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Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020-50 would increase under all the scenarios (relative to the baseline period 1970-2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020-50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020-50. The reservoir would be likely to undergo more flooding in 2020-50. [ABSTRACT FROM AUTHOR]

The impact of climate change on hydrology and water resources is one of the most critical issues facing the world in the next few decades. In particular, there is a need to quantify the risks associated with maintaining the security of resource quantity and quality, and to assess the effectiveness of potential management strategies. In this paper, we assess the impacts of climate variability and change on one aspect of river health. A simple model of Anabaena algal bloom occurrence at a weir pool in the lower Murrumbidgee River, Australia, has been coupled to a catchment model that is used to simulate streamflow, irrigation demand and diversions, dam water storage and releases, and decision-making by both irrigators and managers. Long-term climate data are obtained from a statistical downscaling algorithm, which, when applied to global climate model predictions can provide climate data suitable for driving the coupled model under a variety of climatic scenarios. The coupled model is then used to assess the impact of climate variability and projected climate change on the frequency, duration and magnitude of Anabaena blooms. The impact of two management strategies for bloom control are also assessed and it is shown that even a single, quite simple, resource-neutral, adaptive management strategy has the potential to substantially reduce the occurrence and impact of algal blooms and to more than compensate for the deleterious impacts of climate change. This result supports the notion that planning for the future can lead to positive outcomes in the present. [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: 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]

Atmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7-10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%-20%at a 7-day lead during boreal winter relative to boreal summer.ARhit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Niño-Southern Oscillation (ENSO) conditions and at 7- and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific-North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Niño + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Niña + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill. [ABSTRACT FROM AUTHOR]

Projected climate change effects on hydrology are investigated for the 2041-60 horizon under the A2 emission scenarios using a multimodel approach over two snowmelt-dominated catchments in Canada. An ensemble of 105 members was obtained by combining seven snow models (SMs), five potential evapotranspiration (PET) methods, and three hydrological model (HM) structures. The study was performed using highresolution simulations from the Canadian Regional Climate Model (CRCM-15 km) driven by two members of the third-generation Canadian Coupled Global Climate Model (CGCM3). This study aims to compare various combinations of SM-PET-HM in terms of their ability to simulate streamflows under the current climate and to evaluate how they affect the assessment of the climate change-induced hydrological impacts at the catchment scale. The variability of streamflow response caused by the use of different SMs (degree-day versus degree-day/energy balance), PET methods (temperature-based versus radiation-based methods), and HM structures is evaluated, as well as the uncertainty due to the natural climate variability (CRCM intermember variability). The hydroclimatic simulations cover 1961-90 in the present period and 2041-60 in the future period. The ensemble spread of the climate change signal on streamflow is large and varies with catchments. Using the variance decomposition on three hydrologic indicators, theHMstructure was found to make the most substantial contribution to uncertainty, followed by the choice of the PET methods or natural climate variability, depending on the hydrologic indicator and the catchment. Snow models played a minor, almost negligible role in the assessment of the climate change impacts on streamflow for the study catchments. [ABSTRACT FROM AUTHOR]

Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability. [ABSTRACT FROM AUTHOR]

Recent analyses of extreme hydrological events across the United States, including those summarized in the recent U.S. Third National Climate Assessment (May 2014), show that extremely large (extreme) precipitation and streamflow events are increasing over much of the country, with particularly steep trends over the northeastern United States. The authors demonstrate that the increase in extreme hydrological events over the northeastern United States is primarily a warm season phenomenon and is caused more by an increase in frequency than magnitude. The frequency of extreme warm season events peaked during the 2000s; a secondary peak occurred during the 1970s; and the calmest decade was the 1960s. Cold season trends during the last 30-50 yr are weaker. Since extreme precipitation events in this region tend to be larger during the warm season than during the cold season, trend analyses based on annual precipitation values are influenced more by warm season than by cold season trends. In contrast, the magnitude of extreme streamflow events at stations used for climatological analyses tends to be larger during the cold season: therefore, extreme event analyses based on annual streamflow values are overwhelmingly influenced by cold season, and therefore weaker, trends. These results help to explain an apparent discrepancy in the literature, whereby increasing trends in extreme precipitation events appear to be significant and ubiquitous across the region, while trends in streamflow appear less dramatic and less spatially coherent. [ABSTRACT FROM AUTHOR]

