Spatial uncertainty in bias corrected climate change projections and hydrogeological impacts

The question of which climate model bias correction methods and spatial scales for correction are optimal for both projecting future hydrological changes as well as removing initial model bias has so far received little attention. For 11 climate models (CMs), or GCM/RCM – Global/Regional Climate Model pairing, this paper analyses the relationship between complexity and robustness of three distribution‐based scaling (DBS) bias correction methods applied to daily precipitation at various spatial scales. Hydrological simulations are forced by CM inputs to assess the spatial uncertainty of groundwater head and stream discharge given the various DBS methods. A unique metric is devised, which allows for comparison of spatial variability in climate model bias and projected change in precipitation. It is found that the spatial variability in climate model bias is larger than in the climate change signals. The magnitude of spatial bias seen in precipitation inputs does not necessarily correspond to the magnitude of biases seen in hydrological outputs. Variables that integrate basin responses over time and space are more sensitive to mean spatial biases and less so on extremes. Hydrological simulations forced by the least parameterized DBS approach show the highest error in mean and maximum groundwater heads; however, the most highly parameterised DBS approach shows less robustness in future periods compared with the reference period it was trained in. For hydrological impacts studies, choice of bias correction method should depend on the spatial scale at which hydrological impacts variables are required and whether CM initial bias is spatially uniform or spatially varying. Copyright © 2015 John Wiley & Sons, Ltd.     

Vulnerability of water resources,vegetation productivity and soil erosion to climate change in Mediterranean watersheds

Climate change is expected to increase temperatures and lower rainfall in Mediterranean regions; however, there is a great degree of uncertainty as to the amount of change. This limits the prediction capacity of models to quantify impacts on water resources, vegetation productivity and erosion. This work circumvents this problem by analysing the sensitivity of these variables to varying degrees of temperature change (increased by up to 6·4 °C), rainfall (reduced by up to 40%) and atmospheric CO₂ concentrations (increased by up to 100%). The SWAT watershed model was applied to 18 large watersheds in two contrasting regions of Portugal, one humid and one semi‐arid; incremental changes to climate variables were simulated using a stochastic weather generator. The main results indicate that water runoff, particularly subsurface runoff, is highly sensitive to these climate change trends (down by 80%). The biomass growth of most species showed a declining trend (wheat down by 40%), due to the negative impacts of increasing temperatures, dampened by higher CO₂ concentrations. Mediterranean species, however, showed a positive response to milder degrees of climate change. Changes to erosion depended on the interactions between the decline in surface runoff (driving erosion rates downward) and biomass growth (driving erosion rates upward). For the milder rainfall changes, soil erosion showed a significant increasing trend in wheat fields (up to 150% in the humid watersheds), well above the recovery capacity of the soil. Overall, the results indicate a shift of the humid watersheds to acquire semi‐arid characteristics, such as more irregular river flows and increasingly marginal conditions for agricultural production. Copyright © 2007 John Wiley & Sons, Ltd.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin,USA

An appropriate, rapid and effective response to extreme precipitation and any potential flood disaster is essential. Providing an accurate estimate of future changes to such extreme events due to climate change are crucial for responsible decision making in flood risk management given the predictive uncertainties. The objective of this article is to provide a comparison of dynamically downscaled climate models simulations from multiple model including 12 different combinations of General Circulation Model (GCM)–regional climate model (RCM), which offers an abundance of additional data sets. The three major aspects of this study include the bias correction of RCM scenarios, the application of a newly developed performance metric and the extreme value analysis of future precipitation. The dynamically downscaled data sets reveal a positive overall bias that is removed through quantile mapping bias correction method. The added value index was calculated to evaluate the models' simulations. Results from this metric reveal that not all of the RCMs outperform their host GCMs in terms of correlation skill. Extreme value theory was applied to both historic, 1980–1998, and future, 2038–2069, daily data sets to provide estimates of changes to 2‐ and 25‐year return level precipitation events. The generalized Pareto distribution was used for this purpose. The Willamette River basin was selected as the study region for analysis because of its topographical variability and tendency for significant precipitation. The extreme value analysis results showed significant differences between model runs for both historical and future periods with considerable spatial variability in precipitation extremes. Copyright © 2012 John Wiley & Sons, Ltd.     

More rain,less soil: long‐term changes in rainfall intensity with climate change

This commentary discusses the role of long‐term climate change in driving increases in soil erosion. Assuming that land use and management remain effectively constant, we discuss changes in the ability of rainfall to cause erosion (erosivity), using long daily rainfall data sets from southeast England. An upward trend in mean rainfall per rain day is detected at the century‐plus timescale. Implications for soil erosion and sediment delivery are discussed and evidence from other regions reviewed. We conclude that rates of soil erosion may well increase in a warmer, wetter world. Copyright © 2015 John Wiley & Sons, Ltd.     

