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Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.
Jinghua Xiong; Shenglian Guo; Jiabo Yin; Lei Gu; Feng Xiong. Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin. Remote Sensing 2021, 13, 3023 .
AMA StyleJinghua Xiong, Shenglian Guo, Jiabo Yin, Lei Gu, Feng Xiong. Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin. Remote Sensing. 2021; 13 (15):3023.
Chicago/Turabian StyleJinghua Xiong; Shenglian Guo; Jiabo Yin; Lei Gu; Feng Xiong. 2021. "Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin." Remote Sensing 13, no. 15: 3023.
The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by combining the Xinanjiang (XAJ) conceptual model and LSTM model (XAJ-LSTM) to achieve precise multi-step-ahead flood forecasts. The hybrid model takes flood forecasts of the XAJ model as the input variables of the LSTM model to enhance the physical mechanism of hydrological modeling. Using the XAJ and the LSTM models as benchmark models for comparison purposes, the hybrid model was applied to the Lushui reservoir catchment in China. The results demonstrated that three models could offer reasonable multi-step-ahead flood forecasts and the XAJ-LSTM model not only could effectively simulate the long-term dependence between precipitation and flood datasets, but also could create more accurate forecasts than the XAJ and the LSTM models. The hybrid model maintained similar forecast performance after feeding with simulated flood values of the XAJ model during horizons to . The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.
Zhen Cui; Yanlai Zhou; Shenglian Guo; Jun Wang; Huanhuan Ba; Shaokun He. A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting. Hydrology Research 2021, 1 .
AMA StyleZhen Cui, Yanlai Zhou, Shenglian Guo, Jun Wang, Huanhuan Ba, Shaokun He. A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting. Hydrology Research. 2021; ():1.
Chicago/Turabian StyleZhen Cui; Yanlai Zhou; Shenglian Guo; Jun Wang; Huanhuan Ba; Shaokun He. 2021. "A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting." Hydrology Research , no. : 1.
Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.
Jinghua Xiong; Shenglian Guo; Jiabo Yin. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sensing 2021, 13, 2272 .
AMA StyleJinghua Xiong, Shenglian Guo, Jiabo Yin. Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sensing. 2021; 13 (12):2272.
Chicago/Turabian StyleJinghua Xiong; Shenglian Guo; Jiabo Yin. 2021. "Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River." Remote Sensing 13, no. 12: 2272.
A series of biodegradable cellulose/chitin materials (beads and membranes) were successfully prepared by mixing cellulose with chitin in an NaOH/thiourea–water system and coagulation in a H2SO4 solution. The effects of chitin content on the materials’ mechanical properties, morphology, structure, and sorption ability for heavy metal ions (Pb2+, Cd2+, and Cu2+) were studied by tensile tests, scanning electron micrographs, Fourier transform infrared spectroscopy, and atomic absorption spectrophotometry. The results revealed that the cellulose/chitin blends exhibited relatively good mechanical properties, a homogeneous, microporous mesh structure, and the existence of strong hydrogen bonds between molecules of cellulose and chitin when the chitin content was less than 30 wt%, which indicated a good compatibility of the cellulose/chitin materials. Furthermore, in the same chitin content range, Pb2+, Cd2+, and Cu2+ can be adsorbed efficiently onto the cellulose/chitin beads at pH0 = 5, and the sorption capacity of the beads is more than that of chitin flakes. This shows that the hydrophilicity and microporous mesh structure of the blends are favorable for the kinetics of sorption. Preparation of environmentally friendly cellulose/chitin blend materials provides a simple and economical way to remove and recover heavy metals, showing a potential application of chitin as a functional material.
Dao Zhou; Hongyu Wang; Shenglian Guo. Preparation of Cellulose/Chitin Blend Materials and Influence of Their Properties on Sorption of Heavy Metals. Sustainability 2021, 13, 6460 .
AMA StyleDao Zhou, Hongyu Wang, Shenglian Guo. Preparation of Cellulose/Chitin Blend Materials and Influence of Their Properties on Sorption of Heavy Metals. Sustainability. 2021; 13 (11):6460.
Chicago/Turabian StyleDao Zhou; Hongyu Wang; Shenglian Guo. 2021. "Preparation of Cellulose/Chitin Blend Materials and Influence of Their Properties on Sorption of Heavy Metals." Sustainability 13, no. 11: 6460.
