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1.
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.  相似文献   

2.
Scenario‐neutral assessments of climate change impact on floods analyse the sensitivity of a catchment to a range of changes in selected meteorological variables such as temperature and precipitation. The key challenges of the approach are the choice of the meteorological variables and statistics thereof and how to generate time series representing altered climatologies of the selected variables. Different methods have been proposed to achieve this, and it remains unclear if and to which extent they result in comparable flood change projections. Here, we compare projections of annual maximum floods (AMFs) derived from three different scenario‐neutral methods for a prealpine study catchment. The methods chosen use different types of meteorological data, namely, observations, regional climate model output, and weather generator data. The different time series account for projected changes in the seasonality of temperature and precipitation, in the occurrence statistics of precipitation, and of daily precipitation extremes. Resulting change in mean AMF peak magnitudes and volumes differs in sign between the methods (range of ?6% to +7% for flood peak magnitudes and ?11% to +14% for flood volumes). Moreover, variability of projected peak magnitudes and flood volumes depends on method with one approach leading to a generally larger spread. The differences between the methods vary depending on whether peak magnitudes or flood volumes are considered and different relationships between peak magnitude and volume change result. These findings can be linked to differing flood regime changes among the three approaches. The study highlights that considering selected aspects of climate change only when performing scenario‐neutral studies may lead to differing representations of flood generating processes by the approaches and thus different quantifications of flood change. As each method comes with its own strengths and weaknesses, it is recommended to combine several scenario‐neutral approaches to obtain more robust results.  相似文献   

3.
鄱阳湖流域过去1000 a径流模拟以及对气候变化响应研究   总被引：1，自引：1，他引：0  
张小琳  李云良  于革  张奇 《湖泊科学》2016,28(4):887-898
为研究过去千年尺度径流变化及其对气候变化的响应,以长江中游鄱阳湖流域为研究区,运用气候模式CCSM4和ECHAM5模拟过去1000 a气候数据,空间降尺度后驱动水文模型模拟了鄱阳湖流域过去近千年流域径流序列.利用快速傅里叶变换、小波分析等手段,分析流域极端径流变化特征、周期和该流域旱涝事件发生频率.结果表明:2种气候模式均能反映出中世纪暖期及小冰期阶段的干湿交替变化,且小冰期内中干旱状态维持时间较长;径流的丰枯变化与降水量变化具有较好的对应关系.CCSM4和ECHAM5模式下发生旱涝灾害与极大极小降水事件发生频率基本相同,径流丰枯变化与降水变化周期相近,均具有30 a左右的主周期,10~15、7 a左右的子周期.小波系数模平方图中30 a左右显著的能量信号揭示了该周期与北太平洋气候的主要环流机制的太平洋年代际振荡周期相近,因此,大气环流涛动是造成气候-水文变化的主要原因.研究结果拓展了基于近代60 a观测记录的流域水文变化的认识,探讨了千年时间长度下流域干湿变化特征和水文对气候响应的动力机制,有助于全面系统认识长江中游在全球气候暖化背景下旱涝极端水文事件的发生机制与变化规律.  相似文献   

4.
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.  相似文献   

5.
Water resource assessment on climate change is crucial in water resource planning and management. This issue is becoming more urgent with climate change intensifying. In the current research of climate change impact, climate natural variability (fluctuation) has seldom been studied separately. Many studies keep attributing all changes (e.g. runoff) to climate change, which may lead to wrong understanding of climate change impact assessment. Because of lack of long enough historical series, impacts of climate variability have been always avoided deliberately. Based on Latin hypercube sampling technique, a block sampling approach was proposed for climate variability simulation in this study. The widely used time horizon (1961–1991) was defined as baseline period, and the runoff variation probability affected by climate natural variability was analysed. Allowing for seven future climate projections in total of three GCMs (CSIRO, NCAR, and MPI) and three emission scenarios (A1B, A2, and B1), the impact of future climate change on water resources was estimated in terms of separating the contribution from climate natural variability. Based on the analysis of baseline period, for the future period from 2021 to 2051, the impact of climate natural variability may play a major part, whereas for the period from 2061 to 2091, climate change attributed to greenhouse gases may dominate the changing process. The results show that changes from climate variability possess a comparable magnitude, which highlights the importance to separate impacts of climate variability in assessing climate change, instead of attributing all changes to climate change solely. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

