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To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation. [ABSTRACT FROM AUTHOR]

This paper shows how low-impact development (LID) practices affect the resilience of stormwater drainage system under climate change scenarios. A rainfall-runoff model was calibrated and evaluated a in a tropical watershed located in Midwestern Brazil. An ensemble of 17 general circulation model outputs forced by Representative Concentration Pathways (RCP 4.5 and RCP 8.5) was used to create future climate change scenarios up to 2095. The LID efficiency was evaluated based on the runoff peak reduction and the resilience of stormwater drainage by means of a resilience index. Overall, LID combinations showed a reduction in runoff peak higher than 20%, and the best LID combination had a reduction of as much as 46%. This represents a significant improvement in the resilience against flooding in the study area. Therefore, the findings can contribute to the increase in widespread adoption of LID and can encourage decisionmakers to provide such practices for urban flood management. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on the impact of climate change on road infrastructure in South Africa. The approach used built on previous work associated with the UNU-WIDER Development under Climate Change (DUCC) effort, emphasizing understanding of the impact of climate change on roads. This paper illustrates how climate change effects on road infrastructure can be evaluated at a national and provincial level to produce quantitative estimates of climate change from multiple future scenarios. The results of the study indicate that the national-level climate change cost impact in South Africa will vary between US$116.8 million and US$228.7 million annually in the 2050 decade for the median and maximum climate scenarios if a no-adaptation policy action is taken. Conversely, if a proactive adaptation action is taken, these costs can be reduced to US$55.7 million. The savings in costs arising from climate change impacts based on this policy approach is equivalent to building more than 10,000 km of new, secondary paved road by 2050. The analysis focused on the advantage of a proactive policy approach: the upgrade of road infrastructure where climate impacts will exceed current design and maintenance standards. A stressor-response methodology created by the writers is briefly explained, including the application of 54 potential climate futures using general circulation models (GCMs) approved by the Intergovernmental Panel on Climate Change (IPCC). This paper focuses on provincial-level impacts of both a proactive and a reactive approach. Decadal and average annual costs are detailed through 2100. [ABSTRACT FROM AUTHOR]

Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. [ABSTRACT FROM AUTHOR]

Climate change impact reports by the Intergovernmental Panel on Climate Change (IPCC) are well established throughout the world. By now many local, regional, and central authorities are aware of these impacts and they try to take necessary precautions according to their environmental circumstances. Preliminary decisions cannot be taken unless there is a model for future climate change impacts, especially for rainfall occurrence, amount, and frequency estimations. The main purpose is to combine the general circulation (climate) model (GCM) outputs with the local meteorology station records by a suitable downscaling procedure. In this paper, a statistical quadrangle downscaling model (QDM) is developed for rainfall estimations up to 2100. This model helps to downscale the nearby four scenario rainfall amounts at GCM grids to a desired point. Two main steps in the model structure are the use of a regional dependence function (RDF) for transferring the GCM output data from the four nearest grid points to the location. The second step is to adjust the downscaled scenario monthly rainfalls with the local rainfall records. The GCM monthly rainfall outputs are downloaded from the National Center for Atmospheric Research (NCAR) with special report emission scenario A2. The application of the methodology is presented for Riyadh City, Kingdom of Saudi Arabia (KSA). The RDF is obtained from the most reliable meteorology records at 27 locations that are irregularly scattered all over the country. Then it is used for scenario data transfer from the regular grid points. The adjustment is based on the average statistical properties of concurrent monthly rainfall amounts between 1985 and 1996. The trend component in the GCM remains intact. It has been observed that there is a major increasing trend in the monthly rainfalls after 2050. In the meantime, each decade starting from 2011 gives estimates that indicate steady rainfall increases up to 2050. The QDM indicates a 40% increase in the annual total rainfall amounts, with a 15% standard deviation increase. [ABSTRACT FROM AUTHOR]

