Fuzzy Optimization Neural Network Approach for Ice Forecast in the Inner Mongolia Reach of the Yellow River/Approche d'Optimisation Floue de Réseau de Neurones pour la Prévision de la Glace Dans le Tronçon de Mongolie Intérieure du Fleuve Jaune

Abstract

In ice forecasting, a key problem is the forecast of freeze-up and break-up dates. Ice-water mechanics and the principle of heat-exchange were mainly adopted in previous research. However, the mathematical models in these studies are complex and many parameters are required in relation to upstream and/or downstream gauging stations. Moreover, too many assumptions or simplifications for these parameters and constraints directly lead to low accuracy of the models and limitations as to their practical applications. This paper develops a fuzzy optimization neural network approach for the forecast of freeze-up date and break-up date. The Inner Mongolia reach lies in the top north of the Yellow River, China. Almost every year ice floods occur because of its special geographical location, hydrometeorological conditions and river course characteristics. Therefore, it is of particular importance for ice flood prevention to forecast freeze-up date and break-up date accurately. A case study in this region shows that the proposed methodology may allow obtaining useful results.     

A 200-year precipitation index for the central English Lake District / Un indice de précipitation de 200 ans pour la Région des Lacs en Angleterre

Abstract

Abstract The geographical context and hydroclimatology of the English Lake District means that the region is an important monitor of changes to nationally significant environmental assets. Using monthly rainfall series for sites in and around the central Lake District, a continuous ~200-year precipitation index was constructed for a representative station close to Grasmere. The bridged series shows a significant decline in summer rainfall since the 1960s, offset by increases in winter and spring that are strongly linked to North Atlantic forcing. Over longer time periods, the index exhibits several notable dry (1850s, 1880s, 1890s, 1930s, 1970s) and wet (1820s, 1870s, 1920s, 1940s, 1990s) decades. These patterns are strongly reflected by reservoir inflow series and by indicators of the biological status of the region’s freshwater lakes. It is argued that long-term climate indices will become increasingly important as managers seek to evaluate recent and project environmental changes within the context of long-term natural variability.     

Modelling the temporal variation in snow-covered area derived from satellite images for simulating/forecasting of snowmelt runoff in Turkey/Modélisation de la variation temporelle de la surface enneigée à partir d'images satellitaires pour la simulation/prévision de l'écoulement de fonte nivale en Turquie

Abstract

Monitoring the change of snow-covered area (SCA) in a basin is vitally important for optimum operation of water resources, where the main contribution comes from snowmelt. A methodology for obtaining the depletion pattern of SCA, which is based on satellite image observations where mean daily air temperature is used, is applied for the 1997 water year and tested for the 1998 water year. The study is performed at the Upper Euphrates River basin in Turkey (10 216 km²). The major melting period in this basin starts in early April. The cumulated mean daily air temperature (CMAT) is correlated to the depletion of snow-covered area with the start of melting. The analysis revealed that SCA values obtained from NOAA-AVHRR satellite images are exponentially correlated to CMAT for the whole basin in a lumped manner, where R ² values of 0.98 and 0.99 were obtained for the water years 1997 and 1998, respectively. The applied methodology enables the interpolation between the SCA observations and extrapolation. Such a procedure reduces the number of satellite images required for analysis and provides solution for the cloud-obscured images. Based on the image availability, the effect of the number of images on the quality of snowmelt runoff simulations is also discussed. In deriving the depletion curve for SCA, if the number of images is reduced, the timing of image analysis within the snowmelt period is found very important. Analysis of the timing of satellite images indicated that images from the early and middle parts of the melt period are more important.     

Utility of high-resolution climate model simulations for water resources prediction over the Korean Peninsula: a sensitivity study / Utilité de simulations de modèle climatique à haute résolution pour la prévision des ressources en eau dans la Péninsule Coréenne: étude de sensibilité

Abstract

Abstract The utility of simulations of Global Climate Models (GCMs) for regional water resources prediction and management on the Korean Peninsula was assessed by a probabilistic measure. Global Climate Model simulations of an indicator variable (e.g. surface precipitation or temperature) were used for discriminating high vs low regional observations of a target variable (e.g. watershed precipitation or reservoir inflow). The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two distributions. High resolution Atmospheric Model Intercomparison Project-II (AMIP-II) type GCM simulations performed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and AMIP-I type GCM simulations performed by the Korean Meteorological Research Institute (METRI) were used to obtain information for the indicator variables. Observed mean areal precipitation and temperature, and watershed-outlet discharge values for seven major river basins in Korea were used as the target variables. The results suggest that the use of the climate model nodal output from both climate models in the vicinity of the target basin with monthly resolution will be beneficial for water resources planning and management analysis that depends on watershed mean areal precipitation and temperature, and outlet discharge.