6.
An understanding of the weather drivers of soil erosion necessitates an extended instrumental meteorological series and knowledge of the processes linking climate and hydrology. The nature of such linkages remains poorly understood for the Mediterranean region. This gap is addressed through a composite analysis of long‐term climatic controls on rain erosivity in the Calore River Basin (southern Italy) for the period 1869–2006. Based on a parsimonious interpretation of rainstorm processes, a model (comparable with the Revised Universal Soil Loss Equation) was adapted to generate erosivity values on different time‐aggregation scales (yearly and seasonal). The evolution of the generated series of cumulated and extreme erosivity events was assessed by two return period (T) quantiles via a 22‐year moving window analysis (low return period, T = 2 years; high return period, T = 50 years). Erosivity extremes are shown to be characterized by increasing yearly trends (at a 100‐year rate of ～150 MJ mm ha–1 h–1 for T = 2 years and ～800 MJ mm ha–1 h–1 for T = 50 years), especially during the spring and autumn seasons. Quantile patterns on the extremes are also shown to be decoupled from trends in the cumulated values. The Buishand test was applied to detect the presence of temporal change points, and a wavelet spectrum analysis used for time‐frequency localization of climate signals. A change‐point in the evolution of climate is revealed over the 1970s in the spring series, which correlates to a distinct rain erosivity increase. The results indicate that soil erosion risk tends to rise as a consequence of an escalation of the climate erosive hazard, predominantly between April and November (associated with cultivation and tillage practices). Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

7.
To reconstruct the recent climate history in Kamchatka, a series of repeated precise temperature logs were performed in a number of boreholes located in a broad east-west strip (between 52 and 54°N) in the central part of Kamchatka west of Petropavlovsk-Kamchatski. Within three years more than 30 temperature logs were performed in 10 holes (one up to six logs per hole) to the depth of up to 400 metres. Measured temperature gradients varied in a broad interval 0 to 60 mK/m and in some holes a sizeable variation in the subsurface temperatures due to advective heat transport by underground water was observed. Measured data were compared with older temperature profiles obtained in the early eighties by Sugrobov and Yanovsky (1993). Even when older data are of poorer precision (accuracy of about 0.1 K), they presented valuable information of the subsurface temperature conditions existing 20–25 years ago. Borehole observations and the inverted ground surface temperature histories (GSTHs) used for the paleoclimate reconstruction were complemented with a detailed survey of meteorological data. Namely, the long-term surface air temperature (SAT) and precipitation records from Petropavlovsk station (in operation since 1890) were used together with similar data from a number of local subsidiary meteo-stations operating in Central Kamchatka since 1950. Regardless of extreme complexity of the local meteorological/climate conditions, diversity of borehole sites and calibration of measuring devices used during the whole campaign, the results of the climate reconstruction supported a general warming of about 1 K characteristic for the 20th century, which followed an inexpressive cooler period typical for the most of the 19th century. In the last three to four decades the warming rate has been locally increasing up to 0.02 K/year. It was also shown that the snow cover played a dominant role in the penetration of the climate “signal” to depth and could considerably smooth down the subsurface response to the changes occurred on the surface.  相似文献   

8.
The study evaluates relationships between the North Atlantic Oscillation (NAO) index and winter temperatures (including indices of extremes) over Europe in an ensemble of transient simulations of current global climate models (GCMs). We focus on identification of areas in which the NAO index is linked to winter temperatures and temperature extremes in simulations of the recent climate (1961–2000), and evaluate how these relationships change in climate change scenarios for the late 21st century (2071–2100). Most GCMs are able to reproduce main features of the observed links. The NAO index is more important for cold than warm extremes, which is also reproduced by the GCMs. However, all GCMs underestimate the magnitude of the NAO influence on cold extremes when averaged over northern and western Europe. For future scenarios, the links between the NAO and temperatures are mostly analogous to those in the recent climate, except for one GCM (CM3) in which the influence of the NAO on temperature almost disappears over whole Europe. This suggests that future scenarios from this particular GCM should be evaluated with caution. The NAO index is found to represent a useful covariate that explains an important fraction of variability of cold extremes in winter, and its incorporation into extreme value models for daily temperatures (and their possible changes under climate change) may improve performance of these models and reliability of estimates of extremes and their uncertainty.  相似文献   

