Atmospheric /oceanic climatic influences for improved water management

This dissertation investigated the influence of atmospheric / oceanic variability on streamflow in the continental United States. Unimpaired streamflow for stations in the continental United States and, interdecadal and interannual Pacific Ocean (e.g., El Nino-Southern Oscillation and Pacific Decadal Oscillation) and Atlantic Ocean (e.g., Atlantic Multidecadal Oscillation and North Atlantic Oscillation) climatic variability were identified. Initially, the coupled effects of climatic variability on continental U.S. streamflow, based on the long-term phase (warm / positive or cold / negative) of the interdecadal variable, were identified using nonparametric statistical testing. Next, sea surface temperature variability in the Pacific and Atlantic Oceans, and the resulting continental U.S. streamflow variability, were identified using Singular Value Decomposition. Finally, Pacific and Atlantic Ocean sea surface temperatures (SSTs) were used as predictors in a long lead-time streamflow forecast model applying Partial Least Squares Regression; The major contributions of this dissertation are threefold. First, an evaluation was performed to identify the interdecadal PDO, AMO and NAO\u27s influence on U.S. streamflow, focusing on how each enhanced or dampened the interannual ENSO. This resulted in several new observations, including the enhancement of La Nina during an AMO warm phase in the Southeastern United States. Next, Pacific and Atlantic Ocean SST impacts on continental U.S. streamflow, based on the long-term phase of the interdecadal PDO or AMO, were evaluated. This resulted in a significant relationship between variability in SST and streamflow based on the warm or cold phase of the interdecadal influence. Finally, Pacific and Atlantic Ocean SSTs were utilized and a long lead-time, streamflow forecast model was developed. The use of SSTs resulted in excellent forecast skill for several rivers in the continental United States. The results of this dissertation, including the identification of climatic influences and forecasting of continental U.S. streamflow, will provide useful information to water managers and planners

Incorporating Antecedent Soil Moisture into Streamflow Forecasting

This study incorporates antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin, Colorado/Wyoming (USA). The incorporation of antecedent soil moisture accounts for infiltration and can improve streamflow predictions. Current Natural Resource Conservation Service (NRCS) forecasting methods are replicated, and a comparison is drawn between current NRCS forecasts and proposed forecasting methods using antecedent soil moisture. Current predictors used by the NRCS in regression-based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods utilize antecedent soil moisture as a predictor variable in addition to the predictors noted above. A decision system was used to segregate data based on antecedent soil moisture conditions (e.g., dry, wet or normal). Principal Components Analysis and Stepwise Linear Regression were applied to generate streamflow forecasts, and numerous statistics were determined to measure forecast skill. The results show that when incorporating antecedent soil moisture, the “poor” forecasts (i.e., years in which the NRCS forecast diered greatly from the observed value) were improved, while the overall forecast skill remains unchanged. The research presented shows the need to increase the monitoring and collection of soil moisture data in mountainous western U.S. watersheds, as this parameter results in improved forecast skill

Incorporating Antecedent Soil Moisture into Streamflow Forecasting

This study incorporates antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin, Colorado/Wyoming (USA). The incorporation of antecedent soil moisture accounts for infiltration and can improve streamflow predictions. Current Natural Resource Conservation Service (NRCS) forecasting methods are replicated, and a comparison is drawn between current NRCS forecasts and proposed forecasting methods using antecedent soil moisture. Current predictors used by the NRCS in regression-based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods utilize antecedent soil moisture as a predictor variable in addition to the predictors noted above. A decision system was used to segregate data based on antecedent soil moisture conditions (e.g., dry, wet or normal). Principal Components Analysis and Stepwise Linear Regression were applied to generate streamflow forecasts, and numerous statistics were determined to measure forecast skill. The results show that when incorporating antecedent soil moisture, the “poor” forecasts (i.e., years in which the NRCS forecast differed greatly from the observed value) were improved, while the overall forecast skill remains unchanged. The research presented shows the need to increase the monitoring and collection of soil moisture data in mountainous western U.S. watersheds, as this parameter results in improved forecast skill

