The forecast of reservoir inflow is related to the safe operation of reservoirs, flood control dispatching and the rational allocation of water resources, and its importance is self-evident. However, it is difficult to forecast the inflow of reservoirs, which restricts the application of the forecasting model in the forecasting of inflow of reservoirs in the following aspects:
- The complexity and uncertainty of hydrological processes are affected by various factors such as rainfall, evaporation, infiltration, and storage regulation, and the interaction is complex.
- Challenges in data acquisition and processing, data shortages, anomalies and quality problems affecting the accuracy of forecasts;
- The applicability and difficulty of calibrating the model requires the selection and calibration of the model for a specific watershed, which is a complex process and requires specialized knowledge.
To this end, Four-Faith has launched a distributed hydrological model based on DEM and based on mountain slopes, which can simulate hydrological processes in large watersheds. The core calculation module of the model includes two modules: slope production confluence and river network confluence. The model can effectively predict and simulate the rainfall runoff process, confluence process and hydrological process in the basin, which can be easily and quickly integrated into the reservoir flood control and dispatching business system, and provide core support for the prediction of reservoir inflow.
In view of the difficulties in the forecasting of reservoir inflow, the distributed hydrological model launched by Four-Faith has continued to make efforts in the following aspects, bringing revolutionary changes to reservoir water resources management and flood prevention and disaster reduction.
The prediction period is effectively extended, making decision-making more calm
The distributed hydrological prediction model accesses the grid rainfall prediction data from various meteorological data systems in real time, and cross-checks and calculates with the data of the on-site rainfall gauge station, so as to effectively extend the prediction period through refined simulation and prediction. Gain more time for reservoir managers to develop and adjust scheduling plans to deal with complex hydrological conditions.
▲Grid rainfall forecast
Forecasting frequencies are faster, making information more timely
Due to the limitations of model calculation performance and the frequency of meteorological prediction data update, the traditional inflow forecast often has a large time lag, which is difficult to meet the needs of modern water conservancy management. The distributed hydrological forecasting model can accurately disassemble the sub-basins of the upstream rainfall collection area of the reservoir, and provide distribution computing capabilities, which can achieve high-frequency forecast updates, reflect the hydrological changes of the basin in a timely manner, and provide more accurate and timely information support for reservoir managers.
Figure 2: Extracting the river
Figure 3: Generating small watersheds
Wider use area, make the application more common
The model takes the hillside as the basic unit, which is widely applicable to the north and south regions, and carefully considers the multi-source geographic information databases such as vegetation cover, land use, and remote sensing images. This design allows the model to accurately generalize and analyze climate and soil conditions in different regions, whether humid or dry. This comprehensive and integrated geographic information method ensures that the model can provide accurate simulation of hydrological processes under various environmental conditions, thus providing strong support for the prediction of inflow of large and medium-sized reservoirs, and significantly improving its universality and practical value.
Rely less on historical data, making applications more flexible
For many regions, the lack of historical data is an important factor limiting the accuracy of inbound flow forecasting. However, the distributed hydrological prediction model applies the mechanism of water science, based on the spatial geographic information of the watershed, and reduces the dependence on historical data by predicting the real-time input of rainfall data from the grid in the rainfall collection area. Even in the absence of sufficient historical data, more accurate forecasts can be achieved, providing a viable solution for more regions.
Calibration efficiency makes optimization easier
The calibration of the model is a key part of ensuring the accuracy of its predictions. Most of the parameters of a distributed hydrological prediction model are generally based on actual measurements or derived from the corresponding database. The only parameters that need to be calibrated separately are groundwater conductivity and groundwater storage coefficient. This reduction in parameters greatly reduces the workload and time of the model personnel, while improving efficiency.
Easier deployment, easier applications
The distributed hydrological prediction model software has a complete tool chain and convenient integration methods, which can be easily and quickly integrated into the existing business application system. Whether it is a digital twin platform or a flood control and drought relief information platform, the deployment and application of models can be easily realized. This provides great convenience for water conservancy departments at all levels, and makes it easier and more efficient to improve the ability to forecast inflow flows.