Search

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs.

Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (

Food security is important for human well-being worldwide. However, changing climate, population growth and shrinking land resources are threatening food security in many regions of the world. Jiangsu Province, China, is one such region. It is a major food-producing region of the country but is witnessing rapid population growth and urbanization that is putting pressure on agricultural water and land resources and threatening food security of the region. This paper interprets the nexus between regional water availability and food security in Jiangsu Province under different climate change and socio-economic scenarios of population growth and land resource availability. Climate change scenarios are generated based on historical data and Global Climate Model (GCM) products. Socio-economic scenarios are generated based on population growth and crop planted area projections. The uptake of water and nutrients are considered as two dominant biophysical processes of crop growth and food production. Complementing it is human agency, including human labor, irrigation and land-preparation machinery, which are the factors behind water and nutrient use efficiencies of crops grown. Two dominant crops are considered, rice and wheat, that contribute to 61.4% of total crops produced in the province. Results show that adaptation by human agency is necessary to ensure that food supply meets at least the demand of the province under all climate change and socio-economic scenarios. Under relatively favorable scenarios, labor could replace land-preparing machinery since the level of food production can be easily maintained with abundant water and land availability. Mechanization in agricultural production significantly increases food production under unfavorable conditions, since it improves water and nutrient use efficiencies and leads to higher crop yields. This demonstrates that human agency plays an important role in securing food under stressful scenarios of drier climate, population growth, and contraction of agricultural lands.

Vietnam is a country with diverse terrain and climatic conditions and a dependency on hydropower for a significant proportion of its power needs and as such, is particularly vulnerable to changes in climate. In this paper we apply SWAT (Soil and Water Assessment Tool) derived discharge simulation results coupled with regression analysis to estimate the performance of hydropower plants for Vietnam between 1995 and mid-2014 when both power supply and demand increased rapidly. Our approach is to examine the watershed formed from three large inter-boundary basins: The Red River, the Vietnam Coast and the Lower Mekong River, which have a total area of 977,964 km2. We then divide this area into 7,887 sub-basins with an average area of 131.6 km2 (based on level 12 of HydroSHEDS/HydroBASINS datasets) and 53,024 Hydrological Response Units (HRUs). Next we simulate river flow for the 40 largest hydropower plants across Vietnam. Our validation process demonstrates that the simulated flows are significantly correlated with the gauged inflows into these dams and are able to serve as a good proxy for the inflows into hydropower dams in our baseline energy regression, which captures 87.7% of the variation in monthly power generation. In other results we estimate that large dams sacrifice on average around 18.2% of their contemporaneous production for the purpose of flood control. When we assess Vietnam’s current alignment of dams we find that the current cascades of large hydropower dams appear to be reasonably efficient: each MWh/day increase in upstream generation adds 0.146 MWh/day to downstream generation. The study provides evidence for the multiple benefits of a national system of large hydropower dams using a cascade design. Such a system may help overcome future adverse impacts from changes in climate conditions. However, our results show that there is still room for improvement in the harmonization of cascades in some basins. Finally, possible adverse hydro-ecological impacts due to the proliferation of large upstream dams, including those located beyond Vietnam’s border, need to be carefully considered.

It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management.

While this version is embargoed, a copy is freely available in the ArXiv repository in the following link:https://arxiv.org/abs/2202.04444 This paper investigates the impacts of climate change on groundwater droughts making use of regional projections and standardized indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Groundwater Index (SGI). The method adopted, using historical precipitation and temperature data and water levels collected in monitoring wells, first investigates the possible correlations between meteorological and groundwater indices at each well. Then, if there is a correlation, a linear regression analysis is used to model the relationships between SGIs and SPIs, and SGIs and SPEIs. The same relationships are used to infer future SGIs from SPI and SPEI projections obtained by means of an ensemble of Regional Climate Models (RCMs), under different climate scenarios (RCP 4.5 and RCP 8.5). This methodology has been applied to data collected in northern Tuscany (Italy) in an area served by a water company, where historical series of daily climate variables (since 1934) and daily records for 16 wells, covering the period 2005–2020, are available. The impacts on groundwater have been computed in the short- (2006–2035), medium- (2036–2065) and long-term (2066–2095). The analysis indicates that, in the historical period and for most of the monitoring wells, there is a good correlation between SGIs and SPIs or SPEIs. The results point out that making use of the SGI-SPI relationships, slight variations in the availability of groundwater are expected in the future. However, in a global warming scenario, the influence of temperature on evapotranspiration phenomena cannot be overlooked and, for this reason, the SGI-SPEI relationships seem more suitable to forecast groundwater droughts. According to these relationships, negative effects on groundwater levels in almost all wells are estimated for the future. For the RCP 4.5 scenario, the largest decline in groundwater level is expected in the medium-term, while for the RCP 8.5 scenario future SGIs will significantly decrease over the long-term. Due to the type of data required and its simplicity, this methodology can be applied to different areas of interest for a quick estimate of groundwater availability under climate change scenarios.&nbsp

