Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in-situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model-simulated reservoir inflow with conceptual reservoir operation schemes within a land surface-hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography

Toward improved parameterizations of reservoir operation in ungauged basins: a synergistic framework coupling satellite remote sensing, hydrologic modeling, and conceptual operation schemes

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in-situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model-simulated reservoir inflow with conceptual reservoir operation schemes within a land surface-hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography

Model estimates of China's terrestrial water storage variation due to reservoir operation

Understanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long-term reservoir operation records at large scales, and a process-based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental-scale land surface-hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration-free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two-way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE-based terrestrial water storage anomalies. A 30-year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China\u27s terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China\u27s RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one-fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes

Merging Satellite and Gauge Rainfalls for Flood Forecasting of Two Catchments Under Different Climate Conditions

As satellite rainfall data has the advantages of wide spatial coverage and high spatial and temporal resolution, it is an important means to solve the problem of flood forecasting in ungauged basins (PUB). In this paper, two catchments under different conditions, Xin’an River Basin and Wuding River Basin, were selected as the representatives of humid and arid regions, respectively, and four kinds of satellite rainfall data of TRMM 3B42RT, TRMM 3B42V7, GPM IMERG Early, and GPM IMERG Late were selected to evaluate the monitoring accuracy of rainfall processes in the two catchments on hourly scale. Then, these satellite rainfall data were respectively integrated with the gauged data. HEC-HMS (The Hydrologic Engineering Center\u27s-Hydrologic Modeling System) model was calibrated and validated to simulate flood events in the two catchments. Then, improvement effect of the rainfall merging on flood forecasting was evaluated. According to the research results, in most cases, the Nash–Sutcliffe efficiency coefficients of the simulated streamflow from initial TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) satellite rainfall data were negative at the two catchments. By merging gauge and TRMM rainfall, the Nash–Sutcliffe efficiency coefficient is mostly around 0.7, and the correlation coefficient is as high as 0.9 for streamflow simulation in the Xin\u27an River basin. For the streamflow simulated by merging gauge and GPM rainfall in Wuding River basin, the Nash–Sutcliffe efficiency coefficient is about 0.8, and the correlation coefficient is more than 0.9, which indicate good flood forecasting accuracy. Generally, higher performance statistics were obtained in the Xin\u27an River Basin than the Wuding River Basin. Compared with the streamflow simulated by the initial satellite rainfalls, significant improvement was obtained by the merged rainfall data, which indicates a good prospect for application of satellite rainfall in hydrological forecasting. In the future, it is necessary to further improve the monitoring accuracy of satellite rainfall products and to develop the method of merging multi-source rainfall data, so as to better applications in PUB and other hydrological researches

A Multi-step Prediction Method of Urban Air Quality Index Based on Meteorological Factors Analysis

With the development of science and technology, Industry, transportation and other industries used to discharge a large number of pollutants into the atmosphere, which results in air pollution. When air pollution become serious, it will do great harm to human health. High-precision Air Quality Index(AQI) prediction is as important as weather prediction. People could arrange traveling and their life according to the highly precise prediction results, so as to better protect their own health. Considering a lot of complex factors, we choose several potential meteorological factors and historical data to precisely forecast AQI. The principal component analysis (PCA) is introduced in our method to conduct dimension reduction on nine meteorological factors, in order to reduce noise of data and the complexity of the model calculation, which improves the accuracy of AQI prediction as a result. Then the data of meteorological factors after PCA and historical AQI are input into the multi-step prediction model based on LSSVM to train and refine it. Finally, we set up the experiment with data of meteorological factors and AQI. Experimental results show that the method proposed in this paper has better prediction accuracy over classical ARIMA method and has better generalization than ARIMA method as well

An Improved Xin’anjiang Hydrological Model for Flood Simulation Coupling Snowmelt Runoff Module in Northwestern China

The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins

Flood Forecasting with Merged Satellite Precipitation and Hydrologic Model

Flood forecasting has been an effective way to reduce the potential flood hazards for a sustainable socio-economy. However, the lack of in-situ precipitation records has limited the applicability of flood forecasting with hydrologic models in poorly gauged basins. To address this problem, we aim to develop a flood forecasting framework based on the merged satellite precipitation and a hydrologic model. The framework was then applied to a small basin in the upper Lequan River Basin, Hainan, China for flood forecasting experiments. Results indicate that the combination of merged satellite precipitation and hydrologic model can generally well reproduce the past major flood events occurred in the basin. Our approaches are expected to provide new insights into the flood forecasting in small and poorly gauged basins and can be used to support the sustainable development of the socio-economy