9.
An essential part of hydrological research focuses on hydrological extremes, such as river peak flows and associated floods, because of their large impact on economy, environment, and human life. These extremes can be affected by potential future environmental change, including global climate change and land cover change. In this paper, the relative impact of both climate change and urban expansion on the peak flows and flood extent is investigated for a small‐scale suburban catchment in Belgium. A rainfall‐runoff model was coupled to a hydrodynamic model in order to simulate the present‐day and future river streamflow. The coupled model was calibrated based on a series of measured water depths and, after model validation, fed with different climate change and urban expansion scenarios in order to evaluate the relative impact of both driving factors on the peak flows and flood extent. The three climate change scenarios that were used (dry, wet winter, wet summer) were based on a statistical downscaling of 58 different RCM and GCM scenario runs. The urban expansion scenarios were based on three different urban growth rates (low, medium, high urban expansion) that were set up by means of an extrapolation of the observed trend of urban expansion. The results suggest that possible future climate change is the main source of uncertainty affecting changes in peak flow and flood extent. The urban expansion scenarios show a more consistent trend. The potential damage related to a flood is, however, mainly influenced by land cover changes that occur in the floodplain. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

10.
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 (ETo), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ETo 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 ETo. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ETo 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.  相似文献   

11.
A number of watersheds are selected from the Hydro‐Climate Data Network over southeastern United States to examine possible changes in hydrological time series, e.g. precipitation, introduced by changing climate. Possible changes in monthly precipitation are examined by three different methods to detect second order stationarity, abrupt changes in the variance and smooth changes in quantiles of the time series. An analysis of second order stationarity shows that precipitation in eight of the 56 watersheds display nonstationary behaviour. Change‐point analyses reveal that changes in the long‐term variance of monthly precipitation are only detected for a few sites. As a complementary analysis tool, quantile regression aims to detect potential changes of different percentiles of the monthly precipitation over time. Several sites show diverging trends in the quantiles, which implies that the range and thus variance of the data, is increasing. As distinct change‐points are not identified, this suggests that the effect is small and cumulative. Results are analysed in detail, and possible explanations are provided. This type of thorough analysis provides a basis for understanding the possible redistribution of water cycle. It also provides implications for water resources management and hydrological engineering facility design and planning. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

12.
H. Moradkhani 《水文研究》2014,28(26):6292-6308
In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile‐mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100‐year return level runoffs. Results show an increase in the 100‐year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

13.
The impacts of climate extremes on the terrestrial carbon cycle:A review   总被引：2，自引：0，他引：2  
The increased frequency of climate extremes in recent years has profoundly affected terrestrial ecosystem functions and the welfare of human society. The carbon cycle is a key process of terrestrial ecosystem changes. Therefore, a better understanding and assessment of the impacts of climate extremes on the terrestrial carbon cycle could provide an important scientific basis to facilitate the mitigation and adaption of our society to climate change. In this paper, we systematically review the impacts of climate extremes(e.g. drought, extreme precipitation, extreme hot and extreme cold) on terrestrial ecosystems and their mechanisms. Existing studies have suggested that drought is one of the most important stressors on the terrestrial carbon sink, and that it can inhibit both ecosystem productivity and respiration. Because ecosystem productivity is usually more sensitive to drought than respiration, drought can significantly reduce the strength of terrestrial ecosystem carbon sinks and even turn them into carbon sources. Large inter-model variations have been found in the simulations of drought-induced changes in the carbon cycle, suggesting the existence of a large gap in current understanding of the mechanisms behind the responses of ecosystem carbon balance to drought, especially for tropical vegetation. The effects of extreme precipitation on the carbon cycle vary across different regions. In general, extreme precipitation enhances carbon accumulation in arid ecosystems, but restrains carbon sequestration in moist ecosystems. However, current knowledge on the indirect effects of extreme precipitation on the carbon cycle through regulating processes such as soil carbon lateral transportation and nutrient loss is still limited. This knowledge gap has caused large uncertainties in assessing the total carbon cycle impact of extreme precipitation. Extreme hot and extreme cold can affect the terrestrial carbon cycle through various ecosystem processes. Note that the severity of such climate extremes depends greatly on their timing, which needs to be investigated thoroughly in future studies. In light of current knowledge and gaps in the understanding of how extreme climates affect the terrestrial carbon cycle, we strongly recommend that future studies should place more attention on the long-term impacts and on the driving mechanisms at different time scales.Studies based on multi-source data, methods and across multiple spatial-temporal scales, are also necessary to better characterize the response of terrestrial ecosystems to climate extremes.  相似文献   

