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Extreme integrated vapor transport (IVT) is a crucial driving factor of extreme precipitation events (EPEs). This paper presents a complex network‐based characterization of propagations from extreme IVT to EPEs. Specifically, the propagations are tracked from extreme IVT to EPEs by event synchronization; and then the source zones of extreme IVT contributing to EPEs are identified by two‐layer complex network. A case study is devised for North America based on the daily NCEP/NCAR Reanalysis 1 from 1948 to 2021. Overall, eight communities of EPEs are identified: the west coast of United States (US) tend to receive substantial EPEs from the Pacific Ocean; the Gulf of Alaska tends to receive oceanic EPEs propagating inland; western Canada typically experiences large amount of out tendencies and the EPEs tend to accumulate in the Baffin Island and Labrador Peninsula; the southeastern US and the northern Great Plains tend to experience northward propagations from Mexico. Along the west coast of North America, the propagations from extreme IVT to EPEs typically originate from the eastern North Pacific between 160°W and 110°W, and make landfalls in 4 days. These propagations are influenced by anomalous cyclonic circulations developing over the Gulf of Alaska forced by eastward Rossby waves. The coincidence rate of these propagations with atmospheric rivers is, respectively, 85.31% in autumn, 91.35% in winter, 73.94% in spring, and 64.52% in summer. Overall, the observed propagations from extreme IVT to EPEs yield insights into the mechanism of atmospheric moisture transport and the predictability of precipitation. Plain Language Summary: Owing to different synoptic systems such as atmospheric rivers, low‐level jets, and tropical cyclones, there exist profound propagations from extreme integrated vapor transport to extreme precipitation. Focusing on North America, this paper is concentrated on the complex network underlying extreme integrated vapor transport and extreme precipitation events. It is found that the Rocky Mountains play an important part in shaping the propagations from extreme integrated vapor transport to extreme precipitation events. In particular, along the west coast of North America, extreme integrated vapor transport originating from the eastern North Pacific tends to be blocked by the Rocky Mountains, causing rising/cooling and leading to extreme precipitation. Overall, along the Rocky Mountains, there exist eight communities of extreme precipitation. Key Points: There exists a complex network on propagations from extreme integrated vapor transport to extreme precipitation events in North AmericaIn the west coast of North America, the propagations typically originate from eastern North Pacific and make landfalls in 4 daysThe coincidence rate of propagations with atmospheric rivers is, respectively, above 90% and 60% in boreal winter and summer [ABSTRACT FROM AUTHOR]

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]

Ensemble climatological forecasts play a critical part in benchmarking the predictive performance of hydroclimatic forecasts. Accounting for the skewness and censoring characteristics of hydroclimatic variables, ensemble climatological forecasts can be generated by the log, Box‐Cox and log‐sinh transformations, by the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and by the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. This paper is concentrated on the reliability of the 12 types of ensemble climatological forecasts. Specifically, mathematical formulations are presented and large‐sample tests are devised to verify the forecast reliability for the Multi‐Source Weighted‐Ensemble Precipitation version 2 across the globe. Climatological forecasts of monthly precipitation over 18,425 grid cells are generated for 30 years under leave‐one‐year‐out cross validation, leading to 6,633,000 (12 × 18425 × 30) sets of ensemble climatological forecasts. The results point out that the reliability of climatological forecasts considerably varies across the 12 methods, particularly in regions with high hydroclimatic variability. One observation is that climatological forecasts tend to deviate from the distributions of observations when there is inadequate flexibility to fit precipitation data. Another observation is that ensemble spreads can be overly wide when there exist overfits of sample‐specific noises in cross validation. Through the tests of global precipitation, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Overall, the investigations can serve as a guidance on the uses of transformations, distributions and non‐parametric methods in generating climatological forecasts. Plain Language Summary: Ensemble climatological forecasts have been extensively used as the benchmark to evaluate forecast skill. That is, forecasts generated by a certain forecasting model are skillful when they outperform climatological forecasts and otherwise they are not. In practice, ensemble climatological forecasts are generated by different methods, including the log, Box‐Cox and log‐sinh transformations, the combinations of the Bernoulli distribution with the Gaussian, Gamma, log‐normal, generalized extreme value, generalized logistic and Pearson type III distributions and the non‐parametric resampling, empirical cumulative distribution function and kernel density estimation methods. It is important to investigate pros and cons of different types of climatological forecasts. Focusing on the reliability, that is, statistical consistency between forecasts and observations, this paper has devised large‐sample tests of global monthly precipitation. The results show that owing to hydroclimatic variability, different types of climatological forecasts exhibit varying characteristics of reliability. On the one hand, climatological forecasts can deviate from observations when there is inadequate flexibility to fit precipitation data, especially for the Bernoulli‐Gaussian distribution. On the other hand, ensemble spreads can be too wide when there exist overfits of sample‐specific noises in cross validation. Among the 12 methods, the robustness of the log‐sinh transformation and the Bernoulli‐Gamma distribution is highlighted. Key Points: Climatological forecasts can be generated by using data transformations, statistical distributions and non‐parametric methodsThe reliability of climatological forecasts generated by different methods is shown to vary considerably in large‐sample testsThe robustness of log‐sinh transformation and Bernoulli‐Gamma distribution is illustrated through the tests of global precipitation [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]

