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Early flood warning for Linyi Watershed by the GRAPES/XXT Model Using TIGGE Data

Acta meteorologica Sinica 2012年第1期26卷 103-111页

作者：徐精文张万昌郑子彦矫梅燕陈静 College of Resources and Environment Sichuan Agricultural University Institute of Atmospheric Physics Chinese Academy of Sciences Center for Earth Observation and Digital Earth Chinese Academy of Sciences China Meteorological Administration National Meteorological Center CMA

Early and effective flood warning is essential for reducing loss of life and economic damage. Three global ensemble weather prediction systems of the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, and the US National Centers for Environmental Prediction （NCEP） in THORPEX （The Observing System Research and Predictability Experiment） In- teractive Grand Global Ensemble （TIGGE） archive are used in this research to drive the Global/Regional Assimilation and PrEdiction System （GRAPES） to produce 6-h lead time forecasts. The output （precipita- tion, air temperature, humidity, and pressure） in turn drives a hydrological model XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL）, the hybrid model that combines the TOPMODEL （a topography based hydrological model） and the Xinanjiang model, for a case study of a flood event that lasted from 18 to 20 July 2007 in the Linyi watershed. The results show that rainfall forecasts by GRAPES using TIGGE data from the three forecast centers all underestimate heavy rainfall rates; the rainfall forecast by GRAPES using the data from the NCEP is the closest to the obser- vation while that from the CMA performs the worst. Moreover, the ensemble is not better than individual members for rainfall forecasts. In contrast to corresponding rainfall forecasts, runoff forecasts are much better for all three forecast centers, especially for the NCEP. The results suggest that early flood warning by the GRAPES/XXT model based on TIGGE data is feasible and this provides a new approach to raise preparedness and thus to reduce the socio-economic impact of floods.

关键词： TIGGE GRAPES flood warning XXT rainfall-runoffprocess

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Modeling tropical river runoff: A time dependent approach

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Research in Cold and Arid Regions 2014年第3期6卷 247-256页

作者： Rashmi Nigam Sudhir Nigam Sushil K. Mittal Dept. of Mathematics University Institute of Technology Rajiv Gandhi Technical UniversityBhopal India Dept. of Civil Engineering Lakshmi Narain College of Technology & ScienceBhopal India Dept. of Civil Engineering Maulana Azad National Institute of TechnologyBhopal India

Forecasting of rainfall and subsequent river runoff is important for many operational problems and applications related to hydrology. Modeling river runoff often requires rigorous mathematical analysis of vast historical data to arrive at reasonable conclusions. In this paper we have applied the stochastic method to characterize and predict river runoffofthe perennial Kulfo River in southem Ethiopia. The time series analysis based auto regressive integrated moving average （ARIMA） approach is applied to mean monthly runoff data with 10 and 20 years spans. The varying length of the input runoff data is shown to influence the forecasting efficiency of the stochastic process. Preprocessing of the runoff time series data indicated that the data do not follow a seasonal pattern. Our forecasts were made using parsimonious non seasonal ARIMA models and the results were compared to actual 10-year and 20-year mean monthly runoff data of the Kulfo River. Our results indicate that river runoff forecasts based upon the 10-year data are more accurate and efficient than the model based on the 20-year time series.

关键词： flood warning stochastic process ARIMA evaluation univariate, performance error

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