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Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

This paper examines the controls on supercooled liquid water content (SLWC) and drop number concentrations (Nt,CDP) over the Payette River basin during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. During SNOWIE, 27.4% of 1-Hz in situ cloud droplet probe samples were in an environment containing supercooled liquid water (SLW). The interquartile range of SLWC, when present, was found to be 0.02-0.18 g m23 and 13.3-37.2 cm23 for Nt,CDP, with the most extreme values reaching 0.40-1.75 g m23 and 150-320 cm23 in isolated regions of convection and strong shear-induced turbulence. SLWC and Nt,CDP distributions are shown to be directly related to cloud-top temperature and ice particle concentrations, consistent with past research over other mountain ranges. Two classes of vertical motions were analyzed as potential controls on SLWC and Nt,CDP, the first forced by the orography and fixed in space relative to the topography (stationary waves) and the second transient, triggered by vertical shear and instability within passing synoptic-scale cyclones. SLWC occurrence and magnitudes, and Nt,CDP associated with fixed updrafts were found to be normally distributed about ridgelines when SLW was present. SLW was more likely to form at low altitudes near the terrain slope associated with fixed waves due to higher mixing ratios and larger vertical air parcel displacements at low altitudes. When considering transient updrafts, SLWC and Nt,CDP appear more uniformly distributed over the flight track with little discernable terrain dependence as a result of time and spatially varying updrafts associated with passing weather systems. The implications for cloud seeding over the basin are discussed. [ABSTRACT FROM AUTHOR]

In Part II, two classes of vertical motions, fixed (associated with vertically propagating gravity waves tied to flow over topography) and transient (associated primarily with vertical wind shear and conditional instability within passing weather systems), were diagnosed over the Payette River basin of Idaho during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). This paper compares vertical motions retrieved from airborne Doppler radial velocity measurements with those from a 900-m-resolution model simulation to determine the impact of transient vertical motions on trajectories of ice particles initiated by airborne cloud seeding. An orographic forcing index, developed to compare vertical motion fields retrieved from the radar with the model, showed that fixed vertical motions were well resolved by the model while transient vertical motions were not. Particle trajectories were calculated for 75 cross-sectional pairs, each differing only by the observed and modeled vertical motion field. Wind fields and particle terminal velocities were otherwise identical in both trajectories so that the impact of transient vertical circulations on particle trajectories could be isolated. In 66.7% of flight-leg pairs, the distance traveled by particles in the model and observations differed by less than 5 km with transient features having minimal impact. In 9.3% of the pairs, model and observation trajectories landed within the ideal target seeding elevation range (>2000 m), whereas, in 77.3% of the pairs, both trajectories landed below the ideal target elevation. Particles in the observations and model descended into valleys on the mountains' lee sides in 94.2% of cases in which particles traveled less than 37 km. [ABSTRACT FROM AUTHOR]

Multiyear climate predictions provide climate outlooks from years to a decade in advance. As multiyear temperature predictions become more mainstream and skillful, guidance is needed to assist practitioners who wish to explore this maturing field. This paper demonstrates the process and considerations of incorporating multiyear temperature predictions into water resources planning. Multiyear temperature predictions from the Community Earth System Model Decadal Prediction Large Ensemble are presented as discrete and probabilistic products and are used to force two common hydrologic modeling approaches: conceptual and empirical. The approaches are demonstrated to simulate streamflow in the upper Colorado River basin watershed in Colorado, where diagnostics show that increasing temperatures are associated with decreasing streamflows. Using temperature information for lead years 2-6, two analyses are performed: (i) a retrospective hindcast for the climatological period (1981-2010) and (ii) a blind forecast for 2011-15. For the retrospective hindcast, including temperature information improved the percent error as compared with climatology. For the blind forecast, the multiyear temperature prediction for warming was skillful, but the corresponding multiyear average streamflow predictions from both approaches were counterintuitive: with the predicted warming, the multiyear average streamflow was predicted to be lower than the climatological mean; however, the observed multiyear average streamflow was higher than the climatological mean. This was due to above-average precipitation during the prediction time frame, particularly for one of the years. With that year removed, the multiyear streamflow average became lower than the climatological mean. Temperature provides a marginal source of streamflow predictability, but there will be substantial uncertainty until prediction skill for year-to-year climate variability, especially for precipitation, increases. [ABSTRACT FROM AUTHOR]

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme''s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output. This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work. [ABSTRACT FROM AUTHOR]

