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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]

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]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages. [ABSTRACT FROM AUTHOR]

This study quantifies the spatial distribution of precipitation patterns on an annual basis for southeast Louisiana. To compile a long-term record of 24-h rainfall, rainfall reports collected by National Weather Service (NWS) cooperative observers were gathered from National Climatic Data Center (NCDC) archives, private collections of observational data held at regional and local libraries, NWS offices, and local utility providers. The reports were placed into a digital database in which each station’s record was subjected to an extensive quality control process. This process produced a database of daily rainfall reports for 59 south Louisiana stations for the period 1836–2002, with extensive documentation for each site outlining the differences between the study’s data and the data available from the NCDC Web page. A statistical methodology was developed to determine if the four NCDC climate divisions for southeast Louisiana accurately depict average monthly rainfall for the area. This method employs cluster analysis, using Euclidean distance as the measure of dissimilarity for the clustering technique. To resolve missing rainfall observations, an imputation scheme was developed that uses the two most similar stations (based on Euclidean distance) to determine appropriate values for missing rainfall observations. Results from this testing structure show statistical evidence of precipitation microclimates across south Louisiana at higher spatial scales than those of the NCDC climate zones. Quantifying the spatial extent of daily precipitation and documenting historical trends of precipitation provides critical design information for regional infrastructure within this highly vulnerable area of the central Gulf Coast region. [ABSTRACT FROM AUTHOR]

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings. [ABSTRACT FROM AUTHOR]

The rapid development of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) precipitation product provides new opportunities for a wide range of Earth system and natural hazard applications. Spatiotemporal averaging is a common method for IMERG users to acquire suitable resolutions specific to their research or application purpose and has a direct impact on the overall quality of IMERG precipitation estimates. Here, three different IMERG, version 06 (V06), latency run products (i.e., early, late, and final) are assessed against a ground-based benchmark along a continuous series of spatiotemporal resolutions over the Huai River basin (HuaiRB) between June 2014 and May 2017. In general, IMERG products better capture the spatial pattern of precipitation, and demonstrate better reliability, in the southern portion of the HuaiRB relative to its northern region. Furthermore, the degradation of spatiotemporal resolution is associated with better rain/no-rain determination and the consistent improvement of rainfall product performance metrics. This improvement is more pronounced for IMERG products at fine spatiotemporal resolution. However, due to the presence of autocorrelated errors, the performance improvement associated with the degradation of spatiotemporal resolution is less than theoretical expectations assuming purely uncorrelated errors. Component analysis indicates that while both temporal and spatial aggregation do not mitigate temporally autocorrelated errors, temporal averaging can remove spatially autocorrelated error. Hence, temporal averaging is found to be more effective than spatial averaging for improving the quality of IMERG products. These results will inform users of the reliability of IMERG products at different spatiotemporal scales and assist in unifying former disparate validation assessments applied at different scales within the literature. [ABSTRACT FROM AUTHOR]

Flash droughts are extreme phenomena that have been identified using two different approaches. The first approach identifies these events based on unusually rapid intensification rates, whereas the second approach implicitly identifies short-term features. This latter approach classifies flash droughts into two types, namely, precipitation deficit and heat wave flash droughts (denoted as PDFD and HWFD). In this study, we evaluate these two approaches over the Yellow River basin (YRB) to determine which approach provides more accurate information about flash droughts and why. Based on the concept of intensification rate, a new quantitative flash drought identification method focused on soil moisture depletion during the onset–development phase is proposed. Its performance was evaluated by comparing the onset time and spatial dynamics of the identified flash droughts with PDFD and HWFD events identified using the second approach. The results show that the rapid-intensification approach is better able to capture the continuous evolution of a flash drought. Since the approach for identifying PDFD and HWFD events does not consider changes in soil moisture with time, it cannot ensure that the events exhibit rapid intensification, nor can it effectively capture flash droughts' onset. Evaluation of the results showed that the chosen hydrometeorological variables and corresponding thresholds, particularly that of temperature, are the main reasons for the poor performance of the PDFD and HWFD identification approach. This study promotes a deeper understanding of flash droughts that is beneficial for drought monitoring, early warning, and mitigation. [ABSTRACT FROM AUTHOR]

