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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. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]

Highlights • High resolution catchment scale rainfall predictions were generated from 8 GCMs. • Multiple GCMs is preferable over a best model to account for the future variability. • Simulated climate change has limited impact on pollutant treatment performance. • Larger WSUD systems are recommended to account for the future variability. • Bigger rainwater tank is suggested for same harvesting reliability in the future. Abstract 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 (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability. [ABSTRACT FROM AUTHOR]

Summary: Climate change impact studies are often based on coarse scale projections of general circulation models (GCMs) under greenhouse gases emissions scenarios. Outputs from GCMs have then to be downscaled to obtain information relevant to hydrologic studies. This paper investigates the uncertainty in catchment-scale precipitation scenarios due to (1) the emissions scenario, (2) the configuration of the GCM, and (3) the downscaling method. Two emissions scenarios, six global climate models and four downscaling methods are used to build climate change scenarios for three catchment case studies in the UK. Missing combinations are reconstructed by using a variance decomposition algorithm. The highly increasing variance towards the end of the century is shown to be largely due to the spread of results from different GCMs. Multimodel averaged results at the catchment scale generally show an increase in the amplitude of the annual cycle of precipitation, with wetter winters and drier summers. Hydrologists and water resources planners make use of downscaled climate scenarios, often with little regard for the performance of scenario construction methods, for informing decisions on water resources and flood risk management policies and projects. This paper shows that both the downscaling method and the multimodel building scheme applied have a significant impact on the seasonal precipitation regime that may in turn lead to quite different conclusions in impacts assessments. [Copyright &y& Elsevier]

Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed. [ABSTRACT FROM AUTHOR]

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.’69 to Dec.’94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.’95–Dec.’05) and forecast (Jan.’06–Dec.’35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90–95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season. [ABSTRACT FROM AUTHOR]

• Quantile-quantile rainfall mapping methods are extended to improve streamflow simulations. • Extensions correct rainfall bias at multiple time scales and model rainfall autocorrelation. • Methods reduce bias in rainfall sequencing metrics for downscaled GCM simulations. • Methods reduce bias in streamflow simulations forced by downscaled GCM simulations. Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated. All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall. [ABSTRACT FROM AUTHOR]

Summary Bias correction is considered as a critical post-processing step to remove systematic errors and improve the quality of General Circulation Model (GCM) simulations before their use in climate change impact assessment applications. A majority of the bias correction approaches correct for biases either at a single time scale or at multiple pre-specified time scales. An inappropriate or insufficient selection of time scales may lead to improper or sub-optimal bias corrected outputs, especially when persistence attributes across a range of scales are of interest. In this paper, we present a new bias correction approach that works in the frequency space and is independent of specific time scales. The approach is named as frequency-based bias correction (FBC). The usefulness of the approach is demonstrated by applying it to the monthly rainfall simulations of MIROC5 GCM over Australia and comparing the results with two other approaches, namely, empirical quantile mapping and recursive nesting bias correction, in cross validation. The comparison is based on the reproduction of various observed distribution and persistence attributes. Cross-validation results indicate that the proposed approach shows similar performance in terms of reproducing the first- and second-order moments of observed precipitation time series, however, outperforms with regard to persistence attributes. The approach shows high potential for use in downscaling and other climate change impact assessment studies, especially for the planning and design of hydrologic systems that are sensitive to the characterisation of persistence in the hydrologic time series. [ABSTRACT FROM AUTHOR]