Pakistan relies greatly on the water of the Upper Indus basin (UIB) for its vast agriculture in the Indus Basin Irrigation System (IBIS). There are already concerns about the extent of unsustainable water use in the IBIS, particularly of groundwater, and these concerns are likely to increase in the future due to population increase and associated increased demand for food. In addition, climate change, continuing reservoir sedimentation in the major dams, and other impacts on water supply and demand are likely to have an impact on the practical implementation of the landmark inter-provincial Water Apportionment Accord (the Accord), which governs surface water allocation in the IBIS. There is, therefore, a need to assess the sustainability of irrigation in light of the potential variations of water availability in the basin under a series of plausible climate change and reservoir sedimentation scenarios. In this paper, we examine the impact of continuing reservoir sedimentation and climate change by 2050 on the water balance of canal commands in the IBIS. Firstly, we used a river system model, the first to include water management rules as in the Accord, to propagate climate induced changes on flows in the UIB and their impact on IBIS flow regulation and canal deliveries (Icw), with and without reservoir sedimentation. Secondly, we used a selection of General Circulation Models (GCMs) that showed skill at simulating the local climate of the IBIS to assess the projected changes to rainfall (Pn) and evapotranspiration (ETa). The analysis revealed that water balances in IBIS canal command areas are more significantly affected by changed inflows from the UIB than by localised climate induced changes (i.e. Pn and ETa). Continuing reservoir sedimentation in the dams to 2050 will diminish the capacity of the system to store flows during the wet summer Kharif season for its later release in the dry winter Rabi season. This will decrease the supply of Icw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median Icw, equivalent to 1.89 km3), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median Pn, equivalent to 0.13 km3). Sedimentation combined with climate change decreases the median Icw by 51 mm (20%, equivalent to 2.91 km3) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ETa in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of Icw in Rabi and increasing it in Kharif. Concurrently, declining Icw could lead to a substantial reduction in crop areas in the Rabi season. To avoid this undesirable scenario, Pakistan needs to explore both demand and supply management options to achieve food and economic security, as well as controlling sedimentation rates in reservoirs.

This paper describes a scenario-based approach for evaluating the cascading impacts of sea level rise (SLR) and coastal flooding on emergency responses. The analysis is applied to Lower Manhattan, New York City, considering FEMA’s 100- and 500-year flood scenarios and New York City Panel on Climate Change (NPCC2)’s high-end SLR projections for the 2050s and 2080s, using the current situation as the baseline scenario. Service areas for different response timeframes (3-, 5- and 8-min) and various traffic conditions are simulated for three major emergency responders (i.e. New York Police Department (NYPD), Fire Department, New York (FDNY) and Emergency Medical Service (EMS)) under normal and flood scenarios. The modelling suggests that coastal flooding together with SLR could result in proportionate but non-linear impacts on emergency services at the city scale, and the performance of operational responses is largely determined by the positioning of emergency facilities and the functioning of traffic networks. Overall, emergency service accessibility to the city is primarily determined by traffic flow speed. However, the situation is expected to be further aggravated during coastal flooding, with is set to increase in frequency and magnitude due to SLR.