The quality and vertical distribution of precipitation are of utmost importance in hydrological modeling studies in glacierized catchments, which have been facing known challenges in the quality, distribution, and magnitude of observed as well as gridded precipitation due to complex topography. The purpose of the study is to reveal the vertical distribution of precipitation based onmultiple datasets and evaluate the suitability of these datasets for hydrological applications and in the upper Indus basin (UIB). The performance of five gridded precipitation datasets, that is, APHRODITE, CFSR, Princeton Global Meteorological Forcing Dataset for Land SurfaceModeling (PGMFD), TRMM, andHighAsia Refined analysis (HAR), was evaluated against the observed precipitation (OBS) during 2001-07. A corrected reference precipitation dataset was constructed based on the water andmass balance using inversemodelingmethods. TRMMwas identified as the best dataset to represent the spatial and temporal distribution of OBS precipitation. The runoff coefficients for OBS, APHRODITE, TRMM, and PGMFD were greater than 1, indicating that these datasets were underestimated and unable to close the water balance in UIB. The mean annual corrected precipitation was estimated as 593mmyr-1, while the main water-producing elevation zone was located between 3900 and 6600m. CFSR had the highest correlation and lowest biaswith corrected precipitation along the vertical profile. It is concluded that theOBS andmost of the gridded precipitation are insufficient to sustain the water andmass balance inUIB. It is recommended to correct the precipitation based on mass balance at high altitudes (especially along the main water-producing zone) in glacierized catchments before its application in hydrological modeling studies. [ABSTRACT FROM AUTHOR]

Seasonal variations in precipitation (P) and potential evapotranspiration (ET0) are critical for regional hydrometeorological studies and water resource management. The sinusoidal function is widely used to describe the seasonal pattern of P and ET0. However, high errors occur either in the arid places or in places with hyperseasonal precipitation. These limitations are intrinsic properties of the sinusoidal climate descriptor and remain a barrier to provide insight into regional water-energy partitions and hydrologic similarity and predictability. In this study, we used a Gaussian framework as an alternative to describe seasonal variations in P and ET0 regimes in the Yellow River basin (YRB). The results show that the Gaussian framework provides a good approximation to the seasonal pattern of P and has a strong regional applicability for reproducing the monthly PandET0. This allows us to assess the climate seasonality characterizing the regional balance between water supply and energy availability using δP, δET0, and aridity index. The climate seasonality indicates that the balance between water supply and energy availability has a switch in about 32% of the grid cells during the seasonal cycle from1982 to 2015. These grid cells are mostly located in regions with average annual precipitation above 550 mm. In the northwest region of the YRB, which has a dry climate, the amount of potential evapotranspiration always exceeds the precipitation. We argue that the Gaussian function provides a quantitative conceptual framework for accurate assessment of regional water supply and energy availability and offers a penetrating insight into hydrometeorology. [ABSTRACT FROM AUTHOR]

A time lag exists between precipitation P falling and being converted into terrestrial water. The responses of terrestrial water storage (TWS) and its individual components to P over the global scale, which are vital for understanding the interactions and mechanisms between climatic variables and hydrological components, are not well constrained. In this study, relying on land surface models, we isolate five component storage anomalies from TWS anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment mission (GRACE): canopy water storage anomalies (CWSA), surface water storage anomalies (SWSA), snow water equivalent anomalies (SWEA), soil moisture storage anomalies (SMSA), and groundwater storage anomalies (GWSA). The responses of TWSA and of the individual components of TWSA to P are then evaluated over 168 global basins. The lag between TWSA and P is quantified by calculating the correlation coefficients between GRACE-based TWSA and P for different time lags, then identifying the lag (measured in months) corresponding to the maximum correlation coefficient. A multivariate regression model is used to explore the relationship between climatic and basin characteristics and the lag between TWSA and P. Results show that the spatial distribution of TWSA trend presents a similar global pattern to that of P for the period January 2004–December 2013. TWSA is positively related to P over basins but with lags of variable duration. The lags are shorter in the low- and midlatitude basins (1–2 months) than those in the high-latitude basins (6–9 months). The spatial patterns of the maximum correlations and the corresponding lags between individual components of the TWSA and P are consistent with those of the GRACE-based analysis, except for SWEA (3–8 months) and CWSA (0 months). The lags between GWSA, SMSA, and SWSA to P can be arranged as GWSA > SMSA ≥ SWSA. Regression analysis results show that the lags between TWSA and P are related to the mean temperature, mean precipitation, mean latitude, mean longitude, mean elevation, and mean slope. [ABSTRACT FROM AUTHOR]

Hydrometeorological links to high streamflow events (HSFEs), 1950–2014, for the Mystic and Charles watersheds in the Metro Boston region of Massachusetts are examined. HSFEs are defined as one or more continuous days of streamflow above the mean annual maxima for a selected gauge in each basin. There are notable differences in the HSFEs for these two basins. HSFEs last from 1 to 3 days in the Mystic basin, while HSFEs for the Charles can last from 3 to 9 days. The majority of Mystic HSFEs are immediately preceded by extreme precipitation (occurring within 24 h), while only half of those for the Charles are preceded by extreme precipitation (in this case occurring 2–5 days earlier). While extreme precipitation events are often linked to HSFEs, other factors are often necessary in generating high streamflow, particularly for the Charles, as more than 50% of HSFEs occur at times when streamflow, soil moisture, and total precipitation are statistically above average for a period of at least 2 weeks before the HSFE. Approximately 52% and 80% of HSFEs occur from February to June for the Mystic and Charles, respectively, and these HSFEs are frequently linked to the passage of strong coastal lows, which produce extreme precipitation in the form of both rain and snow. For these coastal lows, Mystic HSFEs are linked to a strong moisture feed along the Massachusetts coastline and intense precipitation, while Charles HSFEs are linked to strong cyclones located off the Mid-Atlantic and longer-duration precipitation. [ABSTRACT FROM AUTHOR]