Long‐term extending cultivation activities resulted in the world's worst soil erosion on the Chinese Loess Plateau (LP). By converting cropland into vegetated land, the Grain for Green Project (GfGP)—the world's largest investment revegetation project—effectively alleviates the soil erosion on the LP. However, during the GfGP implementation, the positive effect of cropland to the revegetation and soil erosion control has been underestimated to date, hindering a comprehensive evaluation to the effect of cropland on ecological restoration. Here, we evaluated the effect of the GfGP on soil erosion control across the LP, analyzed the dominant driver of the LP vegetation greening, and further identified the contributions of croplands to this world's largest revegetation project. We found that the vegetation of the LP was significantly improved and its leaf area increased by 1.23 × 105 km2 after the implementation of the GfGP, which contributed 42% to the decrease of the LP soil loss. Among them, our results show that cropland contributed 39.3% to the increased leaf areas of the LP, higher than grassland (36.3%) and forestland (14.3%). With the reduction of agricultural area, the contribution of cropland to the increased leaf areas in the LP was still the largest, which was mainly due to the increase in cropland utilization intensity. This study highlights the significance of the GfGP in soil erosion control and revises our understanding of the role of cropland in ecological restoration and society development. Plain Language Summary: Long‐term extending cultivation activities resulted in the world's worst soil erosion on the Chinese Loess Plateau (LP). For controlling this soil erosion, the Grain for Green Project (GfGP)—the world's largest investment revegetation project—was carried out. This was followed by the conversion of large amounts of cropland to vegetated land. However, this manuscript offers evidence indicating that cropland, rather than grassland and forestland, contributed the most to the vegetation improvement of the LP during the implementation of GfGP. We find that vegetation improvement after the GfGP could explain 42% of the reduction in the LP soil loss. The contribution of cropland to the LP greening was 39.3%, higher than that of grassland (36.3%) and forestland (14.3%). Although the area of cropland was greatly reduced after the implementation of GfGP, the contribution of cropland to the LP vegetation greening was still dominant, which was mainly due to the intensive cropland utilization. The positive effect of cropland on ecological restoration indicates its significant potential for future human land‐use management and revegetation management. Key Points: Vegetation greening reduces the sediment yield of the Loess Plateau by 42%Cropland contributes 39.3% to the Loess Plateau vegetation greening, higher than grassland (36.3%) and forestland (14.3%)Cropland dominates the Loess Plateau greening, mainly due to the improvement of cropping intensity [ABSTRACT FROM AUTHOR]

