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• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

• We analyzed trends and breakpoints for three streamflow signatures of drying patterns in 540 non-perennial streams over CONUS • Nonlinear changes in streamflow are more common than linear changes for all three stream intermittency signatures. • The past behavior of non-perennial streams might be a poor predictor of future drying patterns Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days , the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management. [ABSTRACT FROM AUTHOR]

• Three models were used to cover model structure uncertainty for each catchment. • AET is larger in the karst-dominated than the adjacent nonkarst-dominated catchment. • AET of karst-dominated catchment is more sensitive to the change in climate. • The karst-dominated catchment will experience much drier hydrological projections. Karst systems are well-known for their special features, such as epikarst zone and quick flow, compared to other hydrological systems. However, few studies explored the difference in hydrological responses to climate change between karst and nonkarst catchments. In this paper, two adjacent catchments dominated by karst and nonkarst in Southwest China (subtropical monsoon climate) were selected to investigate their different hydrological behaviors under future climates. To account for potential model structure uncertainties, three different karst hydrological models and three traditional catchment models were used to simulate these two catchments. The results demonstrate that different models yield comparable streamflow simulations within each catchment, allowing for the focus to be on the impact of climate change and catchment hydrological characteristics. The karst-dominated catchment is modeled to have larger actual evapotranspiration (AET) than the nonkarst-dominated catchment under similar climate conditions, with the AET of the former being more sensitive to changes in precipitation and potential evapotranspiration. These differences are primarily due to the presence of the epikarst in the unsaturated zone of the karst system, which can store more infiltration rainwater for evapotranspiration. Both catchments are expected to experience a wetter projection in the wet season and a drier projection in the dry season in the future. However, the karst-dominated catchment will face a drier climate with a significant increase in precipitation in just two months (July and August). Additionally, because of a higher sensitivity of monthly AET to climate change, the karst-dominated catchment will experience a greater monthly discharge reduction in the first two wet months (April and May), a relatively lower discharge increase in other wet months, and a lower monthly discharge in dry months, while the monthly discharge of the nonkarst-dominated catchment increases in the wet season and decrease in the dry season. Furthermore, the karst-dominated catchment will have a much lower annual discharge than the nonkarst-dominated catchment, particularly during extremely dry years in the future period due to its larger AET consumption. These all suggest that the karst-dominated catchment will be more vulnerable to droughts. Overall, due to the drier climate and the different hydrological backgrounds of the karst-dominated catchment, it is expected to have much drier hydrological conditions in the future compared to the nonkarst-dominated catchment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bias adjustment improves accuracy and consistency of simulated hydrological signatures. • Not a single bias adjustment method enhances performance in all analyzed signatures. • Univariate distribution scaling (DS) performs well, especially in rainfall-driven catchments. • Multivariate methods perform better for low-flow signatures in snowmelt-driven catchments. Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biases were introduced, but their benefits for impact studies and accurate streamflow projections are still debated. In this paper, we evaluated the ability of two state-of-the-art, advanced multivariate bias-adjustment methods to accurately reproduce 16 hydrological signatures, and compared their performance against two parsimonious univariate bias-adjustment methods based on a multi-criteria performance evaluation. The results indicated that all bias-adjustment methods considerably reduced biases and increased the consistency of simulated hydrological signatures. The added value of multivariate methods in maintaining dependence structures between precipitation and temperature was not systematically reflected in the resulting hydrological signatures, as they were generally outperformed by univariate methods. The benefits of multivariate methods only emerged for low-flow signatures in snowmelt-driven catchments. Based on these findings, we identified the most suitable bias-adjustment methods for water-resources management in Nordic regions under a changing climate, and provided practical guidelines for the selection of bias-adjustment methods given specific research targets and hydroclimatic regimes. [ABSTRACT FROM AUTHOR]

• Relative impacts of human activities and climate change on river flow are assessed. • The proposed methodology is a stochastic version of the fixing-changing method. • The uncertainties of the models' parameters are analyzed using the DREAM algorithm. • To evaluate the methodology, it was applied to the Zayandehrud basin in Iran. • Results show the applicability and efficiency of the proposed methodology. Climate change and human activities are two important factors affecting surface runoff. In water resource planning and management, it is usually important to separate the contribution of the mentioned factors to the runoff change. In this paper, a new methodology is proposed to obtain the relative impact of human activities and climate change on streamflow under uncertainty. In this methodology, the breakpoint of the annual time series of the observed runoff is estimated and the time series is divided into two study periods; before and after the breakpoint, namely the "natural" and "impacted" periods. The Jazim monthly water balance model is used for monthly runoff simulation. To incorporate the effect of snow in simulating runoff, two Jazim-based monthly water balance models with Rao and Al Wagdany (1995) and McCabe and Markstrom (2007) snow modules are used. Then, the monthly water balance models are calibrated and verified for the natural and impacted periods. The main parameters of the water balance models are optimized using the Genetic Algorithms. The uncertainty of the parameters of the models is analyzed using differential evolution adaptive Metropolis (DREAM) algorithm, which is based on the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm provides the posterior probability distribution functions (PDFs) of parameters of the runoff simulation models. Next, the PDFs of the contribution rate (CR) of the climate change and human activities to runoff are determined using the fixing-changing method by taking into account the PDFs of the models' parameters. To show the applicability and efficiency of the proposed methodology, it is applied to the Zayandehrud sub-basin in Iran. Using the Standard Normal Homogeneity Test (SNHT), Pettitt and Buishand range tests, one breakpoint at 2006 is detected in runoff time series. Through the monthly rainfall-runoff simulations and the fixing-changing method, it is estimated that decrease in the runoff after the breakpoint can be attributed to climate change by 57.8% and 45%, using the Jazim-Rao & Al Wagdany model and the Jazim-McCabe & Markstrom model, respectively. The results of the uncertainty analysis of the parameters of the water balance models show that the parameters of LSMX (maximum moisture capacity of the lower soil layer) and K 3 (deep percolation coefficient) are the most significant parameters in the uncertainty of runoff simulated by both the models. According to the PDFs of contribution rate (CR) of climate change and human activities which are obtained using each rainfall-runoff model, it can be inferred that the Jazim-McCabe & Markstrom model significantly decreased the uncertainty of estimations. [ABSTRACT FROM AUTHOR]