14.
This paper presents the results of an investigation into the problems associated with using downscaled meteorological data for hydrological simulations of climate scenarios. The influence of both the hydrological models and the meteorological inputs driving these models on climate scenario simulation studies are investigated. A regression‐based statistical tool (SDSM) is used to downscale the daily precipitation and temperature data based on climate predictors derived from the Canadian global climate model (CGCM1), and two types of hydrological model, namely the physically based watershed model WatFlood and the lumped‐conceptual modelling system HBV‐96, are used to simulate the flow regimes in the major rivers of the Saguenay watershed in Quebec. The models are validated with meteorological inputs from both the historical records and the statistically downscaled outputs. Although the two hydrological models demonstrated satisfactory performances in simulating stream flows in most of the rivers when provided with historic precipitation and temperature records, both performed less well and responded differently when provided with downscaled precipitation and temperature data. By demonstrating the problems in accurately simulating river flows based on downscaled data for the current climate, we discuss the difficulties associated with downscaling and hydrological models used in estimating the possible hydrological impact of climate change scenarios. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

15.
The increasing frequency and/or severity of extreme climate events are becoming increasingly apparent over multi‐decadal timescales at the global scale, albeit with relatively low scientific confidence. At the regional scale, scientific confidence in the future trends of extreme event likelihood is stronger, although the trends are spatially variable. Confidence in these extreme climate risks is muddied by the confounding effects of internal landscape system dynamics and external forcing factors such as changes in land use and river and coastal engineering. Geomorphology is a critical discipline in disentangling climate change impacts from other controlling factors, thereby contributing to debates over societal adaptation to extreme events. We review four main geomorphic contributions to flood and storm science. First, we show how palaeogeomorphological and current process studies can extend the historical flood record while also unraveling the complex interactions between internal geomorphic dynamics, human impacts and changes in climate regimes. A key outcome will be improved quantification of flood probabilities and the hazard dimension of flood risk. Second, we present evidence showing how antecedent geomorphological and climate parameters can alter the risk and magnitude of landscape change caused by extreme events. Third, we show that geomorphic processes can both mediate and increase the geomorphological impacts of extreme events, influencing societal risk. Fourthly, we show the potential of managing flood and storm risk through the geomorphic system, both near‐term (next 50 years) and longer‐term. We recommend that key methods of managing flooding and erosion will be more effective if risk assessments include palaeodata, if geomorphological science is used to underpin nature‐based management approaches, and if land‐use management addresses changes in geomorphic process regimes that extreme events can trigger. We argue that adopting geomorphologically‐grounded adaptation strategies will enable society to develop more resilient, less vulnerable socio‐geomorphological systems fit for an age of climate extremes. © 2016 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.  相似文献   

16.
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.  相似文献   

17.
One of the most significant anticipated consequences of global climate change is the increased frequency of hydrologic extremes. Predictions of climate change impacts on the regime of hydrologic extremes have traditionally been conducted using a top‐down approach. The top‐down approach involves a high degree of uncertainty associated with global circulation model (GCM) outputs and the choice of downscaling technique. This study attempts to explore an inverse approach to the modelling of hydrologic risk and vulnerability to changing climatic conditions. With a focus targeted at end‐users, the proposed approach first identifies critical hydrologic exposures that may lead to local failures of existing water resources systems. A hydrologic model is used to transform inversely the main hydrologic exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological situations is investigated under present and future climatic scenarios by means of a generic weather generator. The weather generator, linked with GCMs at the last step of the proposed methodology, allows the creation of an ensemble of different scenarios, as well as an easy updating, when new and improved GCM outputs become available. The technique has been applied in Ontario, Canada. The results show significant changes in the frequency of hydro‐climatic extremes under future climate scenarios in the study area. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