The popular probability integral transform (PIT) uniform plot presents informative empirical illustrations of five types of ensemble forecasts, that is, reliable, under‐confident, over‐confident, negatively biased and positively biased. This paper has built a novel two‐stage framework upon the PIT uniform plot to quantitatively examine the forecast attributes of bias and reliability. The first stage utilizes the test statistic on bias to examine whether the mean of PIT values is equal to the theoretical mean of standard uniform distribution. Then, the second stage uses the test statistic on reliability to examine whether the mean squared deviation from the theoretical mean is equal to the theoretical variance of standard uniform distribution. Therefore, by using the two‐tailed bootstrap hypothesis testing, the first stage identifies unbiased ensemble forecasts, negatively biased forecasts and positively biased forecasts; the second stage focuses on unbiased ensemble forecasts to furthermore identify reliable forecasts, under‐confident forecasts and over‐confident forecasts. Numerical experiments are devised for the National Centers for Environmental Prediction's Climate Forecast System version 2 ensemble forecasts of global precipitation. The results highlight the existence of various shapes of the PIT uniform plots. Due to extreme values of observed precipitation, the PIT uniform plots in some cases can substantially deviate from the 1:1 line even though the mean and variance of ensemble forecasts are respectively in accordance with the mean and variance of observations. Nevertheless, the two‐stage framework along with the two test statistics serves as a robust tool for the verification of ensemble hydroclimatic forecasts. Plain Language Summary: Ensemble hydroclimatic forecasts provide useful information for water resources planning and management. The probability integral transform (PIT) uniform plot presents informative visualizations of five types of ensemble forecasts, that is, reliable, under‐confident, over‐confident, negatively biased and positively biased. While the use of PIT uniform plot is for individual grid cells, this paper proposes a novel two‐stage framework along with two test statistics for the examinations of forecast bias and reliability at the global scale. Specifically, the test of bias in the first stage identifies unbiased, negatively biased and positively biased ensemble forecasts; and then, the test of reliability in the second stage is applied to unbiased ensemble forecasts so as to identify reliable, under‐confident and over‐confident ensemble forecasts. Numerical experiments are devised to investigate the bias and reliability for ensemble global precipitation forecasts generated by the National Centers for Environmental Prediction's Climate Forecast System version 2. The five types are effectively identified for raw ensemble forecasts and also for post‐processed forecasts generated by the linear scaling, quantile mapping and Gamma‐Gaussian models. The results demonstrate the effectiveness of the two‐stage framework and highlight that forecast post‐processing can play an important part to provide more useful information for decision‐making. Key Points: The probability integral transform uniform plots facilitate the characterization of bias and reliability for five types of ensemble hydroclimatic forecastsA two‐stage framework along with two non‐parametric test statistics is developed to identify the five types of ensemble forecastsThe forecast attributes of bias and reliability are effectively evaluated for the National Centers for Environmental Prediction's Climate Forecast System version 2 ensemble forecasts of global 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]

Under global warming, a novel category of extreme events has become increasingly apparent, where flood and hot extremes occur in rapid succession, causing significant damages to infrastructure and ecosystems. However, these bivariate compound flood‐hot extreme (CFH) hazards have not been comprehensively examined at the global scale, and their evolution under climate warming remains unstudied. Here, we present the first global picture of projected changes in CFH hazards by using a cascade modeling chain of CMIP6 models, satellite and reanalysis data sets, bias correction, and hydrological models. We find an increasing percentage of floods will be accompanied by hot extremes under climate change; the joint return periods of CFHs are projected to decrease globally, particularly in the tropics. These decreasing joint return periods are largely driven by changes in hot extremes and indicate a likely increase of CFH hazards, and ultimately highlight the urgent need to conduct adaptation planning for future risks. Plain Language Summary: Climate change alters the Earth's energy budget and accelerates the hydrological cycle, bringing new hazards such as temporally compounding flood and hot extremes. Rapid transitions from devastating floods to deadly heat, or vice versa, which used to be rare, are already occurring under the present climate and bring new threats to infrastructure and the public. However, these bivariate CFH hazards have been poorly understood at the global scale, and their future evolution in the context of climate change has not yet been assessed. Here, we provide the first systematic assessment of projected changes and attributions in the multivariate hazards of global flood‐hot extremes. We find that the fraction of flooding accompanied by hot extremes could rise markedly under global warming. Changes in hot extremes dominate the exacerbation of global CFH hazards, especially in tropical climate zones. Our study identifies the tropics as the new global hotspot of flood‐hot extreme events in a warming future, and reveals an increasing global risk of unexpected sequential wet‐hot extremes, highlighting the need to better prepare adaptation and mitigation solutions. Key Points: We present the first global assessment of projected changes in compound river flood‐hot extremesFuture flood‐hot extremes are mainly driven by changes in hot extremesSubstantial increases in compound flood‐hot extremes are projected in tropical regions [ABSTRACT FROM AUTHOR]