Water environmental capacity (WEC) is an essential indicator for effective environmental management. The designed low water flow condition is a prerequisite to determine WEC and is often based on the stationarity assumption of low water flow series. As the low water flow series has been remarkably disturbed by climate change as well as reservoirs operation and water acquisition, the stationarity assumption might bring risk for WEC planning. As the reservoir operation and water acquisition under climate change can be simulated by a water resources allocation model, the low water flow series outputted from the model are the simulations of the disturbances and often show nonstationary conditions. After estimating the designed low water flow through nonstationary frequency analysis from these low water flow series, the WEC under the nonstationary conditions can be determined. Thus, the impacts of water resources allocation on WEC under climate change can be quantitatively assessed. The mid-lower reaches of the Hanjiang River basin in China were taken as a case study due to the intensive reservoir operation and water acquisition under the climate change. A representative concentration pathway scenario (RCP4.5) was employed to project future climate, and a Soil and Water Assessment Tool (SWAT) model was employed to simulate water availability for driving the Interactive River-Aquifer Simulation (IRAS) model for allocating water. Water demand in 2016 and 2030 were selected as baseline and future planning years, respectively. The results show that water resources allocation can increase the amount of WEC due to amplifying the designed low water flow through reservoir operation. Larger regulating capacities of water projects can result in fewer differences of WEC under varied water availability and water demand conditions. The increasing local water demand will decrease WEC, with less regulating capacity of the water projects. Even the total available water resources will increase over the study area under RCP4.5. More water deficit will be found due to the uneven temporal-spatial distribution as well as the increasing water demand in the future, and low water flow will decrease, which further leads to cut down WEC. Therefore, the proposed method for determining the WEC can quantify the risk of the impacts of water supply and climate change on WEC to help water environmental management.
Yujie Zeng; Dedi Liu; Shenglian Guo; Lihua Xiong; Pan Liu; Jiabo Yin; Jing Tian; Lele Deng; Jiayu Zhang. Impacts of Water Resources Allocation on Water Environmental Capacity under Climate Change. Water 2021, 13, 1187 .
AMA StyleYujie Zeng, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Jiabo Yin, Jing Tian, Lele Deng, Jiayu Zhang. Impacts of Water Resources Allocation on Water Environmental Capacity under Climate Change. Water. 2021; 13 (9):1187.
Chicago/Turabian StyleYujie Zeng; Dedi Liu; Shenglian Guo; Lihua Xiong; Pan Liu; Jiabo Yin; Jing Tian; Lele Deng; Jiayu Zhang. 2021. "Impacts of Water Resources Allocation on Water Environmental Capacity under Climate Change." Water 13, no. 9: 1187.
Terrestrial water storage (TWS) plays an important role in the global water cycle. Measuring dynamic changes in TWS is essential for water resources management, weather-related hazard monitoring and agricultural production. The Gravity Recovery and Climate Experiment (GRACE) and its following mission (GRACE-Follow on) have provided monthly terrestrial water storage anomalies (TWSA) at a quasi-global scale since April 2002. This study bridges the data gap between the two generations of satellites using Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network models over mainland China. We systematically examine the spatiotemporal continuity of GRACE and GRACE-Follow on missions based on the spherical harmonics (SH) and mass concentration blocks (mascons) solutions as well as GLDAS-Noah data across mainland China during 2002–2020. Results show that the GRACE-Follow on data of CSR SH/CSR mascons reduces the uncertainty of TWSA in China from 5.26/6.96 to 3.83/4.65 mm/month, respectively. ML-predicted TWSA fits well with in-situ observations during the gap between GRACE and GRACE-Follow on satellites. Modeled TWSA also shows continuity between GRACE and GRACE-Follow on satellites. During the period 2002–2020, reconstructed TWSA significantly decreased from − 0.03 mm/a to − 1.06 mm/a (p < 0.05) with spatial heterogeneity for the whole of China. In many areas of China, TWSA increased rapidly in the range of 5–15 mm/a (p < 0.05), while north and southwest China experienced a decrease between − 5 and − 25 mm/a (p < 0.05). This study provides bridged GRACE data and reveals the variability of TWSA in mainland China, which may contribute to a better understanding of climate change and water resources management.
Jinghua Xiong; Jiabo Yin; Shenglian Guo; Louise Slater. Continuity of terrestrial water storage variability and trends across mainland China monitored by the GRACE and GRACE-Follow on satellites. Journal of Hydrology 2021, 599, 126308 .
AMA StyleJinghua Xiong, Jiabo Yin, Shenglian Guo, Louise Slater. Continuity of terrestrial water storage variability and trends across mainland China monitored by the GRACE and GRACE-Follow on satellites. Journal of Hydrology. 2021; 599 ():126308.
Chicago/Turabian StyleJinghua Xiong; Jiabo Yin; Shenglian Guo; Louise Slater. 2021. "Continuity of terrestrial water storage variability and trends across mainland China monitored by the GRACE and GRACE-Follow on satellites." Journal of Hydrology 599, no. : 126308.