Irrigation is the major user of total water use in most of the tropical countries located in Southeast Asia. Therefore, knowledge on future changes in irrigation demand in the context of climate change is very important for long-term planning and management of water resources of the region. This paper investigated the changes in irrigation water demand in an intensive irrigated area of Malaysia under climate change scenario. Global circulation models (GCMs) outputs are downscaled using statistical downscaling model (SDSM) to project the future changes in rainfall and temperature at local scale. The irrigation water demand estimation model known as CROPWAT is calibrated and validated with historical data and then used to project the future changes in irrigation demand under SDSM projected climatic condition. The results show that temperature and rainfall in the area will increase by 0.2° C and 4% per decade, respectively over the time period 2020-2099. The changes in climate will reduce irrigation water demand at a rate of 0.9% per decade in the area. However, the irrigation demand will be much more variable in future, and therefore, the major challenge in water resources management will be to handle the uncertainty. It is expected that the seasonal and annual variations in irrigation demand projected by the study will help in operating reservoirs and sustainable management of water resources in the region in the context of climate change. [ABSTRACT FROM AUTHOR]

The potential impact of climate change on the existing drainage infrastructure has been an essential aspect of many hydrological studies. Climate change that will increase the intensity of precipitation will also increase the magnitude of the design discharge and thus would probably result in adverse effects on the existing drainage facilities. This paper aims to evaluate the potential impact of rainfall intensity variation due to climate change on the existing drainage infrastructure by investigating whether (1) the stormwater drainage infrastructure designed for preclimate conditions is able to sustain future higher discharges, and (2) new design guidelines are necessary to be established to include the potential rainfall intensity variation due to climate change. A case study was conducted using 34 years of rainfall data (1980-2013) obtained from the National Climatic Data Center (NCDC). These data and the weather scenario data projected by the four run global circulation model (GCMs) under the three emissions scenarios coupled with stochastic weather generator model which generates dailyweather time series statisticallywere used to update the current intensity-duration-frequency curve to reflect the rainfall intensity variation due to climate change. Furthermore, runoff simulation using a storm and sanitary software program was performed to analyze whether the increase in the intensity of rainfall due to climate changewould have an adverse impact on the current drainage systemto convey excess runoff. The results showed thatmost elements of the current drainage infrastructure in the boundary of the study areawere inadequate to convey excess runoff. However, climate change magnifies the problems that already exist in an aging drainage infrastructure. Furthermore, considering the rehabilitation or replacement rates of these aging infrastructures are relatively slow, emphasismust not lie only on the future climate change, but also on identifying the weak spots in the system coupled with, the economic and environmental factors before any changes in design criteria can be recommended due to climate change. [ABSTRACT FROM AUTHOR]

Future projection of air temperature and precipitation due to climate change plays an important role in shaping the water resources management plans in arid and semi-arid countries such as the United Arab Emirates (UAE). The effect of local topography, land use, and sealand contrasts are generally dwarfed in the coarse global circulation models [ocean atmosphere global climate models (OAGCMs)] results. Statistical downscaling (SD) is a statistical tool relating OAGCM outputs (predictors) to finer local observed weather data (predictands) to improve future predictions. SD is used in this study to downscale the Hadley Model (HadCM3) predictors using local observations at two stations representing the dominating bioclimatic zones in the UAE. The paper proposes a rigorous and systematic methodology to guide the selection of dominant predictors in the projection scenarios. The proposed methodology is applied for the UAE but can be easily adopted for any other area. The calibrated model, used to project future scenarios in the region, revealed a range of increase of the annual mean maximum temperature of 2.79-3.80°C and a range of reduction of annual precipitation of 16.80-37.00% by 2080 at the considered stations. Impacts of climate change on the UAE's water resources management are discussed in light of the downscaled estimates for temperature and precipitation. Because a major portion of the water resources is devoted to agriculture, temperature impacts are quite significant. However, the impacts of precipitation are hardly detectable at the national scale. Instead, the impacts of reduced precipitation will be of more localized significance in mountainous regions that are critically dependent on precipitation for water resources. [ABSTRACT FROM AUTHOR]

