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1.
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
2.
Projecting climate change impacts on stream flow regimes with tracer‐aided runoff models ‐ preliminary assessment of heterogeneity at the mesoscale 
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下载免费PDF全文 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
3.
Quantification of uncertainty in the assessment of future streamflow under changing climate conditions 下载免费PDF全文
下载免费PDF全文 Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change. 相似文献
4.
Climate change impacts on surface water resources in the Rheraya catchment (High Atlas,Morocco) 总被引：2，自引：2，他引：0
This study aimed to quantify possible climate change impacts on runoff for the Rheraya catchment (225 km2) located in the High Atlas Mountains of Morocco, south of Marrakech city. Two monthly water balance models, including a snow module, were considered to reproduce the monthly surface runoff for the period 1989?2009. Additionally, an ensemble of five regional climate models from the Med-CORDEX initiative was considered to evaluate future changes in precipitation and temperature, according to the two emissions scenarios RCP4.5 and RCP8.5. The future projections for the period 2049?2065 under the two scenarios indicate higher temperatures (+1.4°C to +2.6°C) and a decrease in total precipitation (?22% to ?31%). The hydrological projections under these climate scenarios indicate a significant decrease in surface runoff (?19% to ?63%, depending on the scenario and hydrological model) mainly caused by a significant decline in snow amounts, related to reduced precipitation and increased temperature. Changes in potential evapotranspiration were not considered here, since its estimation over long periods remains a challenge in such data-sparse mountainous catchments. Further work is required to compare the results obtained with different downscaling methods and different hydrological model structures, to better reproduce the hydro-climatic behaviour of the catchment.
EDITOR M.C. AcremanASSOCIATE EDITOR R. Hirsch 相似文献
5.
A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes 下载免费PDF全文
下载免费PDF全文 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
6.
Statistical downscaling of extremes of precipitation and temperature and construction of their future scenarios in an elevated and cold zone 总被引：5，自引：4，他引：1
Reliable projections of extremes at finer spatial scales are important in assessing the potential impacts of climate change
on societal and natural systems, particularly for elevated and cold regions in the Tibetan Plateau. This paper presents future
projections of extremes of daily precipitation and temperature, under different future scenarios in the headwater catchment
of Yellow River basin over the 21st century, using the statistical downscaling model (SDSM). The results indicate that: (1)
although the mean temperature was simulated perfectly, followed by monthly pan evaporation, the skill scores in simulating
extreme indices of precipitation are inadequate; (2) The inter-annual variabilities for most extreme indices were underestimated,
although the model could reproduce the extreme temperatures well. In fact, the simulation of extreme indices for precipitation
and evaporation were not satisfactory in many cases. (3) In future period from 2011 to 2100, increases in the temperature
and evaporation indices are projected under a range of climate scenarios, although decreasing mean and maximum precipitation
are found in summer during 2020s. The findings of this work will contribute toward a better understanding of future climate
changes for this unique region. 相似文献
7.
《水文科学杂志》2013,58(4):727-738
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. 相似文献
8.