Global warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further affect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What’s more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. In this study, a cascade model chain is developed to evaluate the impacts of projected climate change and human activities on optimal allocation of water resources. Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton–Markov (CA–Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios. Then the runoff simulation is based on the Soil and Water Assessment Tool (SWAT) hydrological model with necessary inputs under the future conditions. Finally, the optimal water resources allocation model is established based on the evaluation of water supply and water demand. The Han River basin in China was selected as a case study. The results show that: (1) the annual runoff indicates an increasing trend in the future in contrast with the base period, while the ascending rate of the basin under RCP 4.5 is 4.47%; (2) a nonlinear relationship has been identified between the optimal allocation of water resources and water availability, while a linear association exists between the former and water demand; (3) increased water supply are needed in the water donor area, the middle and lower reaches should be supplemented with 4.495 billion m3 water in 2030. This study provides an example of a management template for guiding the allocation of water resources, and improves understandings of the assessments of water availability and demand at a regional or national scale.
Jing Tian; Shenglian Guo; Lele Deng; Jiabo Yin; Zhengke Pan; Shaokun He; Qianxun Li. Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China. Scientific Reports 2021, 11, 1 -18.
AMA StyleJing Tian, Shenglian Guo, Lele Deng, Jiabo Yin, Zhengke Pan, Shaokun He, Qianxun Li. Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China. Scientific Reports. 2021; 11 (1):1-18.
Chicago/Turabian StyleJing Tian; Shenglian Guo; Lele Deng; Jiabo Yin; Zhengke Pan; Shaokun He; Qianxun Li. 2021. "Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China." Scientific Reports 11, no. 1: 1-18.
Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a non‐monotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In‐situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tpp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP8.5), which involves 31 CMIP5 climate models, eleven CMIP6 climate members, a daily bias correction method and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift towards warmer temperature bins, resulting in 10 to 30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates.
Jiabo Yin; Shenglian Guo; Pierre Gentine; Sylvia C. Sullivan; Lei Gu; Shaokun He; Jie Chen; Pan Liu. Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming? Water Resources Research 2021, 57, 1 .
AMA StyleJiabo Yin, Shenglian Guo, Pierre Gentine, Sylvia C. Sullivan, Lei Gu, Shaokun He, Jie Chen, Pan Liu. Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming? Water Resources Research. 2021; 57 (2):1.
Chicago/Turabian StyleJiabo Yin; Shenglian Guo; Pierre Gentine; Sylvia C. Sullivan; Lei Gu; Shaokun He; Jie Chen; Pan Liu. 2021. "Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?" Water Resources Research 57, no. 2: 1.
As one of the most crucial indices of sustainable development and water security, water resources carrying capacity (WRCC) has been a pivotal and hot-button issue in water resources planning and management. Quantifying WRCC can provide useful references on optimizing water resources allocation and guiding sustainable development. In this study, the WRCCs in both current and future periods were systematically quantified using set pair analysis (SPA), which was formulated to represent carrying grade and explore carrying mechanism. The Soil and Water Assessment Tool (SWAT) model, along with water resources development and utilization model, was employed to project future water resources scenarios. The proposed framework was tested on a case study of China’s Han River basin. A comprehensive evaluation index system across water resources, social economy, and ecological environment was established to assess the WRCC. During the current period, the WRCC first decreased and then increased, and the water resources subsystem performed best, while the eco-environment subsystem achieved inferior WRCC. The SWAT model projected that the amount of the total water resources will reach about 56.9 billion m3 in 2035s, and the water resources development and utilization model projected a rise of water consumption. The declining WRCC implies that the water resources are unable to support or satisfy the demand of ecological and socioeconomic development in 2035s. The study furnishes abundant and valuable information for guiding water resources planning, and the core idea of this model can be extended for the assessment, prediction, and regulation of other systems.
Lele Deng; Jiabo Yin; Jing Tian; Qianxun Li; Shenglian Guo. Comprehensive Evaluation of Water Resources Carrying Capacity in the Han River Basin. Water 2021, 13, 249 .
AMA StyleLele Deng, Jiabo Yin, Jing Tian, Qianxun Li, Shenglian Guo. Comprehensive Evaluation of Water Resources Carrying Capacity in the Han River Basin. Water. 2021; 13 (3):249.
Chicago/Turabian StyleLele Deng; Jiabo Yin; Jing Tian; Qianxun Li; Shenglian Guo. 2021. "Comprehensive Evaluation of Water Resources Carrying Capacity in the Han River Basin." Water 13, no. 3: 249.