This study develops a four-module-based multimodel framework, coupled with the projections of general circulation models (GCMs), a hydrological model (SWAT), and two sets of evaluation indicators [Indicators of Hydrologic Alteration (IHA) and River Regime Index (RRI)], to systematically quantify the response of ecohydrological regimes to climatic change. The Jinsha River Basin (JRB) was selected as a target area for both framework verification and application analysis because of its precious natural conditions, privileged abundant hydropower, and extremely rich fish resources. Results revealed that: (1) temperature and precipitation in the JRB would show increasing trends to varying degrees in the future. The increase in temperature would be much higher than that in precipitation, and the increment would be more significant during the end of the century (2068–2097). (2) The future runoff in the JRB may face an overall decreasing trend, leading to more and more frequent drought disasters. (3) The disturbances in the ecohydrological regime would result in more concentrated runoff and smoother hydrological pulse fluctuations in the JRB, meaning human-assisted participation would be required for the conservation of reproduction and development of ecological resources like Coreius guichenoti. Our findings suggest that effective management of future watershed resources can only be accomplished if an in-depth and comprehensive hydrological evaluation of changing climate is made, and the protection and sustainable development of ecological resources of the JRB in the future will require the participation of stakeholders. Figuring out the alteration in ecohydrological regimes under natural variation and its potential hazards may guide the degree of human participation in futural river protection. [ABSTRACT FROM AUTHOR]

A mathematical model is developed to describe changes in ice volume in the cryosphere. Modeling the cryosphere may be useful in assessing future climate impacts currently captured by global circulation models (GCMs) by providing an opportunity to validate GCMs. Leveraging the dominating effects of freezing and thawing in the cryosphere to simplify relevant heat transport equations allows for the derivation of a mathematical model that can be solved exactly. Such exact solutions are useful in investigating other climatic components that may be similarly analyzed for possible GCM validation. The current trend in GCM advancement is to increase the complexity and sophistication of the various heat transport effects that are represented in the governing mathematical model in cumulative form as the heat forcing function. In this paper, simplified models are developed whose solution can be directly compared with available data forms representing temperature and ice volume during the Pleistocene. With careful integration of the Pleistocene temperature term in the mathematical solution, the well-known cumulative departure method can be resolved from the mathematical solution using a two-term expansion of the corresponding Taylor series. This simplification is shown to be a good approximation of the Pleistocene ice volume for given Pleistocene temperatures. [ABSTRACT FROM AUTHOR]

Enhanced flood hazard due to global warming and climate change imposes an additional threat to safety and serviceability of river crossing bridges. This study proposes an integrated approach to assess how climate change might affect bridge performance under multihazard conditions involving floods and earthquakes. The approach is used for an existing bridge spanning over the San Joaquin River, California. The multihazard impact considering climate change is evaluated in terms of risk and resilience of the bridge. Future flood projections under climate change are obtained from general circulation model simulations in conjunction with a macroscale hydrological model. Enhanced intensities of future design floods are observed to cause higher expected scour at around bridge piers. This resulted in significant rise in risk (−21%) and drop in resilience (−14%) of the bridge, when compared to no climate change scenario, at a specific seismic hazard level. To reduce possible consequences, ripraps are applied around piers as a climate change adaptation measure. Size of the riprap is determined based on the maximum expected design flood flow at the bridge site over the projection period. The applied adaptive measure is observed to be cost-effective through a cost-benefit analysis performed over the remaining bridge service life. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) is of great importance in assessing the potential impacts of climate changes on hydrological cycles and the global energy balance. The spatiotemporal change of ET0 and the drought response over Poyang Lake watershed of China from 2011 to 2100 are the main concern in this work. Based on the meteorological data and the output of the general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we used the Penman–Monteith formula and downscaling model to calculate the history and future ET0 in Poyang Lake watershed, respectively. Major results are drawn as follows. First, the annual average ET0 decreased during 1961–2014 and the average ET0 of the basin is high in the north and south, but low in the middle. The ET0 was most dominated by sunshine duration in the spring, summer, and fall and by relative humidity in the winter. Second, the downscaling model has a good simulation effect, and the GCM data-downscaling simulation results are significantly improved after the deviation correction. Third, under the representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, ET0 in the Poyang Lake watershed will increase over the next three periods, with the middle future (2041–2070) as the largest increase period. The spatial distribution of ET0 is generally high in the east and low in the west. Fourth, under the RCP 8.5 scenario, the drought index (DI) of the watershed showed an increasing trend, the seasonal distribution of DI is fall>summer>spring>winter , and the Ganjiang River subbasin will be the key prevention area for future drought risks. The results can provide basic data support for the optimal management of water resources and scientific response to the impact of climate change on agricultural production in the watershed for associated policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Uncertainty inherent in precipitation predictions from general circulation model (GCMs) may lead urban drainage systems to be underdesigned (or overdesigned) in the future. This issue can be mitigated with the use of risk analysis models. In this study, a decision-making tool, developed based on six models (minimin, minimax, expected value, Hurwicz, Savage, and scenario-based multiobjective robust optimization), was used to select GCM/representative concentration pathways (RCP) scenarios that would lead to robust designs of an urban drainage system located in Fortaleza, Brazil. The implementation costs of the studied drainage system were estimated using runoff derived from rainfall predictions from six GCMs and two RCPs. After applying the proposed decision-making tool, three GCM/RCP scenarios were selected for yielding the most resilient and reliable designs. The range of feasible GCM/RCP scenarios reflects the level of optimism or pessimism held by a decision maker. We strongly recommend that this method be incorporated in urban drainage system design in order to help municipal planners make better decisions in view of climate change uncertainty. [ABSTRACT FROM AUTHOR]