Summary In this paper, an updated global map of the current climate zoning and of its projections, according to the Köppen–Geiger classification, is first provided. The map at high horizontal resolution (0.5° × 0.5°), representative of the current (i.e. 1961–2005) conditions, is based on the Climate Research Unit dataset holding gridded series of historical observed temperature and precipitation, while projected conditions rely on the simulated series, for the same variables, by the General Circulation Model CMCC-CM. Modeled variables were corrected for their bias and then projections of climate zoning were generated for the medium term (2006–2050) and long term (2056–2100) future periods, under RCP 4.5 and RCP 8.5 emission scenarios. Results show that Equatorial and Arid climates will spread at the expenses of Snow and Polar climates, with the Warm Temperate experiencing more moderate increase. Maps of climate zones are valuable for a wide range of studies on climate change and its impacts, especially those regarding the water cycle that is strongly regulated by the combined conditions of precipitation and temperature. As example of large scale hydrological applications, in this work we tested and implemented a spatial statistical procedure, the geographically weighted regression among climate zones’ surface and mean annual discharge (MAD) at hydrographic basin level, to quantify likely changes in MAD for the main world rivers monitored through the Global Runoff Data Center database. The selected river basins are representative of more than half of both global superficial freshwater resources and world’s land area. Globally, a decrease in MAD is projected both in the medium term and long term, while spatial differences highlight how some areas require efforts to avoid consequences of amplified water scarcity, while other areas call for strategies to take the opportunity from the expected increase in water availability. Also the fluctuations of trends between the medium to long term time frames is viewed in order to pay attention to the lifespan of investments and decisions. Despite preliminary and illustrative, this study suggests how large scale valuable information can be extracted even through indicators derived from statistical spatial modeling procedures, without the pretention to replace more sophisticated studies that allow process based reproduction of inter-annual and intra-annual discharge variability, and on which a robust and comprehensive assessment of future water resource reliability should be however also based. [ABSTRACT FROM AUTHOR]

Summary Return period and risk of extreme hydrological events are critical considerations in water resources management. The stationarity assumption of extreme events for conducting hydrological frequency analysis to estimate return period and risk is now problematic due to climate change. Two different interpretations of return period, i.e. the expected waiting time (EWT) and expected number of exceedances (ENE), have already been proposed in literature to consider nonstationarity in return period and risk analysis by introducing the time-varying moment method into frequency analysis, under the assumption that the statistical parameters are functions only of time. This paper aimed at improving the characterization of nonstationary return period and risk under the ENE interpretation by employing meteorological covariates in the nonstationary frequency analysis. The advantage of the method is that the downscaled meteorological variables from the General Circulation Models (GCMs) can be used to calculate the nonstationary statistical parameters and exceedance probabilities for future years and thus the corresponding return period and risk. The traditional approach using time as the only covariate under both the EWT and ENE interpretations was also applied for comparison. Both approaches were applied to annual minimum monthly streamflow series of two stations in the Wei River, China, and gave estimates of nonstationary return period and risk that were significantly different from the stationary case. The nonstationary return period and risk under the ENE interpretation using meteorological covariates were found more reasonable and advisable than those of the EWT and ENE cases using time alone as covariate. It is concluded that return period and risk analysis of nonstationary low-flow series can be helpful to water resources management during dry seasons exacerbated by climate change. [ABSTRACT FROM AUTHOR]

Summary Many hydro-climatological applications require use of General Circulation Models (GCMs) outputs. However, the raw information as available from GCMs often contains significant systematic biases when compared with observations. This necessitates some kind of statistical adjustment to be carried out on the GCM fields before their use in any application. It is common to correct the GCM simulations by removing the systematic biases at the time scale of interest, usually individually for each GCM variable that is needed. The outcome is a set of bias corrected variables that are not assessed for bias in their joint dependence structure, nor biases in all attributes at other time scales (such as daily and monthly and annual) except the one under consideration. In this paper, we present a bias correction approach that simultaneously adjusts for the biases in multiple variables across multiple time scales. The proposed Multivariate Recursive Nesting Bias Correction (MRNBC) approach simultaneously corrects many GCM variables and repeats the procedure across different levels of temporal aggregation to impart observed distributional and persistence properties at multiple time scales. The bias corrected series exhibits improvements across all variables and over all the time scales considered. The use of the approach in hydrology and water resources related downscaling applications is expected to deliver better results. [ABSTRACT FROM AUTHOR]

