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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]

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 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]

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 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]

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]