In this paper we explored how landscape characteristics such as topography, geology, soils and land cover influence the way catchments respond to changing climate conditions. Based on an ensemble of 15 regional climate models bias-corrected with a distribution-mapping approach, present and future streamflow in 14 neighboring and rather similar catchments in Northern Sweden was simulated with the HBV model. We established functional relationships between a range of landscape characteristics and projected changes in streamflow signatures. These were then used to analyze hydrological consequences of physical perturbations in a hypothetically ungauged basin in a climate change context. Our analysis showed a strong connection between the forest cover extent and the sensitivity of different components of a catchment’s hydrological regime to changing climate conditions. This emphasizes the need to redefine forestry goals and practices in advance of climate change-related risks and uncertainties. [ABSTRACT FROM AUTHOR]

The spatial variability of the hydrological response is controlled by the interaction of two spatial variabilities: climatic forcing and physiographic characteristics. This paper investigates their impact on streamflow modelling throughout a large catchment. They are assessed by successively refining the spatial information of climatic inputs and model parameters, going from the lumped to the sub-catchment or mesh scale. The analysis is conducted according to a spatial split-sample test. For each resolution, the distributed model first calibrated on calibration station(s) is then evaluated in light of this(these) station(s) in addition to validation stations previously treated as ungauged. For that purpose, simulated and observed streamflows are compared focusing on four hydrological signatures: daily runoff, daily regime, flood and low flow. The above framework is applied at the daily time step over a French basin, the Loire catchment at Gien covering 35,707 km 2 , whose streamflow data are available at the catchment outlet and 105 interior points. The results show that spatially distributed climatic forcing greatly enhances the four signatures as the resolution increases, until the right rainfall volume is provided for each simulation point. By taking advantage of the interior calibration stations to spatialise the parameters, the model’s performance progresses again but to a lesser extent. Finally, the catchment’s hydrological behaviour is mainly driven by the spatial variability of the climatic inputs (around 90% over the validation stations), while that of the parameters seems to be of second order. [ABSTRACT FROM AUTHOR]

• Dissolved C fluxes from 843 points on worldwide rivers are analysed. • Mean of DIC fluxes are 92% of the total (7.7 GT C yr−1), while 8% is for DOC fluxes. • Asia exports the highest dissolved C (5.8 GT C yr−1), Africa exports the lowest (0.1 GT C yr−1). • Dissolved C fluxes are higher in tropics due to high interflow contribution to river flow. • Dissolved C fluxes are low in temperate and cold climates due to high groundwater contribution to river flow. Despite dissolved carbon (C) exports from continents being crucial in the connection between terrestrial, atmospheric and oceanic C pools, the relative contribution and process controls on dissolved organic (DOC) and inorganic (DIC) fluxes within river basins are not fully understood. This paper presents an analysis of data from 843 sites worldwide from 45 Web of Science SCI-Expanded (ex ISI) indexed papers that report both the DOC and DIC contents and fluxes in conjunction with chemical elements, such as Si, Na and 13C abundance, to investigate the likely origin, pathways and fate of the dissolved C. The reviewed papers also report on selected environmental factors (e.g., MAP and MAT: mean annual precipitation and temperature respectively; Climate, LU: land use, A: basin surface area). Regarding dissolved C fluxes rivers transported on average, 8.4 GT C yr−1 of dissolved C with 7.7 GT C yr−1 (92%) being DIC and remaining (8%) DOC. Asia has the highest within-river dissolved C transport (5.8 GT C yr−1) followed by Europe (2.0 GT C yr−1), America (0.4 GT C yr−1), and Africa (0.1 GT C yr−1). The dissolved C fluxes tend to increase with river water fluxes and contribution of interflow as seen in the tropical climate, which results in the export of relatively fresh DOC with low aromaticity. In contrast, the lower river water fluxes in sub-tropical and temperate climates corresponds to high concentration of comparatively more decomposed DOC, which pointed to a high proportion of groundwater contribution to the water fluxes. The results also imply that surface area and morphology of the river basins, as well as land use, has insignificant impact on dissolved C dynamics. Such quantitative results, including on the important role of climate on the dynamics of dissolved C in river basins, are helpful in improving predictions on the impact of climate change on the global C cycle. New and innovative multidisciplinary research on the subject is called for. [ABSTRACT FROM AUTHOR]

