The relationship between the El Nino/Southern Oscillation (ENSO) and unimpaired streamflow over the contiguous United States was studied. The ENSO is a warm event in the tropical Pacific Ocean and is considered a significant perturbation of the general atmospheric circulation. EI Nino events have been observed and recorded since 1726. They occur approximately once every 4 years; however, the time interval between successive events varies from 2 to 10 years.

ENSO-related events have important consequences for U.S. atmospheric and weather patterns, but the nature of these consequences depends on the type ofEI Nino. In 1941 and 1983, the major ENSOs of this century, heavy rains were experienced in the West and Southwest and the Colorado River basin. However, most ENSO episodes produce dry conditions in these regions (e.g., that of 1986-1987).

In these studies there was an identification of regions of land that appear to have strong and consistent ENSO-related streamflow signals. Coherent and significant streamflow responses to ENSO forcing are found in four regions of the United States: the gulf of Mexico, the Northeast, the North Central, and the Pacific Northwest.

Streamflows in the Pacific Southwest of the U.S. in relation to the tropical Type 1 EI Nino-Southern Oscillation and La Nina events were specifically studied. The Pacific Southwest streamflow responses to the Type 1 ENSO thermal forcing are characterized by a wet December- July season in the subsequent year of the event. Similarly, a dry February-July season is detected as a period at which the La Nina-streamflow relationship is strong and spatially coherent.

Once an ENSO event sets in, a long-range forecasting utility may be available for these regions. The results of this analysis, which are consistent with previous studies on precipitation and temperature, demonstrate the mid-latitude hydrologic response to the tropical ENSO phenomena.

The research problem studied here is whether regional streamflows can be identified and potentially predicted based on the impact ofEl Nino, La Nina and the Southern Oscillation. The research focused on the following question: Can streamflow and drought in California be predicted by scientifically understanding ENSO and large scale circulation patterns? ENSO is defined as the EI Nino/Southern Oscillation climatic phenomenon. For decades, the prediction of streamflow and drought has intrigued hydrologists; our current research suggests that ENSO strongly affects streamflow and thus could be an important factor in making long range forecasts of streamflow.

Researchers have determined that ENSO events have significant worldwide impacts on such events as precipitation, temperature, floods, droughts and wildfires. ENSO is a warm event in the tropical Pacific Ocean and is considered a significant perturbation of the general atmospheric circulation. EI Nifio events have been observed and recorded since 1726. They occur approximately once every 4 years; however, the time interval between successive events varies from 2 to 10 years.

The research objective was to identify regions of land that appear to have a coherent and consistent ENSO-related signal. The identification of these regions and the predictions of the onset of an ENSO event could then lead to the prediction of climatic anomalies.

The research problem addressed during this study focused on assessing and improving the accuracy of climate reconstructions from tree-ring width records in the Upper Colorado River Basin. The impact of severe sustained droughts (SSD) is a major concern for water availability in the face of increasing population growth in the southwestern United States. This concern is acute in the case of the Colorado River Basin, which includes Wyoming, Colorado, Nevada, Utah, Arizona, New Mexico and California. These states withdraw significant proportions of water for drinking and irrigation from the Colorado River. Since long-term records of hydrologic variability may influence the allocation of water resources, this research has focused on the following question: Are Paleoclimate Reconstructions of Past Severe Drought in the Colorado Basin Accurate Enough to Warrant Changing the Current "Law of the River"?

Soil salinity levels are an important determinant of plant evapotranspiration and carbon uptake. In this dissertation I develop, evaluate, and test a model of plant evapotranspiration and carbon uptake in the context of a saline soil environment, and drive the model using leaf physiological parameters determined from field measurements. This modeling work is performed in the context of three research questions: (1) How are leaf gas exchange parameters characterizing photosynthesis in perennial pepperweed best determined for seasonal scale landscape flux analysis? (2) How can the effects of soil salinity on root water uptake be represented in order to account for changes in the diurnal cycle and in the uptake of carbon dioxide by plants? (3) How sensitive are modeled results to changes in model input parameters, and how may these sensitivities limit the predictive abilities of the model? These questions are assessed using data from a relatively wet pasture-peatlands in the San Francisco Bay - Sacrament River Delta region of California, with the dominant land-cover species perennial pepperweed (Lepidium latifolium), a mildly salt-tolerant and invasive herbaceous weed.

Presented in this research is a characterization of pepperweed as a highly capable invasive species, able to take advantage of local resources such as light, carbon, water, and nitrogen. Modeling results from each section also demonstrate its ability to photosynthesize under higher temperatures and vapor pressure deficits than standard plant models suggest. Incorporating soil salinity into a whole-plant model increases the ability to describe how different soil and atmospheric parameters influence evapotranspiration and photosynthesis in such an environment. The model's sensitivity analysis reveals two pairs of parameters that may constrain each other, and demonstrates how improved measurements of plant conductance and leaf water potential can constrain other portions of the parameter space.