Coupled Oceanic-Atmospheric Variability and U.S. Streamflow

A study of the influence of interdecadal, decadal, and interannual oceanic-atmospheric influences on streamflow in the United States is presented. Unimpaired streamflow was identified for 639 stations in the United States for the period 1951–2002. The phases (cold/negative or warm/positive) of Pacific Ocean (El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and Atlantic Ocean (Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO)) oceanic-atmospheric influences were identified for the year prior to the streamflow year (i.e., long lead time). Statistical significance testing of streamflow, based on the interdecadal, decadal, and interannual oceanic-atmospheric phase (warm/positive or cold/negative), was performed by applying the nonparametric rank-sum test. The results show that in addition to the well-established ENSO signal the PDO, AMO, and NAO influence streamflow variability in the United States. The warm phase of the PDO is associated with increased streamflow in the central and southwest United States, while the warm phase of the AMO is associated with reduced streamflow in these regions. The positive phase of the NAO and the cold phase of the AMO are associated with increased streamflow in the central United States. Additionally, the coupled effects of the oceanic-atmospheric influences were evaluated on the basis of the long-term phase (cold/negative or warm/positive) of the interdecadal (PDO and AMO) and decadal (NAO) influences and ENSO. Streamflow regions in the United States were identified that respond to these climatic couplings. The results show that the AMO may influence La Niña impacts in the Southeast, while the NAO may influence La Niña impacts in the Midwest. By utilizing the streamflow water year and the long lead time for the oceanic-atmospheric variables, useful information can be provided to streamflow forecasters and water managers

Tree-Ring Reconstructions of Streamflow for the Tennessee Valley

This study reports the preliminary results from a statistical screening of tree-ring width records from the International Tree-Ring Data Bank (ITRDB), to evaluate the strength of the hydrological signal, in dendrochronological records from the Tennessee Valley. We used United States Geological Survey (USGS) streamflow data from 11 gages, within the Tennessee Valley, and regional tree-ring chronologies, to analyze the dendroclimatic potential of the region, and create seasonal flow reconstructions. Prescreening methods included correlation, date, and temporal stability analysis of predictors to ensure practical and reliable reconstructions. Seasonal correlation analysis revealed that large numbers of regional tree-ring chronologies were significantly correlated (p ≤ 0.05) with the May–June–July streamflow. Stepwise linear regression was used to create the May–June–July streamflow reconstructions. Ten of the 12 streamflow stations were considered statistically skillful (R2 ≥ 0.40). Skillful reconstructions ranged from 208 to 301 years in length, and were statistically validated using leave-one-out cross validation, the sign test, and a comparison of the distribution of low flow years. The long-term streamflow variability was analyzed for the Nolichucky, Nantahala, Emory, and South Fork (SF) Holston stations. The reconstructions revealed that while most of the Western United States (U.S.). was experiencing some of its highest flow years during the early 1900s, the Tennessee Valley region was experiencing a very low flow. Results revealed the potential benefit of using tree-ring chronologies to reconstruct hydrological variables in the Southeastern U.S., by demonstrating the ability of proxy-based reconstructions to provide useful data beyond the instrumental record

Atlantic Ocean Variability and European Alps Winter Precipitation

Winter precipitation (snowpack) in the European Alps provides a critical source of freshwater to major river basins such as the Danube, Rhine, and Po. Previous research identified Atlantic Ocean variability and hydrologic responses in the European Alps. The research presented here evaluates Atlantic Sea Surface Temperatures (SSTs) and European Alps winter precipitation variability using Singular Value Decomposition. Regions in the north and mid-Atlantic from the SSTs were identified as being tele-connected with winter precipitation in the European Alps. Indices were generated for these Atlantic SST regions to use in prediction of precipitation. Regression and non-parametric models were developed using the indices as predictors and winter precipitation as the predictand for twenty-one alpine precipitation stations in Austria, Germany, and Italy. The proposed framework identified three regions in the European Alps in which model skill ranged from excellent (West Region–Po River Basin), to good (East Region) to poor (Central Region). A novel approach for forecasting future winter precipitation utilizing future projections of Atlantic SSTs predicts increased winter precipitation until ~2040, followed by decreased winter precipitation until ~2070, and then followed by increasing winter precipitation until ~2100

The 2009-2010 El Nino: Hydrologic relief to U.S. regions

Current forecasts by the U.S. National Oceanic and Atmospheric Administration (NOAA) are that the Pacific Ocean will experience El Niño conditions in late 2009 and into 2010. These forecasts are similar to past El Niño events in 1972–1973, 1982–1983, 1986–1987, and 2002–2003. Evaluating the hydrologic conditions for these past El Niño events reveals that during these times, surface water supply conditions improved in many parts of the United States, including the Southeast, Midwest, and Southwest. At the same time, the Pacific Northwest and other specific regions of the United States experienced below-average water supply conditions. This is consistent with the long-established linkages between oceanic-atmospheric phenomena, El Niño, and streamflow [e.g., Kahya and Dracup, 1993; Tootle et al., 2005]

Relationships Between Pacific and Atlantic Ocean Sea Surface Temperatures and U.S. Streamflow Variability