Assessing climate change risk to municipal water supplies is often conducted by hydrologic modeling specific to local watersheds and infrastructure to ensure that outputs are compatible with existing planning frameworks and processes. This study leverages the modeling capacity of an operational National Weather Service River Forecast Center to explore the potential impacts of future climate-driven hydrologic changes on factors important to planning at the Salt Lake City Department of Public Utilities (SLC). Hydrologic modeling results for the study area align with prior research in showing that temperature changes alone will lead to earlier runoff and reduced runoff volume. The sensitivity of average annual flow to temperature varies significantly between watersheds, averaging -3.8% °F-1 and ranging from 21.8% to 26.5% flow reduction per degree Fahrenheit of warming. The largest flow reductions occur during the high water demand months of May-September. Precipitation drives hydrologic response more strongly than temperature, with each 1% precipitation change producing an average 1.9% runoff change of the same sign. This paper explores the consequences of climate change for the reliability of SLC's water supply system using scenarios that include hydrologic changes in average conditions, severe drought scenarios, and future water demand test cases. The most significant water management impacts will be earlier and reduced runoff volume, which threaten the system's ability to maintain adequate streamflow and storage to meet late-summer water demands. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between vegetation in the Liangzhou Oasis in the Upper Shiyang River watershed (USRW) of west-central Gansu, China, and within-watershed precipitation, soil water storage, and oasis self-support. Oases along the base of the Qilian Mountains receive a significant portion of their water supply (over 90%) from surface and subsurface flow originating from the Qilian Mountains. Investigation of vegetation control on oasis water conditions in the USRW is based on an application of a process model of soil water hydrology. The model is used to simulate long-term soil water content (SWC) in the Liangzhou Oasis as a function of (i) monthly composites of Moderate Resolution Imaging Spectroradiometer (MODIS) images of land surface and mean air temperature, (ii) spatiotemporal calculations of monthly precipitation and relative humidity generated with the assistance of genetic algorithms (GAs), and (iii) a 80-m-resolution digital elevation model (DEM) of the area. Modeled removal of vegetation is shown to affect within-watershed precipitation and soil water storage by reducing the exchange of water vapor from the land surface to the air, increasing the air’s lifting condensation level by promoting drier air conditions, and causing the high-intensity precipitation band in the Qilian Mountains to weaken and to be displaced upward, leading to an overall reduction of water to the Liangzhou Oasis. [ABSTRACT FROM AUTHOR]

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov-Sminorv (KS), Anderson-Darling (AD), and chi-square (χ2) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by the Weibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability. [ABSTRACT FROM AUTHOR]

This paper introduces a new, two-part scheme for postprocessing single-valued precipitation forecast to create probabilistic quantitative precipitation forecast (PQPF). This scheme, herein referred to as the mixed-type nonhomogeneous regression (MNHR), combines the use of logistic regression for estimating rainfall intermittency and nonhomogeneous regression for estimation of additional parameters of the conditional distribution. The performance of MNHR is evaluated relative to operational mixed-type meta-Gaussian distribution (MMGD) and the censored, shifted gamma distribution (CSGD) in postprocessing Global Ensemble Forecast System (GEFS) reforecasts averaged over 25 watersheds in the American River basin in California. The results point to superior performance of MNHR relative to MMGD and CSGD in terms of the skill of postprocessed PQPFs at 24- and 96-h accumulation windows. In addition, it is observed that the performance of CSGD tends to trail behindMNHRandMMGDat least for the 24-h window, though the performance differences tend to narrow at higher forecast amounts and longer lead times. Our analyses suggest that CSGD's underperformance arises partly from its tendency to inflate the shift parameter estimates, which is pronounced over the study site possibly because of infrequent rainfall occurrence. By contrast,MNHR's use of logistic regression helps avoid such bias, and its formulation of conditional distribution addresses the lack of skewness of MMGD for higher forecast amounts. Moreover, MHNR-based PQPF exhibits both superior calibration and relatively high sharpness at short lead times and on an unconditional sense, whereas it features lower sharpness relative to the other two suites when conditioned on higher forecast amount. This trade-off between calibration and conditional sharpness warrants further research. [ABSTRACT FROM AUTHOR]

The Colorado River basin (CRB) supplies water to approximately 40 million people and is essential to hydropower generation, agriculture, and industry. In this study, a monthly water balance model is used to compute hydroclimatic water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff) for the period 1901-2014 across the entire CRB. The time series of monthly runoff is aggregated to compute wateryear runoff and then used to identify drought periods in the basin. For the 1901-2014 period, eight basinwide drought periods were identified. The driest drought period spanned years 1901-04, whereas the longest drought period occurred during 1943-56. The eight droughts were primarily driven by winter precipitation deficits rather than warm temperature anomalies. In addition, an analysis of prehistoric drought for the CRB--computed using tree-ring-based reconstructions of the Palmer drought severity index--indicates that during some past centuries drought frequency was higher than during the twentieth century and that some centuries experienced droughts that were much longer than those during the twentieth century. More frequent or longer droughts than those that occurred during the twentieth century, combined with continued warming associated with climate change, may lead to substantial future water deficits in the CRB. [ABSTRACT FROM AUTHOR]

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models. [ABSTRACT FROM AUTHOR]

The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin. [ABSTRACT FROM AUTHOR]

Long-term (20 yr) river discharge records from 30 of the world’s largest river basins have been used to characterize surface hydrologic flows in relation to net precipitation inputs, ocean climate teleconnections, and human land/water use patterns. This groundwork study is presented as a precedent to distributed simulation modeling of surface hydrologic flows in large river basins. Correlation analysis is used as a screening method to classify river basins into categories based on major controls on discharge, for example, climate, land use, and dams. Comparisons of paired station records at upstream and downstream discharge locations within each major river basin suggest that the discharge signals represented in upstream discharge records are sustained in the downstream station records for nearly two-thirds of the drainage basins selected. River basins that showed the strongest localized climate control over historical discharge records, in terms of correlations with net basinwide precipitation rates, are located mainly in the seasonally warm temperate and tropical latitude zones, as opposed to river basins located mainly in the higher latitude zones (above 45°N). Ocean climate indices such as the Niño1+2 and Niño3+4 correlate highly with historical interannual patterns in monthly river discharge for only four of the selected discharge station records, namely, on the Amazon, Congo (Zaire), Columbia, and Colorado (Arizona) Rivers. Historical patterns of cropland development and irrigated areas may explain the weak climate correlations with interannual patterns in monthly river discharge rates for at least one-third of the major river drainages selected from the historical discharge dataset. This paper is part of a special theme issue on land use and ecosystems. [ABSTRACT FROM AUTHOR]

