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Alexandre K. Guetter and Konstantine P. Georgakakos

Abstract

The association between the El Niño/La Niña and seasonal streamflow for the Iowa River is investigated. The seasonal Southern Oscillation index (SOI) was ranked and the extreme quartiles for each season were selected to condition the composite analysis of streamflow. The either concurrent or lagged association between anomalous SOI index and streamflow was obtained with a composite analysis that windowed a 3-yr period. The existence of statistically significant streamflow responses to El Niño and La Niña has been demonstrated for lags ranging from zero to five seasons. The long lag of streamflow-SOI association is attributed to 1) the time to establish global and regional circulation conducive to excess or deficit rainfall in the Midwest and 2) the inertia of anomalous high (low) soil water. Streamflow responses to El Niño and La Niña are out of phase. Above normal streamflow is associated with El Niño, whereas dry conditions are associated with La Niña. Sensitivity analysis of the streamflow-SOI association with respect to the magnitude of SOI seasonal anomalies suggests that winter SOI < −0.73 yields above normal streamflow from fall (three-season lag) to spring (five-season lag), with 70% consistency. Below-normal streamflow during fall is associated with SOI > 0.63 in preceding spring and summer, with 70% and 15% consistency, respectively. Streamflow predictive models conditioned on SOI anomalies were developed for lead times up to five seasons.

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D. Koutsoyiannis, A. Efstratiadis, and K. P. Georgakakos

Abstract

During the last decade, numerous studies have been carried out to predict future climate based on climatic models run on the global scale and fed by plausible scenarios about anthropogenic forcing to climate. Based on climatic model output, hydrologic models attempt then to predict future hydrologic regimes at regional scales. Much less systematic work has been done to estimate climatic uncertainty and to assess the climatic and hydrologic model outputs within an uncertainty perspective. In this study, a stochastic framework for future climatic uncertainty is proposed, based on the following lines: 1) climate is not constant but rather varying in time and expressed by the long-term (e.g., 30 yr) time average of a natural process, defined on a finescale; 2) the evolution of climate is represented as a stochastic process; 3) the distributional parameters of a process, marginal and dependence, are estimated from an available sample by statistical methods; 4) the climatic uncertainty is the result of at least two factors, the climatic variability and the uncertainty of parameter estimation; 5) a climatic process exhibits a scaling behavior, also known as long-range dependence or the Hurst phenomenon; and 6) because of this dependence, the uncertainty limits of the future are affected by the available observations of the past. The last two lines differ from classical statistical considerations and produce uncertainty limits that eventually are much wider than those of classical statistics. A combination of analytical and Monte Carlo methods is developed to determine uncertainty limits for the nontrivial scaling case. The framework developed is applied with temperature, rainfall, and runoff data from a catchment in Greece, for which data exist for about a century. The uncertainty limits are then superimposed onto deterministic projections up to 2050, obtained for several scenarios and climatic models combined with a hydrologic model. These projections indicate a significant increase of temperature in the future, beyond uncertainty bands, and no significant change of rainfall and runoff as they lie well within uncertainty limits.

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Alexandre K. Guetter and Konstantine P. Georgakakos

A record of 50 years of daily outflows through the boundaries of the continental United States has been assembled based on observations recorded by U.S. Geological Survey streamflow stations. Only stations with continuous records from 1939 through 1988 were included. These stations (197 total) are near the outlets of rivers located at the vicinity of the Canadian, Mexican, Atlantic (including the Gulf of Mexico), and Pacific borders of the continental United States. The drainage area of the selected stations covers 77% of the conterminous United States, whereas the existing network of gauging stations covers 83% of the conterminous U.S. area. Station daily data were aggregated over the entire boundary of the United States and were integrated in monthly and annual totals. The 50-year average annual streamflow divergence normalized by the aggregated drainage area is 210.2 mm yr−1 reaching a peak in April with 27.3 mm month−1 and a minimum in September with 8.7 mm month−1. The Mississippi–Missouri Basin comprises 50% of the gauged area and dominates the absolute value of the outflow discharge. Spectral analysis of the monthly outflow anomalies shows an 11-year dominant cycle. The 1939–1988 period contains four notable droughts. Two droughts are partially registered in the limits of the records characterized by the negative anomalies extending from 1939 to 1941 and by the 1987–1988 anomalies for the late 1980s drought. The middle 1950s and early 1960s droughts are fully included in the dataset. Periods of high outflows were registered in the middle 1940s, early 1970s, and early 1980s. Analysis of the spatial coherence of the annual anomalies shows large-scale features, whereas analysis of the monthly anomalies yields the frequency and persistence patterns of floods and droughts. An estimate of the climatological land-surface water budget for the continental United States was done based on recorded precipitation, panevaporation, and outflow. Eigenvector analysis of the monthly outflow residuals per 3° range has been performed to identify the major modes of the spatial correlation structure. The first eight modes explain 66% of the variance of the system and identify the following regions: Atlantic seaboard, Mississippi–Missouri and Ohio River basins, Northeast, Pacific Northwest, Pacific seaboard, Texas Gulf region, North-central, and the Colorado River and Great Basin. Annual and monthly specific outflow aggregates were used to describe the temporal characteristics of the coherent regions. Both time-domain and spectral analyses of the regional outflow anomalies identify the dominant modes of temporal variability.