An evaluation of Pacific and Atlantic Ocean sea surface temperatures (SSTs) and continental U.S. streamflow was performed to identify coupled regions of SST and continental U.S. streamflow variability. Both SSTs and streamflow displayed temporal variability when applying the singular value decomposition (SVD) statistical method. Initially, an extended temporal evaluation was performed using the entire period of record (i.e., all years from 1951 to 2002). This was followed by an interdecadal-temporal evaluation for the Pacific (Atlantic) Ocean based on the phase of the Pacific Decadal Oscillation (PDO) (Atlantic Multidecadal Oscillation (AMO)). Finally, an extended temporal evaluation was performed using detrended SST and streamflow data. A lead time approach was assessed in which the previous year\u27s spring-summer season Pacific Ocean (Atlantic Ocean) SSTs were evaluated with the current water year continental U.S. streamflow. During the cold phase of the PDO, Pacific Ocean SSTs influenced streamflow regions (southeast, northwest, southwest, and northeast United States) most often associated with El Niño–Southern Oscillation (ENSO), while during the warm phase of the PDO, Pacific Ocean SSTs influenced non-ENSO streamflow regions (Upper Colorado River basin and middle Atlantic United States). ENSO and the PDO were identified by the Pacific Ocean SST SVD first temporal expansion series as climatic influences for the PDO cold phase, PDO warm phase, and the all years analysis. Additionally, the phase of the AMO resulted in continental U.S. streamflow variability when evaluating Atlantic Ocean SSTs. During the cold phase of the AMO, Atlantic Ocean SSTs influenced middle Atlantic and central U.S. streamflow, while during the warm phase of the AMO, Atlantic Ocean SSTs influenced upper Mississippi River basin, peninsular Florida, and northwest U.S. streamflow. The AMO signal was identified in the Atlantic Ocean SST SVD first temporal expansion series. Applying SVD, first temporal expansions series were developed for Pacific and Atlantic Ocean SSTs and continental U.S. streamflow. The first temporal expansion series of SSTs and streamflow were strongly correlated, which could result in improved streamflow predictability

Remote Sensing of Water Use Efficiency and Terrestrial Drought Recovery across the Contiguous United States

Ecosystem water-use efficiency (WUE) is defined as the ratio of carbon gain (i.e., gross primary productivity; GPP) to water consumption (i.e., evapotranspiration; ET). WUE is markedly influential on carbon and water cycles, both of which are fundamental for ecosystem state, climate and the environment. Drought can affect WUE, subsequently disturbing the composition and functionality of terrestrial ecosystems. In this study, the impacts of drought on WUE and its components (i.e., GPP and ET) are assessed across the Contiguous US (CONUS) at fine spatial and temporal resolutions. Soil moisture simulations from land surface modeling are utilized to detect and characterize agricultural drought episodes and remotely sensed GPP and ET are retrieved from the moderate resolution imaging spectroradiometer (MODIS). GPP, as the biome vitality indicator against drought stress, is employed to investigate drought recovery and the ecosystems’ required time to revert to pre-drought condition. Results show that drought recovery duration indicates a positive correlation with drought severity and duration, meaning that a protracted drought recovery is more likely to happen following severe droughts with prolonged duration. WUE is found to almost always increase in response to severe (or worse) drought episodes. Additionally, ET anomalies are negatively correlated with drought severity and ET is expected to decrease during severe (or worse) drought episodes. Lastly, the changes of WUE are decomposed in relation to its components and the cross-relation among the variables is revealed and a consistent changing pattern is detected

Forecasting of lower Colorado River Basin streamflow using Pacific Ocean sea surface temperatures and ENSO

The lower Colorado River basin is located in an area of known El Nino-Southern Oscillation (ENSO) influence. A streamflow forecast is developed using Pacific Ocean Sea Surface Temperatures (SSTs) as predictors in addition to a traditional ENSO predictor, such as the Southern Oscillation Index (SOI). Significant regions of SST influence on streamflow were determined using linear correlations (LC). These significant SST regions are then used as predictors in a statistically based exceedance probability model previously applied to streamflow stations in Australia and the U.S. Long lead-time (3 and 6 month) streamflow forecasts were developed for El Nino, La Nina and non-ENSO years for the winter-spring (January-February-March - JFM) season. The use of the SSTs resulted in improved forecasts, based on cross-validated skill scores, when compared to forecasts using the SOI. Additionally, forecast leadtimes were increased when using the SSTs as predictors due to the inability of the SOI to provide an acceptable forecast. Also, the use of SSTs provided an improved forecast for all lead times for non-ENSO seasons when compared to the SOI forecasts. Following the methodology presented, water resource planners in ENSO influenced areas are provided a useful tool for forecasting streamflow