The Russian River in northern California is an important hydrological resource that typically depends on a few significant precipitation events per year, often associated with atmospheric rivers (ARs), to maintain its annual water supply. Because of the highly variable nature of annual precipitation in the region, accurate quantitative precipitation estimates (QPEs) are necessary to drive hydrologic models and inform water management decisions. The basin's location and complex terrain present a unique challenge to QPEs, with sparse in situ observations and mountains that inhibit remote sensing by ground radars. Gridded multisensor QPE datasets can fill in the gaps but are susceptible to both the errors and uncertainties from the ingested datasets and uncertainties due to interpolation methods. In this study a dense network of independently operated rain gauges is used to evaluate gridded QPE from the Multi-Radar Multi-Sensor (MRMS) during 44 precipitation events occurring during the 2015/16 and 2016/17 wet seasons (October–March). The MRMS QPE products matched the gauge estimates of precipitation reasonably well in approximately half the cases but failed to capture the spatial distribution and intensity of the rainfall in the remaining cases. ERA-Interim reanalysis data suggest that the differences in performance are related to synoptic-scale patterns and AR landfall location. These synoptic-scale differences produce different rainfall distributions and influence basin-scale winds, potentially creating regions of small-scale precipitation enhancement or suppression. Data from four profiling radars indicated that a larger fraction of the precipitation in poorly captured events occurred as shallow stratiform rain unobserved by radar. [ABSTRACT FROM AUTHOR]

In this study, the Providing Regional Climates for Impacts Studies (PRECIS) and the Regional Climate Model (RegCM) system as well as the Variable Infiltration Capacity (VIC) macroscale hydrologic model were integrated into a general framework to investigate impacts of future climates on the hydrologic regime of the Athabasca River basin. Regional climate models (RCMs) including PRECIS and RegCM were used to develop ensemble high-resolution climate projections for 1979–2099. RCMs were driven by the boundary conditions from the Hadley Centre Global Environment Model, version 2 with Earth system configurations (HadGEM2-ES); the Second Generation Canadian Earth System Model (CanESM2); and the Geophysical Fluid Dynamics Laboratory Earth System Model with MOM (GFDL-ESM2M) under the representative concentration pathways (RCPs). The ensemble climate simulations were validated through comparison with observations for 1984–2003. The RCMs project increases in temperature, precipitation, and wind speed under RCPs across most of the Athabasca River basin. Meanwhile, VIC was calibrated using the University of Arizona Shuffled Complex Evolution method (SCE-UA). The performance of the VIC model in replicating the characteristics of the observed streamflow was validated for 1994–2003. Changes in runoff and streamflow under RCPs were then simulated by the validated VIC model. The validation results demonstrate that the ensemble-RCM-driven VIC model can effectively reproduce historical climatological and hydrological patterns in the Athabasca River basin. The ensemble-RCM-driven VIC model shows that monthly streamflow is projected to increase in the 2050s and 2080s under RCPs, with notably higher flows expected in the spring for the 2080s. This will have substantial impacts on water balance on the Athabasca River basin, thus affecting the surrounding industry and ecosystems. The developed framework can be applied to other regions for exploration of hydrologic impacts under climate change. [ABSTRACT FROM AUTHOR]

To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP's Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow. [ABSTRACT FROM AUTHOR]

The top 100 basin-scale 1-day precipitation, multiday precipitation, and 1-day streamflow events from 1950 to 2012 are examined for the Ashokan reservoir, a key water source for New York City. Through a cyclone association algorithm, extratropical cyclones (ETCs) are found to be associated with the majority of the top 100 precipitation and streamflow events. Tropical cyclones (TCs) generate the second-most top 100 one-day and multiday precipitation events, with more than two-thirds of these TCs having undergone extratropical transition. Furthermore, TCs that pass over the region are approximately 7 and 4 times more likely to generate a top 100 one-day precipitation and one-day streamflow event, respectively, than ETCs. Lagrangian cyclone track analysis shows cool season ETCs take a more meridional path compared to warm season ETCs. A composite analysis shows that for the top 100 one-day precipitation events, ETCs have relatively less moisture but stronger upper-level support than TCs. Due in part to TCs, heavy precipitation events occur more often in the warm season, whereas high streamflow events occur mainly in the cool season. Despite this difference, approximately 43% of the top 100 events, which represent many of the very strongest events, overlap for all three metrics. While high temperature and specific humidity anomalies accompany all top 100 events, the magnitude of the anomalies is greatest for isolated streamflow events. This analysis provides a reference to forecasters and water managers regarding the relative and synoptic-scale behavior of different storm types for isolated and concurrent precipitation and streamflow events. [ABSTRACT FROM AUTHOR]

