Bias-corrected Regional Climate Models Research Articles

Climate change impacts on summer flood frequencies in two mountainous catchments in China and Switzerland

Abstract Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.

Predicting impacts of climate change on evapotranspiration and soil moisture for a site with subhumid climate

Abstract The current and ongoing climate change over Europe can be characterized by statistically significant warming trend in all seasons. Warming has also an effect on the hydrological cycle through the precipitation intensity. Consequently, the supposed changes in the distribution and amount of precipitation with the continuously increasing temperature may induce a higher rate in water consumption of the plants, thus the adaptation of the plants to the climate change can be critical. The hydrological impact of climate change was studied based on typical environmental conditions of a specific agricultural area in Austria. For this purpose, (1) a monthly step, Thornthwaite-type water balance model was established and (2) the components of the water balance were projected for the 21st century, both (a) with a basic rooting depth condition (present state) and (b) with a (hypothetically) extended rooting depth (in order to evaluate potential adaption strategies of the plants to the warming). To achieve the main objectives, focus was set on calibrating and validating the model using local reference data. A key parameter of the applied model was the water storage capacity of the soil (SOILMAX), represented in terms of a maximum rooting depth. The latter was assessed and modified considering available data of evapotranspiration and soil physical properties. The adapted model was utilized for projections on the basis of four bias corrected Regional Climate Models. An extended rooting depth as a potential adaptation strategy for effects of climate change was also simulated by increasing SOILMAX. The basic simulation results indicated increasing evapotranspiration and soil moisture annual mean values, but decreasing minimum soil moisture for the 21st century. Seasonal examination, however, revealed that a decrease in soil moisture may occur in the growing season towards to the end of the 21st century. The simulations suggest that the vegetation of the chosen agricultural field may successfully adapt to the water scarcity by growing their roots to the possibly maximum.

UNCERTAINTY OF CLIMATE PROJECTIONS AND AN APPROACH UTILIZING CLIMATE MODEL OUTPUTS FOR HYDROLOGIC COMPUTATION IN THE BA RIVER BASIN

A top-down approach begins with Global Climate Models (GCMs) is a common method for assessing climate change impacts on water resources in river basins. To overcome the coarse resolution of GCMs, dynamic downscaling by regional climate models (RCMs) with bias-correction procedures is utilized with the aim to reflect the meteorological features at the river basin scale. However, the results still entail large uncertainties. This paper examines the ability to capture the observed baseline temperature and precipitation (1986-2005) in the Ba River Basin from GCM outputs, RCM outputs, bias-corrected GCM outputs and bias-corrected RCM outputs by analyzing statistical indicators between historical simulations and observed data in 4 temperature and 6 rainfall stations. Bias-corrected results of both GCM and RCM have significantly smaller errors compared to the unbias-corrected ones. The uncertainty of future climate projection for the mid and late 21th century of the bias-corrected GCMs and RCMs are evaluated. It is found that there is still uncertainty in projected results. A concept of “Decision-Scaling” which combines top-down and bottom-up approaches is proposed to assess the climate change impacts on hydrological system to take into account uncertainties of climate projections by models.

Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China

Climate change may affect water resources by altering various processes in natural ecosystems. Dynamic and statistical downscaling methods are commonly used to assess the impacts of climate change on water resources. Objectively, both methods have their own advantages and disadvantages. In the present study, we assessed the impacts of climate change on water resources during the future periods (2020–2029 and 2040–2049) in the upper reaches of the Kaidu River Basin, Xinjiang, China, and discussed the uncertainties in the research processes by integrating dynamic and statistical downscaling methods (regional climate models (RCMs) and general circulation modes (GCMs)) and utilizing these outputs. The reference period for this study is 1990–1999. The climate change trend is represented by three bias-corrected RCMs (i.e., Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA), Regional Climate Model version 4 (RegCM4), and Seoul National University Meso-scale Model version 5 (SUN-MM5)) and an ensemble of GCMs on the basis of delta change method under two future scenarios (RCP4.5 and RCP8.5). We applied the hydrological SWAT (Soil and Water Assessment Tool) model which uses the RCMs/GCMs outputs as input to analyze the impacts of climate change on the stream flow and peak flow of the upper reaches of the Kaidu River Basin. The simulation of climate factors under future scenarios indicates that both temperature and precipitation in the study area will increase in the future compared with the reference period, with the largest increase of annual mean temperature and largest percentage increase of mean annual precipitation being of 2.4°C and 38.4%, respectively. Based on the results from bias correction of climate model outputs, we conclude that the accuracy of RCM (regional climate model) simulation is much better for temperature than for precipitation. The percentage increase in precipitation simulated by the three RCMs is generally higher than that simulated by the ensemble of GCMs. As for the changes in seasonal precipitation, RCMs exhibit a large percentage increase in seasonal precipitation in the wet season, while the ensemble of GCMs shows a large percentage increase in the dry season. Most of the hydrological simulations indicate that the total stream flow will decrease in the future due to the increase of evaporation, and the maximum percentage decrease can reach up to 22.3%. The possibility of peak flow increasing in the future is expected to higher than 99%. These results indicate that less water is likely to be available in the upper reaches of the Kaidu River Basin in the future, and that the temporal distribution of flow may become more concentrated.

