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Predicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management. Plain Language Summary: The prediction of flood event classes is more effective and informative than that of individual events for obtaining comprehensive dynamic characteristics of flood events for early warning and development of emergency plans. However, this is challenging owing to massive flood events with remarkable spatiotemporal heterogeneity and unclear class membership functions. We propose a class prediction approach for flood events using the class membership functions of flood behavior metrics based on frequency analysis and catchment hydrological modeling. This approach deeply mines the behavior characteristics of historical flood events with a strong mathematical basis and cooperates with hydrological models to predict flood classes. Over thousand unimpacted flood events in 68 headstream catchments across China were selected to validate the robustness of the prediction scheme. Our results show that the proposed approach well predicts the flood class with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Our study has strong implications for scientific flood warning research and flood mitigation management. Key Points: A prediction approach for flood event classes is developed using class membership functions of flood regime metrics with hydrological modelClass membership degree is estimated using the frequency distribution function, which fits well with statistical significanceAverage class hit rates are 68.3% ± 0.4% for all the events and predictions of dumpy flood class are better than those of spike flood classes [ABSTRACT FROM AUTHOR]

Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

While deep learning (DL) models are effective in rainfall-runoff modelling, their dependence on data and lack of physical mechanisms can limit their use in hydrology. As there is yet no consensus on the consideration of the fundamental water balance for DL models, this paper presents an in-depth investigation of the effects of water balance constraint on the long-short term memory (LSTM) network. Specifically, based on the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset, the LSTM and its architecturally mass-conserving variant (MC-LSTM) are trained basin-wise to provide rainfall-runoff prediction and then the robustness of the LSTM and MC-LSTM against data sparsity, random parameters initialization and contrasting climate conditions are assessed across the contiguous United States. Through large-sample tests, the results show that the water balance constraint evidently improves the robustness of the basin-wise trained LSTM. On the one hand, as the amount of training data increases from 1 year to 15 years, the incorporation of the water balance constraint into the LSTM network decreases the sensitivity from 95.0 % to 32.7 %. On the other hand, the water balance constraint contributes to the stability of the LSTM for 450 (85 %) basins when there are 3 years' training data. In the meantime, the water balance constraint improves the transferability of the LSTM from the driest years to the wettest years for 318 (67 %) basins. Overall, the in-depth investigations of this paper facilitate insights into the use of DL models for rainfall-runoff modelling. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Extreme riverine floods and hydrodynamic response of downstream lakes are the main focus of catchment flood control, especially under climate change. In this study, future changes in the extreme floods and their hydrodynamic responses are projected by driving the external coupling of the hydrological and hydrodynamic models using the precipitation and temperature outputs of the general circulation models (GCMs) in seven emission scenarios, and the impact of climate change is evaluated. The Baiyangdian Lake and its controlled catchment are selected as our study area. Results show that: (a) both the daily runoff and the lake water levels are well simulated, (b) compared with the baseline scenario, extreme riverine floods are projected to increase evidently throughout the catchment with mean rates of 33.2%, 14.1% and 10.9% in the RCP2.6, 4.5 and 8.5 scenarios, respectively because of increasing precipitation; consequently, the total lake inflow also increases by 13.5%–150.9%, most of which are from Zhulong, Xiaoyi, Tanghe, and Fuhe Rivers, and (c) the water level of the Baiyangdian Lake increases temporally during the flood seasons in all the scenarios, and the increase in water level is 0.27–1.29 m greater than that in the baseline scenario; consequently, the water depth in 55.8% and 73.4% of lake area increases over 1.0 m in the future short‐term and long‐term scenarios, respectively; the inundation area also increases by 23.4%–26.7% in the future scenarios, except in the long‐term RCP4.5 scenario. The northeastern and south‐central regions of Baiyangdian, which are near Xiong'an New Area, have a relatively high inundation risk. Plain Language Summary: In this study, the catchment‐waterbody relationship is revealed by externally coupling a distributed catchment hydrological model with a lake hydrodynamic model to simulate the flood changes throughout the catchment and the hydrodynamic responses of the downstream lake. Future changes in the extreme floods and their hydrodynamic responses are projected in seven emission scenarios, and the impact of climate change is evaluated. The Baiyangdian Lake and its controlled catchment are selected as our study area. Results show that: (a) both the daily runoff at 71.4% of total stations and the lake water levels are well simulated, (b) compared with the baseline scenario, extreme riverine floods are projected to increase evidently because of increasing precipitation, (c) the water level of the Baiyangdian Lake increases temporally during the flood season in all the scenarios; consequently, the water depth in 55.8% and 73.4% of lake area increases over 1.0 m in the future short‐term and long‐term scenarios. The inundation area increases by 23.4%–26.7% in the future scenarios, except in the long‐term RCP4.5 scenario. The northeastern and south‐central regions of Baiyangdian, which are near Xiong'an New Area, have a relatively high inundation risk. Key Points: Future flood changes and their impacts on lake water depth and inundation area are projected using a coupled hydrological‐hydrodynamic modelExtreme riverine floods increase by 10.9%–33.2% in selected RCP scenarios due to increasing precipitationWater depth increases in 93.8%–97.3% of lake area, particularly in regions near Xiong'an New Area with an increase exceeding 1.0 m [ABSTRACT FROM AUTHOR]