Climate change and human activity are two major drivers that alter hydrological cycle processes and cause change in spatio-temporal distribution of water availability. Streamflow, the most important component of hydrological cycle undergoes variation which is expected to be influenced by climate change as well as human activities. Since these two affecting conditions are time dependent, having unequal influence, identification of the change point in natural flow regime is of utmost important to separate the individual impact of climate change and human activities on streamflow variability. Subsequently, it is important as well for framing adaptation strategies and policies for regional water resources planning and management. In this paper, a comprehensive review of different approaches used by research community to isolate the impacts of climate change and human activities on streamflow are presented. The important issues pertaining to different approaches, to make rational use of methodology, are discussed so that researcher and policymaker can understand the importance of individual methodology and its use in water resources management. A new approach has also been suggested to select a representative change point under different scenarios of human activities with incorporation of climate variability/change. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wildfires are becoming more severe and frequent and their effects increasingly expose water resources to stress. • Climate, wildfire, streamflow records (1975 to 2018), topography, and landcover data from 92 catchments were combined to quantify the contribution of wildfire to streamflow variability across Australian temperate forests. • On average wildfire regimes relatively results in 8.8% increases in streamflow across Australian temperate forests. • Wildfire effects on regional streamflow varied spatially depending on hydroclimate factors, particularly precipitation. As wildfires become more frequent and severe, there are concerns regarding their impacts on water yield from forested catchments. While there are many studies in Australia about the effects of individual wildfires on streamflow at fine scales (<1 km2) in specific geographic settings, the effects of wildfire regimes on streamflow at broad spatial scales across temperate forests in Australia are not well understood. In this paper, we combined climate, wildfire, streamflow records (1975 to 2018), topographic, and landcover data from 92 catchments (74 – 4740 km2) in the Australian temperate zone to quantify the contribution of wildfire regimes over time to streamflow variability in different hydroclimatic settings (humid, dry sub-humid, and semi-arid) and geographic regions (Southeast Australia (SEA), Southwest Western Australia (SWWA), and Tasmania (TAS)). Wildfire regimes were represented by two metrics: the burnt area to drainage area (BDA) ratio for wildfire events in each year and a spatially averaged metric of Time Since Fire (TSF), which is a spatial average of time since the last wildfire in the catchment. By comparing prefire and postfire runoff ratios our study found that on average there was a short-term increase in runoff ratio (∼3% in year 1 and ∼6% in year 2 post-fire) after wildfires with BDA > 25%. No influence of fire was found in the long-term (15–20 years after the wildfire) runoff ratio. We found that wildfire regime, measured by TSF, explained ∼8.8% of the variation in annual streamflow across the Australian temperate zone, and that with decreasing TSF (i.e., increased wildfire impact), the average streamflow increased. Streamflow variation explained by wildfire regimes varied with hydroclimate. The explained variance of streamflow by wildfire regimes in semi-arid catchments (23%) and dry sub-humid catchments (13%) were higher than humid catchments (5%). Our results provide a broad-scale understanding of how wildfire regimes influence streamflow variability at broad temporal and spatial scales, and provided important context and baseline information for determining the implications of changes in climate and fire regimes for regional water availability. [ABSTRACT FROM AUTHOR]

• Comparison of Budyko-based methods to separate catchment and climate effects on flow. • Probabilistic nature of catchment parameter and its uncertainty considered. • New, improved Two-Stage Decomposition method proposed for separation. • Proposed method considers uncertainty in evaporative index using bootstrap method. • Proposed method more robust than using any single earlier-practiced method. Streamflow variability is affected by both climate change and direct human impacts within the catchment. Quantification of the role of these two factors have significant implications for water resources. In recent years, Budyko-based analytical methods have been widely used for such quantification due to their simplicity and efficacy. This paper presents a comparison of such methods to attribute changes in streamflow in four Model Parameter Estimation Experiment (MOPEX) catchments in the contiguous United States. Further, a new, improved Two-stage Decomposition method to separate the role of climate and catchment changes on streamflow is proposed, considering the uncertainty in the catchment parameter that further leads to a range of evaporative index values within a catchment. We find that climate and catchment effects are sensitive to the choice of Budyko equation, disaggregation algorithm and approximation technique. Further, the catchment parameter can be expressed probabilistically, to account for the uncertainty because of limited hydro-meteorological records. The proposed approach employs a bootstrap analysis to obtain the stochastic range of the catchment parameter, considering different choices of Budyko equations. We argue that rather than relying on a single Budyko-based approach for separation of climate and catchment effects on streamflow, the proposed method offers a more robust way by considering the entire range of uncertainty of the catchment parameter. [ABSTRACT FROM AUTHOR]

