Search Results (4)

The spatial distribution of water storage capacity has always been the critical content of the study of saturation-excess runoff. Xin’anjiang model uses the water storage capacity curve (WSCC) to characterize the distribution of water storage capacity for runoff yield calculation. However, the mathematical and physical foundations of WSCC are unclear, which is impossible to simulate runoff generation with complex basins accurately. To fill this gap, we considered the dominant role of basin physical characteristics in water storage capacity and developed a new integrated approach to solve the spatial distribution of water storage capacity (L-WSCC) to account for the spatiotemporal dynamics of their impact on runoff generation. The main contribution of L-WSCC was to confer WSCC more physical meaning and the spatial distribution of water storage capacity was explicitly represented more accurately, so as to better express the runoff generation and provide a new approach for runoff yield calculation in non-data basin. L-WSCC was applied to Misai basin in China and promising results had been achieved, which verified the rationality of the method (the mean Nash–Sutcliffe efficiency (NSE):0.86 and 0.82 in daily and hourly scale, respectively). Compared with WSCC, the performance of L-WSCC was improved (mean NSE: 0.82 > 0.78, mean absolute value of flood peak error (PE): 12.74% < 21.66%). Moreover, the results of local sensitivity analyses demonstrated that land-use and land cover was the major driving factor of runoff yield (the change of mean absolute error (ΔMAE): 131.38%). This work was significant for understanding the mechanisms of runoff generation, which can be used for hydrological environmental management and land-use planning. Full article

To protect ecologies and the environment by preventing floods, analysis of the impact of climate change on water requires a tool capable of considering the rainfall-runoff processes on a small scale, for example, 10 m. As has been shown previously, hydrologic models are good at simulating rainfall-runoff processes on a large scale, e.g., over several hundred km², while hydraulic models are more advantageous for applications on smaller scales. In order to take advantages of these two types of models, this paper coupled a hydrologic model, the Xinanjing model (XAJ), with a hydraulic model, the Graphics Processing Unit (GPU)-accelerated high-performance integrated hydraulic modelling system (HiPIMS). The study was completed in the Misai basin (797 km²), located in Zhejiang Province, China. The coupled XAJ–HiPIMS model was validated against observed flood events. The simulated results agree well with the data observed at the basin outlet. The study proves that a coupled hydrologic and hydraulic model is capable of providing flood information on a small scale for a large basin and shows the potential of the research. Full article

To simulate flood processes at the basin level, the GPU-based High-Performance Integrated Hydrodynamic Modelling System (HiPIMS) is gaining interest as computational capability increases. However, the difficulty of coping with rainfall input to HiPIMS reduces the possibility of acquiring a satisfactory simulation accuracy. The objective of this study is to test the sensitivity of the surface runoff coefficient in the HiPIMS source term in the Misai basin with an area of 797 km² in south China. To achieve this, the basin was divided into 909,824 grid cells, to each of which a Manning coefficient was assigned based on its land use type interpreted from remote sensing data. A sensitivity analysis was conducted for three typical flood processes under four types of surface runoff coefficients, assumed a priori, upon three error functions. The results demonstrate the crucial role of the surface runoff coefficient in achieving better simulation accuracy and reveal that this coefficient varies with flood scale and is unevenly distributed over the basin. Full article

Real-time correction models provide the possibility to reduce uncertainties in flood prediction. However, most traditional techniques cannot accurately capture many sources of uncertainty and provide a quantitative evaluation. To account for a wide variety of uncertainties in flood forecasts and overcome the limitations of stationary samples in a changing climate, a Bayesian theory based Self-adapting, Real-time Correction Model (BSRCM) was proposed. BSRCM uses the Autoregressive Moving Average (ARMA (n, m)) model as the prior distribution for the flood hydrograph, and the autoregressive model or order p (AR(p)) as the likelihood function to describe the likelihood relationship between the predicted and observed discharges, on the basis the posterior distribution of real values of discharge at any step can be deduced under the framework of Bayesian theory. Combined with the Xin’anjiang hydrological model, it was applied for flood forecasting in the Misai basin in southern China. Results from this study indicate that: (1) BSRCM can achieve a good precision and perform better than AR(p) in the study region; (2) BSRCM provides not only deterministic results but also rich uncertainty information for real-time correction results, such as the mean, error variance, and confidence intervals of flow discharge at any time during the flood event; (3) BSRCM can achieve better performance with a longer lead time; (4) BSRCM can achieve a good precision even with a small sample for parameter estimates. In addition to good precision, BSRCM can also provide further scientific grounding in flood control, operations and decision making for risk management. Full article