The Huaihe River Basin (HRB) is located in the climate transition zone between north and south of china, where cold and warm air flows are easily encountered, resulting in frequent extreme precipitation events occurred in this area. In this study, in order to extract the spatiotemporal distribution characteristics of extreme precipitation in the HRB, the optimal edge distribution functions were used to fit the precipitation series to obtain the extreme precipitation threshold, and then six indicators were used to describe the spatiotemporal distribution characteristics of extreme precipitation. The results show that the number of occurrences and the amount of precipitation in the HRB are generally greater in the southern part than in the northern part, but the intensity of precipitation in the eastern coastal areas is greater than in the inland areas. The Weibull function has the best fitting effect on both the precipitation and precipitation intensity series in the five zones of the HRB. As the cumulative probability increases, the area with the largest precipitation amount is Zone 1, and the area with the largest precipitation intensity is Zone 3. The spatial variation trends of extreme precipitation and intensity-based extreme precipitation in the HRB are roughly the same. The area with more extreme precipitation is in the southwest of the basin, while the area with higher precipitation intensity is on the eastern coast of the basin. The number of extreme precipitation occurrences has a decreasing trend in most of the basin, and the precipitation amount also has a decreasing trend, but the precipitation intensity has an increasing trend in the southern and northern parts of the basin. Both the start date and end date of extreme precipitation have an increasing trend, indicating that the occurrence time of extreme precipitation has a tendency to delay. This study can provide an important reference for the prevention of extreme precipitation disasters in the HRB. [ABSTRACT FROM AUTHOR]

With the intensification of global warming, extreme precipitation events occur frequently all over the world. Extreme precipitation has brought huge challenges to the development of human society and economy, and it is urgent to strengthen the analysis of the causes of extreme precipitation. In this study, we first extracted precipitation events from the precipitation time series from 1960 to 2018 in the Huaihe River Basin (HRB) and then extracted extreme precipitation events based on precipitation amount and precipitation intensity. The results show that extreme precipitation in the HRB has an increasing trend after 2000, although the increasing trend is not obvious, and the uncertainty of the occurrence of extreme precipitation events had increased. Extreme precipitation mainly had a strong correlation with the surface air temperature in the North Pacific, Indian Ocean, and Qinghai–Tibet Plateau. The El Niño year and the year of abnormal circulation contribute to the production of extreme precipitation in the HRB. In the years when extremely heavy precipitation occurred in the HRB, the subtropical high was northerly. Although most of the water vapor is brought from the Pacific by the East Asian monsoon, the water vapor that causes extreme precipitation in the HRB mainly comes from the Indian Ocean. Through multivariate wavelet coherence (MWC) analysis, annual precipitation is greatly affected by multiple climate variables, while extreme precipitation is greatly affected by a single climate variable. This study provides an important reference for the analysis and prediction of extreme precipitation. [ABSTRACT FROM AUTHOR]

Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long‐term hydrological effects, such as total water yield and baseflow, of this large‐scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970–2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid‐1990s induced a significant decrease in runoff for the Weihe River Basin in semi‐arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long‐term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region‐specific afforestation policy should be considered to deal with forestation‐water quantity trade‐off. Conclusions from this study can help improve decision‐making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place. Key Points: Both precipitation and afforestation explained reduction in runoff for dry basinsForest recovery did not result in significant changes in total runoff for two wet basinsDry season baseflow in two wet basins had an upward trend [ABSTRACT FROM AUTHOR]

[Display omitted] • Five discharge datasets have median correlation coefficients > 0.6 with the observations. • Overestimation of monthly mean flow is more common than underestimation. • GRFR showed the best estimate at around 40% of the locations. • None of the models provides good estimates for rivers and streams in arid regions. High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models. [ABSTRACT FROM AUTHOR]

