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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]

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

This paper 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]