Search

The Budyko-based elasticity method has been widely employed to clarify the driving factors behind runoff changes. However, different formulations of the Budyko framework could produce biases in the elasticity analysis and the assessment errors induced from different formulations of the Budyko framework in the elasticity method remain unclear. Here, we attempt to address this issue by validating the performance of elasticity methods derived from two analytical Budyko equations (Fu's equation and Choudhury's equation), as well as one empirical Budyko equation (Wang-Tang's equation) of the Budyko framework across 22 basins in China. Validations show that the runoff change simulated by the elasticity method derived from the empirical equation has lower errors compared with the two analytical Budyko equations. Results reveal that in the semi-humid environment, the alteration of basin characteristics takes the main responsibility for the runoff change. However, a clear divergence was found in simulated runoff changes among different Budyko-based elasticity methods in humid basins. For parts of the humid basin, the precipitation is the main driver of runoff change from the analytical Budyko-based elasticity methods, while the alteration of basin characteristics is the main derive of the runoff changes according to based on the empirical Budyko-based elasticity method. This difference could be attributed to the variations in the simulated contributions from the alteration of basin characteristics on runoff changes. Generally, our results highlight the importance of validating different Budyko equations when applying the elasticity method to investigate the driver of the runoff changes in humid regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The increasing global climate change has resulted in more frequent drought disasters, and using the drought index to assess spatial and temporal changes accurately is of practical importance. In this study, the Spatio-temporal evolution characteristics of different drought indicators at annual and seasonal scales were analyzed by combining spatial interpolation, correlation analysis, trend tests, and the empirical orthogonal function based on the meteorological drought composite index (MCI) of Penman–Monteith. The study focused on drought research in the Pearl River basin from 1961 to 2020. The results indicated that (1) on the annual scale, drought exhibited a high spatial distribution in the west and low in the east, as well as high in the south and low in the north. The frequency, intensity, and extent displayed a non-significant decreasing trend, with the drought degree being mild in the past ten years. (2) On the seasonal scale, drought exhibited seasonal variability, particularly in spring and autumn, which were inversely distributed. Summer drought was the least severe, with the drought range mostly below 10%, while winter drought was the severest, exceeding 50%, indicating an areawide drought. (3) The consistent change of drought intensity across the basin is the primary mode, and the reverse distribution of east–west and north–south is the secondary mode. The overall higher year of drought intensity is close to 24 years, and the overall lower year is close to 28 years. The study based on the MCI can provide a reference for drought research and management in different regions. [ABSTRACT FROM AUTHOR]

The phenomenon of sudden drought after pluvial (or heavy pluvial after drought) is known as the Dry–Wet Abrupt Alternation (DWAA). This kind of disaster has been evaluated globally based on various DWAA indices. However, few validations have been performed to compare the various DWAA indices. Given that directly validating the identified DWAA events remains difficult because of the limited historical records of DWAA events, in this study, we developed a simple weighting analysis method to cross‐validate the detected DWAA events using three widely‐used indices. The weighting analysis method can be used to estimate the effectiveness of the DWAA indices by calculating the ratio of the synchronous DWAA events (the DWAA events could be synchronously detected by different indices) to the total number of the detected DWAA events. Results suggest that the standard weighted average of precipitation (SWAP) index has better performance for detecting DWAA events in the Pearl River Basin (PRB) compared to other indices. The soil moisture synchronization, meteorological concentration and real DWAA records further testify that the SWAP is more capable of detecting the DWAA events in the PRB. Based on the validation, the SWAP‐derived DWAA events show that both dry–wet (D–W) events and wet–dry events are severe in the upstream sub‐basins in the PRB, while D–W events are more frequent in the downstream PRB. Our study provides a simple but effective method to validate the detected DWAA. The finding underscores the need to validate DWAA detection generated from multiple indicators before further research, and it could help develop adaptation strategies to improve resilience to combined extremes under changing climate. [ABSTRACT FROM AUTHOR]