The climate change induced by global warming is predicted to change the rate of sea level rise (SLR) and that of shoreline shifts. In this study, the SLR as well as the resulting retreat of shorelines has been predicted at a series of locations along the Indian coastline to understand the future of India's beaches. The beach segments near 13 major ports where increasing infrastructure requirements are likely to arise in future are considered. The future SLR is predicted on the basis of an ensemble of general circulation models corresponding to stabilized and worst-case global warming scenarios. This showed that the rate of absolute SLR would be in the range 3.38–5.16 mm/year with a mean of 4.56 mm/year for the stabilized scenario called representative concentration pathway (RCP)-4.5 and 5.36–7.2 mm/year with a mean of 6.63 mm/year for the worst-case RCP-8.5 at the selected locations. The maximum rate of SLR could be experienced near the New Mangalore port area while the minimum could be seen near the Chennai and Ennore ports. Additionally, Cochin and Tuticorin ports could also experience the same SLR as the New Mangalore port for the RCP-8.5 scenario. The shoreline recession in response to SLR was evaluated using Bruun's rule. The results of Bruun's rule indicated that the future recession of shorelines at selected coastal stretches might vary between 14.10–29.22 m and 21.05–45.40 m up to the year 2100 for RCP-4.5 and RCP-8.5 respectively. Similarly, by this time, area-wise maximum horizontal inundation could happen near the Mumbai port region, although it is the Port Blair area where the maximum percentage increase in horizontal inundation could be experienced for both the scenarios. In general, as compared to the west coast locations, the east coast sites would undergo smaller area inundation due to relatively lower SLR and resulting shoreline change. [ABSTRACT FROM AUTHOR]

In cold-region environments, ice-jam floods (IJFs) can result in high water levels in rivers to overtop levees, leading to devastating floods. Since climatic conditions play an important role in ice-jam flooding, there is a growing concern among property developers, insurance companies, government agencies, and communities on future IJF probabilities, especially in the context of changing climate. This study presents a stochastic framework for future IJF risk assessment and hazard mapping. Future hydrological conditions derived from a physically based hydrological model (forced with meteorological inputs from the Canadian regional climate model driven by two atmospheric-ocean general circulation climate models) were coupled to a fully dynamic hydraulic river-ice model to evaluate ice-jam scenarios and subsequent backwater-level profiles for the 2041–2070 period along the Athabasca River at Fort McMurray in Alberta, Canada. The modeling results show that future IJF risk will be lower and flood inundations less severe due to projected changes in climate, hydrology, and ice phenology in the 2041–2070 period compared to the baseline period of 1971–2000. However, extreme IJF events are still probable and can flood a considerable area of the town. [ABSTRACT FROM AUTHOR]

