Hydrological Impact Of Climate Change Research Articles

Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output ...

Differences and sensitivities in potential hydrologic impact of climate change to regional-scale Athabasca and Fraser River basins of the leeward and windward sides of the Canadian Rocky Mountains respectively

Sensitivities to the potential impact of Climate Change on the water resources of the Athabasca River Basin (ARB) and Fraser River Basin (FRB) were investigated. The Special Report on Emissions Scenarios (SRES) of IPCC projected by seven general circulation models (GCM), namely, Japan’s CCSRNIES, Canada’s CGCM2, Australia’s CSIROMk2b, Germany’s ECHAM4, the USA’s GFDLR30, the UK’s HadCM3, and the USA’s NCARPCM, driven under four SRES climate scenarios (A1FI, A2, B1, and B2) over three 30-year time periods (2010–2039, 2040–2069, 2070–2100) were used in these studies. The change fields over these three 30-year time periods are assessed with respect to the 1961–1990, 30-year climate normal and based on the 1961–1990 European Community Mid-Weather Forecast (ECMWF) re-analysis data (ERA-40), which were adjusted with respect to the higher resolution GEM forecast archive of Environment Canada, and used to drive the Modified ISBA (MISBA) of Kerkhoven and Gan (Adv Water Resour 29(6):808–826, 2006). In the ARB, the shortened snowfall season and increased sublimation together lead to a decline in the spring snowpack, and mean annual flows are expected to decline with the runoff coefficient dropping by about 8% per °C rise in temperature. Although the wettest scenarios predict mild increases in annual runoff in the first half of the century, all GCM and emission combinations predict large declines by the end of the twenty-first century with an average change in the annual runoff, mean maximum annual flow and mean minimum annual flow of −21%, −4.4%, and −41%, respectively. The climate scenarios in the FRB present a less clear picture of streamflows in the twenty-first century. All 18 GCM projections suggest mean annual flows in the FRB should change by ±10% with eight projections suggesting increases and 10 projecting decreases in the mean annual flow. This stark contrast with the ARB results is due to the FRB’s much milder climate. Therefore under SRES scenarios, much of the FRB is projected to become warmer than 0°C for most of the calendar year, resulting in a decline in FRB’s characteristic snow fed annual hydrograph response, which also results in a large decline in the average maximum flow rate. Generalized equations relating mean annual runoff, mean annual minimum flows, and mean annual maximum flows to changes in rainfall, snowfall, winter temperature, and summer temperature show that flow rates in both basins are more sensitive to changes in winter than summer temperature.

Distributed modelling of future changes in hydrological processes of Spencer Creek watershed

AbstractThe hydrologic impact of climate change has been largely assessed using mostly conceptual hydrologic models. This study investigates the use of distributed hydrologic model for the assessment of the climate change impact for the Spencer Creek watershed in Southern Ontario (Canada). A coupled MIKE SHE/MIKE 11 hydrologic model is developed to represent the complex hydrologic conditions in the Spencer Creek watershed, and later to simulate climate change impact using Canadian global climate model (CGCM 3·1) simulations. Owing to the coarse resolution of GCM data (daily GCM outputs), statistical downscaling techniques are used to generate higher resolution data (daily precipitation and temperature series). The modelling results show that the coupled model captured the snow storage well and also provided good simulation of evapotranspiration (ET) and groundwater recharge. The simulated streamflows are consistent with the observed flows at different sites within the catchment. Using a conservative climate change scenario, the downscaled GCM scenarios predicted an approximately 14–17% increase in the annual mean precipitation and 2–3 °C increase in annual mean maximum and minimum temperatures for the 2050s (i.e., 2046–2065). When the downscaled GCM scenarios were used in the coupled model, the model predicted a 1–5% annual decrease in snow storage for 2050s, approximately 1–10% increase in annual ET, and a 0·5–6% decrease in the annual groundwater recharge. These results are consistent with the downscaled temperature results. For future streamflows, the coupled model indicated an approximately 10–25% increase in annual streamflows for all sites, which is consistent with the predicted changes in precipitation. Overall, it is shown that distributed hydrologic modelling can provide useful information not only about future changes in streamflow but also changes in other key hydrologic processes such as snow storage, ET, and groundwater recharge, which can be particularly important depending on the climatic region of concern. The study results indicate that the coupled MIKE SHE/MIKE 11 hydrologic model could be a particularly useful tool for understanding the integrated effect of climate change in complex catchment scale hydrology. Copyright © 2010 John Wiley & Sons, Ltd.

Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

AbstractA major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

Assessing Hydrological Impacts of Climate Change: Modeling Techniques and Challenges

Climate Change refers to any systematic change in the long-term statistics of climate elements (such as tem- perature, pressure, or winds) sustained over several decades or longer time periods. General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere and resulting global climate change. They are currently the most credible tools available for simulating the re- sponse of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate vari- ables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75 0 longitude X 3.75 0 latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related vari- ables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspira- tion, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. Hydrologic implications of global climate change are usually assessed by downscaling appro- priate predictors simulated by General Circulation Models (GCMs). Conventionally rainfall is first downscaled with dy- namic or statistical downscaling and then the predicted rainfall is used in hydrologic models to forecast hydrologic scenar- ios of future. Although this methodology is widely practiced, there are some limitations: (a) uncertainty resulting from the use of multi- ple GCMs, scenarios, downscaling models is seldom considered; (b) local changes (e.g., urbanization, population growth, deforestation) which affect directly the hydrology of a region are considered in a very limited number of studies. The pre- sent paper focuses on these limitations and proposes different approaches to deal with the problems.

Blue Nile Runoff Sensitivity to Climate Change

gypt Abstract: This study describes implementation of hydrological climate change impact assessment t ool utilising a combi- nation of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System. A spatial rainfall generator was used to produce high-resolution (daily , 20km) gridded rainfall data required by the distributed hydrological model from monthly GCM outputs. The combined system was used to assess the sensitivity of upper Blue Nile flows at Diem flow gauging station to changes in future rainfall during th e June-September rainy sea- son based on output from three GCMs. The assessment also incorporated future evapotra nspiration changes over the ba- sin. The climate change scenarios derived in this study were broadly in line with other studies, with the majority of scenar- ios indicating wetter conditions in the future. Translating the impacts in to runoff in the basin showed increased future mean flows, although these would be offset to some degree by rising evapotranspiration. I mpacts on extreme runoff indi- cated the possibility of more severe fl oods in future. These are likely to be exacerbated by land-use changes including overgrazing, deforestation, and improper farming practices. Blue Nile basin flood managers the refore need to continue to prepare for the possibility of more frequent floods by adopting a range of measures to mi nimise loss of life and guard against other flood damage.

Adaptation strategy to hydrological impact of climate change

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Hydrology: catchment vegetation and runoff

The interactions between catchment vegetation and runoff continue to be a staple area of hydrological research. Drawing mainly upon material published since 2002, this report briefly reviews progress in this area with specific reference to: (1) paired and single catchment studies; (2) top-down models; and (3) the likely impact of climate change. Results from a wider range of paired and single catchments studies are revealing the complex relationship between catchment vegetation and runoff and prompting a reassessment of the methodologies used to generalize this relationship. Vegetation appears to have a significant influence on runoff at small scales, which reduces to a second-order influence, relative to aridity, at larger scales. Top-down models of catchment behaviour generally reflect this second-order influence at the large scale. As vegetation responds to CO2 enrichment under climate change, the magnitude and direction of associated changes in runoff remains uncertain. A key element in quantifying the hydrological impact of climate change is the relationship between catchment vegetation and runoff, which continues to be a productive area of research within hydrology.

A conditional random field–based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station‐scale hydrologic variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF‐downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large‐scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high‐dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden‐Fletcher‐Goldfarb‐Shanno (L‐BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June–September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium‐resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies

AbstractTwo downscaling methods designed for the study of the hydrological impact of climate change on the Seine basin in France are tested for present climate. First, a multivariate statistical downscaling (SD) methodology based on weather typing and conditional resampling is described. Then, a bias correction technique for dynamical downscaling based on quantile–quantile mapping is introduced. To evaluate the end‐to‐end SD methodology, the atmospheric forcing derived from the large‐scale circulation (LSC) of the ERA40 reanalysis by SD is used to force a hydrological model. Simulated discharges reproduce historical values reasonably well. Next, the dynamical and statistical approaches are compared using the Météo–France ARPEGE general circulation model in a variable resolution configuration (resolution around 60 km over France). The ARPEGE simulation is downscaled using the two methodologies, and hydrological simulations are performed. Regarding downscaled temperature and precipitation, the statistical approach is more efficient in reproducing the temporal and spatial autocorrelation properties. The simulated river discharges from the two approaches are nevertheless very similar: the two methods reproduce well the seasonal cycle and the daily distribution of streamflows. Finally, the results of the study are discussed from a practical impact study perspective. Copyright © 2007 Royal Meteorological Society