This article presents the data collected and analyzed using the University of Iowa's X-band polarimetric (XPOL) radars that were part of the spring 2013 hydrology-oriented Iowa Flood Studies (IFloodS) field campaign, sponsored by NASA's Global Precipitation Measurement (GPM) Ground Validation (GV) program. The four mobile radars have full scanning capabilities that provide quantitative estimation of the rainfall at high temporal and spatial resolutions over experimental watersheds. IFloodS was the first extensive test of the XPOL radars, and the XPOL radars demonstrated their field worthiness during this campaign with 46 days of nearly uninterrupted, remotely monitored, and controlled operations. This paper presents detailed postcampaign analyses of the high-resolution, research-quality data that the XPOL radars collected. The XPOL dual-polarimetric products and rainfall are compared with data from other instruments for selected diverse meteorological events at high spatiotemporal resolutions from unprecedentedly unique and vast data generated during IFloodS operations. The XPOL data exhibit a detailed, complex structure of precipitation viewed at multiple range resolutions (75 and 30 m). The inter-XPOL comparisons within an overlapping scanned domain demonstrate consistency across different XPOL units. The XPOLs employed a series of heterogeneous scans and obtained estimates of the meteorological echoes up to a range oversampling of 7.5 m. A finer-resolution (30 m) algorithm is described to correct the polarimetric estimates for attenuation at the X band and obtain agreement of attenuation-corrected products with disdrometers and NASA S-band polarimetric (NPOL) radar. The paper includes hardware characterization of Iowa XPOL radars conducted prior to the deployment in IFloodS following the GPM calibration protocol. [ABSTRACT FROM AUTHOR]

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins. [ABSTRACT FROM AUTHOR]

The upper Colorado River basin (UCRB) is one of the primary sources of water for the western United States, and increasing temperatures likely will elevate the risk of reduced water supply in the basin. Although variability in water-year precipitation explains more of the variability in water-year UCRB streamflow than water-year UCRB temperature, since the late 1980s, increases in temperature in the UCRB have caused a substantial reduction in UCRB runoff efficiency (the ratio of streamflow to precipitation). These reductions in flow because of increasing temperatures are the largest documented temperature-related reductions since record keeping began. Increases in UCRB temperature over the past three decades have resulted in a mean UCRB water-year streamflow departure of −1306 million m3 (or −7% of mean water-year streamflow). Additionally, warm-season (April through September) temperature has had a larger effect on variability in water-year UCRB streamflow than the cool-season (October through March) temperature. The greater contribution of warm-season temperature, relative to cool-season temperature, to variability of UCRB flow suggests that evaporation or snowmelt, rather than changes from snow to rain during the cool season, has driven recent reductions in UCRB flow. It is expected that as warming continues, the negative effects of temperature on water-year UCRB streamflow will become more evident and problematic. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and soil freeze–thaw (FT) are two relatively active surface parameters that are significant to the sustainable development of the water–land–air–plant–human nexus. Over time, regional or global SM and FT datasets with different spatial resolutions have been developed. In response to the requirements of multiscale product validation and multisource uncertainty tracking, a soil moisture and soil temperature (ST) monitoring network in the Qinghai Lake Basin (QLB-NET) was established in September 2019. The QLB-NET is characterized by densely distributed in situ sites (82 sites) measuring SM and ST at 5-, 10-, and 30-cm depths, with 60 sites in a large-scale network covering an area of 36 km × 40 km and 22 sites evenly distributed across two small-scale 1 km × 1 km networks. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., 36 km × 36 km, 25 km × 25 km, and 0.25° × 0.25°. When statistics are correspondingly performed over 50 out of 54, 25 out of 29, and 25 out of 28 sites, the results are described as follows: 1) the standard deviation of the mean SM varies between 0.0127 and 0.0196 m3 m−3, with the corresponding difference between the upper and lower quartiles being less than 0.02 m3 m−3; 2) the ground freeze–thaw state can be correctly identified with high probabilities ranging from 85.3% to 100% on two freeze–thaw transitional dates. The QLB-NET observed datasets are distributed online and will be continuously updated through cooperation with the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), facilitating product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. SIGNIFICANCE STATEMENT: This study aims to introduce the newly constructed QLB-NET in detail, including the site deployment strategy, the installation and maintenance, the sensor calibration, and the characteristics and quality of the in situ SM and ST measurements. Quantitative analyses of the in situ measurements show that the QLB-NET can provide stable and reliable ground truth for SM and FT over coarse grid scales, e.g., the SMAP 36 km × 36 km grid. The high-quality QLB-NET measurements will facilitate research of product validation and uncertainty tracking, spatiotemporal analysis of SM change and FT transition, optimization of the SM and FT retrieving algorithms and scaling methods, and development of the mountainous microwave radiative transfer model. [ABSTRACT FROM AUTHOR]