Monthly precipitation hindcasts/projections for various climate change scenarios have been available for over a decade. More recently, daily precipitation hindcasts/projections using downscaled bias-corrected constructed analog (BCCA) have garnered wide usage for climate change investigations. Dry bias and drizzle effect reported in previous studies are among known limitations of downscaled BCCA precipitation hindcasts/projections which affect end-user applications. End users also require a reliable representation of precipitation sequence as well as physical consistency of precipitation series for climate change impact assessment applications. This study focuses on the evaluation of the daily sequence of downscaled BCCA precipitation hindcasts. Herein, direct use of BCCA daily precipitation hindcasts are further assessed to determine their suitability to study daily soil moisture dynamics at the field-scale for central Oklahoma climatic conditions. Three daily precipitation data sets were considered: (1) the 1961–1999 BCCA precipitation hindcasts for a 12 km grid in central Oklahoma, (2) the 1961–1999 spatially interpolated daily precipitation data used in the BCCA downscaling procedure, and (3) the 1961–1999 observed daily precipitation observations at the Weatherford COOP Weather Station located within the 12 km BCCA grid. The BCCA daily precipitation hindcasts showed a larger number of rainy days, lower rainfall amounts per rainy day, and longer sequences of consecutive rainy-day clusters than found in observations. These differences were large enough to suggest that BCCA daily precipitation hindcasts may not reflect the characteristics of actual precipitation observations at a point location, i.e., weather station. Additional analyses at Walnut Creek, Iowa and Moorhead, Mississippi confirmed the results are not location specific but numerically induced as a result of the downscaling process. The underlying cause for the noted differences was traced back to the differences in spatial scales of the BCCA outputs and observed daily precipitation at a station. Thus, caution is advised to end users using BCCA daily rainfall hindcasts/projections directly in local and field-scale water investigations, particularly for applications requiring reliable representation of precipitation sequence. Alternatively, a statistical downscaling method based on stochastic weather generation that includes wet-day dry-day transition probabilities would provide the desired temporal disaggregation and sequencing of daily rainfall events. [ABSTRACT FROM AUTHOR]

The objectives of this study are to simulate the Thornthwaite moisture index (TMI) for zones within the Atlantic provinces of Canada (APC) during three 30-year periods in the 21st century and to estimate the interactive effect of TMI and simulated freight traffic loads on the deterioration of pavement structure during the same period. Regional Highways 1, 2, 7, 15, 16, 102, and 104 connecting the APC are considered as the case study. Integration of spatial input-output and transportation models simulates freight movements on the selected regional highways during the period of 2012-2100. TMI is estimated using downscaled average monthly precipitation and temperature at 34 stations within the APC. Simulated traffic loads and TMI are applied to mechanistic modeling of roughness progression on the pavement structure. The findings of this study show that an increase in TMI can cause 11-68% increase of roughness progress rate on pavement structure. [ABSTRACT FROM AUTHOR]

To project the effects of climate-induced change on aquatic environments, it is necessary to know the thermal constraints affecting different fish species and to acquire time series of the current and projected water temperature (WT). A regression between the WT at individual stations and the ambient air temperature (AT) at nearby weather stations could represent the easiest practical method of estimating the WT for an entire region. Assuming that the grid-averaged observations of AT correspond to the AToutput from a general circulation model (GCM) simulation, this study constructed a regression curve between the observations of the local WT and the concurrent GCM-simulated surface AT, minimizing the difference between the time series of the measured and modeled WT, which implicitly includes downscaling to local conditions. The regression model shows excellent performance in capturing the WT trend in response to the AT of the GCM. The projected WT under the global-warming scenario shows a 1.5-2.5°C increase for the period 2080-2100. The maximum/minimum WT shows an amount of change similar to that of the mean values. The results also predict that the WT will increase during most seasons by 2100 but that only minor changes will occur during the summers. [ABSTRACT FROM AUTHOR]

An assessment has been conducted of the potential effect of climate change on runoff in Spain in the 21st century. Runoff depths were calculated with a precipitation runoff model that used as input downscaled global climate model (GCM) outputs. The spatial and temporal resolution of the calculations was and 1 month, respectively. The assessment consisted of comparing runoff values of the baseline period, 1961-1990, with those of three 21st century periods, 2011-2040, 2041-2070, and 2071-2100, all estimated with simulated temperature and precipitation time series. Twelve climate simulations (i.e., combinations of a GCM, a greenhouse gas emissions scenario, and a downscaling algorithm), and whose variability reflects the uncertainty over the future climate, were considered. Based on the results, a decline in runoff is to be expected throughout the country. With respect to the baseline period, and depending on the climate simulation considered, runoff depths are expected to change in the range of to in 2011-2040, to in 2041-2070, and 0 to in 2071-2100. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) use transient climate simulations to predict climate conditions in the future. Coarse-grid resolutions and process uncertainties necessitate the use of downscaling models to simulate precipitation. However, in the downscaling models, with multiple GCMs now available, selecting an atmospheric variable from a particular model which is representative of the ensemble mean becomes an important consideration. The variable convergence score (VCS) provides a simple yet meaningful approach to address this issue, providing a mechanism to evaluate variables against each other with respect to the stability they exhibit in future climate simulations. In this study, VCS methodology is applied to 10 atmospheric variables of particular interest in downscaling precipitation over India and also on a regional basis. The nested bias-correction methodology is used to remove the systematic biases in the GCMs simulations, and a single VCS curve is developed for the entire country. The generated VCS curve is expected to assist in quantifying the variable performance across different GCMs, thus reducing the uncertainty in climate impact-assessment studies. The results indicate higher consistency across GCMs for pressure and temperature, and lower consistency for precipitation and related variables. Regional assessments, while broadly consistent with the overall results, indicate low convergence in atmospheric attributes for the Northeastern parts of India. [ABSTRACT FROM AUTHOR]