Summary This paper presents an assessment of the effects of climate change on river flow regimes in representative English catchments, using the UKCP09 climate projections. These comprise a set of 10,000 coherent climate scenarios, used here (i) to evaluate the distribution of potential changes in hydrological behaviour and (ii) to construct relationships between indicators of climate change and hydrological change. The study uses six catchments, and focuses on change in average flow, high flow (Q5) and low flow (Q95). There is a large range in hydrological change in each catchment between the plausible UKCP09 climate projections, with differences between catchments largely due to differences in catchment geology and baseline water balance. The range in change between the UKCP09 projections is in most catchments smaller than the range between changes with scenarios based on the CMIP3 ensemble of climate models (although the shape of the distribution of changes is different), and earlier UK scenarios produce changes that tend towards the lower (drier) end of the UKCP09 range. The difference between emissions scenarios is small compared to the range across the 10,000 scenarios. Changes in high flows are largely driven by changes in winter precipitation, whilst changes in low flows are determined by changes in summer precipitation and temperature. [ABSTRACT FROM AUTHOR]

Summary Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500–5000 km 2 ) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index ( I M ) and temperature variability index ( I TV ), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity ( I M < 80%), I M is a better predictor of model performance improvement than I TV ; whereas for catchments with high moisture homogeneity ( I M > 80%), I TV is a better predictor of performance improvement than I M . Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow). [ABSTRACT FROM AUTHOR]

Summary: Conventional techniques for quantifying and then managing flood risks are invalid under ‘non-stationary’ climate conditions. Trend detection and attribution are problematic given that the outcome depends on the start and end date of the record, choice of index and test statistic, assumed behaviour of the system, and many non-climatic confounding factors. Analyses are further hampered by short and non-homogeneous flow records. In this paper, we use an objective weather classification scheme to reconstruct the atmospheric drivers of fluvial flood occurrence and magnitude in England, Scotland and Wales since the 1870s. We demonstrate the index using long (>50year) annual maximum (AMAX) and peak over threshold (POT) flood records for 114 stations. Synoptic indices show modest skill at hindcasting multi-decadal variations in flood frequency at national, regional and catchment scales, but not for flood magnitudes. Flood rich episodes are identified in the periods 1908–1934, 1977–1988 and from 1998 onwards. We find that five weather types account for 68% of flood occurrence, and just three types were linked to the most widespread winter floods. These flood-generating systems generally show no sustained changes in frequency, persistence, relative contribution, or rain-bearing properties since the 1930s. However, there are emergent patterns in the day-to-day persistence (declining) and mean precipitation yield (rising) of anticyclonic weather types that merit further investigation. Based on our evaluation, we recommend use of objective weather indices derived from observed atmospheric pressure patterns when interpreting fluvial flood risk linked to climate drivers. [Copyright &y& Elsevier]

Summary: This paper presents an assessment of the impacts of climate change on a series of indicators of hydrological regimes across the global domain, using a global hydrological model run with climate scenarios constructed using pattern-scaling from 21 CMIP3 (Coupled Model Intercomparison Project Phase 3) climate models. Changes are compared with natural variability, with a significant change being defined as greater than the standard deviation of the hydrological indicator in the absence of climate change. Under an SRES (Special Report on Emissions Scenarios) A1b emissions scenario, substantial proportions of the land surface (excluding Greenland and Antarctica) would experience significant changes in hydrological behaviour by 2050; under one climate model scenario (Hadley Centre HadCM3), average annual runoff increases significantly over 47% of the land surface and decreases over 36%; only 17% therefore sees no significant change. There is considerable variability between regions, depending largely on projected changes in precipitation. Uncertainty in projected river flow regimes is dominated by variation in the spatial patterns of climate change between climate models (hydrological model uncertainty is not included). There is, however, a strong degree of consistency in the overall magnitude and direction of change. More than two-thirds of climate models project a significant increase in average annual runoff across almost a quarter of the land surface, and a significant decrease over 14%, with considerably higher degrees of consistency in some regions. Most climate models project increases in runoff in Canada and high-latitude eastern Europe and Siberia, and decreases in runoff in central Europe, around the Mediterranean, the Mashriq, central America and Brasil. There is some evidence that project change in runoff at the regional scale is not linear with change in global average temperature change. The effects of uncertainty in the rate of future emissions is relatively small. [Copyright &y& Elsevier]

Summary: In this paper we proposed: (1) an algorithm of glacier melt, sublimation/evaporation, accumulation, mass balance and retreat; (2) a dynamic Hydrological Response Unit approach for incorporating the algorithm into the Soil and Water Assessment Tool (SWAT) model; and (3) simulated the transient glacier retreat and its impacts on streamflow at basin scale. Application of the enhanced SWAT model in the Manas River Basin (MRB) in the Tianshan Mountains in northwest China, shows that the approach is viable as evidenced by a Nash–Sutcliff efficiency of 0.65 and a percent bias of −3.7% for daily streamflow and water balance, respectively. The results indicate that the glacier area decreased by 11% during the simulation period from 1961 to 1999, which is within the range of records from other glaciers. On average, glacier melt contributed 25% to streamflow, although glacier area accounts for only 14% of the catchment drainage area in the MRB. Glacier melt was positively correlated to temperature change (R 2 =0.70, statistical significance P <0.001) and negatively correlated to precipitation (R 2 =0.20, statistical significance P <0.005). The results indicate that glacier melt was more sensitive to temperature change than to precipitation change, implying that modeling the effects of climate change with increasing temperatures and decreasing precipitation should be further studied. [ABSTRACT FROM AUTHOR]