CMADS-Driven Simulation and Analysis of Reservoir Impacts on the Streamflow with a Simple Statistical Approach

The reservoir operation is a notable source of uncertainty in the natural streamflow and it should be represented in hydrological modelling to quantify the reservoir impact for more effective hydrological forecasting. While many researches focused on the effect of large reservoirs only, this study developed an online reservoir module where the small reservoirs were aggregated into one representative reservoir by employing a statistical approach. The module was then integrated into the coupled Noah Land Surface Model and Hydrologic Model System (Noah LSM-HMS) for a quantitative assessment of the impact of both large and small reservoirs on the streamflow in the upper Gan river basin, China. The Noah LSM-HMS was driven by the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) with a very good performance and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.89, which proved to be more effective than the reanalysis data from the National Centers for Environmental Prediction (NCEP) over China. The simulation results of the integrated model indicate that the proposed reservoir module can acceptably depict the temporal variation in the water storage of both large and small reservoirs. Simulation results indicate that streamflow is increased in dry seasons and decreased in wet seasons, and large and small reservoirs can have equally large effects on the streamflow. With the integration of the reservoir module, the performance of the original model is improved at a significant level of 5%

Toward Improved Parameterizations of Reservoir Operation in Ungauged Basins: A Synergistic Framework Coupling Satellite Remote Sensing, Hydrologic Modeling, and Conceptual Operation Schemes

Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in‐situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model‐simulated reservoir inflow with conceptual reservoir operation schemes within a land surface‐hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography.Key Points: Satellite remote sensing can improve the representation of ungauged reservoirs and streamflow simulations in hydrologic models. A reservoir operation scheme for ungauged reservoirs is extended and tailored to the use of remotely sensed reservoir operation data. Reservoir operation schemes with storage‐based model structures can be more reliable in reservoir simulations under a changing flow regime.National Key Research and Development Program of China http://dx.doi.org/10.13039/501100012166Belt and Road Special Foundation of the State Key Laboratory of Hydrology‐Water Resources and Hydraulic EngineeringGerman Research FoundationGerman Federal Ministry of Science of Educationhttps://doi.org/10.5281/zenodo.7190469https://global-surface-water.appspot.com/downloadhttps://doi.org/10.18738/T8/DF80WGhttps://aviso-data-center.cnes.fr

The Optimization of Water Storage Timing in Upper Yangtze Reservoirs Affected by Water Transfer Projects

To alleviate regional disparities in water resource distribution and consequent scarcity, China has initiated and planned a series of inter-basin water transfer projects using the Yangtze River Basin as the source. These projects are expected to divert approximately 33.4 billion cubic meters of water annually from the Yangtze River Basin. The implementation of these water transfer projects will inevitably alter the hydrological conditions in the upper reaches of the Yangtze River, impacting the reservoir storage strategies of cascading hydroelectric stations under current end-of-flood-season operational plans. This study quantitatively assesses the impact of water transfer projects on end-of-flood-season reservoir storage in cascading systems using the reservoir fullness ratio as an indicator. Employing reservoir storage analysis models, optimization techniques, and flood risk assessment methods, we simulated reservoir storage processes to evaluate associated flood risks and derive an optimized timing strategy for cascading reservoir storage. The results indicate that advancing the reservoir filling schedule by five days for both the Baihetan and Three Gorges dams can offset the adverse impacts of water transfer projects on reservoir storage efficiency. This adjustment restores the reservoir fullness ratio to levels observed in scenarios without water transfers while still meeting flood control requirements. After optimizing the timing of reservoir filling, the electricity generation capacity for the Baihetan and Three Gorges dams increased by 1.357 and 3.183 billion kWh, respectively, under non-transfer scenarios. In water transfer scenarios, the electricity generation for the Baihetan and Three Gorges dams increased by 1.48 and 2.759 billion kWh, respectively. By optimizing reservoir filling schedules, we not only improved the reservoir fullness ratio but also enhanced the electricity generation efficiency of the cascading systems, offering valuable insights for future reservoir operation optimization