Prediction of snow in regional and global hydrological models has been a difficult task due to errors in the forcing data, subgrid-scale variability in the snowpack, and uncertain model physics. This dissertation conducts thorough studies of uncertainties that are concerned with snow modeling, in particular for high mountain areas. First, an in-depth analysis of uncertainties associated with meteorological forcing data in the Sierra Nevada was performed. The use of ensemble forcing data with a reasonable degree of uncertainty and model parameter adjustments did not overcome the low-bias in simulating snow states using a simple two-layer (2-L) snow model in the Variable Capacity Infiltration (VIC) land surface model (LSM). To reveal the uncertainty related to model parameterization, a multi-layer (M-L) soil-snow model with more complexity has been developed. This dissertation examines the impact of model complexity on snow simulations in high mountains by comparing the M-L model and the 2-L model. While the current VIC LSM solves state variables for soil and snow separately, the new M-L model solves state variables for the integrated soil-snow system simultaneously. This dissertation has found that the complex M-L model performs better than the 2-L model overall, in particular during the melting season, but the added complexity did not significantly remove the uncertainty, which is similar to some other researchers' findings. This conclusion has led this dissertation research to data assimilation work to investigate the uncertainty problem from a different angle.

The data assimilation approach was taken to discover the hidden facets of uncertain land surface processes that could not be explained by the complex M-L soil-snow model. This research uses a multiscale data assimilation scheme that allows for incorporation of data with different scales. As an extension of the traditional state space model (e.g., Kalman filtering), the multiscale data assimilation incorporates data at different scales by computing their conditional probabilities in a scale-recursive way. The multiscale assimilation scheme has been embedded into the M-L soil-snow model of the VIC LSM. This dissertation applies the new assimilation system to the West Coast region to examine the impact of snow data assimilation at the regional scale as well as at the local scale. The assimilation at the local and regional scales showed promise by reducing biases in simulating snow states in the region. In addition, this research shows the impact of snow data assimilation on energy flux and streamflow simulations.

The Amazon basin is vulnerable to extreme drought events that can severely impact the local economy and ecosystems, with consequences for the global climate and ecology. However, hydrologic droughts have been mostly studied in the context of semi-arid environments and temperate regions and little is known on droughts on tropical, wet environments such as the Amazon. Also, the limited hydrologic data have been an obstacle in advancing the understanding of Amazon hydrologic systems and their response to droughts. This work employs new data analysis and stochastic simulation methods to reveal important features of droughts in the Amazon basin.

First, spatial-temporal patterns of hydrologic droughts are identified from existing streamflow data, using principal component analysis and Monte Carlo simulations to account for the uncertainty due to missing data. Second, hydrologic droughts are investigated at the watershed scale. A conceptual model is developed to quantify groundwater and subsurface water storages and fluxes during droughts, using existing hydrologic data at four representative Amazon watersheds. Then, the functioning of the typical deep soil columns and deep-rooted vegetation systems during droughts is further examined at the point scale. A one-dimensional, soil-vegetation hydrologic model is coupled with a stochastic rainfall and evapotranspiration model, calibrated with hourly data from conventional gauges and eddy covariance towers, to compute statistical characteristics of water balance components: evaporation of intercepted water, transpiration, deep percolation, and change in soil water storage. Finally, the dependency of evapotranspiration on cloudiness and soil moisture is analyzed using statistical hypothesis testing and stochastic simulations of rainfall and evapotranspiration, using observations of heat fluxes at the land surface, obtained from eddy covariance towers.

Results show distinct modes of spatial-temporal variability of droughts, with marked differences among sub-basins, especially between northern and southern rivers. A significant trend towards more intense droughts is found in the southern sub-basins. Correlations of drought indices with climatic anomalies originating from the Pacific and Atlantic Oceans are significant, but do not explain all the temporal variability of the major patterns of droughts. That suggests that hydrologic processes at smaller scales might play important roles in shaping drought characteristics. At the watershed scale, the propagation of rainfall deficits through the hydrologic systems highlights the importance of Amazon subsurface hydrology in promoting delays of timing, amplification of duration and attenuation of magnitude of deficits in subsurface, groundwater, and river systems. When annual rainfall is below average, the greater release of watershed water storage contributes to streamflow, helping to attenuate deficits. Moreover, propagation of streamflow deficits from upstream to downstream river portions affects drought characteristics in some rivers. At the point scale, the simulations of the soil-vegetation system reveal that, when rainfall is scarce, the drainage out of the bottom of the deepest soil layer is severely reduced and soil water storage decreases, while evapotranspiration is less impacted. The relationship between deep percolation and average soil moisture is found to vary in the wet and dry seasons. Finally, evapotranspiration is found to be more dependent on cloudiness, rather than soil moisture. The association of rainfall deficits with increased evapotranspiration can potentially exacerbate droughts, although the magnitude of that effect is not large at the interannual time scale. Those results highlight the potential of statistical and stochastic simulation methods in the study of the hydrology of data-scarce regions. Furthermore, they identify and quantify important hydrologic processes that shape the magnitude and duration of droughts in wet environments, which helps to better assess the vulnerability of such sensitive systems.