Home About us Contact
|
Home About us Contact | ||
|
|
||
Hydrological Forecasting (hydrological + forecasting)
Selected AbstractsLong-term Hydrological Forecasting in Cold Regions: Retrospect, Current Status and ProspectGEOGRAPHY COMPASS (ELECTRONIC), Issue 5 2009Alexander N. Gelfan The influence of long-term snow accumulation on the runoff conditions several months afterwards is a distinct hydrological characteristic of cold regions, which creates opportunities for long-term (seasonal and subseasonal) hydrological forecasting in these regions. We consider evolution of the long-term forecasting approaches from the deterministic data-based index methods to the hydrological model-based ensemble approaches. Of key interest in this review are the methods developed and used in operational practice in Russia and in the USA, with the emphasis being placed on the methods used in Russia, which may be less familiar to international hydrological society. Following a description of the historical context, we review recent developments that place emphasis on problems relating to the uncertainty of the weather conditions for the lead time of the forecast. We conclude with a personal view of the prospects for the future development of long-term hydrological forecasting techniques. [source] Geostatistical Analysis of RainfallGEOGRAPHICAL ANALYSIS, Issue 2 2010David I. F. Grimes Rainfall can be modeled as a spatially correlated random field superimposed on a background mean value; therefore, geostatistical methods are appropriate for the analysis of rain gauge data. Nevertheless, there are certain typical features of these data that must be taken into account to produce useful results, including the generally non-Gaussian mixed distribution, the inhomogeneity and low density of observations, and the temporal and spatial variability of spatial correlation patterns. Many studies show that rigorous geostatistical analysis performs better than other available interpolation techniques for rain gauge data. Important elements are the use of climatological variograms and the appropriate treatment of rainy and nonrainy areas. Benefits of geostatistical analysis for rainfall include ease of estimating areal averages, estimation of uncertainties, and the possibility of using secondary information (e.g., topography). Geostatistical analysis also facilitates the generation of ensembles of rainfall fields that are consistent with a given set of observations, allowing for a more realistic exploration of errors and their propagation in downstream models, such as those used for agricultural or hydrological forecasting. This article provides a review of geostatistical methods used for kriging, exemplified where appropriate by daily rain gauge data from Ethiopia. La precipitación puede ser modelada como un campo aleatorio correlacionado espacialmente sobrepuesto a un valor de fondo (background) medio. Dadas estas propiedades, resulta apropiado utilizar métodos geoestadísticos para el análisis de datos registrados con pluviómetros distribuidos en estaciones meteorológicas. Existen sin embargo, ciertas características de este tipo de datos que deben ser tomados en cuenta para producir resultados útiles:a) la distribución de datos tiende a ser mixta y no ser normal; b) las observaciones son heterogéneas y de escasa densidad espacial; y c) los patrones de correlación espacial son varían considerablemente en el tiempo y espacio. Numerosos estudios han demostrado ya que un análisis geoestadístico riguroso ofrece mejores resultados que las otras técnicas de interpolación disponibles para este tipo de datos. Cabe resaltar que en la aplicación de estas técnicas, el uso de variogramas climatológicos y el tratamiento apropiado de áreas lluviosas versus áreas no lluviosas son consideraciones importantes. El análisis geoestadístico de lluvias tiene además la ventaja de estimar promedios areales con facilidad, proporcionar una estimación espacial de la incertidumbre, y la posibilidad de incorporar información secundaria (ej. topografía) en el modelo. Asimismo, los métodos geoestadísticos también facilitan la generación de campos de lluvia que son consistentes con las observaciones. Esto hace posible exploraciones más realistas del error y la estimación de su propagación en modelos aplicados subsecuentemente, como por ejemplo en los modelos utilizados en predicción agrícola e hidrológica. Los autores reseñan los métodos geoestadísticos utilizados para krijeage o krijeado (kriging) mediante ejemplos de su uso apropiado con datos pluviométricos en Etiopia. [source] Long-term Hydrological Forecasting in Cold Regions: Retrospect, Current Status and ProspectGEOGRAPHY COMPASS (ELECTRONIC), Issue 5 2009Alexander N. Gelfan The influence of long-term snow accumulation on the runoff conditions several months afterwards is a distinct hydrological characteristic of cold regions, which creates opportunities for long-term (seasonal and subseasonal) hydrological forecasting in these regions. We consider evolution of the long-term forecasting approaches from the deterministic data-based index methods to the hydrological model-based ensemble approaches. Of key interest in this review are the methods developed and used in operational practice in Russia and in the USA, with the emphasis being placed on the methods used in Russia, which may be less familiar to international hydrological society. Following a description of the historical context, we review recent developments that place emphasis on problems relating to the uncertainty of the weather conditions for the lead time of the forecast. We conclude with a personal view of the prospects for the future development of long-term hydrological forecasting techniques. [source] Aims, challenges and progress of the Hydrological Ensemble Prediction Experiment (HEPEX) following the third HEPEX workshop held in Stresa 27 to 29 June 2007ATMOSPHERIC SCIENCE LETTERS, Issue 2 2008Jutta Thielen Abstract Since several years, users of weather forecasts have begun to realize the benefit of quantifying the uncertainty associated with forecasts rather than relying on single value forecasts. At the same time, hydrologists and water managers have begun to explore the potential benefit of ensemble prediction systems (EPS) for hydrological applications. The Hydrologic Ensemble Prediction Experiment (HEPEX) is an international project that aims to foster the development of probabilistic hydrological forecasting and corresponding decision making tools. Since 2004, HEPEX has provided discussion opportunities for hydrological and meteorological scientists involved in the development, testing, and operational management of forecasting systems, and end users. Copyright © 2008 Royal Meteorological Society [source] | ||||||||||