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1.
Abstract

A methodology has been developed and applied to an eastern Nebraska, USA, case study to estimate the space-time distribution of daily precipitation under climate change. The approach is based on the analysis both of the type and of the Markov properties of atmospheric circulation patterns (CPs), and a stochastic linkage between daily (here 500 hPa) CP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily CPs corresponding to 1 × CO2 and 2 × CO2 are considered. Time series of both local and regional precipitation corresponding to each of those cases were simulated and their statistical properties were compared. Under the dry continental climate of eastern Nebraska, a highly variable spatial response to climate change was obtained. Most of the local and the regional average precipitation values reflect, under 2 × CO2, a somewhat wetter and a more variable precipitation regime in eastern Nebraska. The sensitivity of the results to the GCM utilized should be considered.  相似文献   

2.
Abstract

To investigate the consequences of climate change on the water budget in small catchments, it is necessary to know the change of local precipitation and temperature. General Circulation Models (GCM) cannot provide regional climate parameters yet, because of their coarse resolution and imprecise modelling of precipitation. Therefore downscaling of precipitation and temperature has to be carried out from the GCM grids to a small scale of a few square kilometres. Daily rainfall and temperature are modelled as processes conditioned on atmospheric circulation. Rainfall is linked to the circulation patterns (CPs) using conditional probabilities and conditional rainfall amount distribution. Both temperature and precipitation are downscaled to several locations simultaneously taking into account the CP dependent spatial correlation. Temperature is modelled using a simple autoregressive approach, conditioned on atmospheric circulation and local areal precipitation. The model uses the classification scheme of the German Weather Service and a fuzzy rule-based classification. It was applied in the Aller catchment for validation using observed rainfall and temperature, and observed classified geopotential pressure heights. GCM scenarios of the ECHAM model were used to make climate change predictions (using classified GCM geopotential heights); simulated values agree fairly well with historical data. Results for different GCM scenarios are shown.  相似文献   

3.
In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.  相似文献   

4.
C. Pilling  J. A. A. Jones 《水文研究》1999,13(17):2877-2895
Nationwide changes in spatially well‐resolved patterns of British runoff were investigated under two climate change scenarios derived from general circulation model (GCM) output. A physical process‐based hydrological model (HYSIM) was used to simulate effective runoff across a 10 km×10 km British grid under baseline and future climate conditions. A gridded baseline climatology for precipitation and the Penman variables was used to validate HYSIM across Britain using grid cell‐specific parameters derived from land use and soil type. The climate change scenarios were constructed from the Hadley Centre's high resolution equilibrium GCM (UKHI) for 2050 and transient GCM (UKTR) for 2065. Future effective runoff was simulated under both scenarios by applying changes in precipitation and the Penman variables to the baseline climatology. Annual effective runoff is shown to increase throughout most of Britain under the UKHI scenario for 2050, whilst it decreases over much of England and Wales under the UKTR scenario for 2065. Both scenarios show an increasing gradient in runoff between a wetter northern Britain and a drier south‐eastern Britain. This gradient is more pronounced under the UKTR scenario. Changes in effective runoff for winter and summer show an increase in seasonality under both scenarios. Winter runoff is shown to increase most in northern Britain under both scenarios, whilst summer runoff is shown to experience major reductions over much of England and Wales under the UKTR scenario. If these simulations are realized, Britain may expect an accentuated north to south‐east imbalance in available water resources. If this is combined with a temporal imbalance suggested by the increased seasonality, there could be problems for the future management of British water resources. Copyright © 1999 John Wiley & Sons, Ltd.  相似文献   

5.
This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

6.
Three-dimensional general circulation models (GCMs) are 'state-of-the-art' tools for projecting possible changes in climate. Scenarios constructed for the Czech Republic are based on daily outputs of the ECHAM-GCM in the central European region. Essential findings, derived from validating, procedures are summarized and changes in variables between the control and perturbed experiments are examined. The resulting findings have been used in selecting the most proper methods of generating climate change projections for assessing possible hydrological and agricultural impacts of climate change in selected exposure units. The following weather variables have been studied: Daily extreme temperatures, daily mean temperature, daily sum of global solar radiation, and daily precipitation amounts. Due to some discrepancies revealed, the temperature series for changed climate conditions (2×CO 2 ) have been created with the help of temperature differences between the control and perturbed runs, and the precipitation series have been derived from an incremental scenario based on an intercomparison of the GCMs' precipitation performance in the region. Solar radiation simulated by the ECHAM was not available and, therefore, it was generated using regression techniques relating monthly means of daily extreme temperatures and global radiation sums. The scenarios published in the paper consist of monthly means of all temperatures, their standard deviations, and monthly means of solar radiation and precipitation amounts. Daily weather series, the necessary input to impact models, are created (i) by the additive or multiplicative modification of observed weather daily series or (ii) by generating synthetic time series with the help of a weather generator whose parameters have been modified in accord with the suggested climate change scenarios.  相似文献   