The Wenchuan earthquake caused rapid and gradual changes to the local geomorphology in the earthquake-stricken area. Extreme rainfall events can have strong impacts on local geomorphic evolution and alter the risks of subsequent geohazards and flooding. However, there is still lack of understanding of long-term geomorphic and fluvial evolution in face of changing future climate. In this paper, we first perform the future extreme rainfall projection and then analyze the landscape evolution variation under future climate change with the CAESAR-Lisflood model. A new approach to applying the ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset at the local scale is presented, and a future landscape evolution analysis is conducted. We used spatial (tailoring process) and statistical temporal downscaling methods with NEX-GDDP data to project future extreme rainfall in combination with the CAESAR-Lisflood model to simulate the landscape evolution in response to climate change. The results showed that the future rainfall projection model can provide the most robust and effective representative of future climate change scenarios at the local scale, the geological and geomorphological environment of the basin continues to change dynamically after the earthquake, and nearly 27 years (~2035) are needed before the geomorphology of the basin stabilizes, and then, the yearly yield remains at an average rate of 1 × 104 m3. Post-earthquake mountainous area response and recovery characteristics to a changing future climate can provide important information on future geohazard projections and measures to be taken.

A water supply system can be impacted by rainfall reduction due to climate change, thereby reducing its supply potential. This highlights the need to understand the system resilience, which refers to the ability to maintain service under various pressures (or disruptions). Currently, the concept of resilience has not yet been widely applied in managing water supply systems. This paper proposed three technically robust indictors to assess the resilience of a water supply system. A case study analysis was undertaken of the Water Grid system of Queensland State, Australia, to showcase how the proposed indicators can be applied to assess resilience. The research outcomes confirmed that the use of resilience indicators is capable of identifying critical conditions in relation to the water supply system operation, such as the maximum allowable rainfall reduction for the system to maintain its operation without failure. Additionally, resilience indicators also provided useful insight regarding the sensitivity of the water supply system to a changing rainfall pattern in the context of climate change, which represents the system’s stability when experiencing pressure. The study will help in the quantitative assessment of resilience and provide improved guidance to system operators to enhance the efficiency and reliability of a water supply system.

Policymakers in arid and semiarid basins face hard choices on water policies needed for adaptation to climate change. Hydro-economic modeling is a state-of-the art approach that can be used to guide the design and implementation of these policies in basins. A major gap in developments of hydro-economic modeling to date has been the weak integration of physically-based representations of water sources and uses such as the interaction between ground and surface water resources, to inform complex basin scale policy choices. This paper presents an integrated hydro-economic modeling framework to address this gap with application to an important and complex river basin in Spain, the Jucar basin, for the assessment of a range of climate change scenarios and policy choices. Results indicate that in absence of adequate policies protecting water resources and natural ecosystems, water users will strategically deplete reservoirs, aquifers and river flows for short-term adaptation to climate change, disregarding the impacts on the environment and future human activities. These impacts can be addressed by implementing sustainable management policies. However, these policies could have disproportionate costs for some stakeholders groups, and their opposition may undermine attempts at sustainable policy. These tradeoffs among water policy choices are important guides to the design of policies aimed at basin-wide adaptation to climate change.

Summary Urban pluvial flood are attracting growing public concern due to rising intense precipitation and increasing consequences. Accurate risk assessment is critical to an efficient urban pluvial flood management, particularly in transportation sector. This paper describes an integrated methodology, which initially makes use of high resolution 2D inundation modeling and flood depth-dependent measure to evaluate the potential impact and risk of pluvial flash flood on road network in the city center of Shanghai, China. Intensity–Duration–Frequency relationships of Shanghai rainstorm and Chicago Design Storm are combined to generate ensemble rainfall scenarios. A hydrodynamic model (FloodMap-HydroInundation2D) is used to simulate overland flow and flood inundation for each scenario. Furthermore, road impact and risk assessment are respectively conducted by a new proposed algorithm and proxy. Results suggest that the flood response is a function of spatio-temporal distribution of precipitation and local characteristics (i.e. drainage and topography), and pluvial flash flood is found to lead to proportionate but nonlinear impact on intra-urban road inundation risk. The approach tested here would provide more detailed flood information for smart management of urban street network and may be applied to other big cities where road flood risk is evolving in the context of climate change and urbanization.