This study investigates warm‐season extreme precipitation events (EPEs) with the focus on the less‐attended weak convection (WeEPEs) using spaceborne radar observations. WeEPEs are compared to EPEs with intense convection (InEPEs), including their frequency/location, convective structures, and storm environments. WeEPEs and InEPEs both account for about 30% of the EPEs, highlighting the importance of WeEPEs. Overland WeEPEs mainly occur over key monsoon regions and maximize over windward sides of coastal mountains likely due to orographic enhancement. WeEPEs develop under conditions with deep moist profiles and strengthened low‐level onshore flows. Radar, microwave, and lightning proxies all suggest a weak updraft and limited ice contents in WeEPEs. However, vertical radar profiles of WeEPEs exceptionally increase (∼15 dBZ) downward below the melting level, indicating substantial raindrop growth and/or concentration increase through very active warm‐rain processes. This study provides useful guidelines for evaluating high‐resolution model simulations and satellite rainfall retrievals on extreme precipitation. Plain Language Summary: When extreme precipitation occurs, one will easily connect it to intense storms such as those producing numerous lightning flashes. However, some rainstorms display little severe signatures, therefore, they are even more challenging from the monitoring or nowcasting point of view. This study investigates the warm‐season extreme precipitation events (EPEs thereafter) with weak convection (weak upward motion indicated by satellite proxy) in the tropics and subtropics using 15 years of satellite observations. Surprisingly, EPEs with weak convection (WeEPEs) over land are as frequent (∼30%) as EPEs with intense convection, highlighting the significance of WeEPEs. Over land, WeEPEs preferentially develop over deep monsoon regions and their maxima are located over or near coastal mountains, suggesting the important role of orographic lifting/convergence in producing WeEPEs. Spaceborne radar, microwave, and lightning observations all indicate that WeEPEs have a weak convective intensity and limited ice contents above the melting level. WeEPEs are featured by vertical radar profiles persistently increase downward below the melting level (by ∼15 dBZ). Our findings collectively suggest that heavy rainfall in WeEPEs is largely caused by precipitation process in the warm‐cloud layer, instead of the melting of large amount of ice particles. Key Points: Nearly 30% warm‐season extreme precipitation events (EPEs) have only weak convection, similar fraction as EPEs with intense convectionMost weak convective EPEs (WeEPEs) are situated over the ITCZ, Asian Monsoon, and South American Monsoon, especially coastal regionsWeEPEs are featured by limited ice‐phase process and vertical radar profiles markedly increasing downward below the freezing level [ABSTRACT FROM AUTHOR]