• Frequent changes in groundwater trend direction are detected according to time series length. • Groundwater levels in France show a low-frequency variability that could explain these changes. • Groundwater level trends are highly sensitive to the presence of low-frequency variations. Estimating groundwater level evolution is a major issue in the context of climate change. Groundwater is a key resource and can even account in some countries for more than half of the water supply. Groundwater trend estimates are often used for describing this evolution. However, the estimated trend obviously strongly depends on available time series length, which may be caused by the existence of long-term variability of groundwater resources. In this paper, using a groundwater level database in Metropolitan France as an example, we address this issue by exploring how much trend estimates are sensitive to low-frequency variability of groundwater levels. Database consists of relatively undisturbed groundwater level time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping). Frequent changes in trend direction and magnitude are detected according to time series length, which can eventually lead to contradictory interpretations of the groundwater resource evolution, as presented in first part of this article. To assess whether low-frequency variability – known to originate from climate variability – can induce such modifications of trends, we explored in a second step the multi-time scale variability of groundwater levels using a methodology based on discrete wavelet transform. Most of the time series displaying changing trends depending on time series length corresponded to aquifers with high-amplitude low-frequency variability of groundwater levels. Two predominant low-frequency components were detected: multi-annual (∼7 years) and decadal (∼17 years). We finally examined how much those two low-frequency components may affect trend estimates on the longer time period available. For this purpose, we individually removed each of both components from the original times series by discrete wavelet filtering and re-estimated trends in the filtered groundwater level time series. The results showed that the groundwater level trends were highly sensitive to the presence of any of these low-frequency components, which may then strongly influence the estimated trends either by exaggerating or mitigating them. These results emphasize that i) attributing the estimated trends only to climate change would be hazardous given the large influence of low-frequency variability on groundwater level trends, ii) estimation of trends in hydrological projections resulting from General Circulation Models (GCM) outputs in which low-frequency variability is not well represented would be subject to strong uncertainty, iii) a potential change in the amplitude of internal climate variability – e.g. increasing or decreasing low-frequency variability – in the next decades may lead to substantial changes in groundwater level trends. [ABSTRACT FROM AUTHOR]

• IBIS may be more impacted by climate change in UIB than local hydrology. • Sedimentation combined with climate change exacerbates dry season water shortages. • Water availability reductions in the dry season could impact Pakistan's food security. 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 (I cw), 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 (P n) and evapotranspiration (ET a). 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. P n and ET a). 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 I cw 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 I cw , equivalent to 1.89 km3), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median P n , equivalent to 0.13 km3). Sedimentation combined with climate change decreases the median I cw 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 ET a in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of I cw in Rabi and increasing it in Kharif. Concurrently, declining I cw 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. [ABSTRACT FROM AUTHOR]

Summary: Multi-scale temporal variability of precipitation has an established relationship with floods and droughts. In this paper, we present the diagnostics on the ability of 16 General Circulation Models (GCMs) from Bias Corrected and Downscaled (BCSD) World Climate Research Program’s (WCRP’s) Coupled Model Inter-comparison Project Phase 3 (CMIP3) projections and 10 Regional Climate Models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to represent multi-scale temporal variability determined from the observed station data. Four regions (Los Angeles, Las Vegas, Tucson, and Cimarron) in the Southwest United States are selected as they represent four different precipitation regions classified by clustering method. We investigate how storm properties and seasonal, inter-annual, and decadal precipitation variabilities differed between GCMs/RCMs and observed records in these regions. We find that current GCMs/RCMs tend to simulate longer storm duration and lower storm intensity compared to those from observed records. Most GCMs/RCMs fail to produce the high-intensity summer storms caused by local convective heat transport associated with the summer monsoon. Both inter-annual and decadal bands are present in the GCM/RCM-simulated precipitation time series; however, these do not line up to the patterns of large-scale ocean oscillations such as El Nino/La Nina Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Our results show that the studied GCMs/RCMs can capture long-term monthly mean as the examined data is bias-corrected and downscaled, but fail to simulate the multi-scale precipitation variability including flood generating extreme events, which suggests their inadequacy for studies on floods and droughts that are strongly associated with multi-scale temporal precipitation variability. [Copyright &y& Elsevier]

Summary: 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. [Copyright &y& Elsevier]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]

