Variability of United States Runoff and its Climate Teleconnections

1. Variability of United States Runoff and its Climate Teleconnections E.P. Maurer(1), D.P. Lettenmaier(2) and N.J. Mantua(3) (1)Department of Civil Engineering, Santa Clara University, Santa Clara, CA 95053 (2)Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle WA 98195 (3)Climate Impacts Group, Joint Institute for the Study of the Atmosphere and Ocean, Box 354235, University of Washington, Seattle, WA 98195 Poster: H22B-0917 3 Principal Components of Distributed Runoff and Climate Teleconnections Abstract Understanding the space-time variability of runoff has important implications for climate because of the linkage of runoff and evapotranspiration, as well as practical implications for the prediction of drought and floods. In contrast to climate variables like precipitation and temperature, there has to date been relatively little work evaluating climate teleconnections of runoff, in part because of the absence of data sets that lend themselves to commonly used techniques in climate analysis like principal components analysis (PCA). We examine the space-time variability of runoff over North America between latitudes 25 and 53 degrees north, which is the spatial domain of the North American Land Data Assimilation System (N-LDAS) for which a 50-year retrospective data set of runoff and other land surface water cycle variables has been produced at 1/8 degree latitude-longitude spatial resolution. Past efforts to investigate spatial patterns of runoff variability in the United States have utilized streamflow observations, which have three important drawbacks: first, the location of the observations is highly non-uniform, second, routing effects associated with the conversion of runoff to streamflow confound the interpretation of the observed variables, and third, river impoundments and diversions affect the observations by varying amounts. By using derived spatially distributed runoff which represents natural (no effects of routing, or diversions or impoundments) conditions, we are able to avoid these shortcomings. Using the 50-year 1/8 degree data set, accumulated to monthly amounts, we determine climatic teleconnections using common climate indices (such as Niño3.4 and NAO), by season for lead times of months to a year. High and low values of climatic indices are evaluated separately, which allows independent interpretation of the telconnections of different climatic anomalies to the runoff variability. We identify patterns of runoff variability that are not revealed with observed datasets, especially where observations are sparse. A greater number of significant climate-runoff relationships are exhibited for runoff patterns on the east and west coasts and southern interior, with fewer for Northern interior runoff patterns in the Upper Mississippi and Missouri river regions. Rarely do both the positive and negative phases of any climatic index show significant teleconnection with a particular pattern of runoff variability, lead time and season. PC loadings are plotted below, illustrating coherent modes of runoff variability 10 PCs were rotated (using the Varimax technique). Variability is revealed in the northern Great Plains that is not detectable in studies using sparse gauge observations. Two of these patterns are the Upper Missouri/Canadian prairie and Lower Missouri, with both regions experiencing devastating floods within the past 10 years. Climate signals exhibiting 95% significant teleconnection to the indicated pattern of runoff variability for the designated season and lead time. A (+) indicates the positive phase of this index is significantly correlated with the runoff pattern, and a (-) indicates the negative phase. Rows indicate runoff season, and columns indicate lead time in seasons. Runoff season Runoff variance explained by pattern For seasons and locations such as these examples, where no climate indices show a significant relationship to runoff, any land surface influence may be more important (see box 4 below) 1 Motivation and Research Questions • Improved understanding of hydrologic variability is needed to advance prediction of floods and droughts. • Principal components (PC) analysis (PCA) provides a tool to characterize variability of earth system variables. • PCA has been used to examine streamflow variability, using observed gauge flows to determine modes of variability. • PCA on spatially distributed runoff can avoid the problems in using observed streamflows, such as non-uniform spatial distribution of observations, effects of river impoundments or diversions, and routing through the contributing basin. • PCs of spatially distributed runoff be examined to reveal climatic teleconnections and relationships of runoff to the land surface, to show potential sources of runoff predictability. • These inspired the following questions: • Do different patterns of runoff variability emerge with spatially distributed runoff data as compared to the irregularly-spaced stream gauge data? • What are the SST and climatic teleconnections to the patterns of runoff variability? • Where and at what lead times are the soil moisture and snow conditions significantly related to the modes of runoff variability? 10 highest and lowest values of anomaly in 50-year record selected; PC magnitude for each set of 10 compared for significant difference from 0 • Climate Teleconnection Analysis • Significant climate teleconnections at different lead times for each pattern are shown in the table to the right. • High and low signals were evaluated separately: • Climate teleconnections are generally weaker at longer lead times. • More climate teleconections exist for Southern patterns in DJF and MAM, and for Northern patterns for MAM and JJA. • In most cases where one phase of a climate signal has a significant teleconnection its opposite does not. • AO and NAO are significantly related to runoff at long lead times, and can provide additional runoff predictability. 4 5 Bonus Section: Land Surface Influence on Runoff Variability Summary and Conclusions Soil Moisture: Low magnitude DJF runoff has high SM relationship. Low values (dry conditions) show stronger relationships to runoff. Snow water equivalent: shaded blocks indicate grid cells with significant relationships between SWE and runoff pattern. Relationships follow snow melt: snow produces MAM runoff variability in the East, MAM and JJA runoff variability in the West. • By including a large domain and using a spatially distributed runoff data set, runoff variability was revealed that could not be obtained directly using observed streamflow • Through a lead of 1 season, snow water equivalent is a significant predictor for runoff variability, and is strongest in the Pacific Northwest and Far West. • Soil moisture provides widespread predictability at short lead times, and is more frequently a significant predictor in its dry state than wet. • By a lead of 1 season, soil moisture has its primary influence on runoff variability in the Western U.S. • Both soil moisture and snow explain significant portions of runoff variability where climate signals do not, showing that the two sources of predictability can be complementary. 2 Data and Domain The runoff, soil moisture, and snow water equivalent data used are those presented by Maurer et al. (2002; J. Climate 15(22):3237-51), derived by driving a hydrologic model with gridded observed meteorology. The output including runoff, soil moisture, and snow, compared favorably to available observations. The domain is North America between latitudes 53ºN and 25ºN, coinciding with the Land Data Assimilation System-North America (N-LDAS) region. Climate indices were obtained from publicly available sources and include: Niño 3.4 (for ENSO signal) Pacific Decadal Oscillation index (PDO) Arctic Oscillation index (AO) North Atlantic Oscillation index (NAO) North Pacific index (NP), which mirrors the Pacific North America index (PNA)