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1.
Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster–Shafer (D–S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D–S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D–S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D–S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster–Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D–S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D–S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.  相似文献   

2.
In accounting for uncertainties in future simulations of hydrological response of a catchment, two approaches have come to the fore: deterministic scenario‐based approaches and stochastic probabilistic approaches. As scenario‐based approaches result in a wide range of outcomes, the role of probabilistic‐based estimates of climate change impacts for policy formulation has been increasingly advocated by researchers and policy makers. This study evaluates the impact of climate change on seasonal river flows by propagating daily climate time series, derived from probabilistic‐based climate scenarios using a weather generator (WGEN), through a set of conceptual hydrological models. Probabilistic scenarios are generated using two different techniques. The first technique used probabilistic climate scenarios developed from statistically downscaled scenarios for Ireland, hereafter called SDprob. The second technique used output from 17 global climate models (GCMs), all of which participated in CMIP3, to generate change factors (hereafter called CF). Outputs from both the SDprob and the CF approach were then used in combination with WGEN to generate daily climate scenarios for use in the hydrological models. The range of simulated flow derived with the CF method is in general larger than those estimated with the SDprob method in winter and vice versa because of the strong seasonality in the precipitation signal for the 17 GCMs. Despite this, the simulated probability density function of seasonal mean streamflow estimated with both methods is similar. This indicates the usefulness of the SDprob or probabilistic approach derived from regional scenarios compared with the CF method that relies on sampling a diversity of response from the GCMs. Irrespective of technique used, the probability density functions of seasonal mean flow produced for four selected basins is wide indicating considerable modelling uncertainties. Such a finding has important implications for developing adaptation strategies at the catchment level in Ireland. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

3.
General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.  相似文献   

4.
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   

5.
Abstract

This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow.

Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263.  相似文献   

6.
Abstract

Climate change is recognized to be one of the most serious challenges facing mankind today. Driven by anthropogenic activities, it is known to be a direct threat to our food and water supplies and an indirect threat to world security. Increase in the concentration of carbon dioxide and other greenhouse gases in the atmosphere will certainly affect hydrological regimes. The consequent global warming is expected to have major implications on water resources management. The objective of this research is to present a general approach for evaluating the impacts of potential climate change on streamflow in a river basin in the humid tropical zone of India. Large-scale global climate models (GCMs) are the best available tools to provide estimates of the effect of rising greenhouse gases on rainfall and temperature. However the spatial resolution of these models (250 km?×?250 km) is not compatible with that of watershed hydrological models. Hence the outputs from GCMs have to be downscaled using regional climate models (RCMs), so as to project the output of a GCM to a finer resolution (50 km?×?50 km). In the present work, the projections of a GCM for two scenarios, A2 and B2 are downscaled by a RCM to project future climate in a watershed. Projections for two important climate variables, viz. rainfall and temperature are made. These are then used as inputs for a physically-based hydrological model, SWAT, in order to evaluate the effect of climate change on streamflow and vegetative growth in a humid tropical watershed.

Citation Raneesh, K. Y. & Santosh, G. T. (2011) A study on the impact of climate change on streamflow at the watershed scale in the humid tropics. Hydrol. Sci. J. 56(6), 946–965.  相似文献   

7.
Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.  相似文献   

8.
An ensemble of stochastic daily rainfall projections has been generated for 30 stations across south‐eastern Australia using the downscaling nonhomogeneous hidden Markov model, which was driven by atmospheric predictors from four climate models for three IPCC emissions scenarios (A1B, A2, and B1) and for two periods (2046–2065 and 2081–2100). The results indicate that the annual rainfall is projected to decrease for both periods for all scenarios and climate models, with the exception of a few scenarios of no statistically significant changes. However, there is a seasonal difference: two downscaled GCMs consistently project a decline of summer rainfall, and two an increase. In contrast, all four downscaled GCMs show a decrease of winter rainfall. Because winter rainfall accounts for two‐thirds of the annual rainfall and produces the majority of streamflow for this region, this decrease in winter rainfall would cause additional water availability concerns in the southern Murray–Darling basin, given that water shortage is already a critical problem in the region. In addition, the annual maximum daily rainfall is projected to intensify in the future, particularly by the end of the 21st century; the maximum length of consecutive dry days is projected to increase, and correspondingly, the maximum length of consecutive wet days is projected to decrease. These changes in daily sequencing, combined with fewer events of reduced amount, could lead to drier catchment soil profiles and further reduce runoff potential and, hence, also have streamflow and water availability implications. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