Hydrological response of Chamelia watershed in Mahakali Basin to climate change

Chamelia (catchment area = 1603 km2), a tributary of Mahakali, is a snow-fed watershed in Western Nepal. The watershed has 14 hydropower projects at various stages of development. This study simulated the current and future hydrological system of Chamelia using the Soil and Water Assessment Tool (SWAT). The model was calibrated for 2001–2007; validated for 2008–2013; and then applied to assess streamflow response to projected future climate scenarios. Multi-site calibration ensures that the model is capable of reproducing hydrological heterogeneity within the watershed. Current water balance above the Q120 hydrological station in the forms of precipitation, actual evapotranspiration (AET), and net water yield are 2469 mm, 381 mm and 1946 mm, respectively. Outputs of five Regional Climate Models (RCMs) under two representative concentration pathways (RCPs) for three future periods were considered for assessing climate change impacts. An ensemble of bias-corrected RCM projections showed that maximum temperature under RCP4.5 (RCP8.5) scenario for near-, mid-, and far-futures is projected to increase from the baseline by 0.9 °C (1.1 °C), 1.4 °C (2.1 °C), and 1.6 °C (3.4 °C), respectively. Minimum temperature for the same scenarios and future periods are projected to increase by 0.9 °C (1.2 °C), 1.6 °C (2.5 °C), and 2.0 °C (3.9 °C), respectively. Average annual precipitation under RCP4.5 (RCP8.5) scenario for near-, mid-, and far-futures are projected to increase by 10% (11%), 10% (15%), and 13% (15%), respectively. Based on the five RCMs considered, there is a high consensus for increase in temperature but higher uncertainty with respect to precipitations. Under these projected changes, average annual streamflow was simulated to increase gradually from the near to far future under both RCPs; for instance, by 8.2% in near-, 12.2% in mid-, and 15.0% in far-future under RCP4.5 scenarios. The results are useful for planning water infrastructure projects, in Chamelia and throughout the Mahakali basin, to ensure long-term sustainability under climate change.

Hydrological response to future land-use change and climate change in a tropical catchment

ABSTRACTHydrological response to expected future changes in land use and climate in the Samin catchment (278 km2) in Java, Indonesia, was simulated using the Soil and Water Assessment Tool model. We analysed changes between the baseline period 1983–2005 and the future period 2030–2050 under both land-use change and climate change. We used the outputs of a bias-corrected regional climate model and six global climate models to include climate model uncertainty. The results show that land-use change and climate change individually will cause changes in the water balance components, but that more pronounced changes are expected if the drivers are combined, in particular for changes in annual streamflow and surface runoff. The findings of this study will be useful for water resource managers to mitigate future risks associated with land-use and climate changes in the study catchment.

Impact of climate change and climate anomalies on hydrologic and biogeochemical processes in an agricultural catchment of the Chesapeake Bay watershed, USA

Nutrient export from agricultural landscapes is a water quality concern and the cause of mitigation activities worldwide. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. This research quantifies the impact of climate change and climate anomalies on hydrology, nutrient cycling, and greenhouse gas emissions in an agricultural catchment of the Chesapeake Bay watershed. We force a calibrated model with seven downscaled and bias-corrected regional climate models and derived climate anomalies to assess their impact on hydrology and the export of nitrate (NO3-), phosphorus (P), and sediment, and emissions of nitrous oxide (N2O) and di-nitrogen (N2). Model-average (±standard deviation) results indicate that climate change, through an increase in precipitation and temperature, will result in substantial increases in winter/spring flow (10.6 ± 12.3%), NO3- (17.3 ± 6.4%), dissolved P (32.3 ± 18.4%), total P (24.8 ± 16.9%), and sediment (25.2 ± 16.6%) export, and a slight increases in N2O (0.3 ± 4.8%) and N2 (0.2 ± 11.8%) emissions. Conversely, decreases in summer flow (−29.1 ± 24.6%) and the export of dissolved P (−15.5 ± 26.4%), total P (−16.3 ± 20.7%), sediment (−20.7 ± 18.3%), and NO3- (−29.1 ± 27.8%) are driven by greater evapotranspiration from increasing summer temperatures. Decreases in N2O (−26.9 ± 15.7%) and N2 (−36.6 ± 22.9%) are predicted in the summer and driven by drier soils. While the changes in flow are related directly to changes in precipitation and temperature, the changes in nutrient and sediment export are, to some extent, driven by changes in agricultural management that climate change induces, such as earlier spring tillage and altered nutrient application timing and by alterations to nutrient cycling in the soil.