A regional watershed model was developed for watersheds contributing to Long Island Sound, including the Connecticut River basin. The study region covers approximately 40 900 km2, extending from a moderate coastal climate zone in the south to a mountainous northern New England climate zone dominated by snowmelt in the north. The input data indicate that precipitation and temperature have been increasing for the last 46 years (1961-2006) across the region. Minimum temperature has increased more than maximum temperature over the same period (1961-2006). The model simulation indicates that there was an upward trend in groundwater recharge across most of the modeled region. However, trends in increasing precipitation and groundwater recharge are not significant at the 0.05 level if the drought of 1961-67 is removed from the time series. The trend in simulated snowfall is not significant across much of the region, although there is a significant downward trend in southeast Connecticut and in central Massachusetts. To simulate future trends, two input datasets, one assuming high carbon emissions and one assuming low carbon emissions, were developed from GCM forecasts. Under both of the carbon emission scenarios, simulations indicate that historical trends will continue, with increases in groundwater recharge over much of the region and substantial snowfall decreases across Massachusetts, Connecticut, southern Vermont, and southern New Hampshire. The increases in groundwater recharge and decreases in snowfall are most pronounced for the high emission scenario. [ABSTRACT FROM AUTHOR]

Spring snowpack is an important water resource in many river basins in the United States in areas where snowmelt comprises a large part of the annual runoff. Increasing temperatures will likely reduce snowpacks in the future, resulting in more winter runoff and less available water during the summer low-flow season. As part of the National Climate Change Modeling Project by the U.S. Geological Survey, distributed watershed-model output was analyzed to characterize areal extent and water-equivalent volumes of spring snowpack for a warming climate. The output from seven selected watershed models from the mountainous western United States and one model from coastal Maine in the northeastern United States shows a future of declining spring snowpack. Snow-cover area (SCA) and snow-water equivalent (SWE) were used to compare the spring snowpack for current conditions (2006) with three time periods in the future (2030, 2060, and 2090) using three Intergovernmental Panel on Climate Change (IPCC) emission scenarios published in the 2007 Special Report on Emission Scenarios (SRES): A2, B1, and A1B. Distributed SWE and SCA values were sorted into elevation zones in each basin. The change in spring snowpack over time was greater than the change among different emission scenarios, suggesting that, even for a globally reduced carbon emission scenario, large decreases in SWE are likely to occur. The SRES A2 scenario resulted in the greatest decrease in SWE for six of the basins, and the SRES B1 and A1B scenarios resulted in the greatest decrease in one basin each. [ABSTRACT FROM AUTHOR]

The large-scale equatorial circulation known as the Madden–Julian oscillation (MJO) has been shown to impact tropical cyclone activity in several basins around the globe. In this paper, the author utilizes an MJO index created by Wheeler and Hendon to examine its impacts on tropical genesis and intensification in the Atlantic. Large differences in frequency and intensity of tropical cyclone activity are seen, both in the tropical Atlantic as well as in the northwest Caribbean and Gulf of Mexico depending on the MJO phase. Coherent changes in upper- and lower-level winds and relative humidity are likely responsible for these differences. Since the MJO shows potential predictability out to about two weeks, the relationships discussed in this paper may be useful for short-term predictions of the probability of tropical cyclone activity in the Atlantic as a complement to the already available longer-term seasonal predictions. [ABSTRACT FROM AUTHOR]

The Brahmaputra River of South Asia is the fourth largest river in the world in terms of annual discharge. The lower Brahmaputra River basin is susceptible to catastrophic flooding with major social, economic, and public health impacts. There is relatively little rainfall and snowpack information for the watershed, and the system is poorly understood hydrologically. Using a combination of available remotely sensed and gauge data, this study analyzes snow cover, rainfall, and monsoon period discharge for a 14-yr time period (1986–99). It is found that interannual rainfall variability is low and is a weak predictor of monsoon discharge volumes. Strong evidence is found, however, that maximum spring snow cover in the upper Brahmaputra basin is a good predictor of the monsoon flood volume. Despite the temporal and spatial limitations of the data, this study’s analysis demonstrates the potential for developing an empirical tool for predicting large flood events that may allow an annual early window for mitigating flood damages in the lower Brahmaputra basin, home to 300 million people. [ABSTRACT FROM AUTHOR]

Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade-1, with smaller lakes warming at a faster rate. Complementing regression and space--time analyses showed similar results (R² = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximumorminimum air temperature. Nomajor hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers. [ABSTRACT FROM AUTHOR]

Regional warming and associated changes in hydrologic systems pose challenges to water supply management in river basins of the western United States and call for improved understanding of the spatial and temporal variability of runoff. We apply a network of total width, subannual width, and delta blue intensity tree-ring chronologies in combination with a monthly water balance model to identify droughts and their associated precipitation P and temperature T footprints in the Truckee–Carson River basin (TCRB). Stepwise regression gave reasonably accurate reconstructions, from 1688 to 1999, of seasonal P and T (e.g., R2 = 0.50 for May–September T). These were disaggregated to monthly values, which were then routed through a water balance model to generate "indirectly" reconstructed runoff. Reconstructed and observed annual runoff correlate highly (r = 0.80) from 1906 to 1999. The extended runoff record shows that twentieth-century droughts are unmatched in severity in a 300-yr context. Our water balance modeling reconstruction advances the conventional regression-based dendrochronological methods as it allows for multiple hydrologic components (evapotranspiration, snowmelt, etc.) to be evaluated. We found that imposed warming (3° and 6°C) generally exacerbated the runoff deficits in past droughts but that impact could be lessened and sometimes even reversed in some years by compensating factors, including changes in snow regime. Our results underscore the value of combining multiproxy tree-ring data with water balance modeling to place past hydrologic droughts in the context of climate change. Significance Statement: We show how water balance modeling in combination with tree-ring data helps place modern droughts in the context of the past few centuries and a warming climate. Seasonal precipitation and temperature were reconstructed from multiproxy tree-ring data for a mountainous location near Lake Tahoe, and these reconstructions were routed through a water balance model to get a record of monthly runoff, snowmelt, and other water balance variables from 1688 to 1999. The resulting extended annual runoff record highlights the unmatched severity of twentieth-century droughts. A warming of 3°C imposed on reconstructed temperature generally exacerbates the runoff anomalies in past droughts, but this effect is sometimes offset by warming-related changes in the snow regime. [ABSTRACT FROM AUTHOR]