This work presents a methodology to make statistical significant and robust inferences on climate change from an ensemble of model simulations. This methodology is used to assess climate change projections of the Iberian daily-total precipitation for a near-future (2021–2050) and a distant-future (2069–2098) climates, relatively to a reference past climate (1961–1990).Climate changes of precipitation spatial patterns are estimated for annual and seasonal values of: (i) total amount of precipitation (PRCTOT), (ii) maximum number of consecutive dry days (CDD), (iii) maximum of total amount of 5-consecutive wet days (Rx5day), and (iv) percentage of total precipitation occurred in days with precipitation above the 95th percentile of the reference climate (R95T). Daily-total data were obtained from the multi-model ensemble of fifteen Regional Climate Model simulations provided by the European project ENSEMBLES. These regional models were driven by boundary conditions imposed by Global Climate Models that ran under the 20C3M conditions from 1961 to 2000, and under the A1B scenario, from 2001 to 2100, defined by the Special Report on Emission Scenarios of the Intergovernmental Panel on Climate Change.Non-parametric statistical methods are used for significant climate change detection: linear trends for the entire period (1961–2098) estimated by the Theil-Sen method with a statistical significance given by the Mann-Kendall test, and climate-median differences between the two future climates and the past climate with a statistical significance given by the Mann-Whitney test. Significant inferences of climate change spatial patterns are made after these non-parametric statistics of the multi-model ensemble median, while the associated uncertainties are quantified by the spread of these statistics across the multi-model ensemble. Significant and robust climate change inferences of the spatial patterns are then obtained by building the climate change patterns using only the grid points where a significant climate change is found with a predefined low uncertainty.Results highlight the importance of taking into account the spread across an ensemble of climate simulations when making inferences on climate change from the ensemble-mean or ensemble-median. This is specially true for climate projections of extreme indices such CDD and R95T. For PRCTOT, a decrease in annual precipitation over the entire peninsula is projected, specially in the north and northwest where it can decrease down to 400 mm by the middle of the 21st century. This decrease is expected to occur throughout the year except in winter. Annual CDD is projected to increase till the middle of the 21st century overall the peninsula, reaching more than three weeks in the southwest. This increase is projected to occur in summer and spring. For Rx5day, a decrease is projected to occur during spring and autumn in the major part of the peninsula, and during summer in northern Iberia. Finally, R95T is projected to decrease around 20% in northern Iberia in summer, and around 15% in the south-southwest in autumn. 相似文献
9.
Regional climate models (RCMs) have emerged as the preferred tool in hydrological impact assessment at the catchment scale. The direct application of RCM precipitation output is still not recommended; instead, a number of alternative methods have been proposed. One method that has been used is the change factor methodology, which typically uses changes to monthly mean or seasonal precipitation totals to develop change scenarios. However, such simplistic approaches are subject to significant caveats. In this paper, 18 RCMs covering the UK from the ENSEMBLES and UKCP09 projects are analysed across different catchments. The ensembles' ability in capturing monthly total and extreme precipitation is outlined to explore how the ability to make confident statements about future flood risk varies between different catchments. The suitability of applying simplistic change factor approaches in flood impact studies is also explored. We found that RCM ensembles do have some skill in simulating observed monthly precipitation; however, seasonal patterns of bias were evident across each of the catchments. Moreover, even apparently good simulations of extreme rainfall can mis‐estimate the magnitude of flood‐generating rainfall events in ways that would significantly affect flood risk management. For future changes in monthly mean precipitation, we observe the clear ‘drier summers/wetter winters’ signal used to develop current UK policy, but when we look instead at flood‐generating rainfall, this seasonal signal is less clear and greater increases are projected. Furthermore, the confidence associated with future projections varies from catchment to catchment and season to season as a result of the varying ability of the RCM ensembles, and in some cases, future flood risk projections using RCM outputs may be highly problematic. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
10.
Statistical downscaling of extreme daily precipitation,evaporation, and temperature and construction of future scenarios 总被引：4，自引：0，他引：4
Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation, and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature. For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2 and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation process over the Dongjiang River basin. In pre‐flood seasons (April to June), the mixing of the dry and cold air originated from northern China and the moist warm air releases excessive rainstorms to this basin, while in post‐flood seasons (July to October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
11.
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
12.
Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed 总被引：12，自引：6，他引：6
Muhammad Zia Hashmi Asaad Y. Shamseldin Bruce W. Melville 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(4):475-484
Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing
climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the
complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be
useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling
of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling
techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as
they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a
multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to
simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal
average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in
the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to
the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations
are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have
similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate
change impact studies of this nature. 相似文献
13.
Sharif Joorabian Shooshtari Mehdi Gholamalifard Mahmood Azari Roberto Serrano-Notivoli Juan Ignacio López-Moreno 《水文科学杂志》2017,62(16):2655-2673
We evaluated the potential impacts of future land cover change and climate variability on hydrological processes in the Neka River basin, northern Iran. This catchment is the main source of water for the intensively cultivated area of Neka County. Hydrological simulations were conducted using the Soil and Water Assessment Tool. An ensemble of 17 CMIP5 climate models was applied to assess changes in temperature and precipitation under the moderate and high emissions scenarios. To generate the business-as-usual scenario map for year 2050 we used the Land Change Modeler. With a combined change in land cover and climate, discharge is expected to decline in all seasons except the end of autumn and winter, based on the inter-model average and various climate models, which illustrated a high degree of uncertainty in discharge projections. Land cover change had a minor influence on discharge relative to that resulting from climate change. 相似文献
14.