9.
Abstract

A real-time operational methodology has been developed for multipurpose reservoir operation for irrigation and hydropower generation with application to the Bhadra reservoir system in the state of Karnataka, India. The methodology consists of three phases of computer modelling. In the first phase, the optimal release policy for a given initial storage and inflow is determined using a stochastic dynamic programming (SDP) model. Streamflow forecasting using an adaptive AutoRegressive Integrated Moving Average (ARIMA) model constitutes the second phase. A real-time simulation model is developed in the third phase using the forecast inflows of phase 2 and the operating policy of phase 1. A comparison of the optimal monthly real-time operation with the historical operation demonstrates the relevance, applicability and the relative advantage of the proposed methodology.  相似文献   

10.
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   

11.
The paper presents the outcomes of a study conducted to analyse water resources availability and demand in the Mahanadi River Basin in India under climate change conditions. Climate change impact analysis was carried out for the years 2000, 2025, 2050, 2075 and 2100, for the months of September and April (representing wet and dry months), at a sub‐catchment level. A physically based distributed hydrologic model (DHM) was used for estimation of the present water availability. For future scenarios under climate change conditions, precipitation output of Canadian Centre for Climate Modelling and Analysis General Circulation Model (CGCM2) was used as the input data for the DHM. The model results show that the highest increase in peak runoff (38%) in the Mahanadi River outlet will occur during September, for the period 2075–2100 and the maximum decrease in average runoff (32·5%) will be in April, for the period 2050–2075. The outcomes indicate that the Mahanadi River Basin is expected to experience progressively increasing intensities of flood in September and drought in April over the considered years. The sectors of domestic, irrigation and industry were considered for water demand estimation. The outcomes of the analysis on present water use indicated a high water abstraction by the irrigation sector. Future water demand shows an increasing trend until 2050, beyond which the demand will decrease owing to the assumed regulation of population explosion. From the simulated future water availability and projected water demand, water stress was computed. Among the six sub‐catchments, the sub‐catchment six shows the peak water demand. This study hence emphasizes on the need for re‐defining water management policies, by incorporating hydrological response of the basin to the long‐term climate change, which will help in developing appropriate flood and drought mitigation measures at the basin level. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

12.
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   

13.
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   

14.
Min Li  Ting Zhang  Ping Feng 《水文研究》2019,33(21):2759-2771
With the intensification of climate change, its impact on runoff variations cannot be ignored. The main purpose of this study is to analyse the nonstationarity of runoff frequency adjusted for future climate change in the Luanhe River basin, China, and quantify the different sources of uncertainties in nonstationary runoff frequency analysis. The advantage of our method is the combination of generalized additive models in location, scale, and shape (GAMLSS) and downscaling models. The nonstationary GAMLSS models were established for the nonstationary frequency analysis of runoff (1961–2010) by using the observed precipitation as a covariate, which is closely related to runoff and contributes significantly to its nonstationarity. To consider the nonstationary effects of future climate change on future runoff variations, the downscaled precipitation series in the future (2011–2080) from the general circulation models (GCMs) were substituted into the selected nonstationary model to calculate the statistical parameters and runoff frequency in the future. A variance decomposition method was applied to quantify the impacts of different sources of uncertainty on the nonstationary runoff frequency analysis. The results show that the impacts of uncertainty in the GCMs, scenarios, and statistical parameters of the GAMLSS model increase with increasing runoff magnitude. In addition, GCMs and GAMLSS model parameters have the main impacts on runoff uncertainty, accounting for 14% and 83% of the total uncertainty sources, respectively. Conversely, the interactions and scenarios make limited contributions, accounting for 2% and 1%, respectively. Further analysis shows that the sources of uncertainty in the statistical parameters of the nonstationary model mainly result from the fluctuations in the precipitation sequence. This result indicates the necessity of considering the precipitation sequence as a covariate for runoff frequency analysis in the future.  相似文献   

