Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets

Reliable runoff modeling is essential for water resource allocation and management. However, a key uncertainty source is that the true precipitation field is difficult to measure, making reliable runoff modeling still challenging. To account for this uncertainty, this study developed a two-step approach combining ensemble average and cumulative distribution correction (i.e., EC) to incorporate information from the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model and multiple remotely sensed precipitation datasets. In the EC approach, firstly, the ensemble average is applied to construct transitional fluxes using the reproduced runoff information, which is yielded by applying various remotely sensed precipitation datasets to drive the GR4J model. Subsequently, the cumulative distribution correction is applied to enhance the transitional fluxes to model runoff. In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. Meanwhile, the EC method exhibited a more stable performance than the ensemble averaging method under different incorporation scenarios. However, the single precipitation dataset-based approaches tended to underestimate runoff (regression coefficients < 1), and there were similar errors between the calibration and validation stages. To further illustrate the effectiveness of the EC model, five watersheds (including the PW) of different hydrometeorological features were used to test the EC model and its benchmarks. The results show that both the EC model and the ensemble averaging had good transferability, but the EC model had better performance across all the test watersheds. Conversely, the single precipitation dataset-based approaches exhibited significant regional variations and, therefore, had low transferability. The current study concludes that the EC approach can be a robust alternative to model runoff and highlights the value of the incorporation of multiple precipitation datasets in runoff modeling

Study on the Staged Operation of a Multi-Purpose Reservoir in Flood Season and Its Effect Evaluation

A reasonable analysis of flood season staging is significant to the management of floods and the alleviation of water shortage. For this paper, the case of the Chengbi River Reservoir in China was selected for study. Based on fractal theory, the flood season is divided into several sub-seasons by using four indexes (multi-year average daily rainfall, multi-year maximum rainfall, multi-year average daily runoff, and multi-year maximum daily runoff) in this study. Also the Benefit-Risk theory is applied to evaluate the effects of staged dispatching. The results show that the flood season of the Chengbi River basin should be divided into the pre-flood season (13 April–6 June), the main flood season (7 June–9 September) and the post-flood season (10 September–31 October). After adjusting the flood limit water level for sub-season and benefit assessment, the probability of exceedance after reservoir flood season operation increases by 0.13×10-5, the average annual expected risk is 0.2264 million RMB, and the average annual benefit increases by 0.88–1.62 million RMB. The benefits obtained far outweigh the risks, indicating the importance of staging the flood season

Research on Reservoir Optimal Operation Based on Long-Term and Mid-Long-Term Nested Models

In order to solve the problem that the existing optimal operation model of reservoirs cannot coordinate the contradiction between long-term and short-term benefits, the paper nested the long-term optimal operation and mid-long-term optimal operations of reservoirs and established the multi-objective optimal operation nested model of reservoirs. At the same time, based on this model, the optimal control mode is determined when there are errors in the predicted runoff. In the optimal scheduling nested model, the dynamic programming algorithm is used to determine the long-term optimal scheduling solution, and the genetic algorithm is used to solve the mid-long-term optimal scheduling. The optimal control mode is determined by three indicators: power generation benefit, water level over limit risk rate and the not-exploited water volume. The results show that, on the premise of meeting the flood control objectives, the nested model optimal dispatching plan has higher benefits than the long-term optimal dispatching plan and the actual dispatching plan, which verifies the superiority of the nested model in the reservoir optimal dispatching problem. When there is error in predicting runoff, among the water level control mode, flow control mode and output control mode, the average power generation benefit of output control mode is 150.05 GW&middot;h, the low-risk rate of water level overrun is 0.29, and the not-exploited water volume is 39,270 m3. Compared with the water level control mode and the flow control mode, the output control mode has the advantages of higher power generation efficiency, lower water level over limit risk rate and less not-exploited water volume. Therefore, from the perspective of economic benefit and risk balance, the output control mode in the optimization scheduling nested mode is the optimal control mode

Accuracy Analysis of IMERG Satellite Rainfall Data and Its Application in Long-term Runoff Simulation

