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Extreme precipitation events (EPEs) have garnered considerable social concerns due to their hazardous and destructive nature. To identify the possible causes of EPEs in China, this paper presents an in-depth investigation of how EPEs coincide with atmospheric conditions, i.e., atmospheric instability, moisture availability and moisture convergence, as well as driving variables, i.e., vertical velocity, relative humidity and air temperature. Specifically, the classic coincidence probability is devised to explicitly relate 72-h EPEs to convective available potential energy (CAPE), precipitable water (PW), vertical velocity at 700 hPa (verV), relative humidity at 700 hPa (RH), average air temperature between 850 and 500 hPa (Tavg) as well as air temperature difference between 850 and 500 hPa (Tdiff). The results show that at the annual timescale, EPEs in Southeast and Southwest China are dominantly controlled by verV, in North and Central China by PW and in Northwest China by CAPE roughly. At the seasonal timescale, the spatial distributions of coincidence probability values and "competition" among atmospheric conditions and driving variables exhibit similar patterns as observed throughout the entire year except for December–January–February. Moreover, the diagnostic plots generated for three case study regions in China provide valuable insights into the temporal evolution of precipitation events, cumulative distribution function curves of influential factors and dominant controlling factors of EPEs. This paper contributes to understandings of the dominant controlling factors of EPEs for the whole of China. The spatial patterns of EPEs and their related atmospheric conditions and driving variables yield useful information for rainstorm forecasting and disaster risk management. Plain Language Summary: Atmospheric conditions, i.e., atmospheric instability, moisture availability and wind convergence, and driving variables, i.e., vertical velocity, relative humidity and air temperature, play important parts in the occurrence of extreme precipitation events (EPEs). In this paper, the 72-h EPEs in China are explicitly related to atmospheric conditions and driving variables by using the classic coincidence probability. Specifically, the relationships are measured by the ratio of the time when precipitation and the given factor both reaching at extreme to the whole time in EPEs. Assuming that the factor exhibiting the highest coincidence probability influences EPEs the most, it is observed that at the annual timescale, vertical velocity at 700 hPa has the greatest impact on EPEs in Southeast and Southwest China as well as part of Northeast China, precipitable water in North and Central China and convective available potential energy in Northwest China. Also, it is found that the results at the seasonal timescale are consistent with that throughout the entire year except for winter. Overall, the results of this paper can help us to understand the possible causes of EPEs and serve in rainstorm forecasting and risk management at grid cell level for the whole of China. [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]

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]

The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). Due to the harsh climate environment, there are few meteorological stations in its territory, and the measured meteorological data are also less. To overcome the impact of lack of measured meteorological data, the China Meteorological Forcing Dataset (CMFD) was used to couple with the meteorological data, to make the meteorological data more representative of the average climate state of the SRYR. Furthermore, we used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect of meteorological data. Then the six global climate models (GCMs) were integrated through deep learning (DL) algorithm to make it closer to the changes in weather data after correction. The future water resource changes of SRYR were obtained by coupling the SWAT model with the integrated meteorological data. The results show that the CMFD dataset has a high precision in the SRYR, and that the average relative error (RE), coefficient of determination (R2), Nash–Sutcliffe efficiency (Ens) with the measured temperature and precipitation are 10.66%, 0.99, and 0.93, respectively. The CMFD dataset can be used for meteorological data integration. After the meteorological data integration, the Ens between simulated runoff and observed runoff increased from 0.64 to 0.70, and the RE reduced from 13.4 to 10.3%. Under the future climate scenario, the runoff in the SRYR shows a decreasing trend, the average annual runoff decreased by 11.89%, and the distribution of runoff during the year changes is even more dramatic. This shows that the SRYR faces the risk of water resources reduction, which will bring challenges to water resources management in the SRYR. This study provides a new idea for meteorological data correction. [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]