Summary Transboundary data sharing is widely recognised as a necessary element in the successful handling of water-related climate change issues, as it is a means towards integrated water resources management (IWRM). However, in practice it is often a challenge to achieve it. The Mekong River Commission (MRC), an inter-governmental agency established by Cambodia, Lao PDR, Thailand and Vietnam, has adopted IWRM in its water strategy plan in order to properly manage the transboundary waters of the Mekong River. In this context, data sharing procedures were institutionalised and have been officially implemented by the four member countries since 2001. This paper uses a systematic approach to identify the extent of data sharing and the factors influencing the willingness of key individuals in the Vietnam National Mekong Committee and its Primary Custodians to share data. We find that the initial objectives of the Procedures for Data and Information Exchange and Sharing (PDIES) have not been fully achieved and, further, that Vietnam has much to gain and little to lose by engaging in data sharing in the MRC context. The primary motivation for data sharing stems from the desire to protect national benefits and to prevent upstream countries from overexploiting the shared water resources. However, data sharing is hindered by a lack of national regulations in the Vietnam context concerning data sharing between state agencies and outdated information management systems.

Summary Management of water resources in Texas (United States) is a challenging endeavor due to rapid population growth in the recent past coupled with significant spatiotemporal variations in climate. While climate conditions impact the availability of water, over-usage and lack of efficient management further complicate the dynamics of supply availability. In this paper, we provide the first look at the impact of climate change projections from an ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) on 21st century drought characteristics under three future emission trajectories: Representative Concentration Pathway (RCP) 2.6, RCP 4.5 and RCP 8.5, using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). In addition, we evaluate the performance of the ensemble in simulating historical (1950–1999) observations from multiple climate divisions in Texas. Overall, the ensemble performs better in simulating historical temperature than precipitation. In semi-arid locations such as El Paso and Laredo, decreasing precipitation trends are projected even under the influence of climate policies represented by the RCP 4.5. There is little variability in the SPI across climate divisions and across RCPs. The SPEI, on the other hand, generally shows a decreasing trend toward the latter half of the 21st century, with multi-year droughts becoming the norm under the RCP 8.5, particularly in regions that are already dry, such as El Paso. Less severe droughts are projected for the sub-humid eastern edge of the state. Considering that state water planning agencies are already forecasting increased water shortages over the next 50 years, we recommend proactive approaches to risk management such as adjusting the planning tools for potential recurrence of multi-year droughts in regions that are already water-stressed.

In this paper, we present a comparative study of bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations/models. In traditional bias correction schemes, the statistics of the simulated model outputs are adjusted to those of the observation data. However, the model output and the observation data are only one case (i.e., realization) out of many possibilities, rather than being sampled from the entire population of a certain distribution due to internal climate variability. This issue has not been considered in the bias correction schemes of the existing climate change studies. Here, three approaches are employed to explore this issue, with the intention of providing a practical tool for bias correction of daily rainfall for use in hydrologic models ((1) conventional method, (2) non-informative Bayesian method, and (3) informative Bayesian method using a Weather Generator (WG) data). The results show some plausible uncertainty ranges of precipitation after correcting for the bias of RCM precipitation. The informative Bayesian approach shows a narrower uncertainty range by approximately 25–45% than the non-informative Bayesian method after bias correction for the baseline period. This indicates that the prior distribution derived from WG may assist in reducing the uncertainty associated with parameters. The implications of our results are of great importance in hydrological impact assessments of climate change because they are related to actions for mitigation and adaptation to climate change. Since this is a proof of concept study that mainly illustrates the logic of the analysis for uncertainty-based bias correction, future research exploring the impacts of uncertainty on climate impact assessments and how to utilize uncertainty while planning mitigation and adaptation strategies is still needed.

Reoccurring drought through the early 2000s has caused a serious water scarcity issue in the Colorado River Basin. Previous modeling studies have focused on the impact of climate change without considering the adaptive behaviors of farmers and under-utilized Indian water rights. In this paper, we use a coupled agent-based water resource model (ABM) to investigate how the adaptive decisions of farmers can affect water resource management under both climate change impacts and fully utilized Indian water right conditions. We used five General Circulation Model projections with RCP8.5 scenarios for the study. The results of farm-level decision-making showed different responses in irrigated areas that were changing due to climate change impact. While winter precipitation changes might partially explain the behavior changes, no specific pattern could be concluded based on their location. Also, farmers’ responses about annual water diversion showed more significant inter-year variation compared to irrigated areas. Basin-level metrics showed that climate change impacts will generally worsen water scarcity issues as measured in Navajo Reservoir storage, flow to Lake Powell, and instream flow requirement. But these basin-level water scarcity metrics cannot reflect individual farm-level impacts under climate change, which is why modeling the bottom-up management actions is necessary. When the under-utilized Indian water rights are fully used, it is more likely to trigger the shortage sharing agreement due to the higher tribal water depletion. Evaluation of model uncertainty and a more realistic setup for adaptive actions under drought contingency plans are suggested for future research.

