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Flow Series (flow + series)
Selected AbstractsNonlinear determinism in river flow: prediction as a possible indicatorEARTH SURFACE PROCESSES AND LANDFORMS, Issue 7 2007Bellie SivakumarArticle first published online: 6 DEC 200 Abstract Whether or not river flow exhibits nonlinear determinism remains an unresolved question. While studies on the use of nonlinear deterministic methods for modeling and prediction of river flow series are on the rise and the outcomes are encouraging, suspicions and criticisms of such studies continue to exist as well. An important reason for this situation is that the correlation dimension method, used as a nonlinear determinism identification tool in most of those studies, may possess certain limitations when applied to real river flow series, which are always finite and often short and also contaminated with noise (e.g. measurement error). In view of this, the present study addresses the issue of nonlinear determinism in river flow series using prediction as a possible indicator. This is done by (1) reviewing studies that have employed nonlinear deterministic methods (coupling phase-space reconstruction and local approximation techniques) for river flow predictions and (2) identifying nonlinear determinism (or linear stochasticity) based on the level of prediction accuracy in general, and on the prediction accuracy against the phase-space reconstruction parameters in particular (termed as the ,inverse approach'). The results not only provide possible indications to the presence of nonlinear determinism in the river flow series studied, but also support, both qualitatively and quantitatively, the low correlation dimensions reported for such. Therefore, nonlinear deterministic methods are a viable complement to linear stochastic ones for studying river flow dynamics, if sufficient caution is exercised in their applications and in interpreting the outcomes. Copyright © 2006 John Wiley & Sons, Ltd. [source] Apparent/spurious multifractality of data sampled from fractional Brownian/Lévy motionsHYDROLOGICAL PROCESSES, Issue 15 2010Shlomo P. Neuman Abstract Many earth and environmental variables appear to be self-affine (monofractal) or multifractal with spatial (or temporal) increments having exceedance probability tails that decay as powers of , , where 1 < , , 2. The literature considers self-affine and multifractal modes of scaling to be fundamentally different, the first arising from additive and the second from multiplicative random fields or processes. We demonstrate theoretically that data having finite support, sampled across a finite domain from one or several realizations of an additive Gaussian field constituting fractional Brownian motion (fBm) characterized by , = 2, give rise to positive square (or absolute) increments which behave as if the field was multifractal when in fact it is monofractal. Sampling such data from additive fractional Lévy motions (fLm) with 1 < , < 2 causes them to exhibit spurious multifractality. Deviations from apparent multifractal behaviour at small and large lags are due to nonzero data support and finite domain size, unrelated to noise or undersampling (the causes cited for such deviations in the literature). Our analysis is based on a formal decomposition of anisotropic fLm (fBm when , = 2) into a continuous hierarchy of statistically independent and homogeneous random fields, or modes, which captures the above behaviour in terms of only E + 3 parameters where E is Euclidean dimension. Although the decomposition is consistent with a hydrologic rationale proposed by Neuman (2003), its mathematical validity is independent of such a rationale. Our results suggest that it may be worth checking how closely would variables considered in the literature to be multifractal (e.g. experimental and simulated turbulent velocities, some simulated porous flow velocities, landscape elevations, rain intensities, river network area and width functions, river flow series, soil water storage and physical properties) fit the simpler monofractal model considered in this paper (such an effort would require paying close attention to the support and sampling window scales of the data). Parsimony would suggest associating variables found to fit both models equally well with the latter. Copyright © 2010 John Wiley & Sons, Ltd. [source] Impact of time-scale of the calibration objective function on the performance of watershed modelsHYDROLOGICAL PROCESSES, Issue 25 2007K. P. Sudheer Abstract Many of the continuous watershed models perform all their computations on a daily time step, yet they are often calibrated at an annual or monthly time-scale that may not guarantee good simulation performance on a daily time step. The major objective of this paper is to evaluate the impact of the calibration time-scale on model predictive ability. This study considered the Soil and Water Assessment Tool for the analyses, and it has been calibrated at two time-scales, viz. monthly and daily for the War Eagle Creek watershed in the USA. The results demonstrate that the model's performance at the smaller time-scale (such as daily) cannot be ensured by calibrating them at a larger time-scale (such as monthly). It is observed that, even though the calibrated model possesses satisfactory ,goodness of fit' statistics, the simulation residuals failed to confirm the assumption of their homoscedasticity and independence. The results imply that evaluation of models should be conducted considering their behavior in various aspects of simulation, such as predictive uncertainty, hydrograph characteristics, ability to preserve statistical properties of the historic flow series, etc. The study enlightens the scope for improving/developing effective autocalibration procedures at the daily time step for watershed models. Copyright © 2007 John Wiley & Sons, Ltd. [source] Downscaling of global climate models for flood frequency analysis: where are we now?HYDROLOGICAL PROCESSES, Issue 6 2002Christel Prudhomme Abstract The issues of downscaling the results from global climate models (GCMs) to a scale relevant for hydrological impact studies are examined. GCM outputs, typically at a spatial resolution of around 3° latitude and 4° longitude, are currently not considered reliable at time scales shorter than 1 month. Continuous rainfall-runoff modelling for flood regime assessment requires input at the daily or even hourly time-step. A review of the different methodologies suggested in the literature to downscale GCM results at smaller spatial and temporal resolutions is presented. The methods, from simple interpolation to more sophisticated dynamical modelling, through multiple regression and weather generators, are, however, mostly based directly on GCM outputs, sometimes at daily time-step. The approach adopted is a simple, empirical methodology based on modelled monthly changes from the HadCM2 greenhouse gases experiment for the time horizon 2050s. Three daily rainfall scenarios are derived from the same set of monthly changes, representing different possible changes in the rainfall regime. The first scenario represents an increase of the occurrence of frontal systems, corresponding to a decrease in the rainfall intensity; the second corresponds to an increase in convective storm-type rainfall, characterized by extreme events with higher intensity; the third one assumes an increase in the monthly rainfall without any change in rainfall variability. A continuous daily rainfall-runoff model, calibrated for the Severn catchment, was used to generate daily flow series for the 1961,90 baseline period and the 2050s, and a peaks-over-threshold analysis was undertaken to produce flood frequency distributions for the two time horizons. Though the three scenarios lead to an increase in the magnitude and the frequency of the extreme flood events, the impact is strongly influenced by the type of daily rainfall scenario applied. We conclude that if the next generation of GCMs produce more reliable rainfall variance estimates, then more appropriate ways of deriving rainfall scenarios could be developed using weather generators rather than empirical methods. Copyright © 2002 John Wiley & Sons, Ltd. [source] MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2009B. P. Weissling Abstract:, The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km2 watershed in south-central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight-day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r2 = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log-transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base-flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash-Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log-space) for the full 15-month period, ,0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ,32%. The contribution of parameter uncertainties to model discrepancy was evaluated. [source] | ||||||||||