Summary: A climate shift in the mid 1970s reduced rainfalls in south-western Australia by 10–15% and inflows into reservoirs that supply the city of Perth (population 1.8m) by more than half. The region has a Mediterranean climate, similar to other areas in the world experiencing reductions in rainfall and rises in temperatures. Rainfall–runoff modelling has indicated that streamflows may reduce by a further quarter by 2030 or by half if a dry future climate is experienced. Groundwater levels on the coastal plain in south-western Australia have fallen since the mid 1970s where unconfined aquifers are covered with perennial vegetation, including under the main water supply aquifer for Perth. Modelled projections are that groundwater levels in most areas will continue to fall through to 2030 under most future climate scenarios. Projected streamflows and groundwater levels indicate reduced water availability but these need to be converted to projected water yields, i.e. the amount of water that can be diverted for consumptive use. This paper reports how projections of future streamflow and groundwater levels were used to estimate 2030 divertible water yields for a 62,500km2 area in south-western Australia. These yields were then compared with estimates of water demands in 2030 to identify areas of water surplus and deficit under clearly defined assumptions. The methods used to define future yields are based on sets of rules that could be varied by water managers if desired. Surface water yields are estimated to decrease by about 24% (possible range of −4% to −49%) which is similar to the projected reduction in runoff (−25% with a range of −7% to −42%). Groundwater yields are projected to fall by only about 2% (range of+2% to −7%) because of reductions in evapotranspiration and drainage losses as watertables fall where groundwater levels are close to the surface. In addition, recharge remains relatively high under cleared areas used for non-irrigated agriculture. In some areas with high groundwater recharge and little groundwater use groundwater levels may continue to rise even under a hotter and drier future climate. While overall groundwater yields may be little affected there are very important groundwater resources, especially those covered by perennial vegetation, which will experience large reductions in available yield under most projected future climates. Under current per-capita water consumption levels, rapid population and economic growth, along with the reductions in water yields, are expected to result in appreciable water deficits developing near Perth and some regional cities in south-western Australia by 2030. Where both surface water and groundwater resources are available for use, groundwater is anticipated to become increasingly used in future. [Copyright &y& Elsevier]

Summary: The effect of future climate scenarios on surface water and groundwater resources has been shown to have a consequent impact on water-dependent ecosystems. A regional-scale analysis of changes in water resources as a result of changing climate was undertaken in south-western Australia; a region with a large number of nationally and internationally recognised water-dependent ecosystems. The paper examines the potential environmental impacts of a substantial reduction in rainfall and an increase in temperature as projected by global climate models (GCMs) on river and groundwater-dependent terrestrial vegetation by 2030. Climate change effects on environmentally significant flow regimes were evaluated by applying the climate projections from 15 GCMs under three global warming ‘scenarios to rainfall–runoff and groundwater models. It was estimated that under a dry future climate scenario, the frequency of river flow rates important for ecological communities is likely to be reduced by up to 2months per year, which is likely to cause a significant level of stress to ecological communities. Additionally, the duration of no-flow periods may increase by more than 120days in some streams. Under a dry future climate scenario, groundwater-dependent vegetation is projected to be at a high risk in over 19% of its current habitat area. Water-dependent ecosystems are also projected to be affected by an increase in groundwater abstraction. The results indicate that the projected impacts of future climate conditions are not likely to be uniform across the region but overall they could cause a continuing threat to water-dependent ecosystems. The implementation of water management plans should place particular emphasis on the ecological water requirements in 6 of the 13 river catchments in south-western Australia and in areas where groundwater abstraction is high. The climate projections and ecological impacts are a continuation of the trends that have taken place in the past three decades. [Copyright &y& Elsevier]