Precipitation forecasts generated by global climate models (GCMs) are valuable for environmental modelling and management. At the seasonal timescale, GCMs' forecast skills are generally attributed to low-frequency climate signals, particularly to the internal climate variability mode of El Niño-Southern Oscillation (ENSO). This study proposes a diagnostic method targeting the correlation coefficients for both Niño3.4 index and GCM forecasts in relating them to global precipitation. The similarities versus differences are identified. For the Climate Forecast System version 2 (CFSv2) forecasts, regions with significantly positive anomaly correlation corresponding to significant ENSO teleconnection are highlighted, and regions with significantly positive anomaly correlation in the absence of significant ENSO teleconnection are revealed. As December–January–February (DJF) is the peak season of ENSO, it is estimated that for every 100 grid points with significant ENSO teleconnection, ENSO teleconnection determines significantly positive anomaly correlation for 37 of them; the ratio is respectively 34/100 in March–April–May (MAM), 30/100 in June–July–August (JJA), and 38/100 in September–October–November (SON). It is found that anomaly correlation benefits substantially from ENSO teleconnection over Southern Africa, Central America/Mexico, Amazon and Central North America in DJF, Southeast Asia, North-East Brazil and Central Asia in MAM, Southeast Asia and West Coast South America in JJA, and Southeast Asia, Australia and New Zealand in SON. Limited contributions of ENSO teleconnection to anomaly correlation are observed for North Europe in DJF, West Cost South America and East North America in MAM, North-East Brazil and Central America/Mexico in JJA, and Amazon in SON. Overall, the proposed method contributes to disentangling the sources of GCM forecast skill of seasonal precipitation and fostering better seasonal forecast benchmarking and diagnosis. [ABSTRACT FROM AUTHOR]

Integrated water quantity and quality simulations have become a popular tool in investigations on global water crisis. For integrated and complex models, conventional uncertainty estimations focus on the uncertainties of individual modules, e.g., module parameters and structures, and do not consider the uncertainties propagated from interconnected modules. Therefore, this study investigated all the uncertainties of integrated water system simulations using the GLUE (i.e., generalized likelihood uncertainty estimation) method, including uncertainties associated with individual modules, propagated uncertainties associated with interconnected modules, and their combinations. The changes in both acceptability thresholds of GLUE and the uncertainty estimation results were also investigated for different fixed percentages of total number of iterations (100000). Water quantity and quality variables (i.e., runoff and ammonium nitrogen) were selected for the case study. The results showed that module uncertainty did not affect the runoff simulation performance, but remarkably weakened the water quality responses as the fixed percentage increased during calibration and validation periods. The propagated uncertainty from hydrological modules could not be ignored for water quality simulations, particularly during validation. The combination of module and propagated uncertainties further weakened the water quality simulation performance. The uncertainty intervals became wider owing to an increase in the fixed percentages and introduction of more uncertainty sources. Moreover, the acceptability threshold had a negative nonlinear relationship with the fixed percentage. The fixed percentages (∼20.0%–30.0%) were proposed as the acceptability thresholds owing to the satisfactory simulation performance and noticeably reduced uncertainty intervals they produced. This study provided methodological foundations for estimating multiple uncertainty sources of integrated water system models. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are used by major climate centers worldwide for global climate forecasting, and predictive performance is one of the most important issues in GCM forecast applications. In addition to spatial plotting that illustrates anomaly correlation at individual grid cells, this study proposes a novel local indicator of spatial association (LISA) of anomaly correlation (herein, LISAAC) for GCM seasonal forecasts of global precipitation. LISAAC is built upon local Moran's I by relating anomaly correlation at neighboring grid cells to one another. While local Moran's I takes the grand mean of anomaly correlation as the benchmark, LISSAC considers the original value of anomaly correlation in the mathematical formulation. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts, which are initialized in January, February,..., and June, of the global precipitation in June, July, and August. Three metrics—LISAAC, local Moran's I, and original anomaly correlation—are applied to investigate the predictive performance. In comparison with local Moran's I, LISAAC can identify clusters of positive, neutral, and negative anomaly correlations. In comparison with anomaly correlation, LISAAC can capture outliers of positive (negative) anomaly correlation surrounded by negative (positive) anomaly correlation. Overall, the results highlight that LISAAC can serve as a useful tool for evaluating the predictive performance of GCM seasonal forecasts of global precipitation. [ABSTRACT FROM AUTHOR]

In flood control operation, the maximum release from a reservoir is minimized to lessen flood risks. Two properties of the minimax problem are derived by formulating the multi-period decision process as a recursive two-stage model. First, the cost-to-go function, which represents the maximum release in the remaining periods, is a non-decreasing function of the carryover storage. Second, monotonic relationships exist between the initial storage of the two-stage model and the optimal decisions of release and carryover storage. The two properties hold not only in the deterministic case with a given streamflow scenario, but also in the stochastic case with an ensemble of streamflow scenarios. The monotonic relationships are incorporated into the dynamic programming (DP) and sampling stochastic DP (SSDP). Two novel algorithms-improved DP (IDP) and improved SSDP (ISSDP)-are developed. The algorithms are applied to a case study of the Danjiangkou Reservoir in Central China. IDP and ISSDP respectively obtain the same decisions as DP and SSDP, and they are more computationally efficient. The execution times of IDP and ISSDP increase linearly with the number of storage discretizations, while those of DP and SSDP increase quadratically. With 1000 discretizations of reservoir storage, IDP and ISSDP derive optimal decisions at 0.939 and 97.453 s, respectively, whereas DP and SSDP finish at 115.931 and 6372.915 s, respectively. These results suggest that IDP and ISSDP can be useful tools for flood control operation - testing different flood scenarios and determining the optimal decisions. [ABSTRACT FROM AUTHOR]

