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1.
Relatively few studies have addressed water management and adaptation measures in the face of changing water balances due to climate change. The current work studies climate change impact on a multipurpose reservoir performance and derives adaptive policies for possible future scenarios. The method developed in this work is illustrated with a case study of Hirakud reservoir on the Mahanadi river in Orissa, India, which is a multipurpose reservoir serving flood control, irrigation and power generation. Climate change effects on annual hydropower generation and four performance indices (reliability with respect to three reservoir functions, viz. hydropower, irrigation and flood control, resiliency, vulnerability and deficit ratio with respect to hydropower) are studied. Outputs from three general circulation models (GCMs) for three scenarios each are downscaled to monsoon streamflow in the Mahanadi river for two future time slices, 2045–65 and 2075–95. Increased irrigation demands, rule curves dictated by increased need for flood storage and downscaled projections of streamflow from the ensemble of GCMs and scenarios are used for projecting future hydrologic scenarios. It is seen that hydropower generation and reliability with respect to hydropower and irrigation are likely to show a decrease in future in most scenarios, whereas the deficit ratio and vulnerability are likely to increase as a result of climate change if the standard operating policy (SOP) using current rule curves for flood protection is employed. An optimal monthly operating policy is then derived using stochastic dynamic programming (SDP) as an adaptive policy for mitigating impacts of climate change on reservoir operation. The objective of this policy is to maximize reliabilities with respect to multiple reservoir functions of hydropower, irrigation and flood control. In variations to this adaptive policy, increasingly more weightage is given to the purpose of maximizing reliability with respect to hydropower for two extreme scenarios. It is seen that by marginally sacrificing reliability with respect to irrigation and flood control, hydropower reliability and generation can be increased for future scenarios. This suggests that reservoir rules for flood control may have to be revised in basins where climate change projects an increasing probability of droughts. However, it is also seen that power generation is unable to be restored to current levels, due in part to the large projected increases in irrigation demand. This suggests that future water balance deficits may limit the success of adaptive policy options.  相似文献   

2.
In this study we discuss probabilistic forecasts of Citarum River streamflow, which supplies 80 % of the water demands in Jakarta, Indonesia, based on general circulation model (GCM) output, for the September–November (SON) season. Retrospective forecasts of precipitation made over the period 1982–2010 with two coupled-ocean atmosphere GCMs, initialized in August, are used in conjunction historical streamflow records, with a cross-validated regression model. Pearson’s product moment correlation skill values of 0.58–0.67 are obtained, with relative operating characteristic scores of 0.67–0.84 and 0.74–0.92 for the lower and upper tercile categories of flows respectively. Both GCMs thus demonstrate promising ability to forecast below/above normal streamflow for the Citarum River flow during the SON season.  相似文献   

3.
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   

4.
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   

5.
Water scarcity issues in the Johor River Basin (JRB) could affect the populations of Malaysia and Singapore. This study provides an overview of future hydro-meteorological droughts using climate projections from an ensemble of four Coordinated Regional Climate Downscaling Experiments – Southeast Asia (CORDEX-SEA) domain outputs under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios for the 2021–2050 and 2071–2100 periods. The climate projections were bias corrected using the quantile mapping approach before being incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to examine the meteorological and hydrological droughts, respectively. Overall, future annual precipitation, streamflow, and maximum and minimum temperatures are projected to change by about ?44.2 to 24.3%, ?88.7 to 42.2%, 0.8 to 3.7ºC and 0.7 to 4.7ºC, respectively. The results show that the JRB is likely to receive more frequent meteorological droughts in the future.  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.
Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.  相似文献   

8.
I. W. Jung  D. H. Bae  B. J. Lee 《水文研究》2013,27(7):1033-1045
Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow‐dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain‐dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi‐model projections. Seasonal flow changes were projected using the combination of 13 atmosphere–ocean general circulation model simulations and three semi‐distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

9.
To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non‐parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960–1989 data, we assessed climate forecasts from six GCMs: CGCM1‐A, HadCM3, NCAR‐CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R2 about 0·7), and averaging over three decades closely reconstructed the monthly pattern (R2 = 0·94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (?3%, 2005–2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (?9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about ? 15%) and annual flows (about ? 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

10.
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   

11.
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12.
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   

13.
This study addresses a need to document changes in streamflow and base flow (groundwater discharge to streams) in Hawai‘i during the past century. Statistically significant long‐term (1913–2008) downward trends were detected (using the nonparametric Mann–Kendall test) in low‐streamflow and base‐flow records. These long‐term downward trends are likely related to a statistically significant downward shift around 1943 detected (using the nonparametric Pettitt test) in index records of streamflow and base flow. The downward shift corresponds to a decrease of 22% in median streamflow and a decrease of 23% in median base flow between the periods 1913–1943 and 1943–2008. The shift coincides with other local and regional factors, including a change from a positive to a negative phase in the Pacific Decadal Oscillation, shifts in the direction of the trade winds over Hawai‘i, and a reforestation programme. The detected shift and long‐term trends reflect region‐wide changes in climatic and land‐cover factors. A weak pattern of downward trends in base flows during the period 1943–2008 may indicate a continued decrease in base flows after the 1943 shift. Downward trends were detected more commonly in base‐flow records than in high‐streamflow, peak‐flow, and rainfall records. The decrease in base flow is likely related to a decrease in groundwater storage and recharge and therefore is a valuable indicator of decreasing water availability and watershed vulnerability to hydrologic changes. Whether the downward trends will continue is largely uncertain given the uncertainty in climate‐change projections and watershed responses to changes. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