Summary: Despite the increasing use of regional climate model (RCM) simulations in hydrological climate-change impact studies, their application is challenging due to the risk of considerable biases. To deal with these biases, several bias correction methods have been developed recently, ranging from simple scaling to rather sophisticated approaches. This paper provides a review of available bias correction methods and demonstrates how they can be used to correct for deviations in an ensemble of 11 different RCM-simulated temperature and precipitation series. The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data. The second evaluation was based on the combined influence of corrected RCM-simulated temperature and precipitation on hydrological simulations of monthly mean streamflow as well as spring and autumn flood peaks for five catchments in Sweden under current (1961–1990) climate conditions. Finally, the impact on hydrological simulations based on projected future (2021–2050) climate conditions was compared for the different bias correction methods. Improvement of uncorrected RCM climate variables was achieved with all bias correction approaches. While all methods were able to correct the mean values, there were clear differences in their ability to correct other statistical properties such as standard deviation or percentiles. Simulated streamflow characteristics were sensitive to the quality of driving input data: Simulations driven with bias-corrected RCM variables fitted observed values better than simulations forced with uncorrected RCM climate variables and had more narrow variability bounds. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces an approach to route discharge with a variable river flow velocity based on the Manning–Strickler formula within large scale hydrological models. The approach has been developed for the global scale hydrological model WaterGAP and model results have been analyzed focusing on Europe. The goal was to find a method that is simple enough to derive the required parameters from globally available data while being sophisticated enough to deliver realistic flow velocity estimates for a large variety of environmental conditions. The river bed roughness (Manning’s n) is approximated in a spatially explicit way based on topography, the location of urban population, and river sinuosity. The hydraulic radius is estimated from actual river discharge, and river bed slope is derived by combining a high resolution DEM, a 5arc min drainage direction map, and river sinuosity. The modeled river flow velocity has been validated against data of US gauging stations. The representation of lateral transport has clearly been improved compared to the constant flow velocity applied in older model versions. The effect of incorporating variable flow velocities as compared to a constant flow velocity is largest on flood discharge, which generally increases in large rivers. The impact on monthly discharge hydrographs is marginal only. WaterGAP has been driven by three climate change projections for the 2050s to assess climate change impacts on flow velocity, and on the residence time of water in the European river system. Results indicate a decrease in residence times for Northern Europe and an increase for parts of the Mediterranean. [Copyright &y& Elsevier]

Summary: Water companies in the United Kingdom have considered climate change in their water resources plans for more than a decade through studies funded by UK Water Industry Research (UKWIR). This paper presents an initial assessment of the impact of the UK Climate Projections 2009 (UKCP09) on river flows at a national scale for the 2020s under the A1B scenario and the implications for water resource planning. A daily hydrological modelling framework based on two conceptual model structures and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology has been applied to 70 catchments across the UK. A Latin Hypercube Sampling approach was adopted to handle the probabilistic nature of UKCP09. Results show a decrease in mean annual flow over most of the UK, with negative median values of all monthly changes except in winter over the western and northern mountainous areas. Furthermore the results indicate a high likelihood of a significant decline in summer flows. An analysis of variance shows that the main uncertainty in river flow changes comes from the spread in climate projections. Finally results are found to be consistent with a previous UKWIR assessment based on individual projections from 6 Global Climate Models (GCMs) under the A2 scenario. The reduction in summer low-flows, critical for water resources, appears however more limited with UKCP09. Although most expected changes are within natural variability, the drier conditions overall and the greater spread of results with respect to previous assessments indicates a need for testing the robustness of water resource plans. [Copyright &y& Elsevier]

Summary: Most of the plantation impact studies reported in literature normally use either one of the sensitivity-based approach or a hydrological model with few actually comparing the impact results from these different approaches. This paper investigates the impacts of increase or decrease in plantations and climate variability on streamflow using two approaches: the sensitivity-based approach (including a non-parametric model and six Budyko framework based models) and the hydrological modelling approach (using Xinanjiang and SIMHYD models) for three medium sized catchments in Australia. The results from the nine methods show that both plantation expansion/reduction and climatic differences can have major effects on catchment streamflow. There is a small variability in the reduction or increase in streamflow estimated by the nine methods. The results from this study show that the estimates of plantation impacts from the dynamic hydrological models are similar to those from the commonly used sensitivity-based approaches. The sensitivity-based approaches are only applicable where long term datasets are available and they only provide results at a mean annual time scale. The hydrological models simulate plantation impacted streamflow time series and so they can be used to estimate the relative contributions of land cover changes and climate change/variability at a daily, monthly or annual time-step. The outputs from the hydrological models can also be used to investigate the impacts of plantation expansion or reduction and climate change/variability on different runoff characteristics. [ABSTRACT FROM AUTHOR]

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 Niño (La Niña), 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. [Copyright &y& Elsevier]

Summary: A simple but general probability adjustment procedure is proposed for creating climate-series/probability sets that reflect historical frequencies adjusted for climate forecast information. Often forecast information is given as the conditional probability of below-normal, normal, and above-normal temperature or rainfall depths, though forecast information also can be described by the conditional mean and standard deviation of key variables such as seasonal runoff. Probability adjustment methods developed by Croley and by Wilks assign the same probability to all climate series in selected categories. This results in a discontinuity at the interval boundaries, and can seriously misrepresent the mean and variance of the conditional distribution of key variables. The proposed adjustment is based on a frequency model of the unconditional and conditional distributions of key variables. This framework allows derivation of a consistent and smooth set of probabilities for climate series across the entire range of a key variable reflecting the change in the likelihood of each individual climate series. Examples show the improvements obtained in the approximation of the moments and the cumulative distribution function. The paper also considers multivariate adjustments to reflect several forecasts considering different time periods, different regions, or different variables. Examples for normal and non-normal cases illustrate the care needed to ensure that the cross-correlations among climate variables are correctly represented, otherwise the joint dependence is easily distorted. [ABSTRACT FROM AUTHOR]