Satellite-retrieved and atmospheric reanalysis precipitation can bridge the spatiotemporal gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in hydrological monitoring and modelling. To correct precipitation occurrence and intensity simultaneously, this study develops a three-stage blending approach to integrate three multi-satellite precipitation datasets (IMERG Final, TMPA 3B42V7 and PERSIANN-CDR), the ERA5 atmospheric reanalysis product and a gauge dataset within a dynamic framework. Firstly, the systematic biases of the four members were individually corrected by combining the local intensity scaling and ratio bias correction methods. Then, the “state weights” used for determining wet/dry events were optimized by evaluating a score function of the four bias-corrected members. Thirdly, the “intensity weights” were optimized using the cuckoo search (CS) algorithm and the Bayesian Model Averaging (BMA) method, respectively. The three-stage blending approach produced dynamic weights varying both spatially and temporally, and the performance was thoroughly evaluated over mainland China. Results show that the three-stage dynamic scheme performs better than individual datasets and two-stage blending methods in terms of all eight statistical metrics, and the CS algorithm outperforms the BMA method in the third stage. By randomly sampling validation sites using K-fold experiments, the developed algorithm also demonstrates a superior performance in ungauged regions. After interpolating and normalizing blending parameters of all gauges to entire domain using ordinary kriging, a new blended precipitation dataset with a daily 0.25° scale was produced. Four hydrological models are forced by blended and primary precipitations in 238 catchments over China, further confirming that the developed approach can facilitate hydrological modelling demonstrated by improving the Kling-Gupta efficiency of simulated streamflow by 12–35%.
Jiabo Yin; Shenglian Guo; Lei Gu; Ziyue Zeng; Dedi Liu; Jie Chen; Youjiang Shen; Chong-Yu Xu. Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling. Journal of Hydrology 2020, 593, 125878 .
AMA StyleJiabo Yin, Shenglian Guo, Lei Gu, Ziyue Zeng, Dedi Liu, Jie Chen, Youjiang Shen, Chong-Yu Xu. Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling. Journal of Hydrology. 2020; 593 ():125878.
Chicago/Turabian StyleJiabo Yin; Shenglian Guo; Lei Gu; Ziyue Zeng; Dedi Liu; Jie Chen; Youjiang Shen; Chong-Yu Xu. 2020. "Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling." Journal of Hydrology 593, no. : 125878.
Satellite altimetry can fill the spatial gaps of in-situ gauging networks especially in poorly gauged regions. Although at a generally low temporal resolution, satellite altimetry has been successfully used for water surface elevation (WSE) estimation and hydrodynamic modeling. This study aims to investigate the contribution of WSE from both short-repeat and geodetic altimetry to hydrodynamic model calibration, and also explore the contribution of the new Sentinel-3 mission. Two types of data sources (i.e., in-situ and satellite altimetry) are investigated together with two roughness cases (i.e., spatially variable and uniform roughness) for calibration of a hydrodynamic model (DHI MIKE 11) with available bathymetry. A 150 km long reach of Han River in China with rich altimetry and in-situ gauging data is selected as a case study. Results show that the performances of the model calibrated by satellite altimetry-derived datasets are acceptable in terms of Root Mean Square Error (RMSE) of simulated WSE. Sentinel-3A can support hydrodynamic model calibration even though it has a relatively low temporal resolution (27-day repeat cycle). The CryoSat-2 data with a higher spatial resolution (7.5 km at the Equator) are proved to be more valuable than the Sentinel-3A altimetry data with a low spatial resolution (104 km at the Equator) for hydrodynamic model calibration in terms of RMSE values of 0.16 and 0.18 m, respectively. Moreover, the spatially variable roughness can also improve the model performance compared to the uniform roughness case, with decreasing RMSE values by 2–14%. Our finding shows the value of satellite altimetry-derived datasets for hydrodynamic model calibration and therefore supports flood risk assessment and water resources management.
Youjiang Shen; Dedi Liu; Liguang Jiang; Jiabo Yin; Karina Nielsen; Peter Bauer-Gottwein; Shenglian Guo; Jun Wang. On the Contribution of Satellite Altimetry-Derived Water Surface Elevation to Hydrodynamic Model Calibration in the Han River. Remote Sensing 2020, 12, 4087 .
AMA StyleYoujiang Shen, Dedi Liu, Liguang Jiang, Jiabo Yin, Karina Nielsen, Peter Bauer-Gottwein, Shenglian Guo, Jun Wang. On the Contribution of Satellite Altimetry-Derived Water Surface Elevation to Hydrodynamic Model Calibration in the Han River. Remote Sensing. 2020; 12 (24):4087.