人工神经网络模型预测气候变化对博斯腾湖流域径流影响 总被引：9，自引：3，他引：6
温室气体排放量增加造成气候变化,对全球资源环境产生重要影响．本文利用人工神经网络模型建立月降水、气温与径流关系,利用开都河流域降水、气温、径流资料对模型进行训练和验证,通过试算法确定网络模型结构,气温升高和降水量增加对径流影响的敏感程度分析表明,气温升高和降水增加对该区域径流影响较大,且气温升高的影响更为显著,径流增加主要集中在夏季,根据区域气候模型（RCMs）推算的CO2加倍情况下西北地区气候的可能变化,预测位于博斯腾湖流域的开都河大山口站年径流量增加38．6％,其中夏季增加71．8％,冬季增加11．4％。 相似文献
15.
1990s长江流域降水趋势分析 总被引：2，自引：0，他引：2
依据国家气象局提供的实测月降水和日降水资料,运用Mann-Kendall(M-K)非参数检验法验证了降水趋势,并通过空间插补法,由点扩展到面,分析了1990s长江流域降水变化特征,发现1990s长江流域降水变化以降水在时间和空间分布上的集中度的增加为主要特点:时间上,年降水的增加趋势以冬季1月和夏季6月降水的集中增加为主;一日降水量大于等于50mm的暴雨日数和暴雨量在1990s也有了较明显的增加.空间上,年降水、夏季降水、冬季降水的增加都以中下游区的增加为主,尤其以鄱阳湖水系、洞庭湖水系的降水增加为主.1990s长江流域春季和秋季降水的减少以5月和9月两个汛期月份的降水减少为主,除金沙江水系和洞庭湖水系等少数地区外,流域大部分地区降水呈减少趋势.上述1990s出现的降水趋势明显与近年来全球变暖背景下长江流域各地区不同的温度及水循环变异有关. 相似文献
16.
Alison C. Rudd Alison L. Kay Steven C. Wells Timothy Aldridge Steven J. Cole Elizabeth J. Kendon Elizabeth J. Stewart 《水文研究》2020,34(1):139-149
Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods. Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential water-related impacts of climate change; such assessments often focus on broad-scale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, high-resolution (<4 km) convection-permitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made. At the same time, there has been an increasing demand for more robust and timely real-time forecast and alert information on SWF. In the UK, a real-time SWF Hazard Impact Model framework has been developed. The system uses 1-km gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels. Here, a set of high-resolution (1.5 km and 12 km) RCM data has been used as input to a grid-based hydrological model over southern Britain to simulate Current (1996–2009) and Future (~2100s; RCP8.5) surface runoff. Counts of threshold-exceedance for surface runoff and precipitation (at 1-, 3- and 6-hr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higher-resolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12-km RCM output that leads to the largest percentage increase in impacts. The added-value of high-resolution climate model data for hydrological modelling is from capturing the more intense convective storms in surface runoff estimates. 相似文献
17.
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
18.
Using raw regional climate model outputs for quantifying climate change impacts on hydrology 下载免费PDF全文
下载免费PDF全文 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. 相似文献
19.
DIONYSIA PANAGOULIA 《水文科学杂志》2013,58(6):525-547
Abstract The long term hydrological response of a medium-sized mountainous catchment to climate changes has been examined, The climate changes were represented by a set of hypothetical scenarios of temperature increases coupled with precipitation and potential evapotranspiration changes. Snow accumulation and ablation, plus runoff from the study catchment (the Mesochora catchment in central Greece) were simulated under present (historical) and altered climate conditions using the US National Weather Service snowmelt and soil moisture accounting models. The results of this research obtained through alternative scenarios suggest strongly that all the hypothetical climate change scenarios would cause major decreases in winter snow accumulation and hence increases in winter runoff, as well as decreases in spring and summer runoff. The simulated changes in annual runoff were minor compared with the changes in the monthly distribution of runoff. Attendant changes in the monthly distribution of soil moisture and actual evapotranspiration would also occur. Such hydrological results would have significant implications on future water resources design and management. 相似文献