Parameter calibration is a key and difficult issue for a hydrological model. Taking the Jinjiang Xixi watershed of south-east China as the study area, we proposed methods to improve the calibration of two very sensitive parameters, Muskingum K and initial loss, in the Hydrologic Engineering Center hydrologic modelling system (HEC-HMS) model. Twenty-three rainstorm flood events occurring from 1972 to 1977 were used to calibrate the model using a trial-and-error approach, and a relationship between initial loss and initial discharge for these flood events was established; seven rainstorm events occurring from 1978 to 1979 were used to validate the two parameters. The influence of initial loss change on different return-period floods was evaluated. A fixed Muskingum K value, which was calibrated by assuming a flow wave velocity at 3 m/s, could be used to simulate a flood hydrograph, and the empirical power-function relationship between initial loss and initial discharge made the model more applicable for flood forecasting. The influence of initial loss on peak floods was significant but not identical for different flood levels, and the change rate of peak floods caused by the same initial loss change was more remarkable when the return period increased.
To reveal the impacts of land‐use change on flood frequency distribution, a method to contradiction restore the largest‐gauged annual flood series to current land‐use conditions was proposed, based on the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS), with a newly developed iterative asymptotic method to calibrate the model parameters. Using the Xixi Basin on the southeastern coast of China as a case‐study, the HEC‐HMS model was applied to forward restore the largest annual floods between 1956 and 2011 by using the land‐use conditions of 2010. The flood peak flow series derived from forward restoration were used for flood frequency analysis. The results showed that (a) the iterative asymptotic method could calibrate the initial loss ratio and wave velocity relatively well. The physical meaning of the parameter values obtained was clear. The overall model simulation result was satisfactory, with Nash–Sutcliffe efficiency coefficients of 0.827 and 0.843 in the calibration and verification periods, respectively. (b) The calibration method effectively addressed the difficulty in determining the model parameters needed for resolving the restoration of the impacts of land‐use changes on the largest‐gauged annual flood peak flows and provided a newer HEC‐HMS‐based restoration approach for nonstationary flood frequency analysis. (c) Urbanization in the Xixi Basin caused a degradation in forested and arable lands, as well as in grasslands. Its main impact on the flood frequency distribution was that the average flood peak flow increased from 2,633.32 to 2,889.48 m3s−1and the changes in the coefficient of variation and coefficient of skewness were very small.
The lockdown and the strict regulation measures implemented by Chinese government due to the outbreak of the COVID-19 pandemic not only decelerated the spread of the virus but also brought a positive effect on the nationwide atmospheric quality. In this study, we extended our previous research on remotely sensed estimation of PM2.5 concentrations in Yangtze River Delta region (i.e., YRD) of China from 2019 to the strict regulation period of 2020 (i.e., 24 Jan, 2020-31 Aug, 2020). Unlike the method using aerosol optical depth (AOD) developed in previous studies, we validated the possibility of moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance (i.e., MODIS TOA) at 21 bands in estimating the PM2.5 concentrations in YRD region. Two random forests (i.e., TOA-sig RF and TOA-all RF) incorporated with different MODIS TOA datasets were developed, and the results showed that the TOA-sig RF model performed better with R 2 of 0.81 ( RMSE = 8.07 μg/m3) than TOA-all RF model with R 2 of 0.79 ( RMSE = 9.13 μg/m3). The monthly averaged PM2.5 exhibited the highest value of 50.81 μg/m3 in YRD region in January 2020 and sharply decreased from February to August 2020. The annual mean PM2.5 concentrations derived by TOA-sig RF model were 47.74, 32.14, and 21.04 μg/m3 in winter, spring, and summer in YRD during the strict regulation period of 2020, respectively, showing much lower values than those in 2019. Our research demonstrated that the PM2.5 concentrations could be effectively estimated by using MODIS TOA reflectance at 21 bands and the random forest.
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