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy balance-based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long-term climatology and interannual variability in ET derived from a terrestrial water budget (ETWB) for 671 gauged basins across the contiguous United States. All three ETEB products have low spatial bias and accurately capture interannual variability of ETWB in the central United States, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on interannual time scales, ΔTWS and error in precipitation estimates are nonnegligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications. [ABSTRACT FROM AUTHOR]

The 2013 federal Colorado River Basin Water Supply and Demand Study projected the water imbalance between future supply and demand to increase. The Colorado water supply (WS) exemplifies a pronounced quasi-decadal oscillation (QDO) of 10-20 years throughout its historical record; however, this QDO feature is unaccounted for in the climate models used to project the future WS. Adjacent to the Colorado River, the large watershed of the Great Salt Lake (GSL) in Utah records the hydrologicQDOsignal in its water surface, leading previous studies to explore the cause of decadal fluctuations in the lake elevation and assess predictability. This study reports a remarkable coherence between the Colorado WS and the GSL elevation at the 10-20-yr time scale. Analysis of precipitation and terrestrial water storage anomalies suggests a crossbasin connection in the climate and hydrometeorological variations of the Colorado WS and the GSL. The 160-yr-long and well-kept GSL elevation record makes it an effective indicator for the Colorado WS. [ABSTRACT FROM AUTHOR]

The central Amazon forest is projected to experience larger interannual precipitation variability, with uncertain impacts on terrestrial hydrologic fluxes. How surface runoff, groundwater, and evapotranspiration (ET) change as a function of annual precipitation (AP) has large climate and biogeochemical implications. A process-based hydrological model is used to examine the sensitivity of hydrologic budgets and stream discharge Q s generation to AP in an upland Amazon catchment. The authors find that AP strongly controls infiltration, base flow, and surface runoff, but not ET. Hence, AP alone can predict interannual changes in these fluxes except ET. Experiments with perturbed rainfall show the strong control derives from the predominant groundwater component that varies linearly with AP but is insensitive to seasonal rainfall fluctuations. Most rainfall from large storms infiltrates and becomes base flow rather than runoff or ET. Annual baseflow index (BFI; the fraction of stream discharge from base flow) is nearly constant (~0.8) when AP is below ~2500 mm yr−1 and decreases with AP above this value, which represents an inflection point for increased storage-dependent saturation excess. These results indicate that the system is energy limited and groundwater dominated in dry seasons, which implies some resilience of ET to moderate droughts. The results suggest AP is a good predictor for interannual changes in infiltration. Both the seasonal near-surface soil moisture and surface runoff are correlated more strongly to the subsurface fluxes than to precipitation over monthly and annual time scales. Finally, the results confirm the importance of central Amazon groundwater flow and its buffering effect on storms and droughts, implying needed model development in regional to global models. [ABSTRACT FROM AUTHOR]

For 14 alternative climate futures, water yield and snow water equivalent (SWE) throughout the contiguous United States (CONUS) were projected over the twenty-first century using the Variable Infiltration Capacity model (VIC). The futures correspond to climate projections from seven CMIP5 models each forced by two representative concentration pathways for greenhouse gases (RCP4.5 and RCP8.5). With both RCPs, decreases in water yields are projected for roughly two-thirds of the CONUS, and in 60% of that area-mainly at more northern latitudes, where the greatest temperature increases are expected-this occurs despite projected increases in precipitation. The greatest relative decreases in yield are projected for the southern Great Plains and the Southwest, where temperature and precipitation changes combine to decrease yield. Snow accumulation is projected to decrease almost everywhere by the latter half of the century, with the time of peak SWE in some basins projected to occur up to 2 months earlier than it now does. These changes, should they come to pass, will challenge the adaptation capacity of future water management. [ABSTRACT FROM AUTHOR]

Long-term (1901-2012) changes in hydroclimatic variables in the 18 Indian subcontinental basins were examined with hydrology simulated using the Variable Infiltration Capacity model (VIC). Changepoint analysis using the sequential Mann-Kendall test showed two distinct periods (1901-47 and 1948-2012) for the domain-averaged monsoon season (June-September) precipitation. Hydrologic changes for the entire water budget were estimated for both periods. In the pre-1948 period, a majority of the river basins experienced increased monsoon season precipitation, evapotranspiration (ET), and surface water availability (as defined by total runoff). Alternatively, in the post-1948 period, monsoon season precipitation declined in 11 of the 18 basins, with statistically significant trends in one (the Ganges basin), and most (15) basins experienced significant warming trends. Additionally, in the post-1948 period, the mean monsoon season ET and surface water availability declined in eight (with significant declines in four) basins. The results indicate that changes in ET and surface water availability in the pre- and post-1948 periods were largely driven by the changes in the monsoon season precipitation rather than air temperature, despite prominent warming after 1975. Coupled modes of variability of sea surface temperature (SST) and surface water availability indicated El Niño-Southern Oscillation (ENSO) as the leading mode. The second mode was identified as the trend mode for surface water availability in the subcontinental river basins, which was largely driven by SST anomalies in the Indian and Atlantic Ocean regions. This indicates that surface water availability in India's subcontinental basins may be affected in the future in response to changes in large-scale climate variability. [ABSTRACT FROM AUTHOR]