Chicago/Turabian StyleYoujiang Shen; Dedi Liu; Liguang Jiang; Jiabo Yin; Karina Nielsen; Peter Bauer-Gottwein; Shenglian Guo; Jun Wang. 2020. "On the Contribution of Satellite Altimetry-Derived Water Surface Elevation to Hydrodynamic Model Calibration in the Han River." Remote Sensing 12, no. 24: 4087.
The equivalent frequency regional composition (EFRC) method and most likely regional composition (MLRC) method have been widely used for design flood estimation in cascade reservoir system. The EFRC method assumes an identical exceedance probability of design flood quantile between the upstream reservoir site (EFRC-1) or intermediate basin (EFRC-2) and the downstream reservoir site. The MLRC method statistically considers the actual inter-correlation between floods of different sub-basins and determines the flood regional composition by selecting the one with maximum occurrence likelihood. The advances, limitations, inherent connections and differences of these two methods have never been theoretically investigated in literature. In this study, the EFRC and MLRC methods are comprehensively compared through theoretical derivation, statistical experiment and applicability assessment. The main findings are: (1) The difference between EFRC-1 and MLRC methods tends to be smaller with the increase of correlation of floods across different sub-basins, and these two methods are mathematically equivalent in terms of totally correlated floods. (2) The difference between EFRC-2 and MLRC methods are impacted by both the correlation and the variances of annual maximum flood series of upstream and downstream reservoirs. (3) The EFRC method is reasonable when the correlation coefficient exceeds 0.7 for one or two reservoirs; while the MLRC method tends to be more reasonable and practical for complex cascade reservoirs. (4) Compared with original design flood results, the design flood of downstream reservoirs in the Wu River decrease significantly due to the regulation of upstream reservoirs. The flood limited water levels (FLWLs) of downstream reservoirs can be raised without increasing flood control risks, which can increase 174.3 million kW·h annual hydropower generation in flood season.
Feng Xiong; Shenglian Guo; Jiabo Yin; Jing Tian; Muhammad Rizwan. Comparative study of flood regional composition methods for design flood estimation in cascade reservoir system. Journal of Hydrology 2020, 590, 125530 .
AMA StyleFeng Xiong, Shenglian Guo, Jiabo Yin, Jing Tian, Muhammad Rizwan. Comparative study of flood regional composition methods for design flood estimation in cascade reservoir system. Journal of Hydrology. 2020; 590 ():125530.
Chicago/Turabian StyleFeng Xiong; Shenglian Guo; Jiabo Yin; Jing Tian; Muhammad Rizwan. 2020. "Comparative study of flood regional composition methods for design flood estimation in cascade reservoir system." Journal of Hydrology 590, no. : 125530.
Censored data (CD) of floods, i.e., the combination of systematic data (SD) and historical data, can help improve the robustness of flood frequency analysis, due to its temporal information expansion. However, in nonstationary flood frequency analysis, the approach to utilize the CD has rarely been investigated. In this study, a covariate‐based nonstationary flood frequency analysis framework based on various likelihood functions using the generalized extreme value (GEV) distribution was built to utilize the censored data, with uncertainty considered. This framework was applied to the study of the annual maximum flood frequency of the Yichang gauging station 44 km downstream of the Three Gorges Dam over the period from 1470 to 2017. A summer precipitation anomaly and a reservoir index were used as covariates to explain the variation of the distribution parameters. The results show that for either the SD or CD, the nonstationary models are preferred to the stationary ones by the deviance information criterion, and these nonstationary models may prove to be practical in engineering application, due to the acceptable uncertainty range in flood quantiles derived from covariates. Compared to the stationary or nonstationary models based on the SD, the corresponding model based on the CD results in a higher posterior mean and a smaller posterior standard deviation for the shape parameter of the GEV distribution. It is concluded that the use of historical information under the nonstationary frequency analysis framework may be remarkable in reducing design flood uncertainty, especially for the very small exceedance probability at the tail.
Bin Xiong; Lihua Xiong; Shenglian Guo; Chong‐Yu Xu; Jun Xia; Yixuan Zhong; Han Yang. Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River from 1470 to 2017. Water Resources Research 2020, 56, 1 .
AMA StyleBin Xiong, Lihua Xiong, Shenglian Guo, Chong‐Yu Xu, Jun Xia, Yixuan Zhong, Han Yang. Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River from 1470 to 2017. Water Resources Research. 2020; 56 (8):1.
Chicago/Turabian StyleBin Xiong; Lihua Xiong; Shenglian Guo; Chong‐Yu Xu; Jun Xia; Yixuan Zhong; Han Yang. 2020. "Nonstationary Frequency Analysis of Censored Data: A Case Study of the Floods in the Yangtze River from 1470 to 2017." Water Resources Research 56, no. 8: 1.
