Search Results (3)

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, RE_Q, and E_T were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province. Full article

Due to the complicated terrain conditions in montane catchments, runoff formation is fast and complicated, making accurate simulation and forecasting a significant hydrological challenge. In this study, the spatiotemporal variable source mixed runoff generation module (SVSMRG) was integrated with the long short-term memory (LSTM) method, to develop a semi-distributed model (SVSMRG)-based surface flow and baseflow segmentation (SVSMRG-SBS). Herein, the baseflow was treated as a black box and forecasted using LSTM, while the surface flow was simulated using the SVSMRG module based on hydrological response units (HRUs) constructed using eco-geomorphological units. In the case study, four typical montane catchments with different climatic conditions and high vegetation coverage, located in the topographically varying mountains of the eastern Tibetan Plateau, were selected for runoff and flood process simulations using the proposed SVSMRG-SBS model. The results showed that this model had good performance in hourly runoff and flood process simulations for montane catchments. Regarding runoff simulations, the Nash–Sutcliffe efficiency coefficient ($NSE$) and correlation coefficient ($R^2$) reached 0.8241 and 0.9097, respectively. Meanwhile, for the flood simulations, the $NSE$ ranged from 0.5923 to 0.7467, and $R^2$ ranged from 0.6669 to 0.8092. For the 1-, 3-, and 5-h baseflow forecasting with the LSTM method, it was found that model performances declined when simulating the runoff processes, wherein the $NSE$ and $R^2$ between the measured and modeled runoff decreased from 0.8216 to 0.8087 and from 0.9095 to 0.8871, respectively. Similar results were found in the flood simulations, the $NSE$ and $R^2$ values declined from 0.7414–0.5885 to 0.7429–0.5716 and from 0.8042–0.6547 to 0.7936–0.6067, respectively. This means that this new model achieved perfect performance in montane catchment runoff and flood simulation and forecasting with 1-, 3-, 5-h steps. Therefore, as it considers vegetation regulation, the SVSMRG-SBS model is expected to improve runoff and flood simulation accuracy in montane high-vegetation-covered catchments. Full article

The potentially detrimental impact of groundwater discharge into rivers on the ecosystem services provided by the river makes the localization of groundwater discharge areas as well as the quantification of the associated mass fluxes an issue of major interest. However, localizing groundwater discharge zones and evaluating their impact are challenging tasks because of (i) the limited number of suitable tracers and (ii) the high spatio-temporal variability of groundwater/river water interaction in general. In this study, we applied the ubiquitous naturally occurring radioactive noble gas radon (²²²Rn) as an aqueous tracer to localize and quantify groundwater discharge along a 60 km reach of the upper German part of the major river Elbe under drought conditions. All radon data processing was executed with the numerical implicit finite element model FINIFLUX, a radon mass balance-based approach, which has been developed specifically to quantify the groundwater flux into rivers. The model results were compared to the tritium (³H) distribution pattern in the studied river reach. The results of the study proved the applicability of both (i) the methodical approach (i.e., radon as tracer) and (ii) the application of FINIFLUX to drought conditions (with river discharge rates as low as 82 m³/s vs. a long time mean of 300 m³/s). Applying the model, the recorded dataset allowed differentiating between groundwater baseflow, on the one hand, and interflow and surface water runoff distributions to the river, on the other. Furthermore, the model results allowed assessing the location and the intensity of groundwater discharge into the river under low flow conditions. It was also shown that analysing discrete river water samples taken from distinct points in a major stream might lead to slightly incorrect results because of an incomplete mixing of river water and locally discharging groundwater. An integrating sampling approach (as applied for radon) is preferable here. Full article