Patent ID: 11886967
Assignee: WUHAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 2:
3. The long-term streamflow forecast method of claim 1, wherein, the step 3 comprises the following sub-steps:
sub-step 3.1, interpolating observation data and the climate model forecasts to a grid scale matching the VIC distributed hydrological model;
sub-step 3.2, taking a ratio of a mean value of the meteorological data forecasted by the climate model for the months of multiple years and a mean value of the observed meteorological data as a correction factor to perform bias correction on precipitation and maximum and minimum air temperatures forecast by the climate model respectively; if precipitation of an r-th month of a year with a lead time of s months is to be corrected, performing correction in formula: Mcor(s)=M(s)×(Mobs,r/Mpred,r); if maximum and minimum air temperatures of an r-th month of a year with a lead time of s months are to be corrected, performing correction in formula Mcor(s)=M(s)+(Mobs,r/Mpred,r); wherein, Mcor(s) is corrected forecast data, M(s) represents raw forecast data to be corrected, Mobs,r represents a mean value of the observation data, and Mpred,r represents a mean value of the forecast data;
sub-step 3.3, by using Gram-Schmidt orthogonalization method, transforming the observed meteorological data X of a historical period into an orthogonal matrix Yin formula Y=RX, wherein R is a rotation matrix;
sub-step 3.4, calculating an Euclidean distance between the forecast monthly meteorological data to be disaggregated and the observed monthly meteorological data for the historical period, and then selecting K pieces of monthly data with minimum distance and assigning weights, formula is:, W
    ⁡
    (
    k
    )
   
   =
   
    
     1
     /
     k
    
    ⁢
    
     
      ∑
      
       u
       =
       1
      
      K
     
     
      1
      u
     
    
   
  
  ,
 

 wherein k=1, 2, . . . , K;
sub-step 3.5, using these weights as a probabilistic metric to construct a one-dimensional matrix y* of the disaggregated forecast data;
sub-step 3.6, transforming the matrix y* back to an initial space to obtain the daily-scale climate model forecasts x* in the formula x*=RTy*, wherein RT is a transpose matrix of R.