Changing conditions of the climate and underlying surface have altered the rainfall-runoff relationships in many basins, greatly increasing additional challenges in the applicability of hydrological models for studying the hydrological response to those potential changes. However, systematic and simultaneous testing and comparing of both temporal and spatial transferabilities of different hydrological models under changing conditions have not received enough attention. The present study investigates the potential differences between temporal and spatial transferabilities of different hydrological models under different climatic and underlying surface conditions, which are synthesized from two basins in Southern China with 50-year historical records (1966–2015). The transferability of five hydrological models, i.e., XAJ, HBV, SIMHYD, IHACRES and GR4J, is investigated under the synthesised changing conditions by using a new evaluation method, proposed in this study. The results show that: (1) the proposed evaluation method is proved to be effective in evaluating the transferability of the models; (2) for temporal transferability under stationary condition, the five models show similar performances, but for spatial transferability, the performances of complex models (XAJ and HBV) are better than that of the simple model (GR4J); (3) the difference in underlying surface conditions in the target basin affects spatial transferability of the models; (4) hydrological models have much better transferability from dry to wet period than otherwise. This study provides an insight to test temporal and spatial transferabilities of hydrological models in the context of changing climate and underlying surface conditions.
Wushuang Yang; Hua Chen; Chong-Yu Xu; Ran Huo; Jie Chen; Shenglian Guo. Temporal and spatial transferabilities of hydrological models under different climates and underlying surface conditions. Journal of Hydrology 2020, 591, 125276 .
AMA StyleWushuang Yang, Hua Chen, Chong-Yu Xu, Ran Huo, Jie Chen, Shenglian Guo. Temporal and spatial transferabilities of hydrological models under different climates and underlying surface conditions. Journal of Hydrology. 2020; 591 ():125276.
Chicago/Turabian StyleWushuang Yang; Hua Chen; Chong-Yu Xu; Ran Huo; Jie Chen; Shenglian Guo. 2020. "Temporal and spatial transferabilities of hydrological models under different climates and underlying surface conditions." Journal of Hydrology 591, no. : 125276.
The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modelling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has the great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modelling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; and (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue can not only advance water sciences but can also support policy makers toward more sustainable and effective water resources management.
Fi-John Chang; Shenglian Guo. Advances in Hydrologic Forecasts and Water Resources Management. Water 2020, 12, 1819 .
AMA StyleFi-John Chang, Shenglian Guo. Advances in Hydrologic Forecasts and Water Resources Management. Water. 2020; 12 (6):1819.
Chicago/Turabian StyleFi-John Chang; Shenglian Guo. 2020. "Advances in Hydrologic Forecasts and Water Resources Management." Water 12, no. 6: 1819.
As atmospheric moisture capacity is highly sensitive to rising temperatures, precipitation extremes are widely projected to intensify with a warming climate and thus altering the flooding generation regime. Previous works seldomly focused on bivariate flood quantiles under climate change at a national scale, and fewer flooding projections quantified the estimation uncertainty sourced from sample size limitation. This study systematically investigates the changes in bivariate quantiles of flood peak and volume with incorporation of sampling uncertainty for 151 catchments over China, with climate trajectories projected by a set of multi-model ensemble under representative concentration pathway (RCP) 8.5. After correcting the systematical biases of eight CMIP5 GCM outputs, four state-of-the-art hydrological models are driven and validated for each catchment, and the best-simulation model is selected to project future streamflow scenarios. The copula function is employed to construct the joint distribution of flood peak and volume, and then the most likely realizations of bivariate quantiles are derived under different Joint Return Periods (JRPs), with the uncertainty envelope quantified with the area of 90% confidence ellipse by a copula-based parametric bootstrapping uncertainty (C-PBU) approach. Our results project an overall ascending trend of temperature and precipitation over China, and the bivariate flood quantiles and corresponding estimation uncertainty of most catchments in the future period (2056–2100) are much larger than the baseline (1961–2005), despite accompanied by substantial climate model uncertainty and spatial variability in magnitude. Many basins would be subjected to a dramatic increase of flood magnitude by over 50%, while only few basins are projected to experience a decreasing flood risk, suggesting an urgent need to increase societal resilience to a warming climate over China.
Jiabo Yin; Shenglian Guo; Lei Gu; Shaokun He; Huanhuan Ba; Jing Tian; Qianxun Li; Jie Chen. Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China. Journal of Hydrology 2020, 585, 124760 .
AMA StyleJiabo Yin, Shenglian Guo, Lei Gu, Shaokun He, Huanhuan Ba, Jing Tian, Qianxun Li, Jie Chen. Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China. Journal of Hydrology. 2020; 585 ():124760.