Extreme snow ablation can greatly impact regional hydrology, affecting streamflow, soil moisture, and groundwater supplies. Relatively little is known about the climatology of extreme ablation events in the eastern United States, and the causal atmospheric forcing mechanisms behind such events. Studying the Susquehanna River basin over a 50-yr period, here we evaluate the variability of extreme ablation and river discharge events in conjunction with a synoptic classification and global-scale teleconnection pattern analysis. Results indicate that an average of 4.2 extreme ablation events occurred within the basin per year, where some 88% of those events resulted in an increase in river discharge when evaluated at a 3-day lag. Both extreme ablation and extreme discharge events occurred most frequently during instances of southerly synoptic-scale flow, accounting for 35.7% and 35.8% of events, respectively. However, extreme ablation was also regularly observed during high pressure overhead and rain-on-snow synoptic weather types. The largest magnitude of snow ablation per extreme event occurred during occasions of rain-on-snow, where a basinwide, areal-weighted 5.7 cm of snow depth was lost, approximately 23% larger than the average extreme event. Interannually, southerly flow synoptic weather types were more frequent during winter seasons when the Arctic and North Atlantic Oscillations were positively phased. Approximately 30% of the variance in rain-on-snow weather type frequency was explained by the Pacific–North American pattern. Evaluating the pathway of physical forcing mechanisms from regional events up through global patterns allows for improved understanding of the processes resulting in extreme ablation and discharge across the Susquehanna basin. Significance Statement: The purpose of this study is to better understand how certain weather patterns are related to extreme snowmelt and streamflow events and what causes those weather patterns to vary with time. This is valuable information for informing hazard preparation and resource management within the basin. We found that weather patterns with southerly winds were the most frequent patterns responsible for extreme melt and streamflow, and those patterns occurred more often when the Arctic and North Atlantic Oscillations were in their "positive" configuration. Future work should consider the potential for these patterns, and related impacts, to change over time. [ABSTRACT FROM AUTHOR]

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]

A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world's oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world's oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia-Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia-Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa. [ABSTRACT FROM AUTHOR]

The South Asian monsoon is critically important for agricultural production in the region that includes the vast, fertile Indus and Ganges basins. However, the behavior of the South Asian monsoon is not well understood because of its complex nature, and existing instrumental climate records are insufficient for investigating the risks of the low-frequency but high-impact megadroughts that have historically occurred. This paper develops an inverse Palmer drought severity index (PDSI) model to retrieve paleoprecipitation for the region during the time period of 1300-1899, using available data of the water-holding capacity of soil, temperature, and reconstructed PDSI based on the tree-ring analysis of Cook et al.. Temperature data are reconstructed by a regression analysis utilizing an existing temperature reconstruction in an adjacent region and the Pacific decadal oscillation. Based on the retrieved paleoprecipitation, several megadroughts are identified during the reconstruction period. The drought frequency in the Indus basin is higher than that in the Ganges basin. The intensity, frequency, and spatial extent of severe droughts increased from 1300-1899 to 1900-2010. As a signal of climate change, increasing intensity and frequency of severe drought in the Indus and Ganges River basins needs adaptation strategies and drought preparedness measures to secure the food production in this area. [ABSTRACT FROM AUTHOR]

The Ganges-Brahmaputra-Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework. [ABSTRACT FROM AUTHOR]

This paper develops a multivariate mosaic subgrid approach to represent subgrid variability in land surface models (LSMs). The k-means clustering is used to take an arbitrary number of input descriptors and objectively determine areas of similarity within a catchment or mesoscale model grid box. Two different classifications of hydrologic similarity are compared: an a priori classification, where clusters are based solely on known physiographic information, and an a posteriori classification, where clusters are defined based on high-resolution LSM simulations. Simulations from these clustering approaches are compared to high-resolution gridded simulations, as well as to three common mosaic approaches used in LSMs: the 'lumped' approach (no subgrid variability), disaggregation by elevation bands, and disaggregation by vegetation types in two subcatchments. All watershed disaggregation methods are incorporated in the Noah Multi-Physics (Noah-MP) LSM and applied to snowmelt-dominated subcatchments within the Reynolds Creek watershed in Idaho. Results demonstrate that the a priori clustering method is able to capture the aggregate impact of finescale spatial variability with O(10) simulation points, which is practical for implementation into an LSM scheme for coupled predictions on continental-global scales. The multivariate a priori approach better represents snow cover and depth variability than the univariate mosaic approaches, critical in snowmelt-dominated areas. Catchment-averaged energy fluxes are generally within 10%-15% for the high-resolution and a priori simulations, while displaying more subgrid variability than the univariate mosaic methods. Examination of observed and simulated streamflow time series shows that the a priori method generally reproduces hydrograph characteristics better than the simple disaggregation approaches. [ABSTRACT FROM AUTHOR]