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M. B. Sharifi, K. P. Georgakakos, and I. Rodriguez-Iturbe

Abstract

Rainfall data obtained by a highly sensitive raingage have been analyzed for the presence of strange attractors. Analysis of three storms that occurred in Cambridge, Massachusetts revealed, for each storm, the presence of a low-dimensional strange attractor with correlation dimension that was less than 4. The datasets consist of the discrete time series of the interarrival times of one-hundredth of a millimeter rainfall amounts. In all cases, the number of data points in the datasets was at least 3300, which makes the evidence of determinism in storm rainfall strong.

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A. A. Tsonis, J. B. Elsner, and K. P. Georgakakos

Abstract

When the reconstruction of attractors from observables is sought, the Grassberger-Procaccia algorithm for estimating the correlation dimension is often used. An overview of recent developments concerning data requirements and algorithm performance is presented within. In view of these developments the significance of previously estimated dimensions of weather and climate attractors is discussed.

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K. P. Georgakakos, A. A. Carsteanu, P. L. Sturdevant, and J. A. Cramer

Abstract

A prototype meteorological station has been established in Iowa City for the measurement of surface meteorological parameters with accuracy and resolution sufficient to allow for modern dimensional and scaling analyses. The sensor characteristics are presented and an initial analysis of the rain-rate time series of seven storms with high quality data yielded the following results: (a) scaling exists for the short timescales, which in most cases extended up to a few tens of seconds, and it does not extend to the longer timescales for all the storms, and (b) four of the seven storms exhibit hyperbolic behavior of the exceedance probability distributions, with the value of the associated exponent in most cases being near 2 but higher than the values obtained previously from radar and conventional rain gauge data. Analysis of the time series of concurrent surface meteorological variables for a particular spring storm shows that there is interdependence of the time series with an implied reduction of the dimension of the state space of local surface rainfall.

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John O. Roads, Shyh-C. Chen, Alexander K. Guetter, and Konstantine P. Georgakakos

A large-scale, gridpoint, atmospheric, hydrologic climatology consisting of atmospheric precipitable water, precipitation, atmospheric moisture flux convergence, and a residual evaporation for the conterminous United States is described. A large-scale, basin, hydrologic climatology of the same atmospheric variables is also described, as well as residual surface water and streamflow divergence or runoff for various large-scale river basins terminating at the United States boundary.

Climatologically, precipitation, which had a U.S. annual mean of more than 2.1 mm day−1, was largely balanced by evaporation; atmospheric moisture flux convergence was also an important contributor (~0.5 mm day−1), especially during the wintertime, and especially along the U.S. west coast. At the surface, seasonal and anomalous surface water (including snow) variations on the order of 10 cm yr1 were forced by seasonal variations of about 1 mm day−1 in atmospheric moisture flux convergence (precipitation minus evaporation) and streamflow divergence. The strongest seasonal variations were found along the West Coast.

Unlike the climatological means and seasonal variations, atmospheric precipitation anomalies were best related to atmospheric moisture flux convergence anomalies and less well related to the residual evaporation anomalies. Streamflow divergence anomalies were also related to the atmospheric moisture flux convergence anomalies, especially at lags of around 15 days. A better lag relationship occurred between streamflow divergence and precipitation anomalies.

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Jinwon Kim, Norman L. Miller, Alexander K. Guetter, and Konstantine P. Georgakakos

Abstract

A numerical study of precipitation and river flow from November 1994 to May 1995 at two California basins is presented. The Hopland watershed of the Russian River in the northern California Coastal Range and the headwater of the North Fork American River in the northern Sierra Nevada were selected to investigate the hydroclimate, snow budget, and streamflow at different elevations. Simulated precipitation and streamflow at the Hopland basin closely approximated observed values. An intercomparison between the semidistributed TOPMODEL and two versions of the lumped Sacramento model for the severe storm event of January 1995 indicates that both types of models predicted a similar response of river outflows from this basin, with the exception that TOPMODEL predicted a faster recession of river flow with less base flow after precipitation ended. Precipitation in this low-elevation watershed was predominantly in the form of rain, causing a fast streamflow response. The high-elevation Sierra Nevada watershed received most of its precipitation as snowfall. As a result, the frozen water held in surface storage delayed runoff and streamflow. Application of a simple elevation-dependent snowfall and rainfall partitioning scheme showed the significance of finescale terrain variation in the surface hydrology at high-elevation watersheds.

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