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts. [ABSTRACT FROM AUTHOR]

The future changes in drought characteristics were examined on a regional scale for South Korea, in northeastern Asia, using 17 bias-corrected projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) of representative concentration pathway (RCP) scenarios 4.5 and 8.5. The frequency of severe or extreme drought, based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), with time scales of 1, 3, and 12 months (i.e., SPI1, SPI3, SPI12, SPEI1, SPEI3, and SPEI12), was considered, as well as the average duration based on SPEI1. A multimodel ensemble (MME) was produced using selected models, and future changes were investigated in terms of both drought frequency and the average duration for the entire area and four river basins. The changes in drought frequency largely depend on the selection of a drought index, rather than climate projection scenarios. SPEI3 mostly projected future increases in drought frequency, while SPI3 showed varied projections. SPI12 projected decreases in drought frequency for both scenarios in the study area, while differences between river basins were observed for SPEI12. Increases in the average duration of droughts were projected based on SPEI1, indicating an increase in persistent short-term droughts in the future. The results emphasize the importance of regional- and subregional-scale analysis in northeastern Asia. The findings of the study provide valuable information that can be used for drought-related decision-making, which could not be obtained from studies on a global spatial scale. [ABSTRACT FROM AUTHOR]

The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed. [ABSTRACT FROM AUTHOR]

Stable water isotope concentrations were obtained from samples of stream water at 29 sites in a west-east transect across the Southern Alps of New Zealand, where westerly conditions dominate the precipitation regime. The samples were taken from small catchment streams during a time of extended recession as a means of collecting time and space averages of the source precipitation. The isotopic concentrations from sites at either end of the transect lead to a drying ratio estimate of 22%-34% for this region of the Southern Alps. The isotope concentrations increased from west to east, indicating that precipitation in the lee area originates from higher and/or colder condensation than on the windward side. The transect was divided into three regions according to the deuterium-excess ( d excess) results. Increasing d-excess values on the windward side of the mountains were speculated to be a result of some unknown combination of reducing relative evapotranspiration, increasing recycling of water vapor, and increasing nonequilibrating condensation from high-intensity precipitation. Low d-excess values in the region immediately lee of the mountains were consistent with below-cloud evaporation associated with increased hydrometeor drift into the drier lee side. High d excess in the more distant lee side was attributed to a secondary source of moisture (from the east and south). The information gained has supported current concepts of the precipitation processes dominant in the region and has provided additional quantitative measurements with which to validate future precipitation modeling efforts. [ABSTRACT FROM AUTHOR]

Many hydrological hazards are closely connected to local precipitation (extremes), especially in small and urban catchments. The use of regional climate model (RCM) data for small-scale hydrological climate change impact assessment has long been nearly unfeasible because of the low spatial resolution. The RCM resolution is, however, rapidly increasing, approaching the size of small catchments and thus potentially increasing the applicability of RCM data for this purpose. The objective of this study is to explore to what degree subhourly temporal precipitation statistics in an RCM converge to observed point statistics when gradually increasing the resolution from 50 to 6 km. This study uses precipitation simulated by RCA3 at seven locations in southern Sweden during 1995-2008. A positive impact of higher resolution was most clearly manifested in 10-yr intensity-duration-frequency (IDF) curves. At 50 km the intensities are underestimated by 50%-90%, but at 6 km they are nearly unbiased, when averaged over all locations and durations. Thus, at 6 km, RCA3 apparently generates low-frequency subdaily extremes that resemble the values found in point observations. Also, the reproduction of short-term variability and less extreme maxima were overall improved with increasing resolution. For monthly totals, a slightly increased overestimation with increasing resolution was found. The bias in terms of wet fraction and wet spell characteristics was overall not strongly dependent on resolution. These metrics are, however, influenced by the cutoff threshold used to separate between wet and dry time steps as well as the wet spell definition. [ABSTRACT FROM AUTHOR]