This study develops a new method for selecting the most representative climate model for use in the Soil and Water Assessment Tool (SWAT) and evaluates this method in the Maumee River Basin. Downscaled temperature and precipitation data from 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) models were used to drive the SWAT. Our model selection approach uses a sensitivity analysis to evaluate the impacts of temperature and precipitation on streamflow and to determine the optimal weights for both variables. This weighting scheme is then used to rank the performance of all climate models and identify which ones are most representative of climate forcing required by the hydrological model. The results of our sensitivity analysis indicate that the response of streamflow to precipitation is consistent throughout the year, while the response to temperature is more complex. During the warm season, higher temperatures cause evapotranspiration to increase and streamflow to decrease; during the cool season, higher temperatures cause more snowmelt and this causes streamflow to increase. Based on our weighting scheme, the GISS-E2-R was identified as the most representative climate model in the Maumee River Basin. We then compared the performance of SWAT-simulated streamflow using all CMIP5 models. The most representative climate model ranked first based on correlation and mean absolute error, and second based on Nash-Sutcliffe efficiency, while the model selected using a simple average method was ranked fifth, fourth and twelfth, respectively. Based on the evaluation of other key variables, such as sediment transport, nitrogen and phosphorus, the optimal climate model also has a higher correlation and a more representative standard deviation than the other CMIP5 models. The selected model also performs better than the 32-model ensemble mean based on correlation and Taylor’s skill score. When applying this method at different time scales, the responding time of streamflow to climate factors needs to be considered. The model selection method that was developed in this paper can be applied by hydrological modelers to select the most representative climate models for evaluating the impacts of climate change in other watersheds around the world.

Summary Determining evaporation rates is essential for efficient management of reservoirs and water resources, particularly in water-scarce countries such as Australia. Today, it is estimated that open water reservoirs in Australia lose around 40% of their total water storage capacity per year to evaporation. While this loss is of significant concern, the threat of a changing climate has been directing greater focus to how much water will be lost from Australia’s reservoirs in the future. This paper analyses evaporation rates from a large water supply reservoir in South-East Queensland (SEQ), Australia, under current climate and predicted climate change conditions using modelling. Daily meteorological projections from nine global climate models were used in the model DYRESM as the driving forces of the thermodynamics of the reservoir under study. Two future 20-year period simulations were undertaken, one from 2030 to 2050, and the other from 2070 to 2090. The modelled future evaporation rates, as well as water temperatures, were then compared with modelled evaporation rates and temperatures obtained using observed meteorological variables for the period of 1990–2010. The results showed that the evaporation rates from the study reservoir will increase in the future. For the period centred in 2040, the annual evaporation will be approximately 8% higher than the 20-year average annual evaporation estimated for the present climate. A more pronounced increase in evaporation is expected in 2070–2090, with annual evaporation predictions being approximately 15% higher than the baseline evaporation. The main agent behind this increase is higher surface air temperatures in the future. According to the modelling results, the mean annual surface air temperature will grow from the present value of 20.4 °C to 21.5 °C in 2030–2050, and to 23.2 °C in 2070–2090. As a consequence, the mean annual surface water temperatures of the reservoir will increase by 0.9 °C and 1.7 °C in both timeframes, respectively. This will have a significant impact on the evaporation rates, particularly in spring and summer, when the temperature increases will be more significant.

