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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]

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]

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]

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]

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]

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]

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]

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]

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]