Multi-site bias correction of climate model outputs for hydro-meteorological impact studies: An application over a watershed in China

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre-processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi-variate bias correction (MBC) methods—initially proposed to correct the inter-variable correlation or multi-variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution-based single-site bias correction methods are used for comparison. The effects of multi-site correction on hydro-meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi-site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi-site methods consistently reproduce watershed-averaged meteorological variables better than single-site methods, especially for extreme values. In terms of representing hydrological extremes, the multi-site methods generally perform better than the single-site methods, although the benefits vary according to the hydrological index. However, when applying the multi-site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi-site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.     

Sensitivity of runoff and soil erosion to climate change in two Mediterranean watersheds. Part II: assessing impacts from changes in storm rainfall,soil moisture and vegetation cover

The impacts of climate change on storm runoff and erosion in Mediterranean watersheds are difficult to assess due to the expected increase in storm frequency coupled with a decrease in total rainfall and soil moisture, added to positive or negative changes to different types of vegetation cover. This report, the second part of a two‐part article, addresses this issue by analysing the sensitivity of runoff and erosion to incremental degrees of change (from ? 20 to + 20%) to storm rainfall, pre‐storm soil moisture, and vegetation cover, in two Mediterranean watersheds, using the MEFIDIS model. The main results point to the high sensitivity of storm runoff and peak runoff rates to changes in storm rainfall (2·2% per 1% change) and, to a lesser degree, to soil water content (?1·2% per 1% change). Catchment sediment yield shows a greater sensitivity than within‐watershed erosion rates to both parameters: 7·8 versus 4·0% per 1% change for storm rainfall, and ? 4·9 versus ? 2·3% per 1% change for soil water content, indicating an increase in sensitivity with spatial scale due to changes to sediment connectivity within the catchment. Runoff and erosion showed a relatively low sensitivity to changes in vegetation cover. Finally, the shallow soils in one of the catchments led to a greater sensitivity to changes in storm rainfall and soil moisture. Overall, the results indicate that decreasing soil moisture levels caused by climate change could be sufficient to offset the impact of greater storm intensity in Mediterranean watersheds. Copyright © 2009 John Wiley & Sons, Ltd.     

Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin,China

The magnitude and frequency of regional extreme precipitation events may have variability under climate change. This study investigates the time–space variability and statistical probability characteristics of extreme precipitation under climate change in the Haihe River Basin. Hydrological alteration diagnosis methods are implemented to detect the occurrence time, style and degree of alteration such as trend and jump in the extreme precipitation series, and stationarity and serial independence are tested prior to frequency analysis. Then, the historical extreme precipitation frequency and spatio‐temporal variations analyses are conducted via generalized extreme value and generalized Pareto distributions. Furthermore, the occurrence frequency of extreme precipitation events in future is analysed on the basis of the Fourth Assessment Report of the Intergovermental Panel on Climate Change multi‐mode climate models under different greenhouse gases emission scenarios (SRES‐A2, A1B and B1). Results indicate that (1) in the past, alteration of extreme precipitation mainly occurred in the area north of 38°N. Decreasing trends of extreme precipitation are detected at most stations, whereas jump alteration is not obvious at most stations. (2) Spatial variation of estimated extreme precipitation under different return periods shows similarity. Bounded by the Taihang Mountain–Yan Mountain, extreme rainfall in the Haihe River Basin gradually reduces from the southeast to the northwest, which is consistent with the geographical features of the Haihe River Basin. (3) In the future, extreme precipitation with return period 5–20 years accounts for a significant portion of the total occurrence times. The frequency of extreme precipitation events has an increase trend under A1B and A2 scenarios. The total occurrence times of extreme precipitation under A1B senario are not more than that under B1 senario until the 2030s. Copyright © 2012 John Wiley & Sons, Ltd.     

SRES情景下多模式集合对淮河流域未来气候变化的预估

采用偏差修正/空间降尺度方法处理后的IPCC AR4中8个全球海气耦合模式的集合平均结果,分析了SRESA2、A1B和B1情景下淮河流域未来30 a(2011 2040年)相对于现状(1961 1990年)地面温度和降水的可能变化.结果表明:(1)多模式集合能较好地反映流域现状年、季温度和降水的大尺度空间分布特征;对温度和降水的年内分配过程模拟较好,各月温度集合平均与观测值相差0.2℃左右(冬季各月除外),而降水集合平均与观测值相对误差在5%左右(9月除外).(2)不同情景下未来流域年、季温度一致增加,年温度增加幅度在0.85～1.12℃之间;冬、春季温度增加相对明显,而夏、秋季温度增加并不显著;年际和年代际温度增加趋势显著.(3)不同情景下未来流域年降水有增加趋势,增加幅度为0.13%～5.24%,增幅不明显;降水季节变化有增有减,季节、年际和年代际降水变化较为复杂,不同情景下降水空间变化差异显著.     