Flood hedging reservoir operation is when a pre-storm release creates a small flood downstream to reduce the likelihood of a more damaging but uncertain larger flood in the future. Such pre-storm releases before a flood can increase reservoir storage capacity available to capture more severe flood flows, but also can immediately increase downstream flood damage and reduce stored water supply. This study develops an optimization model for pre-storm flood hedging releases and examines some necessary theoretical conditions for optimality, considering hydrologic uncertainty from flood forecasts and engineering uncertainty from flood failures. Theoretically, the ideal optimality condition for pre-storm flood hedging releases is where the current marginal damage from the hedging release equals the future expected marginal damage from storm releases. Additional water supply losses due to pre-storm releases tend to reduce pre-storm flood hedging releases. The overall flood damage cost to be minimized must be a convex function of pre-storm hedging releases for flood hedging to be optimal. Such convexity is determined by the overall flood risk together with the probability distribution of storm forecasts. Increasing the convexity of the failure probability function can induce more pre-storm hedging release. Categorized by flood risk likelihood downstream, forecasted storms that are large, but not yet overwhelming flood management systems, drive optimal flood hedging operation. A wide range of near-optimal hedging releases is observed in numerical examples, providing options for more rational water resources management decisions. [ABSTRACT FROM AUTHOR]

Oceanic evaporation via the East Asian Monsoon (EAM) has been regarded as the major source of precipitation over China, but a recent study estimated that terrestrial evaporation might contribute up to 80% of the precipitation in the country. To explain the contradiction, this study presents a comprehensive analysis of the contribution of oceanic and terrestrial evaporation to atmospheric moisture and precipitation in China's major river basins. The results show that from 1980 to 2010, the mean annual atmospheric moisture (precipitable water) over China was 13.7 mm, 39% of which originates from oceanic evaporation and 61% from terrestrial evaporation. The mean annual precipitation was 737 mm, 43% of which originates from oceanic evaporation and 57% from terrestrial evaporation. Oceanic evaporation makes a greater contribution to atmospheric moisture and precipitation in the East Asian Monsoon Region in South and East China than terrestrial evaporation does. Particularly, for the Pearl River and southeastern rivers, oceanic evaporation contributes approximately 65% of annual precipitation and more than 70% of summer precipitation. Meanwhile, terrestrial evaporation contributes more precipitation in northwest China due to the westerly wind. For the northwestern rivers, terrestrial evaporation from the Eurasian continents contributes more than 70% of precipitation. There is a linear relation between mean annual precipitation and the contribution of oceanic evaporation to precipitation, with a correlation coefficient of 0.92, among the ten major river basins in China. [ABSTRACT FROM AUTHOR]

The flood-mediated connectivity between river channels and floodplains plays a fundamental role in flood hazard mapping and exerts profound ecological effects. The classic nearest neighbor search (NNS) fails to derive this connectivity because of spatial heterogeneity and continuity. We develop two novel data-driven connectivity-deriving approaches, namely, progressive nearest neighbor search (PNNS) and progressive iterative nearest neighbor search (PiNNS). These approaches are illustrated through a case study in Northern Australia. First, PNNS and PiNNS are employed to identify flood pathways on floodplains through forward tracking. That is, progressive search is performed to associate newly inundated cells in each time step to previously inundated cells. In particular, iterations in PiNNS ensure that the connectivity is continuous - the connection between any two cells along the pathway is built through intermediate inundated cells. Second, inundated floodplain cells are collectively connected to river channel cells through backward tracing. Certain river channel sections are identified to connect to a large number of inundated floodplain cells. That is, the floodwater from these sections causes widespread floodplain inundation. Our proposed approaches take advantage of spatial-temporal data. They can be applied to achieve connectivity from hydro-dynamic and remote sensing data and assist in river basin planning and management. [ABSTRACT FROM AUTHOR]