Drought indices are often used for monitoring interannual variability in macroscale hydrology. However, the diversity of drought indices raises several issues: 1) which indices perform best and where; 2) does the incorporation of potential evapotranspiration (PET) in indices strengthen relationships, and how sensitive is the choice of PET methods to such results; 3) what additional value is added by using higher-spatial-resolution gridded climate layers; and 4) how have observed relationships changed through time. Standardized precipitation index, standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index, and water balance runoff (WBR) model output were correlated to water-year runoff for 21 unregulated drainage basins in the Pacific Northwest of the United States. SPEI and WBR with time scales encompassing the primary precipitation season maximized the explained variance in water-year runoff in most basins. Slightly stronger correlations were found using PET estimates from the Penman-Monteith method over the Thornthwaite method, particularly for time periods that incorporated the spring and summer months in basins that receive appreciable precipitation during the growing season. Indices computed using high-resolution climate surfaces explained over 10% more variability than metrics derived from coarser-resolution datasets. Increased correlation in the latter half of the study period was partially attributable to increased streamflow variability in recent decades as well as to improved climate data quality across the interior mountain watersheds. [ABSTRACT FROM AUTHOR]

Development of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP-MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation event verification indicators. The results indicate that 1) precipitation from the DCMAP product may not be suitable for water cycle studies at the watershed scale because of its coarser spatial resolution and 2) ITP-F, WRF-P, and DCMAP-MicroMet precipitation products generally show similar spatial-temporal patterns in HRB but have varying performances between different subbasins. [ABSTRACT FROM AUTHOR]

Understanding the impact of postdam climate feedbacks, resulting from land use/land cover (LULC) variability, on modification of extreme precipitation (EP) remains a challenge for a twenty-first-century approach to dam design and operation. In this study, the Regional Atmospheric Modeling System (RAMS, version 6.0) was used, involving a number of predefined LULC scenarios to address the important question regarding dams and their impoundments: How sensitive are the hydroclimatology and terrain features of a region in modulating the postdam response of climate feedbacks to EP? The study region covered the Owyhee Dam/Reservoir on the Owyhee River watershed (ORW), located in eastern Oregon. A systematic perturbation of the relative humidity in the initial and boundary condition of the model was carried out to simulate EP. Among the different LULC scenarios used in the simulation over the ORW, irrigation expansion in the postdam era resulted in an increase in EP up to 6% in the 72-h precipitation total. The contribution of the reservoir on EP added 8% to the 72-h total when compared to the predam LULC conditions. To address the science question, a previously completed investigation on the Folsom Dam [American River watershed (ARW)] in California was compared with the ORW findings on the basis of contrasting differences in hydroclimatology and terrain features. The results indicate that the postdam LULC change scenarios impact EP of ORW (Owyhee Dam) much greater than the EP of the ARW (Folsom Dam) because of its semiarid climate and flat terrain. EP was less sensitive to LULC changes on the windward side of the mountainous terrain of ARW as compared to the leeward side of the flat terrain of ORW. [ABSTRACT FROM AUTHOR]

Climate change is expected to affect the hydrological cycle, with considerable impacts on water resources. Climate-induced changes in the hydrology of the Rhine River (Europe) are of major importance for the riparian countries, as the Rhine River is the most important European waterway, serves as a freshwater supply source, and is prone to floods and droughts. Here regional climate model data from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project is used to drive the hydrological model Precipitation-Runoff-Evapotranspiration-Hydrotope (PREVAH) and to assess the impact of climate change on the hydrology in the Rhine basin. Results suggest increases in monthly mean runoff during winter and decreases in summer. At the gauge Cologne and for the period 2070-99 under the A1B scenario of the Special Report on Emissions Scenarios, projected decreases in summer vary between −9% and −40% depending on the climate model used, while increases in winter are in the range of +4% to +51%. These projected changes in mean runoff are generally consistent with earlier studies, but the derived spread in the runoff projections appears to be larger. It is demonstrated that temperature effects (e.g., through altered snow processes) dominate in the Alpine tributaries, while precipitation effects dominate in the lower portion of the Rhine basin. Analyses are also presented for selected extreme runoff indices. [ABSTRACT FROM AUTHOR]

Hourly surface precipitation type (Ptype) grids (a total of 408 h from 1 December 2005 through April 20, 2006) were generated by mapping the elevation of the radar-detected brightband height (BBH) to terrain elevation during the 2005/06 observation period of the western Hydrometeorology Testbed (HMT-West) in the North Fork American River basin. BBH Ptype grids were compared to those derived by the standard National Weather Service (NWS) temperature threshold method. In this method, a fixed threshold tem-perature separating rain and snow was applied to hourly 4-km gridded temperature data. The BBH Ptype grids agreed well (>90%) with the temperature threshold-based grids below an elevation of 1524 m. The agreement dropped to below 60% above this elevation, and BBH Ptype produced more rainfall than the temperature-based Ptype. Continuous hourly streamflow simulations were generated using spatially lumped and distributed hydrologie models with and without the BBH Ptype data from 1 October 2005 through 30 September 2006. Simple insertion of BBH Ptype data did not always improve streamflow simulations for the 11 events examined relative to corresponding simulations using temperature threshold-derived precipitation type, possibly because of the use of the models calibrated with the temperature-based Ptype. The simple sensitivity test indicated simulations of both peak flows from midwinter storms and spring snowmelt runoff are affected by errors in precipitation type estimates. [ABSTRACT FROM AUTHOR]