Frequent flood disasters have caused serious damage to karst areas with insufficient measured rainfall data, and the analysis of the applicability of satellite rainfall data in runoff simulation is helpful to the local water management. Therefore, the purpose of this study is to analyze the accuracy of IMERG satellite rainfall data and apply it to long-term runoff simulations in a karst area—the Xiajia River basin, China. First, R (correlation coefficient) and POD (probability of detection) are applied to analyze the accuracy of the IMERG data, and the SWAT model is used for runoff simulation. The results show that the accuracy of the original IMERG data is poor (R range from 0.412 to 0.884 and POD range from 47.33 to 100), and the simulation results are “Unsatisfactory” (NSE (Nash-Sutcliffe efficiency coefficient) ranged from 0.17 to 0.32 and RSR (root mean square standard deviation ratio) ranged from 0.81 to 0.92). Therefore, the GDA correction method is used to correct the original IMERG data, and then the accuracy analysis and runoff simulation are carried out. The results show that the accuracy of the corrected IMERG data is better than that of the original data (R range from 0.886 to 0.987 and POD range from 94.08 to 100), and the simulation results of the corrected IMERG data are “Satisfactory” (NSE is over 0.55 and RSR is approximately 0.65). Therefore, the corrected data have a certain applicability in long-term continuous runoff simulations

The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe

Risk analysis for earth dam overtopping

In this paper, a model of overtopping risk under the joint effects of floods and wind waves, which is based on risk analysis theory and takes into account the uncertainties of floods, wind waves, reservoir capacity and discharge capacity of the spillway, is proposed and applied to the Chengbihe Reservoir in Baise City in Guangxi Zhuang Autonomous Region. The simulated results indicate that the flood control limiting level can be raised by 0.40 m under the condition that the reservoir overtopping risk is controlled within a mean variance of 5×10−6. As a result, the reservoir storage will increase to 16 million m3 and electrical energy generation and other functions of the reservoir will also increase greatly

Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China"

&lt;h4&gt;1. Title: &nbsp;&lt;/h4&gt; &lt;p&gt;Gauge data repository for "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China".&lt;/p&gt; &lt;h4&gt;2. Corresponding author: &nbsp;&lt;/h4&gt; &lt;p&gt;Xingbi Lei ([email protected], [email protected]).&lt;/p&gt; &lt;h4&gt;3. Institution: &nbsp;&lt;/h4&gt; &lt;p&gt;Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education, Nanning 530000, China;&lt;/p&gt; &lt;p&gt;College of Architecture and Civil Engineering, Guangxi University, Nanning 530000, China; &nbsp;&lt;/p&gt; &lt;p&gt;Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530000, China; &nbsp;&lt;/p&gt; &lt;p&gt;Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530000, China; &nbsp;&lt;/p&gt; &lt;p&gt;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 200029, China; &nbsp;&lt;/p&gt; &lt;p&gt;Guangxi Water &amp; Power Design Institute Co., Ltd., Nanning 530023, China. &nbsp;&lt;/p&gt; &lt;h4&gt;4. Description:&lt;/h4&gt; &lt;p&gt;This repository contains gauge data for the research article "Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China", which is currently under review for the journal "Atmospheric Research".&lt;/p&gt; &lt;p&gt;The gauge data used in this study is the daily measurements (2003/01/01-2018/12/31) of 12 stations in the Chengbi River Basin (Southwest China). The data source was the Chengbi River Reservoir Bureau. The data has been quality controlled by the Bureau in accordance with "MWR, PRC,2015. Specification for precipitation observations: SL 21-2015". Please see the journal article for more detailed information.&lt;/p&gt; &lt;h4&gt;5. Citation:&lt;/h4&gt; &lt;p&gt;When using this dataset, please cite "Mo, C. et al., 2024. Comprehensive evaluation and comparison of ten precipitation products in terms of accuracy and stability over a typical mountain basin, Southwest China. Atmospheric Research, 297: 107116.10.1016/j.atmosres.2023.107116". &nbsp;&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt

Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area

As the runoff series exhibit nonlinear and nonstationary characteristics, capturing the embedded periodicity and regularity in the runoff series using a single model is challenging. To account for these runoff characteristics and enhance the forecasting precision, this research proposed a new empirical wavelet transform–particle swarm optimization–support vector machine (EWT–PSO–SVM) hybrid model based on “decomposition-forecasting-reconstruction” for runoff forecasting and investigated its effectiveness in the karst area. First, empirical wavelet transform (EWT) was employed to decompose the original runoff series into multiple subseries. Second, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was applied to forecast every signal subseries. Finally, this study summarized the predictions of the subseries to reconstruct the ultimate runoff forecasting. The developed forecasting model was assessed by applying the monthly runoff series of the Chengbi River Karst Basin, and the composite rating index combined with five metrics was adopted as the performance evaluation tool. From the results of this research, it is clear that the EWT–PSO–SVM model outperforms both the PSO–SVM model and the SVM model in terms of the composite rating index, reaching 0.68. Furthermore, verifying the performance stability, the developed model was also compared with PSO–SVM and SVM models under different input data structures. The comparison demonstrated that the hybrid EWT–PSO–SVM model had a robust performance superiority and was an effective model that can be applied to karst area runoff forecasting

Downscaling Correction and Hydrological Applicability of the Three Latest High-Resolution Satellite Precipitation Products (GPM, GSMAP, and MSWEP) in the Pingtang Catchment, China

The emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications. However, there still exists a shortcoming of coarse spatial resolution when applying these products to small and microscale river basins. In this study, a typical karst watershed in Southwest China—the Pingtang River Basin (PTRB)—was selected, and based on the relationship between precipitation and normalized difference vegetation index (NDVI), aspect, slope, and elevation, we used the geographically weighted regression (GWR) to downscale three SPPs, namely, global precipitation measurement (GPM), global satellite mapping of precipitation (GSMAP), and multisource weighted-ensemble precipitation (MSWEP), to 1 km × 1 km, respectively. Combined with rain gauge stations, the geographical differential analysis (GDA) was used to carry out error corrections to obtain three downscaling correction satellite precipitation products (DC-SPPs) with a 1 km spatial resolution, including DC-GPM, DC -GSMAP, and DC-MSWEP. Several statistical indices were used to perform error evaluation and precipitation capture ability analysis on SPPs and DC-SPPs, and the Grid-Xin’anjiang (the Grid-XAJ) model was used to compare their hydrological utility. The results show the following: (1) The downscaling correction method is effective. GWR can effectively improve the spatial resolution of SPPs, while GDA can reduce errors and further improve the accuracy of precipitation estimation. In addition, (2) the precipitation event characterization capabilities of GPM and GSMAP have been improved after downscaling correction, while the ability to capture precipitation events before and after the MSWEP correction is poor, showing a high hit rate and a high false alarm rate, which is unreliable to monitor precipitation events in the PTRB. Finally, (3) compared with SPPs, the hydrological performances of the three kinds of DC-SPPs have been significantly improved, and the NSE are all above 0.75 with low error. In general, the overall performance of DC-GSMAP is satisfactory. The accuracy of different SPPs after downscaling correction is different, but the applicability has been improved to different degrees

Hydrometeorological Insights into the Forecasting Performance of Multi-Source Weather over a Typical Hill-Karst Basin, Southwest China

Reliable precipitation forecasts are essential for weather-related disaster prevention and water resource management. Multi-source weather (MSWX), a recently released ensemble meteorological dataset, has provided new opportunities with open access, fine horizontal resolution (0.1°), and a lead time of up to seven months. However, few studies have comprehensively evaluated the performance of MSWX in terms of precipitation forecasting and hydrological modeling, particularly in hill-karst basins. The key concerns and challenges are how precipitation prediction performance relates to elevation and how to evaluate the hydrologic performance of MSWX in hill-karst regions with complex geographic heterogeneity. To address these concerns and challenges, this study presents a comprehensive evaluation of MSWX at the Chengbi River Basin (Southwest China) based on multiple statistical metrics, the Soil and Water Assessment Tool (SWAT), and a multi-site calibration strategy. The results show that all ensemble members of MSWX overestimated the number of precipitation events and tended to have lower accuracies at higher altitudes. Meanwhile, the error did not significantly increase with the increased lead time. The “00” member exhibited the best performance among the MSWX members. In addition, the multi-site calibration-enhanced SWAT had reliable performance (Average Nash–Sutcliffe value = 0.73) and hence can be used for hydrological evaluation of MSWX. Furthermore, MSWX achieved satisfactory performance (Nash–Sutcliffe value > 0) in 22% of runoff event predictions, but the error increased with longer lead times. This study gives some new hydrometeorological insights into the performance of MSWX, which can provide feedback on its development and applications