• A model for describing the forecast uncertainty of renewable energy is proposed. • A stochastic scheduling model for wind–photovoltaic–hydropower systems is established. • Uncertainty evolution from single power output to multiple power outputs to power shortage. This paper examines and quantifies the evolution of the uncertainty in forecasting solar- and wind-based electricity generation compensated with hydroelectric power, based on the forecast uncertainties of the three constituents. We used the generalized martingale model of forecast evolution to separately describe the uncertainties of power outputs of wind and photovoltaic systems in the same region. We then superimposed the separate power outputs to obtain the combined power output from these variable renewable energies (VRE). Furthermore, we developed a stochastic recourse model for optimally scheduling hydropower dispatch to compensate VRE and meet scheduled power demands. We applied the new model to hourly performance data obtained from photovoltaic, wind, and hydropower plants with power outputs of 3.1 GW, 2.7 GW, and 3.3 GW, respectively, in the Yalong River Basin in China. Based on the variance of hourly power outputs during spring days with different weather patterns, we found that the uncertainty of the forecasted combined power output of wind and photovoltaic systems is 46% less than that of the forecasted wind power output, and approximately 2% greater than that of the forecasted photovoltaic power output. After hydropower compensated for the power shortage in the combined VRE power output, the uncertainty of meeting prescheduled hourly demand during each of the considered days was reduced by 90%, compared with that without hydropower compensation. When the forecasts were updated dynamically, the uncertainties of the forecasts of the separate power outputs, of the combined power output, and of the power shortage decreased substantially. Thus, the approach proposed in this study offers a scheduling plan for hydropower compensation of VRE on a daily time scale and can also be used to evaluate the risk of power shortage. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

Urbanization-induced landuse changes alter runoff regimes in complex ways. In this study, a detailed investigation of the urbanization impacts on runoff regimes is provided by using multiple runoff metrics and with consideration of landuse dynamics. A catchment hydrological model is modified by coupling a simplified flow routing module of the urban drainage system and landuse dynamics to improve long-term urban runoff simulations. Moreover, multivariate statistical approach is adopted to mine the spatial variations of runoff metrics so as to further identify critical impact factors of landuse changes. The Qing River catchment as a peri-urban catchment in the Beijing metropolitan area is selected as our study region. Results show that: (1) the dryland agriculture is decreased from 13.9% to 1.5% of the total catchment area in the years 2000–2015, while the percentages of impervious surface, forest and grass are increased from 63.5% to 72.4%, 13.5% to 16.6% and 5.1% to 6.5%, respectively. The most dramatic landuse changes occur in the middle and downstream regions; (2) The combined landuse changes do not alter the average flow metrics obviously at the catchment outlet, but slightly increase the high flow metrics, particularly the extreme high flows; (3) The impacts on runoff metrics in the sub-catchments are more obvious than those at the catchment outlet. For the average flow metrics, the most impacted metric is the runoff depth in the dry season (October ∼ May) with a relative change from −10.9% to 11.6%, and the critical impact factors are the impervious surface and grass. For the high flow metrics, the extreme high flow depth is increased most significantly with a relative change from −0.6% to 10.5%, and the critical impact factors are the impervious surface and dryland agriculture; (4) The runoff depth metrics in the sub-catchments are increased because of the landuse changes from dryland agriculture to impervious surface, but are decreased because of the landuse changes from dryland agriculture or impervious surface to grass or forest. The results of this study provide useful information for urban planning such as Sponge City design. [ABSTRACT FROM AUTHOR]

• An integrated postprocessing framework for hydrometeorological ensemble is developed. • The canonical event considered observed precipitation improves the forecast accuracy. • The streamflow simulation can benefit from the seamless hydrologic prediction. • The Bayesian method improves the forecast effect than a single hydrological model. Flood forecasting is a challenging task with major concerns that include forecast uncertainty and lead time. This paper aims to comprehensively address the data correction issue for hydrological models by developing an integrated postprocessing framework for hydrometeorological ensemble forecasts based on three types of ensemble precipitation forecasts (ECMWF, GEFS, and CFSv2). This framework mainly comprises the Ensemble Preprocessor (EPP) and the Bayesian model averaging (BMA) scheme: (1) the EPP eliminates various deviations from the ensemble precipitation forecasts, with the canonical event model extracting useful predictive information and extending the lead time by linking the forecasts seamlessly, and (2) the BMA scheme develops more skillful and reliable probabilistic hydrological forecasts from ensemble streamflow forecasts generated by three hydrological models (XAJ, VIC and DTVGM) driven by the postprocessed ensemble precipitation forecasts. The Ganjiang River Basin was selected as a case study to examine the capacity and efficiency of our developed framework. We found that the performance of the integrated postprocessing of hydrometeorological ensemble forecasts benefitted from a reasonable design of canonical events, especially when the observed precipitation was factored into the canonical events. Streamflow forecasts with increased lead times and accuracy were obtained by employing postprocessed precipitation forecasts instead of raw forecasts as the input for the hydrological models. We also found that the BMA scheme further improved the forecast effect by generating more skillful and reliable forecasts than a single hydrological model, which was verified by a series of statistical indices and uncertainty analysis. Our results underscore the importance of considering both input and output for hydrological models in hydrological forecasting. [ABSTRACT FROM AUTHOR]