Analysis of the 140-year historical record suggests that the inverse relationship between the El Nino-Southern Oscillation (ENSO) and the Indian summer monsoon (weak monsoon arising from warm ENSO event) has broken down in recent decades. Two possible reasons emerge from the analyses. A southeastward shift in the Walker circulation anomalies associated with ENSO events may lead to a reduced subsidence over the Indian region, thus favoring normal monsoon conditions. Additionally, increased surface temperatures over Eurasia in winter and spring, which are a part of the midlatitude continental warming trend, may favor the enhanced land-ocean thermal gradient conducive to a strong monsoon. These observations raise the possibility that the Eurasian warming in recent decades helps to sustain the monsoon rainfall at a normal level despite strong ENSO events.

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Analyses of streamflow, snow mass temperature, and precipitation in snowmelt-dominated river basins in the western United States indicate an advance in the timing of peak spring season flows over the past 50 years. Warm temperature spells in spring have occurred much earlier in recent years, which explains in part the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation are evident for many stations in the western United States. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the melting point; it appears that in this region in particular, modest shifts in temperature are capable of forcing large shifts in basin hydrologic response. It is speculated that these trends could be potentially a manifestation of the general global warming trend in recent decades and also due to enhanced ENSO activity. The observed trends in hydroclimatology over the western United States can have significant impacts on water resources planning and management.

The 132-year historical rainfall record reveals that severe droughts in India have always been accompanied by El Niño events. Yet El Niño events have not always produced severe droughts. We show that El Niño events with the warmest sea surface temperature (SST) anomalies in the central equatorial Pacific are more effective in focusing drought-producing subsidence over India than events with the warmest SSTs in the eastern equatorial Pacific. The physical basis for such different impacts is established using atmospheric general circulation model experiments forced with idealized tropical Pacific warmings. These findings have important implications for Indian monsoon forecasting.

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Abstract. A multivariate, nonparametric time series simulation method is provided to generate random sequences of daily weather variables that "honor" the statistical properties of the historical data of the same weather variables at the site. A vector of weather variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, average wind speed, and precipitation) on a day of interest is resampled from the historical data by conditioning on the vector of the same variables (feature vector) on the preceding day. The resampling is done from the k nearest neighbors in state space of the feature vector using a weight function. This approach is equivalent to a nonparametric approximation of a multivariate, lag 1 Markov process. It does not require prior assumptions as to the form of the joint probability density function of the variables. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, is provided. Results are compared with those from the application of a multivariate autoregressive model similar to that of Richardson [1981]. [1997] differed from the earlier work. They used kernel density estimation to specify the univariate and multivariate probability densities needed for describing the stochastic processes of interest. Precipitation was generated independently from a nonparametric wet/dry spell model , and the other variables on a given day were generated by conditioning on the precipitation magnitude (rather than just the precipitation state) for the day and on the previous day's values for the weather variables.The precipitation amount on a rainy day may also depend on the wind, the temperature, and the humidity as measured by the dew point temperature. Consequently, there is reason to consider dependence of the daily weather process on more than just precipitation as has traditionally been done. Young [1994], in a model similar in spirit to the one presented here, considers such dependence. In the approach adopted in this paper, precipitation is simulated along with the other variables, thereby capturing the mutual dependence of all six weather variables. The simulation strategy used is a direct resampling of the data using a conditional bootstrap based on nearestneighbor probability density estimation. This approach does not require the specification of and estimation of the parameters of a parametric model (e.g., normal or lognormal) for the joint or conditional probability density of the variables.A brief review of traditional methods for simulating weather variables is first provided. The general framework for the resampling strategy proposed here is presented next. The k-nearest-neighbor (k-NN) bootstrap algorithm is outlined. An application of the method to data from Salt Lake City is 3089

In this commentary we suggest that hydrologists and land-surface modelers may be unnecessarily constraining the behavioral agility of very complex physics-based models. We argue that the relatively poor performance of such models can occur due to restrictions on their ability to refine their portrayal of physical processes, in part because of strong a priori constraints in: (i) the representation of spatial variability and hydrologic connectivity, (ii) the choice of model parameterizations, and (iii) the choice of model parameter values. We provide a specific example of problems associated with strong a priori constraints on parameters in a land surface model. Moving forward, we assert that improving hydrological models requires integrating the strengths of the ''physics-based'' modeling philosophy (which relies on prior knowledge of hydrologic processes) with the strengths of the ''conceptual'' modeling philosophy (which relies on data driven inference). Such integration will accelerate progress on methods to define and discriminate among competing modeling options, which should be ideally incorporated in agile modeling frameworks and tested through a diagnostic evaluation approach.

Population growth and a changing climate will tax the future reliability of the Colorado River water supply. Using a heuristic model, we assess the annual risk to the Colorado River water supply for 2008–2057. Projected demand growth superimposed upon historical climate variability results in only a small probability of annual reservoir depletion through 2057. In contrast, a scenario of 20% reduction in the annual Colorado River flow due to climate change by 2057 results in a near tenfold increase in the probability of annual reservoir depletion by 2057. However, our analysis suggests that flexibility in current management practices could mitigate some of the increased risk due to climate change–induced reductions in flows.