Six satellite-based rainfall estimates (SRFE)-namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]-were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba-Shabelle, and Baro-Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003-06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs. [ABSTRACT FROM AUTHOR]

A distributed hydrologic model is used to evaluate how runoff mechanisms-including infiltration excess ( RI), saturation excess ( RS), and groundwater exfiltration ( RG)-influence the generation of streamflow and evapotranspiration (ET) in a mountainous region under the influence of the North American monsoon (NAM). The study site, the upper Sonora River basin (~9350 km2) in Mexico, is characterized by a wide range of terrain, soil, and ecosystem conditions obtained from best available data sources. Three meteorological scenarios are compared to explore the impact of spatial and temporal variations of meteorological characteristics on land surface processes and to identify the value of North American Land Data Assimilation System (NLDAS) forcing products in the NAM region. The following scenarios are considered for a 1-yr period: 1) a sparse network of ground-based stations, 2) raw forcing products from NLDAS, and 3) NLDAS products adjusted using available station data. These scenarios are discussed in light of spatial distributions of precipitation, streamflow, and runoff mechanisms during annual, seasonal, and monthly periods. This study identified that the mode of runoff generation impacts seasonal relations between ET and soil moisture in the water-limited region. In addition, ET rates at annual and seasonal scales were related to the runoff mechanism proportions, with an increase in ET when RS was dominant and a decrease in ET when RI was more important. The partitioning of runoff mechanisms also helps explain the monthly progression of runoff ratios in these seasonally wet hydrologic systems. Understanding the complex interplay between seasonal responses of runoff mechanisms and evapotranspiration can yield information that is of interest to hydrologists and water managers. [ABSTRACT FROM AUTHOR]

A modified drought index, named the spatially normalized--standardized precipitation index (SN-SPI), has been developed for assessing meteorological droughts. The SN--SPI is a variant index to the standardized precipitation index and is based on the probability of precipitation at different time scales, but it is spatially normalized for improved assessment of drought severity. Results of this index incorporate the spatial distribution of precipitation and produce improved drought warnings. This index is applied in the island of Crete, Greece, and the drought results are compared to the ones of SPI. A 30-year-long average monthly precipitation dataset from 130 watersheds of the island is used by the above indices for drought classification in terms of its duration and intensity. Bias-adjusted monthly precipitation estimates from an ensemble of 10 regional climate models were used to quantify the influence of global warming to drought conditions over the period 2010--2100. Results based on both indices (calculated for three time scales of 12, 24, and 48 months) from 3 basins in west, central, and east parts of the island show that 1) the extreme drought periods are the same (reaching 7%% of time) but the intensities based on SN--SPI are lower; 2) the area covered by extreme droughts is 3%% (first time scale), 16%% (second time scale), and 25%% (third time scale), and 96%% (first time scale), 95%% (second time scale), and 80%% (third time scale) based on the SN--SPI and SPI, respectively; 3) concerning the longest time scale (48 months), more than half of the area of Crete is about to experience drought conditions during 28%%, 69%%, and 97%% for 2010--40, 2040--70, and 2070--2100, respectively; and 4) extremely dry conditions will cover 52%%, 33%%, and 25%% of the island for the future 90-year period using 12-, 24-, and 48-month SN--SPI, respectively. [ABSTRACT FROM AUTHOR]