Summary: Quantitative estimates of the hydrologic effects of climate change are essential for understanding and solving potential transboundary water conflicts in the Zerafshan river basin, Central Asia. This paper introduces an identification of runoff generation processes and a detection of changes in hydrological regimes supporting Mann–Kendall trend analysis for streamflows. By this, the effective available and future water resources are identified for the Zerafshan. The results for the subbasins in the upper Zerafshan and for the reference station at the upper catchment outlet indicate that glacier melt is the most significant component of river runoff. The Mann–Kendall trend analysis confirms the regime analysis with the shift in the seasonality of the discharge. Furthermore, the results of the Kendall–Theil Robust Line for predicted long-term discharge trends show a decreasing annual discharge. The experience gained during this study emphasizes the fact that the summer flood, urgently required for the large irrigation projects downstream in Uzbekistan, is reduced and more water will be available in spring. Additionally, following the estimation of future discharges in 50 and 100years the hydrological changes are affecting the seasonal water availability for irrigation. This analysis highlighted that water availability is decreasing and the timing of availability is changing. Hence, there will be more competition between upstream Tajikistan and downstream Uzbekistan. Planned projects within the basin might have to be reconsidered and the changed scenario of water availability needs to be properly taken into account for long-term basin scale water management. [ABSTRACT FROM AUTHOR]

Summary: Miyun Reservoir is the main raw water source for Beijing’s domestic water supply. Flow discharge to Miyun Reservoir decreased drastically over a 50-year period, from 1956 to 2005, and had seriously affected Beijing’s water supply. Climate variability and human activity had been identified as the two main reasons for the decrease in flow. Here, climate variability refers to changes in precipitation and temperature. Impact of human activity includes direct withdrawal of water (referred to as “direct abstraction” in this paper) from the river (primary) or groundwater and indirect impact due to man-made changes in land use and vegetation in the upstream of the reservoir. According to the historical record, “direct abstraction” from the upstream of the reservoir has increased significantly since 1984. The study period was split into two sub-periods, from 1956 to 1983 and from 1984 to 2005. Based on the historical record, annual runoff in the upstream catchment of Miyun Reservoir (i.e., inflow into the reservoir) had decreased from 90.3mm to 41.8mm for the two sub-periods, a decrease of 48.5mm. Over the same period, average annual “direct abstraction” increased from 2.2mm to 13.4mm, an increase of 11.2mm. The latter accounted for 23% (11.2mm/48.5mm) of the decrease in inflow into the reservoir. This study utilised: (1) a distributed hydrological model (geomorphology-based hydrological model, or GBHM) and (2) a climate elasticity model to conduct a quantitative assessment of the impact of climate variability and the indirect impact of human activity on the inflow into the reservoir. Simulation results of GBHM and the climate elasticity model showed that climate impact was accountable for about 55% and 51% of the decrease in reservoir inflow, respectively. The indirect impact of human activity (mainly man-made land use and vegetation changes) accounted for 18% of the decrease in reservoir inflow. [Copyright &y& Elsevier]

Summary: Several studies have shown that statistics of streamflow time series, in particular empirical moments, scale with physical properties of the drainage basin, such as the catchment area. Those scaling laws have been extensively used to estimate statistics of streamflow series at ungauged sites. The role of climate variability and change has not been considered in such models. Further, most studies are based on classical statistics, where parameter uncertainties are usually neglected or not formally considered. In this paper we develop and apply hierarchical Bayesian models, to both assess regional and at-site trends in time in a spatial scaling framework, and simultaneously provide a rigorous framework for assessing and reducing parameter and model uncertainties. The models are tested with reconstructed natural inflow series from over 40 hydropower sites in Brazil with catchments areas varying from 2588 to 823,555km2. Both annual maximum flood series and monthly streamflow are considered. Cross-validated results show that the Hierarchical Bayesian models are able to skillfully estimate monthly and flood flow probability distribution parameters for sites that were not used in model fitting. The models developed can be used to provide record augmentation at sites that have short records, or to estimate flow at ungauged sites, even in the absence of an assumption of time stationarity. Since model uncertainties are accounted for, the precision of the estimates can be quantified and hypotheses tests for regional and at-site trends can be formally made. A formal inclusion of climate predictors to facilitate seasonal forecasting or climate change scenario development is also feasible. This is indicated, but not developed here. [Copyright &y& Elsevier]

Summary: Efficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time. [Copyright &y& Elsevier]

Summary: In recent years, climate change impacts on water resources sectors have been extensively documented. Anticipated changes range from more severe storms to more frequent floods and drought at regional scale. Pressure on water resources and hence on the environment will probably increase with the need of quality data for sustainable design of water resources projects. Therefore, for an optimal network design, hydrometric networks are to be reviewed periodically based on the information needs of the various end users and the perspective for future water resources development. This paper evaluates hydrometric networks to identify essential streamflow stations and critical areas (highly poor network density) within major watersheds across the different provinces of Canada using entropy theory. The method is applied to daily streamflow data and information coefficients such as marginal entropy, joint entropy and transinformation index are used for the identification of important stations as well as critical areas in the basin. The analysis results show that almost all Canadian main watersheds contain deficient hydrometric networks. The most deficient streamflow networks are identified in Alberta (North Saskatchewan, Oldman, and Red Deer basins), Northern Ontario (Hudson Bay basin), and the Northwest Territories. The information might prove to be helpful for decision makers to undertake cost-benefit analyses for hydrometric network updating in each region. [Copyright &y& Elsevier]

