Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

Yang, Sheng Chi; Yang, Tsun-Hua

doi:10.1155/2014/581756

Cited by 18 publications

(12 citation statements)

References 24 publications

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“…Hydrologic models are widely used in applications that are important for society such as flood prediction 1 – 6 , drought monitoring 7 – 10 , infrastructure design 11 – 13 , and reservoir management 14 – 16 . This wide variety of such applications, coupled with the diversity of climatic and physiographic regions and the underlying complexity of hydrologic processes is leading to increasing complexity among these models 17 – 19 .…”

Section: Introductionmentioning

confidence: 99%

The sensitivity of simulated streamflow to individual hydrologic processes across North America

et al. 2022

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Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.

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Section: Introductionmentioning

confidence: 99%

The sensitivity of simulated streamflow to individual hydrologic processes across North America

et al. 2022

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“…Regarding the numerical meteorological forecasts, grid-based ensemble forecasts are usually applied. There are so many applications and improvements for ensemble forecasts [26][27][28][29][30][31][32][33]. The study in [26] used the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) to simulate typhoon tracks.…”

Section: Introductionmentioning

confidence: 99%

A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan

et al. 2021

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Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

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“…ere are several ways of predicting the inflow to the systems starting from simplified tank models [24][25][26][27] to unsteady hydrological routing models [28][29][30][31][32]. Literature shows these approaches are clustered into empirical, conceptual, physical, and data-driven models [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

Karunanayake

Gunathilake

Rathnayake

2020

Applied Computational Intelligence and Soft Computing

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Prediction of water resources for future years takes much attention from the water resources planners and relevant authorities. However, traditional computational models like hydrologic models need many data about the catchment itself. Sometimes these important data on catchments are not available due to many reasons. Therefore, artificial neural networks (ANNs) are useful soft computing tools in predicting real-world scenarios, such as forecasting future water availability from a catchment, in the absence of intensive data, which are required for modeling practices in the context of hydrology. These ANNs are capable of building relationships to nonlinear real-world problems using available data and then to use that built relationship to forecast future needs. Even though Sri Lanka has an extensive usage of water resources for many activities, including drinking water supply, irrigation, hydropower development, navigation, and many other recreational purposes, forecasting studies for water resources are not being carried out. Therefore, there is a significant gap in forecasting water availability and water needs in the context of Sri Lanka. Thus, this paper presents an artificial neural network model to forecast the inflows of one of the most important reservoirs in northern Sri Lanka using the upstream catchment’s rainfall. Future rainfall data are extracted using regional climate models for the years 2021–2050 and the inflows of the reservoir are forecasted using the validated neural network model. Several training algorithms including Levenberg–Marquardt (LM), BFGS quasi-Newton (BFG), scaled conjugate gradient (SCG) have been used to find the best fitting training algorithm to the prediction process of the inflows against the measured inflows. Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. In addition, the forecasted results using the projected climate scenarios clearly showcase the benefit of using the forecasting model in solving future water resource management to avoid or to minimize future water scarcity. Therefore, the validated model can effectively be used for proper planning of the proposed drinking water supply scheme to the nearby urban city, Jaffna in northern Sri Lanka.

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Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

Cited by 18 publications

References 24 publications

The sensitivity of simulated streamflow to individual hydrologic processes across North America

The sensitivity of simulated streamflow to individual hydrologic processes across North America

A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan

Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks

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