Abstract: The present invention provides a method and system for analyzing a spatial probability based on a correspondence relationship between precipitation forecast and teleconnection.

Abstract: The present invention provides a method and system for analyzing precipitation normalization by gradient-based parameter optimization.

Abstract: The present disclosure provides a method for calibrating daily precipitation forecast by using a Bernoulli-Gamma-Gaussian distribution, including the following steps: acquiring daily raw forecast data and observed data; using a Bernoulli distribution to perform precipitation occurrence analysis; using a Gamma distribution to perform precipitation amount analysis on the data that precipitation occurs; using a Gaussian distribution to perform normal transformation on the raw forecast data and the observed data according to the analysis results of the Bernoulli distribution and the Gamma distribution, and obtaining corresponding normalized variables; constructing a bivariate joint normal distribution; constructing a conditional probability distribution of a predictand; and determining whether a forecast to be calibrated is that a precipitation event occurs, determining a conditional probability distribution parameter of the predictand, then randomly sampling the conditional probability distribution of the predic

Abstract: Provides a method for associating a precipitation forecast capability with a teleconnection effect based on coefficients of determination, including following steps: acquiring historical forecast precipitation data, observed precipitation data, and a climate index sample sequence, and obtaining original sample data; establishing regression equations of observed precipitation and forecast precipitation, observed precipitation and a climate index, and observed precipitation and a union set of the forecast precipitation and the climate index, and calculating corresponding coefficients of determination through the regression equations; calculating variances explained by the forecast precipitation alone, by the climate index alone, and by the forecast precipitation and the climate index alone; processing the variances by means of bootstrapping to obtain a reference distribution of the variances; and comparing the original sample data with the reference distribution of the variances to obtain an association result

Abstract: A spatial auto-correlation clustering method for a remote correlation mode. By taking the degree of remote correlation between each spatial grid cell and an adjacent cell thereof into consideration, and on the basis of the definition of a local Moran index, an original value of a correlation coefficient is used without performing centralization processing, thereby improving a local Moran index calculation formula to obtain a new local indicator of spatial auto-correlation (LISAAC), such that the detection of a significant positive or negative remote correlation aggregation range is realized, and the identification of an abnormal value (that is, a non-significant or negative-value grid appears in a significant positive-value area, and a non-significant or positive-value grid appears in a significant negative-value area) is realized.

Abstract: A spatial auto-correlation clustering method for a remote correlation mode. By taking the degree of remote correlation between each spatial grid cell and an adjacent cell thereof into consideration, and on the basis of the definition of a local Moran index, an original value of a correlation coefficient is used without performing centralization processing, thereby improving a local Moran index calculation formula to obtain a new local indicator of spatial auto-correlation (LISAAC), such that the detection of a significant positive or negative remote correlation aggregation range is realized, and the identification of an abnormal value (that is, a non-significant or negative-value grid appears in a significant positive-value area, and a non-significant or positive-value grid appears in a significant negative-value area) is realized.

Abstract: The present disclosure provides a method for calibrating daily precipitation forecast by using a Bernoulli-Gamma-Gaussian distribution, including the following steps: acquiring daily raw forecast data and observed data; using a Bernoulli distribution to perform precipitation occurrence analysis; using a Gamma distribution to perform precipitation amount analysis on the data that precipitation occurs; using a Gaussian distribution to perform normal transformation on the raw forecast data and the observed data according to the analysis results of the Bernoulli distribution and the Gamma distribution, and obtaining corresponding normalized variables; constructing a bivariate joint normal distribution; constructing a conditional probability distribution of a predictand; and determining whether a forecast to be calibrated is that a precipitation event occurs, determining a conditional probability distribution parameter of the predictand, then randomly sampling the conditional probability distribution of the predic

Abstract: The present invention provides a method for calibrating monthly precipitation forecast by using a Gamma-Gaussian distribution, including the following steps: acquiring forecast data of monthly average precipitation in a watershed area and corresponding observed values of the average precipitation in the watershed area as input data; performing fitting on the input data by means of a Gamma distribution function; calculating a cumulative distribution function value of each input data in a corresponding Gamma distribution; transforming the cumulative distribution function values into variables obeying a standard normal distribution; constructing a joint normal distribution according to the variables obeying the standard normal distribution to characterize a correlation between the forecast data and the observed values in the input data; and randomly sampling the observed values according to the correlation, and inversely transforming acquired samples to obtain a calibrated forecast result.

Abstract: The present invention provides a method for analyzing flow regime alterations from reservoir inflow to reservoir outflow, including: acquiring a reservoir inflow data series and a reservoir outflow data series, and determining a local water year of reservoir inflow and reservoir outflow; calculating low and high pulses thresholds of indicators of hydrologic alteration (IHA) for reservoir inflow, and environmental flow thresholds of environmental flow component (EFC) for the reservoir inflow; applying thresholds required for low pulse and high pulse of IHA and the environmental flow thresholds of EFC to reservoir outflow, and respectively calculating IHA parameters and EFC parameters of the inflow and the outflow; and using the range of variability approach (RVA) based on IHA and EFC to analyze the flow regime alterations from the reservoir inflow to the reservoir outflow according to the IHA parameters and the EFC parameters of the reservoir inflow and the reservoir outflow.