7.
Many downscaling techniques have been developed in the past few years for projection of station‐scale hydrological variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K‐nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue‐type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

8.
This research investigates the potential impacts of climate change on stormwater quantity and quality generated by urban residential areas on an event basis in the rainy season. An urban residential stormwater drainage area in southeast Calgary, Alberta, Canada is the focus of future climate projections from general circulation models (GCMs). A regression‐based statistical downscaling tool was employed to conduct spatial downscaling of daily precipitation and daily mean temperature using projection outputs from the coupled GCM. Projected changes in precipitation and temperature were applied to current climate scenarios to generate future climate scenarios. Artificial neural networks (ANNs) developed for modelling stormwater runoff quantity and quality used projected climate scenarios as network inputs. The hydrological response to climate change was investigated through stormwater runoff volume and peak flow, while the water quality responses were investigated through the event mean value (EMV) of five parameters: turbidity, conductivity, water temperature, dissolved oxygen (DO) and pH. First flush (FF) effects were also noted. Under future climate scenarios, the EMVs of turbidity increased in all storms except for three events of short duration. The EMVs of conductivity were found to decline in small and frequent storms (return period < 5 years); but conductivity EMVs were observed to increase in intensive events (return period ≥ 5 years). In general, an increasing EMV was observed for water temperature, whereas a decreasing trend was found for DO EMV. No clear trend was found in the EMV of pH. In addition, projected future climate scenarios do not produce a stronger FF effect on dissolved solids and suspended solids compared to that produced by the current climate scenario. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

9.
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.  相似文献   

10.
In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.  相似文献   

11.
With increasing uncertainties associated with climate change, precipitation characteristics pattern are receiving much attention these days. This paper investigated the impact of climate change on precipitation in the Kansabati basin, India. Trend and persistence of projected precipitation based on annual, wet and dry periods were studied using global climate model (GCM) and scenario uncertainty. A downscaling method based on Bayesian neural network was applied to project precipitation generated from six GCMs using two scenarios (A2 and B2). The precipitation values for any of three time periods (dry, wet and annual) do not show significant increasing or decreasing trends during 2001–2050 time period. There is likely an increasing trend in precipitation for annual and wet periods during 2051–2100 based on A2 scenario and a decreasing trend in dry period precipitation based on B2 scenario. Persistence during dry period precipitation among stations varies drastically based on historical data with the highest persistence towards north‐west part of the basin. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

12.
Abstract

The regional hydroclimatological effect of global climate change has been estimated and compared using a semi-empirical downscaling method with two versions (T21 and T42) of the general circulation model (GCM) developed at the Max Planck Institute for Meteorology, Germany. The comparisons were performed with daily mean temperature and daily precipitation amounts for the continental climate of the state of Nebraska, USA. Both the T21 and the T42 versions resulted in an increase of daily mean temperature under a 2 x C02 climatess. The magnitude of warming was substantially greater for T21 than for T42, except for February and June and at some stations in July where the T42 model suggested greater warming. Both GCMs resulted in a slight decrease in precipitation frequency and an increase in the amount of precipitation on wet days. Here, the T42 model again led to smaller changes. Different locations within Nebraska exhibited somewhat different temperature and precipitation responses with both GCM versions.  相似文献   

13.
On the basis of General Circulation Model (GCM) experiments with increased CO2, many parts of the northern latitudes including western Europe, are expected to have enhanced hydrologic cycles. Using observations of precipitation and streamflow from Ireland, we test for climatic and hydrologic change in this maritime climate of the northeast Atlantic. Five decades of hourly precipitation (at eight sites) and daily streamflow at four rivers in Ireland were investigated for patterns of climate variability. An increase in annual precipitation was found to occur after 1975. This increase in precipitation is most noticeable on the West of the island. Precipitation increases are significant in March and October and are associated with increases in the frequency of wet hours with no change in the hourly intensities. Analysis of streamflow data shows the same trends. Furthermore, analysis of extreme rainfall events show that a much greater proportion of extremes have occurred in the period since 1975. A change also occurred in the North Atlantic Oscillation (NAO) index around 1975. The increased NAO since 1975 is associated with increased westerly airflow circulation in the Northeast Atlantic and is correlated with the wetter climate in Ireland. These climatic changes have implications for water resources management particularly flood analysis and protection.  相似文献   