Recent climate changes have observable impacts on hydrologic processes and will further affect hydrologic systems in the future. The temperature and precipitation modeled with eight global circulation models (GCMs) (selected from 22 GCMs published in the Fourth Assessment of the Intergovernmental Panel on Climate Change) under three typical emission scenarios entitled A1B, A2, and B1 were evaluated in this study for future projections in the Yangtze River Basin, China. The artificial neural network model was used to assess the evolutional trend of hydrologic processes (e.g., streamflow) and the possibility of extreme floods in the Yangtze River Basin by using data generated by selected GCMs under future climate changes. The results indicate that the future annual streamflow tends to decrease in the Yangtze River Basin. The future average annual flow is reduced by compared with that of the historic record (1951-2005) observed at Yichang Hydrologic Station of the Middle Yangtze River, and the percentage of dry years increases by 46.8%. However, at Datong Hydrologic Station of the Lower Yangtze River, the average annual flow is reduced by compared to the historic record and the percentage of normal year increases up to 75.5%. Extreme floods are divided into three categories of catastrophic, great, and large floods. The results show that large floods are most likely to occur in the future, whereas the likelihood of extreme floods is the minimum under the B1 scenario. In the three decades of the 2020s, 2050s, and 2080s, the likelihood of extreme floods at Yichang and Datong Hydrologic Stations tends to increase; the likelihood of extreme floods at Yichang Hydrologic Station is much greater than that of the Datong Hydrologic Station. Variability in the quantity of water in these areas will pose a greater challenge to the integrated deployment and management of water resources in the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

Global warming and its resulting climate change will affect different elements, such as water resources, in the future. One effect is that rainfall becomes very difficult to predict, as it is under the influence of several different elements. In this study, which considers Shahrekord synoptic station in Iran, various sources of uncertainty in rainfall prediction in the future and its effect on dry and wet spells are investigated. In the present research, CCSIRO, CGCM, ECHO-G, HADCM3, ECHAM, and PCM Atmospheric and Ocean General Circulation Model (AOGCM) models and , , , and emission scenarios under three downscaling methods are examined. The results indicate a significant impact of the various downscaling methods on increasing the uncertainty band in rainfall estimation for the future. The AOGCM models in all of the scenarios except are in agreement. The results of wet and dry spells estimation display a long-duration drought at the beginning of the upcoming 30-year period, followed by a long-duration wet spell. [ABSTRACT FROM AUTHOR]

The joint behavior of drought characteristics under climate change is evaluated using the copula method, which has recently attained popularity in the analysis of complex hydrologic systems with correlated variables. Trivariate copulas are applied, in this study, to analyze the major drought variables, including duration, severity, and intensity, in Oregon's Upper Klamath River Basin. Among the variables, results show that duration severity exhibits the strongest correlation, whereas duration intensity exhibits the least correlation. The impact of climate change on future droughts is evaluated using five general circulation models (GCMs) under one emission scenario. Despite more intense extreme events that are expected to occur in most parts of the globe in the future, the results of this study show that the Upper Klamath River Basin in the Pacific Northwest will experience less intense droughts affected by climate change. Compared with historical events, an overall decrease in drought duration and severity is estimated for this study area in the time period of 2020-2090 with maximum drought duration shown to decline from 8 to 5 months. Among the five GCMs employed in this study, GFDL-CM2.1 and CSIRO-MK3.0 are identified as the wettest and driest projections, respectively. High uncertainty associated with GCM products is demonstrated in the analysis of return period by means of bivariate copulas. However, all projections result in larger return periods (i.e., less frequent droughts) compared with historical droughts during the reference period. [ABSTRACT FROM AUTHOR]