Predictive performance is one of the most important issues for practical applications of ensemble hydroclimatic forecasts. While different forecasting studies tend to use different combinations of verification metrics and diagnostic plots, this paper presents a literature survey of popular verification metrics, diagnostic plots and their corresponding forecast attributes. Twenty metrics to quantify predictive performance of ensemble, deterministic and categorical forecasts are detailed; six types of diagnostic plots to visualize the relationship between ensemble forecasts and observations are presented; and the correspondence relationships of verification metrics and diagnostic plots with ten forecast attributes are illustrated. Numerical experiments are devised for raw ensemble precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) to illustrate the verification metrics, diagnostic plots and forecast attributes. The results suggest that verification metrics that pay attention to some similar attributes can be related to one another. Meanwhile, the metrics are not necessarily in full accordance as they tend to reflect different attributes. Time series plots visualize forecasts over a period of interest and graphically illustrate forecast bias, reliability and skill. Quantile range plots provide an overall diagnosis for the association of ensemble forecasts with observations. Overall, verification metrics can be illustrated by diagnostic plots for individual grid cells and also be screened by spatial plots at regional and global scales. The survey presented in this article can serve as a roadmap for the selection of verification metrics, diagnostic plots and forecast attributes for verifying ensemble hydroclimatic forecasts. This article is categorized under: Science of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

Downstream stakeholders are often hindered when performing water management decision making based on effective hydrological modeling and streamflow anticipation, due to alterations of river flow caused by upstream damming. Meanwhile, spaceborne altimeters and imagers can provide valuable data for the upper data‐limited reservoirs by monitoring their operations. This study proposes a novel modeling approach that incorporates reservoir levels from satellite altimetry into hydrological model calibration. Doing so enables model parameter determination and retrieval of upper reservoir inflow for dammed basins, when only the altered downstream flow observations are available. Case studies are performed for two large reservoirs in the Dongjiang basin, a typical dam‐affected area in South China. Two settings for determining the stage‐volume (S‐V) curve are tested: (1) The curves are prefitted by satellite information and (2) the curves are optimized by calibration in conjunction with the model parameters. Results show that the proposed approach is effective in identifying the hydrological model parameters and retrieving the upper reservoir inflow of data‐limited dammed basins. Our analyses also show that the error in satellite altimetry has significant influences and can impede reasonable determination of the model parameters, and the fitted S‐V curves perform more reasonably and robustly than calibrated S‐V curves with respect to retrieving reservoir inflow, and thus, the fitted S‐V curve is more highly recommended. Overall, our approach provides an effective solution for aiding downstream stakeholders and researchers in understanding the upper reservoir operation and analyzing the hydrology of dammed basins, when knowledge about the upstream area is limited. Key Points: A novel satellite‐based modeling approach for hydrological model calibration of dammed basins is proposedHydrological modeling of the case basins is effectively improved after introducing the satellite altimetry reservoir level dataReservoir inflow of the case basins is effectively retrieved by the model calibrated using satellite altimetry data [ABSTRACT FROM AUTHOR]

Observations show that metropolitan areas throughout China, which are experiencing rapid urbanization, may have enhanced extreme precipitation. However, the underlying urban‐induced mechanism is poorly understood, particularly for entire characteristics of extreme precipitation. Focusing on the Beijing metropolitan area, we investigate regional patterns of extreme precipitation characterized by magnitude, frequency, duration, and timing metrics according to daily observations from 1975 to 2015 at 20 weather stations. Urbanization effects are explored by physical metrics of urbanization, including area, complexity, fragmentation, and dominance deduced from five periods of land use maps. Results show that the magnitudes and frequencies of extreme precipitation have decreased over time, the consecutive precipitation days have been extended, and the Julian date of maximum precipitation has been delayed. Temporal trends at ~40% of weather stations are significant. According to precipitation metrics and geographical features, three representative regions are identified: the central urban region, the windward slope of topographic area, and the mountainous region. Compared with the metrics in mountainous region, the magnitudes of windward slope and central urban region are 24.3–60.6% and 5.9–47.3% greater, respectively; the frequencies are increased by 1.17 and 1.10 days, respectively; and the average date of maximum precipitation values are delayed by 7.0 and 4.0 days, respectively. The magnitudes and frequencies are enhanced by expansion of urban area, complexity, fragmentation, dominance, and heat islands. The durations are positive for urban area and dominance but negative for urban complexity, fragmentation, and heat islands. Furthermore, the effects in central urban region are more significant due to high urbanization rates. Key Points: Extreme precipitation metrics in central urban region and windward slope of topographic area are greater than those in mountainous regionMagnitude and frequency of extreme precipitation are enhanced by expansions of urban area, irregularity, patch, dominance, and heat islandsDuration of extreme precipitation is positive for the urban area and dominance but negative for irregularity, patch, and heat islands [ABSTRACT FROM AUTHOR]