15.
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   

16.
This paper presents optimization and uncertainty analysis of operation policies for Hirakud reservoir system in Orissa state, India. The Hirakud reservoir project serves multiple purposes such as flood control, irrigation and power generation in that order of priority. A 10-daily reservoir operation model is formulated to maximize annual hydropower production subjected to satisfying flood control restrictions, irrigation requirements, and various other physical and technical constraints. The reservoir operational model is solved by using elitist-mutated particle swarm optimization (EMPSO) method, and the uncertainty in release decisions and end-storages are analyzed. On comparing the annual hydropower production obtained by EMPSO method with historical annual hydropower, it is found that there is a greater chance of improving the system performance by optimally operating the reservoir system. The analysis also reveals that the inflow into reservoir is highly uncertain variable, which significantly influences the operational decisions for reservoir system. Hence, in order to account uncertainty in inflow, the reservoir operation model is solved for different exceedance probabilities of inflows. The uncertainty in inflows is represented through probability distributions such as normal, lognormal, exponential and generalized extreme value distributions; and the best fit model is selected to obtain inflows for different exceedance probabilities. Then the reservoir operation model is solved using EMPSO method to arrive at suitable operational policies corresponding to various inflow scenarios. The results show that the amount of annual hydropower generated decreases as the value of inflow exceedance probability increases. The obtained operational polices provides confidence in release decisions, therefore these could be useful for reservoir operation.  相似文献   

17.
Climatic and hydrological changes will likely be intensified in the Upper Blue Nile (UBN) basin by the effects of global warming. The extent of such effects for representative concentration pathways (RCP) climate scenarios is unknown. We evaluated projected changes in rainfall and evapotranspiration and related impacts on water availability in the UBN under the RCP4.5 scenario. We used dynamically downscaled outputs from six global circulation models (GCMs) with unprecedented spatial resolution for the UBN. Systematic errors of these outputs were corrected and followed by runoff modelling by the HBV (Hydrologiska ByrånsVattenbalansavdelning) model, which was successfully validated for 17 catchments. Results show that the UBN annual rainfall amount will change by ?2.8 to 2.7% with a likely increase in annual potential evapotranspiration (in 2041–2070) for the RCP4.5 scenario. These changes are season dependent and will result in a likely decline in streamflow and an increase in soil moisture deficit in the basin.  相似文献   

18.
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19.
The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.  相似文献   

20.
Qingjiang River, the second largest tributary of the Yangtze River in Hubei Province, has taken on the important tasks for power generation and flood control in Hubei Province. The Qingjiang River watershed has a subtropical monsoon climate and, as a result, has dramatic diversity in its water resources. Recently, global warming and climate change have seriously affected the Qingjiang watershed’s integrated water resources management. In this article, general circulation model (GCM) and watershed hydrological models were applied to analyze the impacts of climate change on future runoff of Qingjiang Watershed. To couple the scale difference between GCM and watershed hydrological models, a statistical downscaling method based on the smooth support vector machine was used to downscale the GCM’s large-scale output. With the downscaled precipitation and evaporation, the Xin-anjiang hydrological model and HBV model were applied to predict the future runoff of Qingjiang Watershed under A2 and B2 scenarios. The preformance of the one-way coupling approach in simulating the hydrological impact of climate change in the Qingjiang watershed is evaluated, and the change trend of the future runoff of Qingjiang Watershed under the impacts of climate change is presented and discussed.  相似文献   

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