The accuracy of flood forecasts generated using spatially lumped hydrological models can be severely affected by errors in the estimates of areal mean rainfall. The quality of the latter depends both on the size and type of errors in point‐based rainfall measurements, and on the density and spatial arrangement of rain gauges in the basin. Here we use error feedback correction, based on the dynamic system response curve (DSRC) method, to compute updated estimates of the rainfall inputs. The method is evaluated via synthetic and real‐data cases, showing that the method works as theoretically expected. The ability of the method to improve the accuracy of real‐time flood forecasts is then demonstrated using 20 basins of different sizes and having different rain gauge densities. We find that the degree of forecast improvement is more significant for larger basins and for basins with lower rain gauge density. The method is relatively simple to apply and can improve the accuracy and stability of real‐time model predictions without increasing either model complexity and/or the number of model parameters.

The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.

The dynamic system response curve (DSRC) is commonly applied as a real-time flood forecasting error correction method to improve the accuracy of real-time flood forecasting. It has been widely recognized that the least squares (OLS/LS) method, employed by DSRC, breaks down ill-posed problems, and therefore, the DSRC method may lead to deterioration in performance caused by meaningless solutions. To address this problem, a diagnostically theoretical analysis was conducted to investigate the relationship between the numerical solution of the Fredholm equation of the first kind and the DSRC method. The analysis clearly demonstrates the derivation of the problem and has implications for an improved approach. To overcome the unstable problem, a new method using regularization techniques (Tikhonov regularization and L-Curve criterion) is proposed. Moreover, in this study, to improve the performance of hydrological models, the new method is used as an error correction method to correct a variable from a hydrological model. The proposed method incorporates the information from a hydrological model structure. Based on the analysis of the hydrological model, the free water storage of the Xinanjiang rainfall-runoff (XAJ) model is corrected to improve the model’s performance. A numerical example and a real case study are presented to compare the two methods. Results from the numerical example indicate that the mean Nash–Sutcliffe efficiency value (NSE) of the regularized DSRC method (RDSRC) decreased from 0.99 to 0.55, while the mean NSE of DSRC decreased from 0.98 to −1.84 when the noise level was increased. The overall performance measured by four different criteria clearly demonstrates the robustness of the RDSRC method. Similar results were obtained for the real case study. The mean NSE of 35 flood events obtained by RDSRC method was 0.92, which is significantly higher than the mean NSE of DSRC (0.7). The results demonstrate that the RDSRC method is much more robust than the DSRC method. The applicability and usefulness of the RDSRC approach for real-time flood forecasting is demonstrated via the numerical example and the real case study.

The main purpose of this study is to explore the runoff generation mechanism and isotopic variation of precipitation during typhoon and plum rain events in the mountainous region mainly covered with bamboo in southeastern China. The isotopic value of precipitation in plum rain events is more depleted than that of precipitation in typhoon events and has a larger range of d-excess. Typhoon events are affected by frequent temperate and tropical cyclones, the ocean evaporationis expected to be very intenseand marine air parcels move very quickly. As for plum rain events, marine air parcel moves slowly due to the blocking effect of the cold front and air moisture evaporated from the continent plays a significant role in the isotope of precipitation depletion process. The difference of stable isotope values in various water sources allows the two-component hydrological separation to quantify the contribution of the event and pre-event water. The results indicate that the pre-event water accounts for at least 60% of the discharge and the difference in proportion of pre-event water between different types of rain events suggests that the initial state of watershed, rainfall intensity and macropore flow are major control factors of the runoff production mechanism.

Real-time flood forecasting requires accurate and reliable estimates of the uncertainty to make efficient flood event management strategies. However, the accuracy of flood forecasts can be severely affected by errors in the estimates of sediment yield in the loess region. To improve the accuracy of sediment-laden flood forecasts generated using streamflow-sediment coupled (SSC) model, an error feedback correction method based on the dynamic system response curve (DSRC) is proposed. The physical basis of the system response curve is the sediment concentration of the hydrological model. The theoretical basis of the method is the differential of the system response function of the sediment yield time series. The effectiveness of DSRC method is evaluated via an ideal case and three real-data cases with different basin scales of the Yellow River. Results suggest that the DSRC method can effectively improve the accuracy and stability of sediment transport forecasts by providing accurate estimates of the sediment yield errors. The degree of forecast improvement is scale dependent and is more significant for larger basins with lower rain gauge densities. Besides, the DSRC method is relatively simple to apply without the need to modify either the model structure or parameters in real-time flood forecasting.