Abstract: The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three‐dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL‐CM2p1‐aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North‐American Multi‐Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA‐RSMAS‐CCSM3), 45% for COLA‐RSMAS‐CCSM4, 59% for GFDL‐CM2p1, 67% for GFDL‐CM2p1‐aer04, 65% for GFDL‐CM2p5‐FLOR‐A06, 57% for GFDL‐CM2p5‐FLOR‐B01, 48% for NCAR‐CESM1, and 60% for NCEP‐CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts. [ABSTRACT FROM AUTHOR]

ABSTRACT: Atmospheric rivers (ARs), filamentary patterns of strong water vapour fluxes, play a prominent role in global poleward moisture transport and have profound impacts on extreme rainfalls (ERs). Previous AR research has mainly focused on the mid‐latitude regions, whereas the characteristics of ARs in low latitudes and their relationship with local ERs remain largely unknown. This study investigates the spatiotemporal characteristics of ARs over the Bay of Bengal and their relationship with ERs after landing on the northern Indian subcontinent using the ERA‐Interim reanalysis data. During the study period from 1979 to 2011, a total of 149 ARs have been identified, which feature a bimodal temporal pattern with more events observed in May and October. The AR axes generally stretch northeastwards over the bay and land in Bangladesh and Burma. A total of 24% of ARs occurring during tropical cyclones implies a possible connection between them, in addition to the similar intra‐annual distribution. In summer, as the tropical cyclones are weak and the northward water vapour flux decreases due to topographic blocking of the Western Ghats, it is less likely to form intensified water vapour pathway, though the atmospheric humidity is high in the study region. Furthermore, a close correlation between ARs and ERs is manifested. A large proportion of ARs would lead to ERs, with a small fraction of ERs occur after ARs. In addition, although persistent ARs constitute the majority of identified events, rainfall intensity will not be enhanced by the increase in AR duration. This study enriches the knowledge of AR characteristics in low latitudes and provides new pathways to understand the hydrological cycles in the Indian Peninsula and the Bay of Bengal. [ABSTRACT FROM AUTHOR]

Global climate model (GCM) forecasts are an integral part of long-range hydroclimatic forecasting. We propose to use clustering to explore anomaly correlation, which indicates the performance of raw GCM forecasts, in the three-dimensional space of latitude, longitude, and initialization time. Focusing on a certain period of the year, correlations for forecasts initialized at different preceding periods form a vector. The vectors of anomaly correlation across different GCM grid cells are clustered to reveal how GCM forecasts perform as time progresses. Through the case study of Climate Forecast System Version 2 (CFSv2) forecasts of summer precipitation in China, we observe that the correlation at a certain cell oscillates with lead time and can become negative. The use of clustering reveals two meaningful patterns that characterize the relationship between anomaly correlation and lead time. For some grid cells in Central and Southwest China, CFSv2 forecasts exhibit positive correlations with observations and they tend to improve as time progresses. This result suggests that CFSv2 forecasts tend to capture the summer precipitation induced by the East Asian monsoon and the South Asian monsoon. It also indicates that CFSv2 forecasts can potentially be applied to improving hydrological forecasts in these regions. For some other cells, the correlations are generally close to zero at different lead times. This outcome implies that CFSv2 forecasts still have plenty of room for further improvement. The robustness of the patterns has been tested using both hierarchical clustering and k-means clustering and examined with the Silhouette score. [ABSTRACT FROM AUTHOR]

The cover image is based on the Advanced Review Predictive performance of ensemble hydroclimatic forecasts: Verifi cation metrics, diagnostic plots and forecast attributes by Zeqing Huang and Tongtiegang Zhao https://doi.org/10.1002/wat2.1580. [ABSTRACT FROM AUTHOR]