Climate indices for the Baltic states from principal component analysis

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic states (Estonia, Latvia, and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCMs) were used. Principal components were derived for the years 1961–1990. The first three components describe 92 % of the variance in the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed, and it was detected (based on correlation coefficient between principal components and initial variables) that higher values in each index corresponded to locations with (1) less distinct seasonality, (2) warmer climate, and (3) wetter climate. In addition, for the pattern of the first index, the impact of the Baltic Sea (distance to coast) was apparent; for the second, latitude and elevation were apparent, and for the third elevation was apparent. The loadings from the chosen principal components were further used to calculate the values of the climate indices for the years 2071–2100. An overall increase was found for all three indices with minimal changes in their spatial pattern.

On bias correction in drought frequency analysis based on climate models

Assessment of future drought characteristics based on climate models is difficult as climate models usually have bias in simulating precipitation frequency and intensity. In this study, we examine the significance of bias correction in the context of drought frequency and scenario analysis using output from climate models. In particular, we use three bias correction techniques with different emphases and complexities to investigate how they affect the results of drought frequency and severity based on climate models. The characteristics of drought are investigated using regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP). The Standardized Precipitation Index (SPI) is used to compare and forecast drought characteristics at different timescales. Systematic biases in the RCM precipitation output are corrected against the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data and the bias-corrected RCM historical simulations. Preserving mean and standard deviation of NARR precipitation is essential in drought frequency analysis. The results demonstrate that bias correction significantly decreases the RCM errors in reproducing drought frequency derived from the NARR data. Different timescales of input precipitation in the bias corrections show similar results. The relative changes in drought frequency in future scenario compared to historical scenario are similar whether both scenarios are bias corrected or both are not bias corrected.

Assessment of projected climate change in the Carpathian Region using the Holdridge life zone system

In this paper, expected changes in the spatial and altitudinal distribution patterns of Holdridge life zone (HLZ) types are analysed to assess the possible ecological impacts of future climate change for the Carpathian Region, by using 11 bias-corrected regional climate model simulations of temperature and precipitation. The distribution patterns of HLZ types are characterized by the relative extent, the mean centre and the altitudinal range. According to the applied projections, the following conclusions can be drawn: (a) the altitudinal ranges are likely to expand in the future, (b) the lower and upper altitudinal limits as well as the altitudinal midpoints may move to higher altitudes, (c) a northward shift is expected for most HLZ types and (d) the magnitudes of these shifts can even be multiples of those observed in the last century. Related to the northward shifts, the HLZ types warm temperate thorn steppe and subtropical dry forest can also appear in the southern segment of the target area. However, a large uncertainty in the estimated changes of precipitation patterns was indicated by the following: (a) the expected change in the coverage of the HLZ type cool temperate steppe is extremely uncertain because there is no consensus among the projections even in terms of the sign of the change (high inter-model variability) and (b) a significant trend in the westward/eastward shift is simulated just for some HLZ types (high temporal variability). Finally, it is important to emphasize that the uncertainty of our results is further enhanced by the fact that some important aspects (e.g. seasonality of climate variables, direct CO2 effect, etc.) cannot be considered in the estimating process.

Impact of climate change on the timing of strawberry phenological processes in the Baltic States

Climate change has been shown to impact aspects of agriculture and phenology. This study aims to quantify changes in the timing of garden strawberry blooms and harvests in the Baltic States using Regional Climate Models (RCMs). First, parameters for a strawberry phenology model based on the growing degree day (GDD) methodology were determined. Growing degree days were calculated using a modified sine wave method that estimates the diurnal temperature cycle from the daily maximum and minimum temperature. Model parameters include the base temperature and the required cumulative GDD sum, estimated from phenological and meteorological observations in Latvia for the years 2010-2013 via iterative calibration. Then an ensemble of bias-corrected RCM results (ENSEMBLES project) was used as input to the phenological model to estimate the timing of strawberry phenological processes for the years 1951-2099. The results clearly show that strawberry phenological processes can be expected to occur earlier in the future, with a significant change in regional patterns. Differences between coastal and inland regions are expected to decrease over time. The uncertainty of the results was estimated using the RCM ensemble spread, with northern coastal locations showing the largest spread.

Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional climate models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction (BC) of RCM temperature and precipitation for Finland is carried out using different versions of the distribution based scaling (DBS) method. The DBS-adjusted RCM data are used as input of a hydrological model to simulate changes in discharges of four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS-adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period 1961–2000 and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the standard deviation of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

Climate Change Impact on the Hydrology of a Typical Watershed in the Tianshan Mountains

To study the impact of future climatic changes on hydrology in the Kaidu River Basin in the Tianshan Mountains, two sets of future climatic data were used to force a well-calibrated hydrologic model: one is bias-corrected regional climate model (RCM) outputs for RCP4.5 and RCP8.5 future emission scenarios, and the other is simple climate change (SCC) with absolute temperature change of −1~6°C and relative precipitation change of −20%~60%. Results show the following: (1) temperature is likely to increase by 2.2°C and 4.6°C by the end of the 21st century under RCP4.5 and RCP8.5, respectively, while precipitation will increase by 2%~24%, with a significant rise in the dry season and small change in the wet season; (2) flow will change by −1%~20%, while evapotranspiration will increase by 2%~24%; (3) flow increases almost linearly with precipitation, while its response to temperature depends on the magnitude of temperature change and flow decrease is significant when temperature increase is greater than 2°C; (4) similar results were obtained for simulations with RCM outputs and with SCC for mild climate change conditions, while results were significantly different for intense climate change conditions.

Impact of Climate Change on the Hydrology of Upper Tiber River Basin Using Bias Corrected Regional Climate Model

The use of regional climate model (RCM) outputs has been getting due attention in most European River basins because of the availability of large number of the models and modelling institutes in the continent; and the relative robustness the models to represent local climate. This paper presents the hydrological responses to climate change in the Upper Tiber River basin (Central Italy) using bias corrected daily regional climate model outputs. The hydrological analysis include both control (1961–1990) and future (2071–2100) climate scenarios. Three RCMs (RegCM, RCAO, and PROMES) that were forced by the same lateral boundary condition under A2 and B2 emission scenarios were used in this study. The projected climate variables from bias corrected models have shown that the precipitation and temperature tends to decrease and increase in summer season, respectively. The impact of climate change on the hydrology of the river basin was predicted using physically based Soil and Water Assessment Tool (SWAT). The SWAT model was first calibrated and validated using observed datasets at the sub-basin outlet. A total of six simulations were performed under each scenario and RCM combinations. The simulated result indicated that there is a significant annual and seasonal change in the hydrological water balance components. The annual water balance of the study area showed a decrease in surface runoff, aquifer recharge and total basin water yield under A2 scenario for RegCM and RCAO RCMs and an increase in PROMES RCM under B2 scenario. The overall hydrological behaviour of the basin indicated that there will be a reduction of water yield in the basin due to projected changes in temperature and precipitation. The changes in all other hydrological components are in agreement with the change in projected precipitation and temperature.

Future changes in extreme precipitation in the Rhine basin based on global and regional climate model simulations

Abstract. Probability estimates of the future change of extreme precipitation events are usually based on a limited number of available global climate model (GCM) or regional climate model (RCM) simulations. Since floods are related to heavy precipitation events, this restricts the assessment of flood risks. In this study a relatively simple method has been developed to get a better description of the range of changes in extreme precipitation events. Five bias-corrected RCM simulations of the 1961–2100 climate for a single greenhouse gas emission scenario (A1B SRES) were available for the Rhine basin. To increase the size of this five-member RCM ensemble, 13 additional GCM simulations were analysed. The climate responses of the GCMs are used to modify an observed (1961–1995) precipitation time series with an advanced delta change approach. Changes in the temporal means and variability are taken into account. It is found that the range of future change of extreme precipitation across the five-member RCM ensemble is similar to results from the 13-member GCM ensemble. For the RCM ensemble, the time series modification procedure also results in a similar climate response compared to the signal deduced from the direct model simulations. The changes from the individual RCM simulations, however, systematically differ from those of the driving GCMs, especially for long return periods.

