow, glacier, soil- and groundwater, runoff formation and transfer. In order to implement the anthropogenic effect of lake regulations, we created an interface for the hydrodynamic model MIKE11. In this work, we will present the two hydraulically connected Swiss lakes, Walensee (unregulated) and Zurichsee (regulated), that are located on the gradient between snow-covered peaks and urban environments. This catchment area was already affected by water scarcity in isolated years.</p><p>The hydrological projections at the end of the century show minor changes in mean annual lake levels and outflow for both lakes, but there is a pronounced seasonal redistribution of both level and outflow. The changes intensify over time, especially in the scenario without climate change mitigation measures (RCP8.5). In the winter, mean lake levels rise and outflow increases; in the summer, mean lake levels fall and outflow decreases. Walensee’s (unregulated) level change is significantly higher, with a difference of up to 50 cm under RCP8.5, than Zurichsee’s (regulated), which only changes around 5 cm; the changes in outflow are of the same order of magnitude in both lakes. The extremes show an increased frequency of reaching the drawdown limit, but no clear change in frequency of reaching the flood limit.</p><p>In order to estimate future hydrological developments on lakes and downstream rivers, it is important to use models that include the impact of such regulations. Hydrological models including anthropogenic effects allow a separation of climatic and regulatory impacts. Timely hydrological projections are crucial to allow the necessary time horizon for both lake and downstream interests to adapt.</p>

scholarly journals Do changes in climate or vegetation regulate evapotranspiration and streamflow trends in water-limited basins?

2014 ◽  
Vol 11 (10) ◽  
pp. 11183-11202
Author(s):  
Q. Liu ◽  
Z. Yang ◽  
L. Liang ◽  
W. Nan
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Vegetation Type ◽  
Climate Change Impacts ◽  
Hydrological Processes ◽  
Rooting Depth ◽  
Climate Change Scenarios ◽  
The Impact ◽  
Streamflow Trends

Abstract. Interactions between climate change, vegetation, and soil regulate hydrological processes. In this study, it was assumed that vegetation type and extent remained fixed and unchanged throughout the study period, while the effective rooting depth (Ze) changed under climate change scenarios. Budyko's hydrological model was used to explore the impact of climate change and vegetation on evapotranspiration (E) and streamflow (Q) on the static vegetation rooting depth and the dynamic vegetation rooting depth. Results showed that both precipitation (P) and potential evapotranspiration (Ep) exhibited negative trends, which resulted in decreasing trends for dynamic Ze scenarios. Combined with climatic change, decreasing trends in Ze altered the partitioning of P into E and Q. For dynamic scenarios, total E and Q were predicted to be −1.73 and 28.22%, respectively, greater than static scenarios. Although climate change regulated changes in E and Q, the response of Ze to climate change had a greater overall contribution to changes in hydrological processes. Results from this study suggest that with the exception of vegetation type and extent, Ze scenarios were able to alter water balances, which in itself should help to regulate climate change impacts on water resources.

scholarly journals Comparing Meteorological Data Sets in the Evaluation of Climate Change Impact on Hydrological Indicators: A Case Study on a Mexican Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2110
Author(s):  
Juan Alberto Velázquez-Zapata
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Data Sets ◽  
Data Set ◽  
Climate Simulations ◽  
Change Impact ◽  
Data Source

This study evaluates the choice of the meteorological data set in the simulation of the streamflow of a Mexican basin, in the bias correction of climate simulations, and in the climate change impact on hydrological indicators. The selected meteorological data sets come from stations, two interpolated data sets and one reanalysis data set. The climate simulations were taken from the five-member ensemble from the second generation Canadian Earth System Model (CanESM2) under two representative concentration pathways (RCPs), for a reference period (1981–2000) and two future periods (2041–2060 and 2081–2100). The selected lumped hydrological model is GR4J, which is a daily lumped four-parameter rainfall-runoff model. Firstly, the results show that GR4J can be calibrated and validated with the meteorological data sets to simulate daily streamflow; however, the hydrological model leads to different hydrological responses for the basin. Secondly, the bias correction procedure obtains a similar relative climate change signal for the variables, but the magnitude of the signal strongly varies with the source of meteorological data. Finally, the climate change impact on hydrological indicators also varies depending on the meteorological data source, thus, for the overall mean flow, this uncertainty is greater than the uncertainty related to the natural variability. On the other hand, mixed results were found for high flows. All in all, the selection of meteorological data source should be taken into account in the evaluation of climate change impact on water resources.

scholarly journals Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation

2021 ◽  
Vol 25 (3) ◽  
pp. 1307-1332
Author(s):  
Lieke Anna Melsen ◽  
Björn Guse
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
The United States ◽  
Parameter Sensitivity ◽  
Hydrological Models ◽  
The Future ◽  
The Mean ◽  
The Impact

Abstract. Hydrological models are useful tools for exploring the impact of climate change. To prioritize parameters for calibration and to evaluate hydrological model functioning, sensitivity analysis can be conducted. Parameter sensitivity, however, varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity for the mean discharge and the timing of the discharge, within a plausible climate change rate. We investigate whether changes in sensitivity propagate into the calibration strategy and diagnostically compare three hydrological models based on the sensitivity results. We employed three frequently used hydrological models (SAC, VIC, and HBV) and explored parameter sensitivity changes across 605 catchments in the United States by comparing GCM(RCP8.5)-forced historical and future periods. Consistent among all hydrological models and both for the mean discharge and the timing of the discharge is that the sensitivity of snow parameters decreases in the future. Which other parameters increase in sensitivity is less consistent among the hydrological models. In 45 % to 55 % of the catchments, dependent on the hydrological model, at least one parameter changes in the future in the top-5 most sensitive parameters for mean discharge. For the timing, this varies between 40 % and 88 %. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on the processes that become more relevant in future projections also calls for a strict evaluation of the adequacy of the model structure for long-term simulations.

Sign in / Sign up

Export Citation Format

Share Document