The frequency of large-scale anomalous precipitation events over China has increased during 1961–2018. However, it remains challenging to understand the mechanisms associated with these anomalous events showing different spatial patterns. Here, we applied an autoencoder technique to identify large-scale anomalous precipitation events for both observations and model simulations, which were then classified into several patterns through a self-organizing map method. The synoptic behavior and atmospheric circulation background of different anomalous patterns were also analyzed using simultaneous composite analyses. Results show that occurrences of different anomalous precipitation patterns have increased significantly, except those centered in North China, Northeast China, and the Yangtze River basin. The anomalous precipitation patterns manifest various intraseasonal distributions, which are linked to zonal oscillations of the western North Pacific subtropical high (WNPSH) and meridional displacements of East Asia westerly jet (EAJ). Accompanied by the westward movement of WNPSH, anticyclonic systems transport warm moist air from the Indian Ocean and the South China Sea to converge with the cold air caused by anomalous cyclones over the northwest flank of the WNPSH, leading to large-scale anomalous precipitation in these regions. Besides WNPSH, the northward and southward displacements of EAJ also favor the occurrence of anomalous precipitation events in northern and southern China, respectively. Our study also illustrates that the occurrence frequency of anomalous precipitation events is projected to increase remarkably under the Shared Socioeconomic Pathway 5–8.5 (SSP585) scenario by rates of twofold to fourfold. [ABSTRACT FROM AUTHOR]

Precipitation in China is controlled by a combination of multiple weather systems and is susceptible to their spatiotemporal variability under climate warming. Based on the daily precipitation dataset developed by the China Meteorological Administration and ERA5 6-hourly data from 1979 to 2018, we detected tropical cyclones, extratropical cyclones, and fronts affecting mainland China of an altitude less than 1500 m using objective methods and identified their individually associated precipitation. We quantified climatological characteristics of precipitation associated with these three meteorological causes and their proportion of precipitation totals, and evaluated their relative contribution to change in precipitation. Results show that precipitation associated with tropical cyclones (TCP), extratropical cyclones (ETCP), and fronts (FRTP) accounted for 3.0%, 21.2%, and 20.4% of the total precipitation in China, and constituted the largest percentage in August, June, and January, respectively. The total proportion of these three types of precipitation exceeded 50% in more than 55% of China's regions. The high-value regions of corresponding precipitation were directly related to the locations of frequent occurrences of these weather systems. TCP and FRTP increased over the past four decades, while ETCP decreased. The western part of Northeast China and the middle and upper reaches of the Yangtze River show precipitation decreases, 37.8% of which is contributed by ETCP decreases. Precipitation increased in most regions of China during the dry season, 34.1% of which is contributed by FRTP increases. Among them, Northeast (Northwest) China significantly increased it precipitation by 14.4% decade−1 (21.8% decade−1), 33.6% (51.3%) of which is contributed by FRTP increases. [ABSTRACT FROM AUTHOR]

Floods in river deltas are driven by complex interactions between astronomical tides, sea levels, storm surges, wind waves, rainfall‐runoff, and river discharge. Given the anticipated increase in compound flood hazards in river deltas in a warming climate, climate‐informed regional to local extreme water levels (EWLs) is thus critical for decision‐makers to evaluate flood hazards and take adaptation measures. We develop a simple yet computationally efficient stress test framework, which combines historical and projected climatological information and a state‐of‐the‐art hydrodynamic model, to assess future compound coastal‐fluvial flood hazards in river deltas. Our framework is applied in the world's largest single urban area, China's Pearl River Delta (PRD), which is also characterized by densely crossed river network. We find that extreme sea level is the dominant driver causing the compound coastal‐fluvial flood in the PRD over the past 60 years. Meanwhile, there is large spatial heterogeneity of the individual and compound effects of the typhoon intensity, local sea‐level rise, and riverine inflow on coastal‐fluvial floods. In a plausible disruptive scenario (e.g., a 0.50 m sea‐level rise combined with a 9% increase in typhoon intensity in a 2°C warming), the EWL will increase by 0.76 m on average. An additional 1.54 and 0.56 m increase in EWL will occur in the river network and near the river mouth, respectively, if coastal floods coincide with the upstream mean annual flood. Findings from our modeling framework provide important insights to guide adaptation planning in river deltas to withstand future compound floods under climate change. Plain Language Summary: Compound floods pose serious threats to the dense population living in river deltas worldwide. Climate change could increase the compound flood hazards through stronger tropical cyclones, sea level rise, higher extreme precipitation, and river flows. It is urgent to understand how the so‐called grey swan events will evolve under a changing climate in these already flood‐prone areas. Here, grey swan events are high‐consequence events that are beyond people's experience but may be foreseeable and thus can be systematically prepared for. In this study, a storyline‐based framework is proposed to assess the potential grey swan compound floods under a warming climate. We apply this framework to China's Pearl River Delta, the world's largest single urban area. In a plausible disruptive scenario (e.g., a 0.50 m sea‐level rise combined with a 9% increase in typhoon intensity in a 2°C warming), we find that extreme water levels will increase by 0.76 m on average. Our flexible and computationally efficient storyline‐based framework could guide coastal planners to prepare for the grey swan compound flood hazards under climate change. Key Points: A quantitative stress test framework is proposed to assess the compound coastal‐fluvial flood in the river delta at minimal costA state‐of‐the‐art unstructured mesh generation technology is applied to develop an objective and reproducible hydrodynamic modelCompound effects of typhoon intensity, local sea‐level rise, and river inflow on coastal‐fluvial floods are systematically investigated [ABSTRACT FROM AUTHOR]