18.
The Nooksack River has its headwaters in the North Cascade Mountains and drains an approximately 2000 km2 watershed in northwestern Washington State. The timing and magnitude of streamflow in a snowpack‐dominated drainage basin such as the Nooksack River basin are strongly influenced by temperature and precipitation. Projections of future climate made by general circulation models (GCMs) indicate increases in temperature and variable changes in precipitation for the Nooksack River basin. Understanding the response of the river to climate change is crucial for regional water resources planning because municipalities, tribes, and industry depend on the river for water use and for fish habitat. We combine three different climate scenarios downscaled from GCMs and the Distributed‐Hydrology‐Soil‐Vegetation Model to simulate future changes to timing and magnitude of streamflow in the higher elevations of the Nooksack River. Simulations of future streamflow and snowpack in the basin project a range of magnitudes, which reflects the variable meteorological changes indicated by the three GCM scenarios and the local natural variability employed in the modeling. Simulation results project increased winter flows, decreased summer flows, decreased snowpack, and a shift in timing of the spring melt peak and maximum snow water equivalent. These results are consistent with previous regional studies, but the magnitude of increased winter flows and total annual runoff is higher. Increases in temperature dominate snowpack declines and changes to spring and summer streamflow, whereas a combination of increases in temperature and precipitation control increased winter streamflow. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

19.
The northern mid‐high latitudes form a region that is sensitive to climate change, and many areas already have seen – or are projected to see – marked changes in hydroclimatic drivers on catchment hydrological function. In this paper, we use tracer‐aided conceptual runoff models to investigate such impacts in a mesoscale (749 km2) catchment in northern Scotland. The catchment encompasses both sub‐arctic montane sub‐catchments with high precipitation and significant snow influence and drier, warmer lowland sub‐catchments. We used downscaled HadCM3 General Circulation Model outputs through the UKCP09 stochastic weather generator to project the future climate. This was based on synthetic precipitation and temperature time series generated from three climate change scenarios under low, medium and high greenhouse gas emissions. Within an uncertainty framework, we examined the impact of climate change at the monthly, seasonal and annual scales and projected impacts on flow regimes in upland and lowland sub‐catchments using hydrological models with appropriate process conceptualization for each landscape unit. The results reveal landscape‐specific sensitivity to climate change. In the uplands, higher temperatures result in diminishing snow influence which increases winter flows, with a concomitant decline in spring flows as melt reduces. In the lowlands, increases in air temperatures and re‐distribution of precipitation towards autumn and winter lead to strongly reduced summer flows despite increasing annual precipitation. The integration at the catchment outlet moderates these seasonal extremes expected in the headwaters. This highlights the intimate connection between hydrological dynamics and catchment characteristics which reflect landscape evolution. It also indicates that spatial variability of changes in climatic forcing combined with differential landscape sensitivity in large heterogeneous catchments can lead to higher resilience of the integrated runoff response. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

20.
ABSTRACT

In this study, we investigate the temporal oscillations of precipitation extremes in different climate regions of the United States. We apply quantile perturbation analysis to average daily precipitation and, to 1041 weather stations with high-quality data from 1900 to 2016. Moreover, we explore the relationship between the extreme precipitation and different well-known cyclical climate modes. Overall, the analysis of average daily precipitation identifies a drier condition in the middle decades of the twentieth century and, a wetter climate in the early century and recent decades. Moreover, the in situ analysis reveals a significant anomaly, mainly prevalent in the Central and Southern regions of the United States. We applied a finite set of linear regression models with different combinations of cyclical climate modes to inform the variability of anomalies with best performing models. Our results highlight the dominant effect of ENSO and NAO in the wide area of the United States.  相似文献   

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