Extreme precipitation events, and the quantitative precipitation forecasts (QPFs) associated with them, are examined. The study uses data from the Hydrometeorology Testbed (HMT), which conducted its first field study in California during the 2005/06 cool season. National Weather Service River Forecast Center (NWS RFC) gridded QPFs for 24-h periods at 24-h (day 1), 48-h (day 2), and 72-h (day 3) forecast lead times plus 24-h quantitative precipitation estimates (QPEs) from sites in California (CA) and Oregon--Washington (OR--WA) are used. During the 172-day period studied, some sites received more than 254 cm (100 in.) of precipitation. The winter season produced many extreme precipitation events, including 90 instances when a site received more than 7.6 cm (3.0 in.) of precipitation in 24 h (i.e., an ''event'') and 17 events that exceeded 12.7 cm (24 h)−−1 [[5.0 in. (24 h)−−1]]. For the 90 extreme events {{>7.6 cm (24 h)−−1 [[3.0 in. (24 h)−−1]]}}, almost 90%% of all the 270 QPFs (days 1--3) were biased low, increasingly so with greater lead time. Of the 17 observed events exceeding 12.7 cm (24 h)−−1 [[5.0 in. (24 h)−−1]], only 1 of those events was predicted to be that extreme. Almost all of the extreme events correlated with the presence of atmospheric river conditions. Total seasonal QPF biases for all events {{i.e., ≥≥0.025 cm (24 h)−−1 [[0.01 in. (24 h)−−1]]}} were sensitive to local geography and were generally biased low in the California--Nevada River Forecast Center (CNRFC) region and high in the Northwest River Forecast Center (NWRFC) domain. The low bias in CA QPFs improved with shorter forecast lead time and worsened for extreme events. Differences were also noted between the CNRFC and NWRFC in terms of QPF and the frequency of extreme events. A key finding from this study is that there were more precipitation events >7.6 cm (24 h)−−1 [[3.0 in. (24 h)−−1]] in CA than in OR--WA. Examination of 422 Cooperative Observer Program (COOP) sites in the NWRFC domain and 400 in the CNRFC domain found that the thresholds for the top 1%% and top 0.1%% of precipitation events were 7.6 cm (24 h)−−1 [[3.0 in. (24 h)−−1]] and 14.2 cm (24 h)−−1 [[5.6 in. (24 h)−−1]] or greater for the CNRFC and only 5.1 cm (24 h)−−1 [[2.0 in. (24 h)−−1]] and 9.4 cm (24 h)−−1 [[3.7 in. (24 h)−−1]] for the NWRFC, respectively. Similar analyses for all NWS RFCs showed that the threshold for the top 1%% of events varies from ∼∼3.8 cm (24 h)−−1 [[1.5 in. (24 h)−−1]] in the Colorado Basin River Forecast Center (CBRFC) to ∼∼5.1 cm (24 h)−−1 [[3.0 in. (24 h)−−1]] in the northern tier of RFCs and ∼∼7.6 cm (24 h)−−1 [[3.0 in. (24 h)−−1]] in both the southern tier and the CNRFC. It is recommended that NWS QPF performance in the future be assessed for extreme events using these thresholds. [ABSTRACT FROM AUTHOR]

This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund–Potsdam–Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations. [ABSTRACT FROM AUTHOR]

High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods. [ABSTRACT FROM AUTHOR]

An atmospheric general circulation model simulation for 1948-97 of the water budgets for the MacKenzie, Mississippi, and Amazon River basins is presented. In addition to the water budget, passive tracers are included to identify the geographic sources of water for the basins, and the analysis focuses on the mechanisms contributing to precipitation recycling in each basin. While each basin's precipitation recycling has a strong dependency on evaporation during the mean annual cycle, the interannual variability of the recycling shows important relationships with the atmospheric circulation. The MacKenzie River basin recycling has only a weak interannual correspondence with evaporation, where the variations in zonal moisture transport from the Pacific Ocean can affect the basin water cycle. On the other hand, the Mississippi River basin precipitation and recycling have strong interannual correlation on evaporation. The evaporation is related to the moist and shallow planetary boundary layer that provides moisture for convection at the cloud base. At global scales, high precipitation recycling is also found to be partly correlated to warm SSTs in the tropical Pacific Ocean. The Amazon River basin evaporation exhibits small interannual variations, so the interannual variations of precipitation recycling are related to atmospheric moisture transport from the tropical South Atlantic Ocean. Increasing SSTs over the 50-yr period are causing increased easterly transport across the basin. As moisture transport increases, the Amazon precipitation recycling decreases (without real-time varying vegetation changes). In addition, precipitation recycling from a bulk diagnostic method is compared to the passive tracer method used in the analysis. While the mean values of the different recycling methods are different, the interannual variations are comparable between each method. The methods also exhibit similar relationships to the terms of the basin-scale water budgets. [ABSTRACT FROM AUTHOR]

ABSTRACT This study used the atmospheric Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the University of Washington Distributed Hydrology-Soil-Vegetation Model (DHSVM) for the simulation of a complex rain-on-snow flood event that occurred from 28 December 1996 to 3 January 1997 on the 1560-km[sup 2] Snoqualmie River watershed in western Washington. Three control simulations were created with MM5 applied at 36-, 12-, and 4-km horizontal spacing and DHSVM at a horizontal spacing of 100 m. Results showed that the accuracy of the atmospheric fields increased with higher horizontal resolution, although underforecasting of precipitation was evident for all three resolutions. Simulated river flows captured 67% (36 km), 75% (12 kin), and 72% (4 km) of the total flow and 52% (36 km), 58% (12 kin), and 62% (4 km) of the event peak flow. Several sensitivity simulations of the modeling system (4-km spacing only) were conducted to improve on the control simulations. Adjusting the MM5 precipitation using observations led to a streamflow forecast that captured 90% of the total flow. Reduction of the model high-wind speed bias improved the simulated snowmelt, although the resulting effects on streamflow were relatively small. A sensitivity experiment that included the precipitation from an intense rainband that was not captured by MM5 revealed the importance of this high-intensity, short-lived feature; simulated streamflow from this experiment captured 93% of the total flow and over 82% of the peak flow, with a 4-h timing error. A final set of sensitivity simulations, using both a higher- and... [ABSTRACT FROM AUTHOR]