Summary: The distribution-free Mann–Kendall test is widely used for the assessment of significance of trends in many hydrologic and climatic time series. Previous studies have suggested both exact and approximate formulas for the calculation of the variance of the test statistic when the data are serially correlated. This paper outlines a procedure for the calculation of the exact distribution of the Mann–Kendall trend test statistic for persistent data with an arbitrary correlation structure. The particular cases of the AR(1) (first order autoregressive) model and the Fractional Gaussian Noise (FGN) model are presented for sample sizes between 3 and 9. While it has been previously shown that the Normal distribution gives a reasonable approximation to the exact distribution for large values of sample size n, a more accurate approximation based on the Beta distribution is proposed for moderate values of n. The application of the test to small samples is illustrated by testing the significance of recent trends starting in 1990 in 58 world river flow time series. The results confirm the effect of scaling in small samples and the benefits of using the Beta distribution as an approximation. [Copyright &y& Elsevier]

Summary: The objective of this paper is to investigate whether resampling of the output from a regional climate model (RCM) can provide realistic long-duration sequences of precipitation and temperature for the simulation of extreme river flows. This is important to assess the impact of climate change on river flooding. Daily streamflows of the river Meuse in western Europe are considered. Resampling is performed with a nearest-neighbour technique that was already successfully applied to the observed daily precipitation and temperature in the river basin. Streamflows are simulated with the semi-distributed HBV rainfall–runoff model. Two simulations of the KNMI regional climate model RACMO are considered. One of these simulations is driven by the global atmospheric model HadAM3H of the UK Meteorological Office for the period 1961–1990 and the other by ERA40 re-analysis data. Much attention is given to the bias correction of RACMO precipitation. It was found that a relatively simple nonlinear correction adjusting both the biases in the mean and variability led to a better reproduction of observed extreme daily and multi-day precipitation amounts than the commonly used linear scaling correction. This also resulted in more realistic discharge extremes, suggesting that a correct representation of the variability of precipitation is important for the simulation of extreme flood quantiles. For the Meuse basin it is further shown that it is advantageous to correct for the variability of the 10-day precipitation amounts rather than that of the daily amounts. Despite the remaining biases in the RCM data, the simulated extreme flood quantiles correspond quite well with those obtained using observed precipitation and temperature. [Copyright &y& Elsevier]

Climate change impact assessments conventionally assess just the implications of a change in mean climate due to global warming. This paper compares such effects of such changes with those due to natural multi-decadal variability, and also explores the effects of changing the year-to-year variability in climate as well as the mean. It estimates changes in mean monthly flows and a measure of low flow (the flow exceeded 95% of the time) in six catchments in Britain, using the UKCIP98 climate change scenarios and a calibrated hydrological model. Human-induced climate change has a different seasonal effect on flows than natural multi-decadal variability (an increase in winter and decrease in summer), and by the 2050s the climate change signal is apparent in winter and, in lowland Britain, in summer. Superimposing natural multi-decadal variability onto the human-induced climate change increases substantially the range in possible future streamflows (in some instances counteracting the climate change signal), with important implications for the development of adaptation strategies. Increased year-to-year variability in climate leads to slight increases in mean monthly flows (relative to changes due just to changes in mean climate), and slightly greater decreases in low flows. The greatest effect on low flows occurs in upland catchments. [Copyright &y& Elsevier]

The existence of an ice cover has important effects on the streamflow conditions as well as on the heat transfer between water bodies and the overlying atmosphere. This paper investigates the effects of climate variability on the termination of the ice season in the Baltic region. In particular, trends and fluctuations observed in the cryophenological records from this region are analyzed in detail, searching for possible connections with the North Atlantic Oscillation (NAO). The NAO seems to affect mostly the late-winter temperature (January–March) with a significant impact also on the mid-spring (April–May) period, when the air temperature is strongly correlated to the ice dates. A regional analysis shows the existence in the series of winter temperature (JFM) of the same fluctuations as the winter NAO. The same components can be found in the cryophenological records and (partly) in the series of spring temperature. Nevertheless, both ice phenology and spring temperature show the existence of a very well defined trend that is not detectable in the series of winter NAO at time scales of a century or longer. This leads to the conclusion that winter NAO has still a weak, though significant, effect on the regime of spring temperature in the Baltic region and explains the most significant fluctuating components embedded in the cryophenological records. However it is argued that other climatic forcings, related to CO2-induced regional and global warming, acting at the end of the ice season, are able to induce pronounced trends in the regime of spring temperature and have an important impact on the cryosphere leading to the earlier occurrence of ice break-up observed in the last several decades. [Copyright &y& Elsevier]