The focus of this paper is to understand the multi-decadal oscillatory component of variability in the Okavango River system, in southwestern Africa, and its potential evolution through the 21st century under climate change scenarios. Statistical analyses and hydrological modelling are used to show that the observed multi-decadal wet and dry phases in the Okavango River and Delta result from multi-decadal oscillations in rainfall, which are likely to be related to processes of internal variability in the climate system, rather than external natural or anthropogenic forcing. Analyses of changes in this aspect of variability under projected climate change scenarios are based on data from a multi-model ensemble of 19 General Circulation Models, which are used to drive hydrological models of the Okavango River and Delta. Projections for the 21st century indicate a progressive shift towards drier conditions attributed to the influence of increasing temperatures on water balance. It is, however, highly likely that multi-decadal oscillations, possibly of similar magnitude to that of 20th century, will be superimposed on the overall trend. These may periodically offset or amplify the mean drying trend. This effect should be accounted for in water and catchment management and climate change adaptation strategies.

A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.

Summary This paper assesses the future variability of water resources in the short, medium and long terms over a large Sudano-Sahelian catchment in West Africa. Flow simulations were performed with a daily conceptual model. A period of nearly 50 years (1952–2000) was chosen to capture long-term hydro-climatic variability. Calibration and validation were performed on the basis of a multi-objective function that aggregates a variety of goodness-of-fit indices. The climate models HadCM3 and MPI-M under SRES-A2 were used to provide future climate scenarios over the catchment. Outputs from these models were used to generate daily rainfall and temperature series for the 21st century according to: (i) the unbias and delta methods application and (ii) spatial and temporal downscaling. A temperature-based formula was used to calculate present and future potential evapotranspiration (PET). The daily rainfall and PET series were introduced into the calibrated and validated hydrological model to simulate future discharge. The model correctly reproduces the observed discharge at the basin outlet. The Nash-Sutcliffe efficiency criterion is over 89% for both calibration and validation periods, and the volume error between simulation and observation is close to null for the overall considered period. With regard to future climate, the results show clear trends of reduced rainfall over the catchment. This rainfall deficit, together with a continuing increase in potential evapotranspiration, suggests that runoff from the basin could be substantially reduced, especially in the long term (60–65%), compared to the 1961–1990 reference period. As a result, the long-term hydrological simulations show that the catchment discharge could decrease to the same levels as those observed during the severe drought of the 1980s.

Public water supply systems are designed to maintain water supply through extended periods of dry weather without excessive cost or environmental damage. During a drought, water suppliers can take further measures to enhance supplies or reduce demand. The introduction of drought measures is usually formalised in a drought plan, but there is often little evidence that the plan will prove successful during a range of feasible droughts. As the climate changes, recent hydrological data may be a poor guide to future drought, and planned actions may prove insufficient to maintain adequate water supplies. This paper describes a method for testing the resilience of water company drought plans to droughts that are outside recent hydrological experience. Long severe droughts of the nineteenth century provide an opportunity to test water supply system behaviour in a range of realistic droughts. The method developed combines system modelling with an interactive approach that asks water system managers to work through the actions that they would take at different stages of the drought, without knowledge of subsequent drought development. The approach was tested for two contrasting English water resource systems. In both cases, the existing water supply and drought planning measures succeeded in maintaining water supply, but significant demand restrictions and engineering measures had to be introduced. Wider use of the method by water supply planners should allow the refinement of drought and water supply plans, and will also create increased awareness of the actions necessary to manage a range of droughts.

Summary This paper presents an analysis on the hydrological regimes from several rivers flowing from the western slope of the Andes Cordillera. We compiled a database of 40 unimpaired average monthly streamflow records located in central and southern Chile, spanning the region between latitudes 30°S and 40°S and the period between years 1961 and 2006. River categories encompass snowmelt, mixed and rainfall dominated watersheds. The water year hydrograph center of timing (CT) is used as an indicator of flow regime and as a proxy for streamflow distribution throughout the year. The CT for each river is correlated to local climatic variables such as seasonal temperature and precipitation, and to large-scale circulation indexes. We assess CT trends using the Mann–Kendall test for varying time windows within the study period. Overall results for the 1961–2006 period show a significant (95% confidence level) negative trend (CT date shifting towards earlier in the year) for 23 out of the 40 analyzed series. However, when studying time windows within the entire period there seems to be a break in the trend during the late 1970s, with positive or null changes for the latter half of the study period for most of the stations analyzed. We found that even though the CT is significantly correlated (r > 0.5) to temperature indexes in the snowfed watersheds, there has been no significant change in CT timing for most of these watersheds despite documented warming on the region. CT for all types of watersheds present clear correlation to precipitation indexes (r > 0.8), and we observed high and significant correlation between CT and ONI, SOI and MEI indexes for all the study region: higher (lower) precipitation amounts during El Nino (La Nina), rather than temperature variations, seem to be the most important factor controlling CT timing during these episodes. In order of importance, the CT showed higher correlation with annual precipitation amounts and timing, MEI, ONI and SOI, mean winter and spring temperature, and finally PDO. The detected trends are consistent only with trends in precipitation, as rivers that showed high correlation to temperature do not show any trend. These conclusions suggest that rivers on the region show a different response to temperature changes when compared to rivers of similar regime in the northern hemisphere, and that precipitation amount and timing are the source of most of the variability in streamflow timing for both snowmelt and rainfall dominated watersheds. Temperature induced variability in streamflow timing should be studied with more precise hydrological models, as the CT has a complex behavior due to multiple climatical dependencies. Studying such behavior using only historical data might be insufficient for drawing more precise conclusions.

