Optimal Hydrological Model Calibration Strategy for Climate Change Impact Studies

Abstract. Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

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Importance of hydrological uncertainty assessment methods in climate change impact studies

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-501-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 501-553 ◽

Cited By ~ 4

Author(s):

M. Honti ◽

A. Scheidegger ◽

C. Stamm

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Hydrological Model ◽

Uncertainty Assessment ◽

Assessment Method ◽

Calibration Data ◽

Relative Importance ◽

Impact Studies ◽

Uncertainty Sources ◽

Change Impact

Abstract. Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with a recent boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the following decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The source, structure and composition of uncertainty depended strongly on the uncertainty assessment method. This demonstrated that one could arrive to rather different conclusions about predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

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Effect of model calibration strategy on climate projections of hydrological indicators at a continental scale

Climatic Change ◽

10.1007/s10584-020-02874-4 ◽

2020 ◽

Vol 163 (3) ◽

pp. 1287-1306 ◽

Cited By ~ 1

Author(s):

Yeshewatesfa Hundecha ◽

Berit Arheimer ◽

Peter Berg ◽

René Capell ◽

Jude Musuuza ◽

...

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Model Calibration ◽

Large Scale ◽

Hydrological Model ◽

River Basins ◽

Absolute Difference ◽

Catchment Scale ◽

Major River ◽

Change Impact

AbstractThe effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection.

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Fine-resolution climate projections enhance regional climate change impact studies

Eos ◽

10.1029/2007eo470006 ◽

2007 ◽

Vol 88 (47) ◽

pp. 504-504 ◽

Cited By ~ 328

Author(s):

Edwin P. Maurer ◽

Levi Brekke ◽

Tom Pruitt ◽

Philip B. Duffy

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Regional Climate ◽

Regional Climate Change ◽

Climate Projections ◽

Impact Studies ◽

Change Impact ◽

Fine Resolution

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What can we learn from long-term groundwater data to improve climate change impact studies?

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-7621-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 7621-7655 ◽

Cited By ~ 1

Author(s):

S. Stoll ◽

H. J. Hendricks Franssen ◽

R. Barthel ◽

W. Kinzelbach

Keyword(s):

Climate Change ◽

Land Use ◽

Climate Change Impact ◽

Large Scale ◽

Climate Models ◽

Groundwater Resources ◽

Groundwater Dynamics ◽

Impact Studies ◽

Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

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