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Water managers across the United States face the need to make informed policy decisions regarding long-term impacts of climate change on water resources. To provide a scientifically informed basis for this, the evolution of important components of the basin-scale water balance through the end of the twenty-first century is estimated. Bias-corrected and spatially downscaled climate projections, from phase 3 of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme, were used to drive a spatially distributed Variable Infiltration Capacity (VIC) model of hydrologic processes in the Salt-Verde basin in the southwestern United States. From the suite of CMIP3 models, the authors select a five-model subset, including three that best reproduce the historical climatology for the study region, plus two others to represent wetter and drier than model average conditions, so as to represent the range of GCM prediction uncertainty. For each GCM, data for three emission scenarios (A1B, A2, and B1) were used to drive the hydrologic model into the future. The projections of this model ensemble indicate a statistically significant 25% decrease in streamflow by the end of the twenty-first century. The primary cause for this change is due to projected decreases in winter precipitation accompanied by significant (temperature driven) reductions in storage of snow and increased winter evaporation. The results show that water management in central Arizona is highly likely to be impacted by changes in regional climate. [ABSTRACT FROM AUTHOR]

Hydrological processes change from the impacts of climate variability and human activities. Runoff in the upper reaches of the Hun-Taizi River basin, which is mainly covered by forests in northeast China, decreased from 1960 to 2006. The data used in this study were based on runoff records from six hydrological stations in the upper reaches of the Hun-Taizi River basin. Nonparametric Mann-Kendall statistic was used to identify change trends and abrupt change points and consequently analyze the change characteristics in hydrological processes. The abrupt change in the annual runoff in most subcatchments appeared after 1975. Finally, the effects of climate change and land cover change on water resources were identified using regression analysis and a hydrology model. Results of the regression analysis suggest that the correlation coefficients between precipitation and runoff prior to the abrupt change were higher compared with those after the abrupt change. Moreover, using hydrology model analysis, the water yield was found to increase because of the decrease in forest land. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980-2009. This evaporative demand measure is from the 'PenPan' model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales. [ABSTRACT FROM AUTHOR]

Hydrological and climatic characteristics were investigated within the Severn Uplands to determine the interconnection between variables with a focus on understanding changes in hydrological extremes. Time series of precipitation and flows over a 30-year period were analysed for trends in extremes. The Mann-Kendall (MK) non-parametric test was used to detect trends at six rain gauges and five flow gauges across the catchment. Maxima, percentiles and N-day maxima of precipitation and flow distributions were investigated to identify changes in extreme intensity, frequency and persistence, respectively. Results indicated some significant statistical trends at annual, seasonal, and monthly scales. The largest number of significant trends in extreme precipitation was identified at mountainous gauge locations where significant increases in extreme precipitation persistence were detected annually and during spring and winter. Additionally, significant increases in extreme precipitation frequency were observed annually and during autumn and winter. Trends in flow identified an increase in extreme intensity on an annual basis, as well as during winter and the month of July, and a decrease in extreme frequency at certain stations during spring. Changes in climate variables were investigated to identify causal relationships with precipitation and flow hydrological extremes. Extremes were found to coincide with certain weather types, generally characteristic of cyclonic conditions and/or orographic enhancement. These were largely correlated with strong positive and negative stages of the North Atlantic oscillation, particularly during winter. Snow cover records indicated rapid decline in the Severn Uplands and a reduction in snow indicated a link to changes in spring hydrological extremes. All changes seemed to be driven by the underlying climate variable of air temperature, which coincided with changes in observed national and global temperature records. Hydroclimatological changes indicated a possible temporal shift in the occurrence of extreme hydrological events, which was likely to have been affected by changes in catchment physical properties. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]