Chicago/Turabian StyleJiabo Yin; Shenglian Guo; Lei Gu; Shaokun He; Huanhuan Ba; Jing Tian; Qianxun Li; Jie Chen. 2020. "Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China." Journal of Hydrology 585, no. : 124760.
It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.
Yanlai Zhou; Shenglian Guo; Chong-Yu Xu; Fi-John Chang; Jiabo Yin. Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network. Water 2020, 12, 578 .
AMA StyleYanlai Zhou, Shenglian Guo, Chong-Yu Xu, Fi-John Chang, Jiabo Yin. Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network. Water. 2020; 12 (2):578.
Chicago/Turabian StyleYanlai Zhou; Shenglian Guo; Chong-Yu Xu; Fi-John Chang; Jiabo Yin. 2020. "Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network." Water 12, no. 2: 578.
Deriving operating rules for multi-objective cascade reservoir systems is an important challenge in water resources management. To address, this study combines a radial basis function network with an evolutionary algorithm to propose a heuristic input variable selection (HIS) method that extracts reservoir operating rules based on feature selection. For a case study of the Hanjiang cascade reservoirs in China, we initially describe the operating rules with radial basis functions and subsequently refine them based on the HIS method. We select the most suitable input variables for each reservoir conditioned on water supply and power generation targets to derive and optimize the rules with a Pareto-archived dynamically dimensioned search algorithm. From this we can analyze input variable selection and the corresponding impact on multi-objective cascade reservoir operations. The results demonstrate that the HIS method selects the input variables accurately and the reservoir operating rules refined by the method could increase water supply by up to 6.6% and power generation by up to 1.2%. The most suitable input variables for reservoir operation vary depending on reservoir objective, however the HIS method appears effective at selecting the appropriate input variables for individual reservoirs in a cascade system.
Guang Yang; Shenglian Guo; Pan Liu; Xiaofeng Liu; Jiabo Yin. Heuristic Input Variable Selection in Multi-Objective Reservoir Operation. Water Resources Management 2020, 34, 617 -636.
AMA StyleGuang Yang, Shenglian Guo, Pan Liu, Xiaofeng Liu, Jiabo Yin. Heuristic Input Variable Selection in Multi-Objective Reservoir Operation. Water Resources Management. 2020; 34 (2):617-636.
Chicago/Turabian StyleGuang Yang; Shenglian Guo; Pan Liu; Xiaofeng Liu; Jiabo Yin. 2020. "Heuristic Input Variable Selection in Multi-Objective Reservoir Operation." Water Resources Management 34, no. 2: 617-636.
Climate change leads to great impact on hydrological cycle and consequently affects water resources management. Historical strategies are no longer applicable under a changing environment. Therefore, adaptive management, especially adaptive operation rules for reservoirs, has been developed to mitigate the potential adverse impacts of climate change. However, previous studies generally provide a similar framework for adaptation strategies of individual reservoir without consideration of cascade reservoirs in the future scenario. This study derives adapting operation rules for cascade reservoir system based on future projections (2021–2100) of two global climate change models (GCMs). By using Pareto archived dynamically dimensioned search (PA-DDS) algorithm with maximization of water supply and power generation, the performance of the adaptive operation rule curves is compared with the designed operation rule. The results demonstrate that Pareto solutions of the PA-DDS algorithm provide a wider, more optimal range of annual power generation and water supply, and the projection pursuit method can select the best. The adaptive operation rules focusing on power generation can significantly increase the cascade reservoir annual power generation (by 3.7% in GCM-BCC or 4.8% in GCM-BNU), which shows that the proposed method can adapt future climate change.
Shaokun He; Shenglian Guo; Guang Yang; Kebing Chen; Dedi Liu; Yanlai Zhou. Optimizing Operation Rules of Cascade Reservoirs for Adapting Climate Change. Water Resources Management 2019, 34, 101 -120.
AMA StyleShaokun He, Shenglian Guo, Guang Yang, Kebing Chen, Dedi Liu, Yanlai Zhou. Optimizing Operation Rules of Cascade Reservoirs for Adapting Climate Change. Water Resources Management. 2019; 34 (1):101-120.
Chicago/Turabian StyleShaokun He; Shenglian Guo; Guang Yang; Kebing Chen; Dedi Liu; Yanlai Zhou. 2019. "Optimizing Operation Rules of Cascade Reservoirs for Adapting Climate Change." Water Resources Management 34, no. 1: 101-120.