The impact of climate change on extreme precipitation in Sicily,Italy

Increasing precipitation extremes are one of the possible consequences of a warmer climate. These may exceed the capacity of urban drainage systems, and thus impact the urban environment. Because short‐duration precipitation events are primarily responsible for flooding in urban systems, it is important to assess the response of extreme precipitation at hourly (or sub‐hourly) scales to a warming climate. This study aims to evaluate the projected changes in extreme rainfall events across the region of Sicily (Italy) and, for two urban areas, to assess possible changes in Depth‐Duration‐Frequency (DDF) curves. We used Regional Climate Model outputs from Coordinated Regional Climate Downscaling Experiment for Europe area ensemble simulations at a ~12 km spatial resolution, for the current period and 2 future horizons under the Representative Concentration Pathways 8.5 scenario. Extreme events at the daily scale were first investigated by comparing the quantiles estimated from rain gauge observations and Regional Climate Model outputs. Second, we implemented a temporal downscaling approach to estimate rainfall for sub‐daily durations from the modelled daily precipitation, and, lastly, we analysed future projections at daily and sub‐daily scales. A frequency distribution was fitted to annual maxima time series for the sub‐daily durations to derive the DDF curves for 2 future time horizons and the 2 urban areas. The overall results showed a raising of the growth curves for the future horizons, indicating an increase in the intensity of extreme precipitation, especially for the shortest durations. The DDF curves highlight a general increase of extreme quantiles for the 2 urban areas, thus underlining the risk of failure of the existing urban drainage systems under more severe events.     

On the correlation between precipitation and potential evapotranspiration climate change signals for hydrological impact analyses

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ET_o), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ET_o amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ET_o. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ET_o amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation.     

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

Hydrological 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.     

How the performance of hydrological models relates to credibility of projections under climate change

Two approaches can be distinguished in studies of climate change impacts on water resources when accounting for issues related to impact model performance: (1) using a multi-model ensemble disregarding model performance, and (2) using models after their evaluation and considering model performance. We discuss the implications of both approaches in terms of credibility of simulated hydrological indicators for climate change adaptation. For that, we discuss and confirm the hypothesis that a good performance of hydrological models in the historical period increases confidence in projected impacts under climate change, and decreases uncertainty of projections related to hydrological models. Based on this, we find the second approach more trustworthy and recommend using it for impact assessment, especially if results are intended to support adaptation strategies. Guidelines for evaluation of global- and basin-scale models in the historical period, as well as criteria for model rejection from an ensemble as an outlier, are also suggested.     

Possible change in Korean streamflow seasonality based on multi‐model climate projections

Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow‐dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain‐dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi‐model projections. Seasonal flow changes were projected using the combination of 13 atmosphere–ocean general circulation model simulations and three semi‐distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright © 2012 John Wiley & Sons, Ltd.     

Impacts of climate change on water resources in Spain

Abstract

Impacts on water resources produced by climate change can be exacerbated when occurring in regions already presenting low water resources levels and frequent droughts, and subject to imbalances between water demands and available resources. Within Europe, according to existing climate change scenarios, water resources will be severely affected in Spain. However, the detection of those effects is not simple, because the natural variability of the water cycle and the effects of water abstractions on flow discharges complicate the establishment of clear trends. Therefore, there is a need to improve the assessment of climate change impacts by using hydrological simulation models. This paper reviews water resources and their variability in Spain, the recent modelling studies on hydrological effects of climate change, expected impacts on water resources, the implications in river basins and the current policy actions.

Editor Z.W. Kundzewicz

Citation Estrela, T., Pérez-Martin, M.A., and Vargas, E., 2012. Impacts of climate change on water resources in Spain. Hydrological Sciences Journal, 57 (6), 1154–1167.     