Climate change and its impacts on hydrometeorological variables and surface runoff have been demonstrated in many investigations around the world. General circulation models (GCMs) are widely used in climate change studies; however, their applications are limited because of low resolution for regional investigations. Different downscaling models have been developed to overcome this shortcoming, but most of them cannot preserve the monthly characteristics of rainfall that are mostly important in water resources planning and management. In this study, a self-organizing Gaussian-based downscaling (SOGDS) scheme is proposed to preserve the monthly characteristics of rainfall variations. The Gaussian mixture distribution (GMD) was employed to determine the monthly rainfall class based on observed climatic predictors. The monthly characteristics of rainfall were estimated, including the number of dry days (days without rainfall) and the maximum number of wet and dry spells, by developing an artificial neural network (ANN) model. The output of the ANN model was applied to a probabilistic scheme to develop a daily rainfall time series. Finally, the generated daily rainfall time series was used to evaluate the performance of a drainage system in the northeastern part of the Tehran metropolitan area in Iran, to consider climate change impacts. The results indicated that the proposed model was capable of downscaling rainfall by preserving its statistical characteristics when compared to a statistical downscaling model (SDSM), which was used as an alternative model. The results indicated that climate change will significantly increase the flood volume/risk in the study region, which should be considered in the future planning of drainage systems in the study area. [ABSTRACT FROM AUTHOR]

Studies on the effects of anthropogenic climate change have found that the magnitude and frequency of intense precipitation events are expected to increase over the next century for the midwestern United States. The goal of this study was to use statistically downscaled and debiased precipitation projections for the state of Wisconsin derived from 14 general circulation models (GCMs) to assess the projected precipitation changes for the mid-21st century in a way that is relevant to water-resource decision making. The authors analyzed metrics that are relevant to storm-water design, such as the 100-year, 24-h quantile, and the authors also evaluated the changes in a riskassessment context using idealized damage functions that translate precipitation depths into economic damages. The results of our designmetric analysis show that the 100-year, 24-h quantiles for Wisconsin are projected to have significant but modest increases of approximately 11% over the next 50 years. Our risk assessment shows that the largest percent changes in risk for Wisconsin are projected to be in the northeast portion of the state. Both of these analyses will be used as part of the Wisconsin Initiative on Climate Change Impacts (WICCI) to develop climate-change adaptation strategies for communities throughout the state. [ABSTRACT FROM AUTHOR]

TOPMODEL predictions of surface runoff and subsurface flow are fundamentally developed on the basis of the topographic index distribution (TID). The scale dependency of the TID [i.e., dependency on the resolution of the digital elevation model (DEM) data used to compute the topographic indexes] determines that downscaling of the TID computed from a coarser resolution DEM to a finer resolution is needed before the TOPMODEL concepts can be applied to simulate hydrological processes at some larger scales than the scale of hillslopes. It was found that adjusting only the mean values cannot achieve an accurate downscaling of the TID because the difference between 2-m TIDs and the downscaled TIDs from a coarser resolution to 2 m through adjusting only mean values resulted in overestimation of the fraction of the saturation area and surface runoff under wet conditions and underestimation under dry conditions. It was found that downscaling by correcting for scale-dependencies in the first three moments of TIDs produced better predictions. A series of empirical relationships among mean, standard deviation, and coefficient of skewness of TIDs of nine catchments in eastern Tennessee at resolutions of 2, 10, and 100 m and 205 watersheds across the contiguous United States at resolutions of 10 m and 1 km were developed for downscaling TIDs from 1 km to 2 m through approximating TIDs by a 3-parameter gamma distribution function. The errors in the downscaled TIDs from 1 km to 10 m over 205 watersheds across the contiguous United States decreased with increasing watershed size and approached a minimum (approximately 6%) as the watershed drainage area was larger than approximately 500 km2. With the constructed empirical relationships, topographic indexes computed from 1-km DEM can be scaled down to 2 m for reducing errors and uncertainties in the TOPMODEL-based general circulation model (GCM) hydrological simulations. [ABSTRACT FROM AUTHOR]