14.
A number of extensive droughts and destructive floods have occurred in Poland in the last 25 years; hence, projections of low and high river flows are of considerable interest and importance. In the first part of this paper, projections of low and high flows in the rivers of the Vistula and the Odra basins (VOB region), for two future time horizons, are presented. Projections are based on the Soil and Water Assessment Tool (SWAT) hydrological model simulations driven by results of the EURO‐CORDEX experiment under Representative Concentration Pathways 4.5 and 8.5. The VOB region covers most of Poland and parts of five neighboring countries, giving this study an international relevance. In the second part of the paper, a review of projections of low and high flows in rivers in Central and Eastern Europe is presented. Despite a substantial spread of flow projections, the main message of the modelling part is that increases of both low and high flows are dominating. The magnitude of increase of low flow is considerably higher than that of high flow. In other words, future streamflow droughts are projected to be less severe, whereas, in contrast, river floods are projected to increase, which is a challenge for flood risk reduction, water management, and climate change adaptation. There is an overall agreement of our findings for the VOB region with projections of hydrological extremes from large‐scale models forced by EURO‐CORDEX results in the European‐scale studies.  相似文献   

15.
We propose a novel technique for improving a long‐term multi‐step‐ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks‐based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

16.
Currently, an operational strategy for the maintenance of reservoirs is an important issue because of the reduction of reservoir storage from sedimentation. However, relatively few studies have addressed the reliability analysis including uncertainty on the decrease of the reservoir storage by the sedimentation. Therefore, it is necessary that the reduction of the reservoir storage by the sedimentation should be assessed by a probabilistic viewpoint because the natural uncertainty is embedded in the process of the sedimentation. The objective of this study is to advance the maintenance procedures, especially the assessment of future reservoir storage, using the time-dependent reliability analysis with the Bayesian approach. The stochastic gamma process is applied to estimate the reduction of the Soyang dam reservoir storage in South Korea. In estimating the parameters of the stochastic gamma process, the Bayesian Markov chain Monte Carlo (MCMC) scheme using the informative prior distribution through the empirical Bayes method is applied. The Metropolis–Hastings algorithm is constructed and its convergence is checked by the various diagnostics. The range of the expected life time of the Soyang dam reservoir by the Bayesian MCMC is estimated from 111 to 172 years at a 5 % significance level. Finally, it is suggested that improving the assessment strategy in this study can provide valuable information to the decision makers who are in charge of the maintenance of a reservoir or a dam.  相似文献   

17.
Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.  相似文献   

18.
Employing long‐range correlation, complexity features and clustering, this study investigated the influence of dam and lake‐river systems on the Yangtze River flow. The impact of the Gezhouba Dam and the lake systems on streamflow was evaluated by analysing daily streamflow records at the Cuntan, the Yichang and the Datong station. Results indicated no evident influence of the Gezhouba Dam on streamflow changes. Distinct differences in scaling behaviour, long‐range correlation and clustering of streamflow at the Datong station when compared with those at the Cuntan and Yichang stations undoubtedly showed the influence of water storage and the buffering effect of the lake systems between the Datong station and other two hydrological stations on streamflow in the lower Yangtze River basin. Decreased regularity, enhanced long‐range correlation and increased clustering of streamflow in the lower Yangtze River basin due to the effect of water storage of the lake systems were corroborated. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

19.
This paper describes a methodology, based on dynamical systems theory, to model and predict streamflow at the daily scale. The model is constructed by developing a multidimensional phase-space map from observed streamflow signals. Predictions are made by examining trajectories on the reconstructed phase space. Prediction accuracy is used as a diagnostic tool to characterize the nature, which ranges from low-order deterministic to stochastic, of streamflow signals. To demonstrate the utility of this diagnostic tool, the proposed method is first applied to a time series with known characteristics. The paper shows that the proposed phase-space model can be used to make a tentative distinction between a noisy signal and a deterministic chaotic signal.The proposed phase-space model is then applied to daily streamflow records for 28 selected stations from the Continental United States covering basin areas between 31 and 35 079 km2. Based on the analyses of these 28 streamflow time series and 13 artificially generated signals with known characteristics, no direct relationship between the nature of underling stream flow characteristics and basin area has been found. In addition, there does not appear to be any physical threshold (in terms of basin area, average flow rate and yield) that controls the change in streamflow dynamics at the daily scale. These results suggest that the daily streamflow signals span a wide dynamical range between deterministic chaos and periodic signal contaminated with additive noise.  相似文献   

20.
This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

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