The primary data are available from ref. 2. The MOPEX data are available from National Oceanic and Atmospheric Administration website (https://www.nws.noaa.gov/ohd/mopex/mo_datasets.htm). The weekly snow cover data are from Northern Hemisphere EASE-Grid 2.0 Weekly Snow Cover and Sea Ice Extent, Version 4, which is archived in National Snow & Ice Data Center (https://nsidc.org/). The global gridded Berkeley Earth Surface Temperatures (BEST) dataset is from Berkeley Earth (http://berkeleyearth.org/). The soil moisture data are from the Global Land Evaporation Amsterdam Model (GLEAM) version 3 (https://www.gleam.eu). The high-resolution (0.5° × 0.5°) gridded daily precipitation and temperature dataset in China is obtained from Chinese Meteorological Administration (http://www.cma.gov.cn/). The streamflow data of Chinese river basins are available from the authors upon request. Wasko, C., Sharma, A. & Lettenmaier, D. P. Increases in temperature do not translate to increased flooding. Nat. Commun. https://doi.org/10.1038/s41467-019-13613-4 (2019). Yin, J. et al. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 9, 4389 (2018). Yin, J. B. et al. A copula-based analysis of projected climate changes to bivariate flood quantiles. J. Hydrol. 566, 23–42 (2018). Lenderink, G. & Van Meijgaard, E. Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci. 1, 511 (2008). Utsumi, N., Seto, S., Kanae, S., Maeda, E. E. & Oki, T. Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett. 38, 239–255 (2011). Wang, G. et al. The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat. Clim. Change 7, 268–274 (2017). Lemordant, L., Gentine, P., Swann, A. L. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl Acad. Sci. USA 115, 4093–4098 (2018). Roderick, M. L. & Farquhar, G. D. The cause of decreased pan evaporation over the past 50 years. Science 298, 1410–1411 (2002). Diffenbaugh, N. S., Scherer, M. & Ashfaq, M. Response of snow-dependent hydrologic extremes to continued global warming. Nat. Clim. Change 3, 379 (2013). Lu, M., Lall, U., Schwartz, A. & Kwon, H. Precipitation predictability associated with tropical moisture exports and circulation patterns for a major flood in France in 1995. Water Resour. Res. 49, 6381–6392 (2013). Alfieri, L. et al. Global projections of river flood risk in a warmer world. Earths Future 5, 171–182 (2017). Bennett, B. et al. An empirical investigation into the effect of antecedent precipitation on flood volume. J. Hydrol. 567, 435–445 (2018). Cao, M. & Woodward, F. I. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature 393, 249 (1998). Steenhuis, T. S., Winchell, M., Rossing, J., Zollweg, J. A. & Walter, M. F. SCS runoff equation revisited for variable-source runoff areas. J. Irrig. Drain. Eng. 121, 234–238 (1995). Wasko, C. & Sharma, A. Global assessment of flood and storm extremes with increased temperatures. Sci. Rep. 7, 7945 (2017). Download references We are grateful for the funding from National Natural Science Foundation of China (Grant No. 51539009, 51579183), and the “111 Project” Fund of China (B18037). This work is also partly funded by the Ministry of Foreign Affairs of Denmark and administered by Danida Fellowship Centre (File number: 18-M01-DTU). J.Y., P.G. and S.G. led the writing and formatting of this paper. J.Y. and L.G. performed the analysis. S.Z., S.C.S. and P.L. assisted in interpretation of observational data. Y.Z. and L.G. assisted in preparing the figures. All authors reviewed the manuscript. Correspondence to Pierre Gentine or Shenglian Guo or Pan Liu. The authors declare no competing interests. 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If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and Permissions Yin, J., Gentine, P., Guo, S. et al. Reply to ‘Increases in temperature do not translate to increased flooding’. Nat Commun 10, 5675 (2019). https://doi.org/10.1038/s41467-019-13613-4 Download citation Received: 06 January 2019 Accepted: 11 November 2019 Published: 12 December 2019 DOI: https://doi.org/10.1038/s41467-019-13613-4 Nature Communications (2019) Nature Communications (2019) By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
Jiabo Yin; Pierre Gentine; Shenglian Guo; Sha Zhou; Sylvia C. Sullivan; Yao Zhang; Lei Gu; Pan Liu. Reply to ‘Increases in temperature do not translate to increased flooding’. Nature Communications 2019, 10, 1 -5.
AMA StyleJiabo Yin, Pierre Gentine, Shenglian Guo, Sha Zhou, Sylvia C. Sullivan, Yao Zhang, Lei Gu, Pan Liu. Reply to ‘Increases in temperature do not translate to increased flooding’. Nature Communications. 2019; 10 (1):1-5.
Chicago/Turabian StyleJiabo Yin; Pierre Gentine; Shenglian Guo; Sha Zhou; Sylvia C. Sullivan; Yao Zhang; Lei Gu; Pan Liu. 2019. "Reply to ‘Increases in temperature do not translate to increased flooding’." Nature Communications 10, no. 1: 1-5.