Seasonality and magnitude of floods in Switzerland under future climate change

The flood seasonality of catchments in Switzerland is likely to change under climate change because of anticipated alterations of precipitation as well as snow accumulation and melt. Information on this change is crucial for flood protection policies, for example, or regional flood frequency analysis. We analysed projected changes in mean annual and maximum floods of a 22‐year period for 189 catchments in Switzerland and two scenario periods in the 21st century based on an ensemble of climate scenarios. The flood seasonality was analysed with directional statistics that allow assessing both changes in the mean date a flood occurs as well as changes in the strength of the seasonality. We found that the simulated change in flood seasonality is a function of the change in flow regime type. If snow accumulation and melt is important in a catchment during the control period, then the anticipated change in flood seasonality is most pronounced. Decreasing summer precipitation in the scenarios additionally affects the flood seasonality (mean date of flood occurrence) and leads to a decreasing strength of seasonality, that is a higher temporal variability in most cases. The magnitudes of mean annual floods and more clearly of maximum floods (in a 22‐year period) are expected to increase in the future because of changes in flood‐generating processes and scaled extreme precipitation. Southern alpine catchments show a different signal, though: the simulated mean annual floods decrease in the far future, that is at the end of the 21st century. Copyright © 2013 John Wiley & Sons, Ltd.     

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT

Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.     

The impact of climate change on groundwater recharge and runoff in a humid,equatorial catchment: sensitivity of projections to rainfall intensity

Abstract

Projected warming in equatorial Africa, accompanied by greater evaporation and more frequent heavy precipitation events, may have substantial but uncertain impacts on terrestrial hydrology. Quantitative analyses of climate change impacts on catchment hydrology require high-resolution (<50 km) climate data provided by regional climate models (RCMs). We apply validated precipitation and temperature data from the RCM PRECIS (Providing Regional Climates for Impact Studies) to a semi-distributed soil moisture balance model (SMBM) in order to quantify the impacts of climate change on groundwater recharge and runoff in a medium-sized catchment (2098 km²) in the humid tropics of southwestern Uganda. The SMBM explicitly accounts for changes in soil moisture, and partitions effective precipitation into groundwater recharge and runoff. Under the A2 emissions scenario (2070–2100), climate projections from PRECIS feature not only rises in catchment precipitation and modelled potential evapotranspiration by 14% and 53%, respectively, but also increases in rainfall intensity. We show that the common application of the historical rainfall distribution using delta factors to the SMBM grossly underestimates groundwater recharge (i.e. 55% decrease relative to the baseline period of 1961–1990). By transforming the rainfall distribution to account for changes in rainfall intensity, we project increases in recharge and runoff of 53% and 137%, respectively, relative to the baseline period.     

Sensitivity of runoff and soil erosion to climate change in two Mediterranean watersheds. Part I: model parameterization and evaluation

Climate change is expected to effect storm runoff and erosion processes in Mediterranean watersheds at multiple spatial scales. Models are typically applied to estimate these impacts; however, the scarcity of spatially distributed data for parameterization, calibration and validation often prevents application of these models, particularly for larger catchments. This report, the first part of a two‐part article, presents an application and evaluation of the MEFIDIS model for two Mediterranean meso‐scale watersheds (115 and 290 km²) in a data‐scarce environment. A multi‐scale assessment method was used that combines quantitative validation and qualitative evaluation, consisting of three steps: (1) calibration at the small (field) scale using results from rainfall simulation experiments; (2) calibration and validation for catchment‐scale results while changing catchment‐scale parameters only (channel roughness and a parameter controlling the distribution of saturated areas); and (3) qualitative evaluation of within‐watershed erosion processes using empirical estimates of sediment delivery ratio and gully location. The results indicate that calibrating MEFIDIS at the field scale can provide reasonable results for catchment runoff and sediment export and for within‐watershed erosion processes. Copyright © 2009 John Wiley & Sons, Ltd.     

Hydrologic impact of regional climate change for the snow‐fed and glacier‐fed river basins in the Republic of Tajikistan: statistical downscaling of global climate model projections

Climate change due to global warming is a public concern in Central Asia. Because of specific orography and climate conditions, the republic of Tajikistan is considered as the main glacial center of Central Asia. In this study, regional climate change impacts in the two large basins of Tajikistan, Pyanj and Vaksh River basins located in the upstream sector of the Amu Darya River basin are analysed. A statistical regression method with model output statistics corrections using the ground observation data, Willmott archived dataset and GSMaP satellite driven dataset, was developed and applied to the basins to downscale the Global Climate Model Projections at a 0.1‐degree grid and to assess the regional climate change impacts at subbasin scale. It was found that snow and glacier melting are of fundamental importance for the state of the future water resources and flooding at the target basins since the air temperature had a clearly increasing trend toward the future. It was also found that the snowfall will decrease, but the rainfall will increase because of the gradual increase in the air temperature. Such changes may result in an increase in flash floods during the winter and the early spring, and in significant changes in the hydrological regime during a year in the future. Furthermore, the risks of floods in the target basins may be slightly increasing because of the increase in the frequencies and magnitudes of high daily precipitation and the increase in the rapid snowmelt with high air temperatures toward the future. Copyright © 2012 John Wiley & Sons, Ltd.