14.
The 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.  相似文献   

15.
In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961–1990 as well as the validation period of 1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R 2 values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.  相似文献   

16.
The precipitation amounts on wet days at De Bilt (the Netherlands) are linked to temperature and surface air pressure through advanced regression techniques. Temperature is chosen as a covariate to use the model for generating synthetic time series of daily precipitation in a CO2 induced warmer climate. The precipitation-temperature dependence can partly be ascribed to the phenomenon that warmer air can contain more moisture. Spline functions are introduced to reproduce the non-monotonous change of the mean daily precipitation amount with temperature. Because the model is non-linear and the variance of the errors depends on the expected response, an iteratively reweighted least-squares technique is needed to estimate the regression coefficients. A representative rainfall sequence for the situation of a systematic temperature rise is obtained by multiplying the precipitation amounts in the observed record with a temperature dependent factor based on a fitted regression model. For a temperature change of 3°C (reasonable guess for a doubled CO2 climate according to the present-day general circulation models) this results in an increase in the annual average amount of 9% (20% in winter and 4% in summer). An extended model with both temperature and surface air pressure is presented which makes it possible to study the additional effects of a potential systematic change in surface air pressure on precipitation.  相似文献   

17.
S. Rehana  P. P. Mujumdar 《水文研究》2013,27(20):2918-2933
This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U2. Consequently, the reference evapotranspiration, modeled by the Penman–Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

18.
Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.  相似文献   

19.
This study demonstrates the use of spatially downscaled, monthly general circulation model (GCM) rainfall and temperature data to drive the established HyMOD hydrological model to evaluate the prospective effects of climate change on the fluvial run‐off of the River Derwent basin in the UK. The evaluation results of this monthly hydrological model using readily available, monthly GCM data are consistent with studies on nearby catchments employing high‐temporal resolution data, indicating that useful hydro‐climatic planning studies may be possible using standard datasets and modest computational resources. HyMOD was calibrated against 5 km2 gridded UK Climate Projections dataset data and then driven using monthly spatially interpolated (~5 km2) outputs from Hadley Centre Coupled Model, version 3 and the Canadian Centre for Climate Modelling and Analysis for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC‐SRES) A2a and B2a covering the 2020s, 2050s and 2080s. Results for both GCMs project a decrease in annual run‐off in both GCM models and scenarios with higher values in the summer/autumn months, whereas an increase in the later winter months. Both Hadley Centre Coupled Model, version 3 and the Canadian Centre for Climate Modelling and Analysis show higher ranges of uncertainty during the winter season with higher values of run‐off associated with December in all three simulation periods and two scenarios. A seasonal comparison of run‐off simulations shows that both GCMs give similar results in summer and autumn, whereas disparities due to GCM uncertainties are more conspicuous in winter and spring. In this study, both the GCMs under A2a scenario have demonstrated the high possibility of time shift in monthly average peak run‐offs in the Derwent River by 2080s in comparison with the early 21st century. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

20.
The frequency and magnitude of extreme meteorological or hydrological events such as floods and droughts in China have been influenced by global climate change. The water problem due to increasing frequency and magnitude of extreme events in the humid areas has gained great attention in recent years. However, the main challenge in the evaluation of climate change impact on extreme events is that large uncertainty could exist. Therefore, this paper first aims to model possible impacts of climate change on regional extreme precipitation (indicated by 24‐h design rainfall depth) at seven rainfall gauge stations in the Qiantang River Basin, East China. The Long Ashton Research Station‐Weather Generator is adopted to downscale the global projections obtained from general circulation models (GCMs) to regional climate data at site scale. The weather generator is also checked for its performance through three approaches, namely Kolmogorov–Smirnov test, comparison of L‐moment statistics and 24‐h design rainfall depths. Future 24‐h design rainfall depths at seven stations are estimated using Pearson Type III distribution and L‐moment approach. Second, uncertainty caused by three GCMs under various greenhouse gas emission scenarios for the future periods 2020s (2011–2030), 2055s (2046–2065) and 2090s (2080–2099) is investigated. The final results show that 24‐h design rainfall depth increases in most stations under the three GCMs and emission scenarios. However, there are large uncertainties involved in the estimations of 24‐h design rainfall depths at seven stations because of GCM, emission scenario and other uncertainty sources. At Hangzhou Station, a relative change of ?16% to 113% can be observed in 100y design rainfall depths. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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