Reconstructing extreme flows using an airflow index-based stochastic weather generator and a hydrological simulation model

This paper offers an alternative to the use of geomorphological and sedimentological evidence for the reconstruction of flood and low flow frequencies. It is based on a technique developed to estimate the hydrological impact of future climate change and it uses either observed or calculated meteorological parameters. It is possible to use this method directly without modification to ‘hindcast’ events within the period of regional meteorological records, which in areas like the UK extend back over the last 150 years. It should also be possible to extend the approach to earlier periods using reconstructed meteorological parameters based on surrogate data, such as ships' logs. The resulting extreme flow sequences may be used to study past hydrological regimes or to improve estimates of present-day risks by extending the flow records. The technique uses an airflow index-based stochastic weather generator to create hydrometeorological parameters to input into a physically based hydrological simulation model. The method is illustrated here in a reconstruction of daily flow series for the River Wye catchment above Rhayader, mid-Wales, for the period 1889–1998. The method makes use of observed climatic variables for the entire period, with the aim of capturing actual climate variability occurring over the 110-year period. Changes in high flow characteristics are assessed using the mean annual flood (MAF), Q 5 flow and peaks-over-threshold (POT) calculated from the 110-year simulated daily flow series. This particular application shows evidence of a possible trend towards increasing magnitude and frequency of high flow events, which, if continued, would have implications for flood and water resource management. Looking at evidence from the last 110 years helps to place possible future trends within the context of past variations in high flow extremes due to both natural and anthropogenically influenced fluctuations in climate.

Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models

Changes in global climate will have significant impact on local and regional hydrological regimes, which will in turn affect ecological, social and economical systems. However, climate-change impact studies on hydrologic regime have been relatively rare until recently, mainly because Global Circulation Models, which are widely used to simulate future climate scenarios, do not provide hourly or daily rainfall reliable enough for hydrological modeling. Nevertheless, more reliable rainfall series corresponding to future climate scenarios can be derived from GCM outputs using the so called ‘downscaling techniques’. This study applies two types of statistical (a stochastic and a regression based) downscaling techniques to generate the possible future values of local meteorological variables such as precipitation and temperature in the Chute-du-Diable sub-basin of the Saguenay watershed in northern Québec, Canada. The downscaled data is used as input to two different hydrologic models to simulate the corresponding future flow regime in the catchment. In addition to assessing the relative potential of the downscaling methods, the paper also provides comparative study results of the possible impact of climate change on river flow and total reservoir inflow in the Chute-du-Diable basin. Although the two downscaling techniques do not provide identical results, the time series generated by both methods indicates a general increasing trend in the mean daily temperature values. While the regression based downscaling technique resulted in an increasing trend in the mean and variability of daily precipitation values, such a trend is not obvious in the case of precipitation time series downscaled with the stochastic weather generator. Moreover, the hydrologic impact analysis made with the downscaled precipitation and temperature time series as input to the two hydrological models suggest an overall increasing trend in mean annual river flow and reservoir inflow as well as earlier spring peak flows in the basin.

Predicting rainfall statistics in England and Wales using atmospheric circulation variables

Regression models are developed to predict point rainfall statistics, with potential application to downscaling general circulation model (GCM) output for future climates. The models can be used to predict the mean daily rainfall amount and the proportion of dry days for each calendar month at any site in England and Wales, and use the following explanatory variables: (i) geographical (altitude, geographic coordinates, and distance from nearest coast); and (ii) atmospheric circulation variables (mean values of air-flow indices derived from mean sea-level pressure grids). Values predicted by the models, for 10-km grid squares covering the whole of England and Wales, are in reasonable agreement with the 1961-1990 climatology of Barrow et al. (1993). The potential use of the models in hydrological climate change impact studies is discussed.