Various methods have been proposed to downscale the coarse resolution general circulation model (GCM) climatological variables to the fine-scale regional variables; however, fewer studies have been focused on the selection of GCM predictors. Additionally, the results obtained from one downscaling technique may not be robust and the uncertainties related to the downscaling scheme are not realized. To address these issues, the writers employed independent component analysis (ICA) for predictor selection that determines spatially independent GCM variables. Cross-validation of the independent components is employed to find the predictor combination that describes the regional precipitation over the upper Willamette basin with minimum error. These climate variables, along with the observed precipitation, are used to calibrate three downscaling models: multilinear regression (MLR), support vector machine (SVM), and adaptive-network-based fuzzy inference system (ANFIS). The presented method incorporates several GCM grids in the downscaling process that allows considering more predictors in the model calibration and removes the predictors correlation and dependence by ICA. Also, the study uses several downscaling techniques to develop an ensemble of precipitation time series that can be used in hydrologic climate impact assessment. The performance assessment of the results indicates that the procedure is successful in choosing the predictors for downscaling the GCM data both in monthly and seasonal timescales. The study shows that by choosing proper predictors the MLR model is an efficient method for precipitation downscaling. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods describe a statistical relationship between large-scale atmospheric variables such as temperature, humidity, precipitation, etc., and local-scale meteorological variables like precipitation. This study examines the potential predictor variables selected from the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set for downscaling monthly precipitation in Tahtali watershed in Turkey. An approach based on the assessment of all possible regression types was used to select the predictors among the NCEP reanalysis data set, and artificial neural network (ANN)-based downscaling models were designed separately for each station in the basin. The results of the study showed that precipitation, surface and sea level pressures, air temperatures at surface, 850-, 500-, and 200-hPa pressure levels, and geopotential heights at 850- and 200-hPa pressure levels are the most explanatory NCEP/NCAR parameters for the study area. It was concluded that ANN-based downscaling models can be implemented to downscale coarse-scale atmospheric parameters to monthly precipitation at station scale by using the above parameters as inputs in the study area. [ABSTRACT FROM AUTHOR]

This study presents a methodology for assessing impacts of climate change in regional-scale hydrology using the K-nearest neighbor weather generator (WG) model in combination with the outputs of global circulation models (GCMs) and hydrologic rainfall-runoff model. The Nakdong River Basin in Korea is used as a case study. The study applies a systematic approach to select the variables for the WG model from five meteorological variables available in the basin. In addition, the GCMs' projections based on a B1 emission scenario are incorporated into the proposed WG model to reflect the impacts of climate change, consequently generating the meteorological series for 60 years. Meteorological data for historic and two climate scenarios (dry and wet) are generated. The generated time series of meteorological variables are used with the streamflow synthesis and reservoir regulation rainfall-runoff model to calculate the streamflow at 25 sub-basins. The results demonstrate that the WG models combined with the GCMs' outputs are able to provide future weather conditions for the assessment of climate change impacts. One of the major findings of the study is the potential severity of drought impacts that may increase the historical drought duration in the basin up to three times. [ABSTRACT FROM AUTHOR]

There is a vast amount of research on downscaling and bias-correcting general circulation model (GCM) data to a regional scale, but research is lacking on whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida, region, this case study investigates: (1) whether GCM and the downscaled, bias-corrected data are able to replicate important historical climate states, and (2) if climate state or transition probabilities in raw GCMs are preserved or lost in translation in the corrected downscaled data. This has important implications in understanding the limitations of bias-correction methods and shortcomings of future projection scenarios. Results showed that the GCM and downscaled and bias-corrected data did a poor job in capturing historical climate states for wet or dry states as well as variability in precipitation, including some extremes associated with El Niño events. Furthermore, the corrected products ended up creating different cycles compared to the original GCMs. Because the corrected products did not preserve GCM historical transition probabilities, more than likely, similar types of deviations will occur for "future" predictions, and therefore another correction could be applied if desired to reproduce the degree of spatial persistence of atmospheric features and climatic states that are hydrologically important. [ABSTRACT FROM AUTHOR]

Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area. [ABSTRACT FROM AUTHOR]