In this study, the authors contrast four century-long meteorological datasets comprising of two sets of observations [Climate Research Unit (CRU) and Parameter–Elevation Regressions on Independent Slopes Model (PRISM)] and two atmospheric reanalyses [Twentieth Century Reanalysis (20CR) and Florida Climate Institute–Florida State University Land–Atmosphere Regional Reanalysis version 1.0 (FLAReS1.0)] to diagnose the El Niño–Southern Oscillation (ENSO) forced variations on the streamflow in 28 watersheds spread across the southeastern United States (SEUS). The datasets are used to force three different lumped (calibrated) hydrological models with precipitation from these four sources of century-long datasets separately to obtain the median prediction from 1800 (=3 models × 600 simulations per model per watershed per season) multimodel estimates of seasonal mean streamflow across the 28 watersheds in the SEUS for each winter season from 1906 to 2005. The authors then compare and contrast themean streamflow and its variability estimates fromall three of the century-long climate forcings. The multimodel strategy of simulating the seasonal mean streamflow is to reduce the hydrological model uncertainty. The authors focus on the boreal winter season when ENSO influence on the SEUS climate variations is well known. The authors find that the atmospheric reanalysis over the SEUS is able to reasonably capture the ENSO teleconnections as depicted in the CRU and PRISM precipitation datasets. Even the observed decadal modulation of this teleconnection by Atlantic multidecadal oscillation (AMO) is broadly captured. The streamflow in the 28 watersheds also show similar consistency across the four datasets in that the positive correlations of the boreal winter Niño-3.4 SST anomalies with corresponding anomalies of streamflow, the associated shift in the probability density function of the streamflow with the change in phase of ENSO, and the decadal modulation of the ENSO teleconnection by the AMO are sustained in the streamflow simulations forced by all four climate datasets (CRU, PRISM, 20CR, and FLAReS1.0). However, the ENSO signal in the streamflow is consistently much stronger in the southern watersheds (over Florida) of the SEUS across all four climate datasets. During the negative phase of the AMO, however, there is a clear shift of the ENSO teleconnections with streamflow, with winter streamflows in northern watersheds (over the Carolinas) exhibiting much stronger correlations with the ENSO Niño-3.4 index relative to the southern watersheds of the SEUS. This study clearly indicates that the proposed methodology using FLAReS1.0 serves as a viable alternative to reconstruct twentieth-century SEUS seasonal winter hydrology that captures the interannual variations of ENSO and associated decadal variations forced by the AMO. However, it is found that the FLAReS1.0 forced streamflow is far from adequate in simulating the streamflow dynamics of the watershed over the SEUS at a daily time scale. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Variable Infiltration Capacity (VIC) model to the Fraser River basin (FRB) of British Columbia (BC), Canada, over the latter half of the twentieth century. The Fraser River is the longest waterway in BC and supports the world's most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system, but with climate change, it may evolve to a pluvial regime. Thus, the goal of this study is to evaluate the changing contribution of snow to the hydrology of the FRB over the latter half of the twentieth century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1949 to 2006 (water years) at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major subwatersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff ( R). The ratio of the spatially averaged maximum SWE to R ( RSR) is used to quantify the contribution of snow to the runoff in the 11 subwatersheds of interest. From 1949 to 2006, RSR exhibits a significant decline in 9 of the 11 subwatersheds (with p < 0.05 according to the Mann-Kendall test statistics). To determine the sensitivity of RSR, the air temperature and precipitation in the forcing dataset are then perturbed. The ratio RSR decreases more significantly, especially during the 1990s and 2000s, when air temperatures have warmed considerably compared to the 1950s. On the other hand, increasing precipitation by a multiplicative factor of 1.1 causes RSR to decrease. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated system. [ABSTRACT FROM AUTHOR]

SIGNIFICANCE STATEMENT: Water is a limited and critical resource in the western United States. To protect water access for millions of people and ecosystems, and to preserve our ability to irrigate millions of acres of cropland, policymakers and water managers require advanced weather and water prediction systems. To improve these forecast systems in high-altitude complex terrain, the National Oceanic and Atmospheric Administration (NOAA) has deployed instrumentation in the East River watershed of the Upper Colorado River basin as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). SPLASH observations are being used to advance fundamental understanding of mountain weather and water and to advance predictive capabilities across weather and climate time scales. Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry. [ABSTRACT FROM AUTHOR]

Long-term changes in the water budget of lakes in the Tibetan Plateau due to climate change are of great interest not only for the importance of water management, but also for the critical challenge due to the lack of observations. In this paper, the water budget of Nam Co Lake during 1980-2010 is simulated using a dynamical monthly water balance model. The simulated lake level is in good agreement with field investigations and the remotely sensed lake level. The long-term hydrological simulation shows that from 1980 to 2010, lake level rose from 4718.34 to 4724.93 m, accompanied by an increase of lake water storage volume from 77.33 × 109 to 83.66 × 109 m3. For the net lake level rise (5.93 m) during the period 1980-2010, the proportional contributions of rainfall-runoff, glacier melt, precipitation on the lake, lake percolation, and evaporation are 104.7%, 56.6%, 41.7%, −22.2%, and −80.9%, respectively. A positive but diminishing annual water surplus is found in Nam Co Lake, implying a continuous but slowing rise in lake level as a hydrological consequence of climate change. [ABSTRACT FROM AUTHOR]