Projections of climate for the 2080s from an ensemble of global climate models (GCMs) run under a medium–high (A2) emissions scenario are used to simulate changes in groundwater resources of a Chalk aquifer in central-southern England. Few studies that have investigated the impacts of climate change on groundwater resources have addressed uncertainty. In this paper the uncertainty associated with use of a suite of GCM outputs in catchment scale impact studies is quantified. A range of predictions is obtained by applying precipitation and temperature change factors, derived from thirteen GCMs, to a distributed recharge model and a groundwater flow model of the Chalk aquifer of the Marlborough and Berkshire Downs and south-west Chilterns in the UK. The ensemble average suggests there will be a 4.9% reduction in annual potential groundwater recharge across the study area, although this is not statistically significant at the 95% confidence level. The spread of results for simulated changes in annual potential groundwater recharge range from a 26% decrease to a 31% increase by the 2080s, with ten predicting a decrease and three an increase. Whilst annual recharge is not found to change significantly, the multi-model results suggest that the seasonal variation in the groundwater resource will be greater, with higher recharge rates during a reduced period of time in winter. The spread of predictions for changes in river baseflow, at the bottom of the largest river sub-catchment, is from −16 to +33% in March and from −68 to −56% in October. The effects of climate change are shown to depend significantly on the type of land-use. It is concluded that further research is required to quantify the effect of different vegetation types on Chalk covered by different thicknesses of soil and their response to a changing climate.

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.

Summary Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves excluding ecological requirement are generally derived from observed or synthetic flows and have rarely been determined by future flows under climate change. This paper develops an integrated adaptive optimization model (IAOM) for derivation of multipurpose reservoir operating rule curves including ecological operating rule curve under future climate change. Steps in the proposed IAOM include: (1) weather generator module to generate feasible future climate conditions, (2) VIC model as the hydrological simulation module to generate streamflows from those future weather conditions, and (3) multipurpose reservoir optimization module to determine the optimal reservoir operations to deal with climate change. China’s Danjiangkou reservoir in Han River basin is selected for a case study. The results demonstrate that the IAOM provides optimal multipurpose reservoir operating rule curves that reflect the hydrologic characteristics of future climate change. Ecological supply water operation will alleviate negative effect of dam on river ecosystem without reducing conservation benefits and flood control standard. Therefore, they can consult with reservoir administrators if it is useful results for operations.

SummarySoil moisture is an essential component of water variability and change in the landscape. This paper develops a conceptual and analytical framework for linking hydro-climatic change at the surface and soil-groundwater conditions in the subsurface, and quantifying long-term development of soil moisture statistics in a changing climate. Soil moisture is evaluated both in the unsaturated zone and over a fixed soil depth that may also include a variable groundwater table. Long-term variability and change of soil moisture are assessed for a hydro-climatic observation record that extends over the whole 20th century in a major Swedish drainage basin. Frequencies of particularly dry and wet soil moisture events are investigated for different 20-year climatic periods. Results show major increase in the frequency of dry events from the beginning to the end of the 20th century. This indicates increased risk for hydrological and agricultural drought even though the risk for meteorological drought, in terms of precipitation, has decreased in the region. The developed quantification framework can also be used to screen future scenarios of soil moisture change under projected climate change.