• A coupled Agent-based-Riverware model was applied to assess climate change impact. • Farmers response differently in irrigated area to different climate change impacts. • Basin level's water scarcity metrics cannot reflect farmer level's impacts. 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. [ABSTRACT FROM AUTHOR]

• Lower Upper Bound Estimation method used for constructing prediction intervals. • General circulation models applied for downscaling precipitation and temperature. • Results compared with the results of classic Bootstrap method. • Performance of methods was assessed by the criteria of coverage and width. • Prediction intervals for temperature were narrower than precipitation. In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN) based downscaling for mean monthly precipitation and temperature of two stations (Tabriz and Ardabil in North West of Iran) were evaluated using general circulation models (GCMs). PIs were constructed by novel Upper Lower Bound Estimation (LUBE) method in which an ANN with two outputs was constructed for estimating the prediction bounds. Also, Bootstrap method as a classic technique for assessing uncertainty of ANN was used to further examine the proposed LUBE method. In this way, the accuracy of PIs was quantified by coverage and width criteria. Three GCMs, Can-ESM2, BNU-ESM, INM-CM4 and ensemble-GCM (ensemble of mentioned models) were used in four grid points around each of station for evaluating ANN-based downscaling of precipitation and temperature parameters. Comparison between the results of two methods indicated that LUBE method could lead to more reliable results than the Bootstrap method. PIs width and coverage probability were 10–40% lower and 2–10% higher than the Bootstrap method for different GCMs, respectively. Ensemble-GCM led to more accurate results so that computed PIs width and coverage probability were 10–60% lower and 2–20% higher than those for the single GCMs. [ABSTRACT FROM AUTHOR]

• Combining hydrology, social sciences and modelling leads to ownership of solutions. • Modelling, simulation and visualisation results in system understanding and awareness. • For Lima, a megacity with particular challenges, an Action Plan has been adopted. • Adaptation of the simulator and of the methodology developed to other regions is possible. The rapidly growing urban centres throughout the world are facing serious problems due to the fast-changing developments in all of their environmental spheres (nature, society, politics, culture and economics). Therefore, strategic planning becomes even more important in order to develop strategies that allow cities to adapt to new challenges and to prevent or to mitigate negative trends. This paper presents a transdisciplinary approach to water management, which combines adaptation and application of methods from hydrology, social sciences, water engineering and modelling; furthermore, this approach also involves stakeholders in this process. This methodology assists cities in addressing risks by elaborating solutions, which are characterised by ownership and acceptance of the stakeholders involved. The presented methodology proposed here has been applied to the water system of the desert megacity of Lima/Peru. As a city of almost 10 million inhabitants and with an annual rainfall of about 10 mm per year, Lima presents a unique case with particular challenges regarding water supply. In order to assess the changes in precipitation and temperature for the next decades, two global circulation models and three scenarios have been used. Changes in discharge was addressed using the conceptual rainfall-runoff model HBV applied in both the Atlantic and Pacific catchments relevant to the capital city Lima in terms of water supply. A scenario methodology, combining qualitative and quantitative elements, based on the Cross-Impact Balance Analysis, has been developed and applied. From the millions of theoretically possible combinations of future developments of "descriptors" (driving forces) of the water system, four have been identified as the (only) consistent potential developments of the future. Local stakeholders, stemming from a wide range of institutions have been actively involved in the definition of these driving forces and the set of scenarios. The evaluation of these scenarios and potential options to adapt the water system to future developments was carried out by modelling and simulation, using a purpose-built, yet general, simulator which represents the entire water and wastewater system and includes the important inherent feedback loops (e.g. water demand by irrigation, reuse of treated and untreated wastewaters). The setup of the simulator and the implemented models was done in a way that the simulator formed an integral element in the design of strategies and measures, derived by this innovative combination of qualitative scenario building, quantitative modelling and stakeholder participation. As a core result of this process, the Action Plan "Lima 2040" has been developed and adopted, in which the signatories (the main institutions and organisations responsible for the water sector of Lima) commit themselves to specific actions to be implemented over the years to come. [ABSTRACT FROM AUTHOR]