The decrease in size the Aral Sea in central Asia, seen as both lower water levels and reduction in areal extent, has been one of the greatest examples of anthropogenic modification of a natural system in recent history. Many studies have monitored the extent and rate of this water loss and provided estimates on the expected life span of the remaining water. However, with little data for groundwater monitoring in the post-Soviet era, it is unclear what the water balance currently is in the remainder of the watershed. Redistribution of water upstream in the watershed including damming to create reservoirs and groundwater recharge from irrigation has not only deprived the sea of water but also increased evapotranspiration and altered local climate patterns. Using Tropical Rainfall Measurement Mission (TRMM) and Global Precipitation Climatology Centre (GPCC) data, rainfall trends for the Aral Sea watershed were analyzed over 10- and 30-yr periods and only minimal changes in rainfall were detected. Using Gravity Recovery and Climate Experiment (GRACE) gravity data from 2003 to 2012, trends in equivalent water mass were determined for the entire watershed. Estimates show up to 14 km3 of equivalent water mass has been lost from the watershed annually from 2002 to 2013. The mass loss throughout the basin is most likely attributable to increased evapotranspiration due to the inefficient irrigation systems and other human modification increasing the need for international cooperation and conservation programs to minimize negative impacts throughout the region. [ABSTRACT FROM AUTHOR]

The authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute-Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall-runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogs of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, the authors find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root-mean-square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that, over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. The authors contend that, given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty. [ABSTRACT FROM AUTHOR]

Global warming will have direct impacts on regional water resources by accelerating the hydrological cycle. Hydrological simulation is an important approach to studying climate change impacts. In this paper, a snowmelt-based water balance model (SWBM) was used to simulate the effect of climate change on runoff in the Kuye River catchment of the Loess Plateau, China. Results indicated that the SWBM is suitable for simulating monthly discharge into arid catchments. The response of runoff in the Kuye River catchment to climate change is nonlinear, and runoff is more sensitive to changes in precipitation than to changes in temperature. The projections indicated that the Kuye River catchment would undergo more flooding in the 2020s, and global warming would probably shorten the main flood season in the catchment, with greater discharge occurring in August. Although projected changes in annual runoff are uncertain, the possibilities of regional water shortages and regional flooding are essential issues that need to be fully considered. [ABSTRACT FROM AUTHOR]

Since historical (predam) data are traditionally the sole criterion for dam design, future (postdam) meteorological and hydrological variability due to land-use and land-cover change cannot be considered for assessing design robustness. For example, postdam urbanization within a basin leads to definite and immediate increase in direct runoff and reservoir peak inflow. On the other hand, urbanization can strategically (i.e., gradually) alter the mesoscale circulation patterns leading to more extreme rainfall rates. Thus, there are two key pathways (immediate or strategic) by which the design flood magnitude can be compromised. The main objective of the study is to compare the relative contribution to increase in flood magnitudes through direct effects of land-cover change (urbanization and less infiltration) with gradual climate-based effects of land-cover change (modification in mesoscale storm systems). The comparison is cast in the form of a sensitivity study that looks into the response to the design probable maximum flood (PMF) from probable maximum precipitation (PMP). Using the American River watershed (ARW) and Folsom Dam as a case study, simulated peak floods for the 1997 (New Year's) flood event show that a 100% impervious watershed has the potential of generating a flood that is close to design PMF. On the other hand, the design PMP produces an additional 1500m³ s-1 peak flood compared to the actual PMF when the watershed is considered 100% impervious. This study points to the radical need for consideration future land-cover changes up front during the dam design and operation formulation phase by considering not only the immediate effects but also the gradual climatic effects on PMF. A dynamic dam design procedure should be implemented that takes into account the change of land-atmospheric and hydrological processes as a result of land-cover modification rather than relying on historical records alone. [ABSTRACT FROM AUTHOR]

A major research plan entitled "Integrated research on the eco-hydrological process of the Heihe River Basin" was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of inter-disciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated eco-hydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses. [ABSTRACT FROM AUTHOR]

Heavy rainfall hit the Yangtze-Huai Rivers basin (YHRB) of east China several times during the pro-longed 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu pre-cipitation processes, especially at the mesoß and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather sta-tions with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative as-sessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with sub-stantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., >10mmh-1). [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Changes in temperature and precipitation projected from five general circulation models, using one late-twentieth-century and three twenty-first-century emission scenarios, were downscaled to three different baseline conditions. Baseline conditions are periods of measured temperature and precipitation data selected to represent twentieth-century climate. The hydrologic effects of the climate projections are evaluated using the Precipitation-Runoff Modeling System (PRMS), which is a watershed hydrology simulation model. The Almanor Catchment in the North Fork of the Feather River basin, California, is used as a case study. Differences and similarities between PRMS simulations of hydrologic components (i.e., snowpack formation and melt, evapotranspiration, and streamflow) are examined, and results indicate that the selection of a specific time period used for baseline conditions has a substantial effect on some, but not all, hydrologic variables. This effect seems to be amplified in hydrologic variables, which accumulate over time, such as soil-moisture content. Results also indicate that uncertainty related to the selection of baseline conditions should be evaluated using a range of different baseline conditions. This is particularly important for studies in basins with highly variable climate, such as the Almanor Catchment. [ABSTRACT FROM AUTHOR]