Mass-balance parameters derived from a synthetic network of mass-balance glaciers

AbstractGlacier mass-balance parameters such as the equilibrium-line altitude (ELA) play an important role when working with large glacier samples. While the number of observational mass-balance series to derive such parameters is limited, more and more modeled data are becoming available.Here we explore the possibilities of analyzing such 'synthetic' mass-balance data with respect to mass-balance parameters. A simplified energy-balance model is driven by bias-corrected regional climate model output to model mass-balance distributions for 94 glaciers in the Swiss Alps over 15 years. The modeling results in realistic interannual variability and mean cumulative mass balance. Subsequently model output is analyzed with respect to 18 topographic and mass-balance parameters and a correlation analysis is performed. Well-known correlations such as for ELA and median elevation are confirmed from the synthetic data. Furthermore, previously unreported parameter relationships are found such as a correlation of the balance rate at the tongue with the accumulation-area ratio (AAR) and of the glacier elevation range with the AAR. Analyzing modeled data complements in situ observations and highlights their importance: the small number of accurate mass-balance observations available for validation is a major challenge for the presented approach.

Evaluation of future flow variability in ungauged basins: Validation of combined methods

Predicting future flow variability and changes in ungauged basins remains a challenging task not only because of data limitation, but also the various sources of uncertainty involved. However, for future water resources development projects, reliable estimate of future flow variability is essential. In this study, future flow variability and changes are estimated in 90 selected basins across the Province of Ontario using a multi-model approach combining bias correction and regional climate models (RCMs), downscaling and global climate models (GCMs), dual regionalization method and hydrologic model. Raw RCM outputs, bias corrected RCM data, and downscaled GCM outputs are compared to identify the most accurate predictor (precipitation, temperature) series when compared to the observed data for the current period. It is found that the bias correction approach can reduce monthly precipitation and daily temperature errors of regional climate models by about 90% and provides the most accurate precipitation and temperature time series. The bias-corrected RCM data are used with a rainfall-runoff model and a coupled regionalization method to simulate continuous streamflow in both gauged and ungauged basins. It is found that consistent streamflow and baseflow estimate can be obtained whatever the RCM data used. In general, positive (increasing) changes in streamflow are observed for all seasons except for March, April and May where negative (decreasing) trends are dominant in some basins. Future changes in baseflow are mostly positive with smaller amplitude whatever the season for almost all the 90 selected basins. Overall, it is shown that for northern, central and southern regions of Ontario, the amplitude of the variability and changes of future streamflow and baseflow in ungauged basins should be in the range of −10 to +10 mm per month based on most recent RCM simulations.

Modeling anticipated climate change impact on biogeochemical cycles of an acidified headwater catchment

Abstract The aim of this study was to estimate future water balance and streamwater chemistry changes, according to different climate change scenario, in a forested acid-sensitive catchment. The investigated Czech catchment was affected by large anthropogenic acidification in the 20th century. Runoff for the year 2070 and streamwater chemistry for the period 1860–2070 were simulated by the models Brook90 and MAGIC, respectively. The runoff would change from 450 mm a−1 (1990–2006) to 320–402 mm a−1 for the year 2070 according to Brook90 simulations with bias-corrected regional climate model (RCAO) data with A2 emission scenario. Future streamwater SO 4 2 - concentration would maintain at the current level of approximately 90 μeq L−1 (2008–2009) in the case of minimum or no change in runoff, or would increase by 10–30% as a result of projected runoff decrease. The runoff change would increase the concentration of base cations of a similar magnitude compared to the measured 160 μeq L−1 (2008–2009). According to both scenarios, the effect of climate change on pH would be small and would lead to slight re-acidification of streamwater compared to the measured 4.2 (2008–2009). The climate change influence to streamwater chemistry would be small compared to the influence of atmospheric acid deposition. The environmental condition of the stream will probably remain unsatisfactory until the 2070s.

Hydrological impacts of climate change on the Tejo and Guadiana Rivers

Abstract. A distributed daily rainfall–runoff model is applied to the Tejo and Guadiana river basins in Spain and Portugal to simulate the effects of climate change on runoff production, river flows and water resource availability with results aggregated to the monthly level. The model is calibrated, validated and then used for a series of climate change impact assessments for the period 2070–2100. Future scenarios are derived from the HadRM3H regional climate model (RCM) using two techniques: firstly a bias-corrected RCM output, with monthly mean correction factors calculated from observed rainfall records; and, secondly, a circulation-pattern-based stochastic rainfall model. Major reductions in rainfall and streamflow are projected throughout the year; these results differ from those for previous studies where winter increases are projected. Despite uncertainties in the representation of heavily managed river systems, the projected impacts are serious and pose major threats to the maintenance of bipartite water treaties between Spain and Portugal and the supply of water to urban and rural regions of Portugal.