Flow duration curves (FDCs) that represent streamflow regime function through an empirical relationship between the FDC parameters and basin descriptors are widely adopted for hydrologic applications. However, the applications of this method are highly dependent on the availability of observation data. Hence, it is still of great significance to explore the process controls of underpinning regional patterns on streamflow regimes. In this study, we developed a new regionalization method of FDCs to solve the problem of runoff prediction for ungauged mountainous basins. Five empirical equations (power, exponential, logarithmic, quadratic, and cubic) were used to fit the observed FDCs in the 64 mountainous basins in eastern China, and the power model outperforms other models. Stepwise regression was used to explore the differentiated control of 23 basin descriptors on the 13 percentile flows of FDCs, and seven descriptors remained as independent variables for further developing the regional FDCs. Application results with different combinations of these selected descriptors showed that five indices, i.e., average annual rainfall (P), average elevation (H), average gradient (β), average topographic index (TI), and maximum 7d of annual rainfall (Max7d), were the main control factors of FDCs in these areas. Through the regional method, we found that 95.31% of all the basins have NSE values greater than 0.60 and ε (namely the relative mean square error) values less than 20%. In conclusion, our study can guide runoff predictions to help manage booming demands for water resources and hydropower developments in mountainous areas.

For the identification of Reed‐Solomon (RS) codes, the existing methods need to test the possible codeword length and the primitive polynomials exhaustively. Exhaustive search leads to high computational complexity. To overcome this limitation and fulfill the requirement in practice, a fast blind identification method is proposed. Different with most methods, our identification method is discussed in Galois Field of 2. First, by using Gaussian elimination, the bit matrix is transformed to the simplest upper triangular form. If the assumed codeword length is right, the matrix is of deficient rank, and the parameters of an RS code can be estimated according to the rank. The null space of the matrix, defined as the equivalent binary parity check matrix, can also be obtained from the simplification result. Then, a candidate set of primitive polynomials is constructed according to the estimated codeword length. By using the matrix null space, the correctness of a candidate primitive polynomial is tested through matrix analysis. Finally, by combining the estimated parameters, the generator polynomial of an RS code is identified. To decrease the bit errors in the matrix, soft‐decision data is used to select reliable bits. Experimental results show the effectiveness and robustness of our method.

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