The paper aims to quantitatively estimate the role of snowmelt in the spring flood flow and the redistribution of river runoff for the northern (Arctic) part of the western Siberian Plain (the rivers Poluy, Nadym, Pur, and Taz). In this region, the presence of wetlands and thermokarst lakes significantly influences the seasonal redistribution of river discharge. First the study region is described, and the snow regime from in situ observations at the Tarko-Sale meteorological station is analyzed. As Special Sensor Microwave Imager (SSM/I) estimates of snow depth for this region are lower than in situ observations, a correction of the SSM/I snow depth estimates is done using snow parameters measured on the snow transect near the meteorological station Tarko-Sale for 1991-96. This reestimated snow depth is then used to assess the volume of water stored every winter on the watersheds for 1989-2006. This snow product is compared with the spring flood streamflow estimated from in situ observations, and the regional relationship between the snow water storage and flood flow is constructed. The proportion of meltwater that does not reach the main rivers and is thus evaporated or stored by the wetlands is estimated to be on average 30%% (varying from 0%% to 74%%). We observe an increasing trend of this value from 20%%-30%% in the early 1990s to 50%%-60%% in the mid-2000s. This increase could be attributed to several factors such as increased air temperature (leading to increased evaporation, changes in vegetation cover, and active layer depth) and also to human activity. [ABSTRACT FROM AUTHOR]

Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall-runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall-runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the 'distributed' application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall-runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required. [ABSTRACT FROM AUTHOR]

The potential effects of long-term urbanization and climate change on the freshwater resources of the Flint River basin were examined by using the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter watershed model developed to evaluate the effects of various combinations of precipitation, temperature, and land cover on streamflow and multiple intermediate hydrologic states. Precipitation and temperature output from five general circulation models (GCMs) using one current and three future climate-change scenarios were statistically downscaled for input into PRMS. Projections of urbanization through 2050 derived for the Flint River basin by the Forecasting Scenarios of Future Land-Cover (FORE-SCE) land-cover change model were also used as input to PRMS. Comparison of the central tendency of streamflow simulated based on the three climate-change scenarios showed a slight decrease in overall streamflow relative to simulations under current conditions, mostly caused by decreases in the surface-runoff and groundwater components. The addition of information about forecasted urbanization of land surfaces to the hydrologic simulation mitigated the decreases in streamflow, mainly by increasing surface runoff. [ABSTRACT FROM AUTHOR]

This paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated synoptic weather typing as well as cumulative logit and nonlinear regression methods was employed to develop within-weather-type daily rainfall simulation models. In addition, regression-based downscaling was applied to downscale four general circulation model (GCM) simulations to three meteorological stations (i.e., London, Ottawa, and Toronto) within the river basins for all meteorological variables (except rainfall) used in the study. Using downscaled GCM hourly climate data, discriminant function analysis was employed to allocate each future day for two windows of time (2046--65, 2081--2100) into one of the weather types. Future daily rainfall and its extremes were projected by applying within-weather-type rainfall simulation models together with downscaled future GCM climate data. A verification process of model results has been built into the whole exercise (i.e., statistical downscaling, synoptic weather typing, and daily rainfall simulation modeling) to ascertain whether the methods are stable for projection of changes in frequency of future daily rainfall events. Two independent approaches were used to project changes in frequency of daily rainfall events: method I--comparing future and historical frequencies of rainfall-related weather types, and method II--applying daily rainfall simulation models with downscaled future climate information. The increases of future daily rainfall event frequencies and seasonal rainfall totals (April--November) projected by method II are usually greater than those derived by method I. The increase in frequency of future daily heavy rainfall events greater than or equal to 25 mm, derived from both methods, is likely to be greater than that of future daily rainfall events greater than or equal to 0.2 mm: 35%%--50%% versus 10%%--25%% over the period 2081--2100 derived from method II. In addition, the return values of annual maximum 3-day accumulated rainfall totals are projected to increase by 20%%--50%%, 30%%--55%%, and 25%%--60%% for the periods 2001--50, 2026--75, and 2051--2100, respectively. Inter-GCM and interscenario uncertainties of future rainfall projections were quantitatively assessed. The intermodel uncertainties are similar to the interscenario uncertainties, for both method I and method II. However, the uncertainties are generally much smaller than the projection of percentage increases in the frequency of future seasonal rain days and future seasonal rainfall totals. The overall mean projected percentage increases are about 2.6 times greater than overall mean intermodel and interscenario uncertainties from method I; the corresponding projected increases from method II are 2.2--3.7 times greater than overall mean uncertainties. [ABSTRACT FROM AUTHOR]

In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95%% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov--Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. [ABSTRACT FROM AUTHOR]

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement (''downscaling''), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median −−11%%) caused by the hydrologic model''s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen--Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors'' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water. [ABSTRACT FROM AUTHOR]

Based on the changes in the size of closed basin lakes, the author shows that water availability has undergone large fluctuations in response to conditions experienced during full glacial time and during the period of deglaciation. Based on these observations, the author concludes that as the planet warms in response to the buildup of fossil fuel CO2, the distribution of available water will shift. The resulting changes will be particularly noticeable in the world''s drylands. In this paper, the author attempts to invert the response to cold glacial conditions and to interhemispheric temperature differences, which occurred during the interval of deglaciation, to predict how the hydrologic conditions in the western United States might evolve over the next half-century. The tentative conclusion on both scores is that this region will likely undergo a progressive drying. However, because both the glacial and deglacial hydrologic shifts were certainly influenced by the presence of excess ice, as well as by colder temperatures, the author admits that the validity of this inversion is subject to challenge. [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)--Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10%% of months during the cold half of the year (October--March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations. [ABSTRACT FROM AUTHOR]