The atmospheric moisture transport processes are of great importance to the occurrence and intensity of precipitation extremes. In this paper, we review the linkage between processes, including the large‐scale atmospheric circulation, atmospheric moisture transport, and extreme precipitation events. We first summarize the thermodynamic and dynamic processes and moisture transport trackings for historical precipitation extremes. We then focus on the contribution of three major atmospheric moisture transport pathways, that is, atmospheric rivers, low‐level jets, and tropical cyclones, to the occurrence and intensity of regional precipitation extremes. Studies on large‐scale atmospheric circulation driving water vapor transport for precipitation extremes over East Asia and North America were specifically reviewed for the understanding of physical mechanisms and predictability of moisture transport and extreme precipitation events. We then pay more attention to the effects of global warming on atmospheric moisture transport, and thus regional precipitation extremes from the perspectives of thermodynamic and dynamic changes of the atmosphere. In the end, we summarize future research challenges on the physical mechanisms of atmospheric moisture transport that are associated with regional precipitation extremes, especially under a warming climate. This article is categorized under: Science of Water > Water ExtremesScience of Water > Hydrological Processes [ABSTRACT FROM AUTHOR]

The increase in storm surge and compound flood events caused by climate change exacerbates adverse effects on the estuaries. The ADCIRC + SWAN coupled model was simulated using various scenario simulations based on Super Typhoon Hato to quantify the nonlinear interactions of tide-surge-river during extreme events. The results reveal that three drivers, including (1) tide-surge phases, (2) typhoon tracks and wind speeds (3) upstream runoff, affect the extreme water levels by different nonlinear interactions. Changes in tide-surge phases remain the most constructive factor controlling nonlinear effects, with the maximum contribution exceeding 50.46% of the extreme water level. The tide-surge nonlinear residual water level contributing to the extreme water level at high tide falling can reach 0.92 m. The nonlinear effects are exacerbated as typhoon wind speeds and upstream discharges increase. Peak water levels from the compound floods are more than 25% higher than storm surges alone at the river outlets. The nonlinear effects are mainly generated by the convection terms from the tide-surge-river interactions in the estuarine rivers. • Under the nonlinear effects, tides, typhoons, and riverine discharges amplify extreme water levels. • The phase variations in tide-surge, contributing to 50.46% of the extreme water level, remain the dominant nonlinear factor. • Compound floods intensify the TSRI, raising extreme water levels. [ABSTRACT FROM AUTHOR]

• A sigmoid CO 2 concentration stress function f (Ca) was proposed to reflect ET response. • A process-based transpiration model was extended with the f (Ca) to model total ET. • The model with CO 2 stress is sound and robust to couple water and carbon processes. • The mechanism of CO 2 effect and advantage of a f (Ca) in ET models were discussed. Evapotranspiration (ET) is one of the most difficult hydrologic variables to estimate. In the context of climate change, the rise of CO 2 emissions may improve photosynthesis but depress transpiration thus increasing plant water use efficiency (WUE). Therefore, it is of great importance and necessity to take elevated atmospheric CO 2 concentration (Ca) as a limiting factor for improved assessment of ET and WUE toward a better ecosystem management. In this study, we developed and incorporated a Ca stress function into a modified process-based transpiration model and extended its application from transpiration to total evapotranspiration simulation. The original simplified process-based model (denoted as BTA by combining developers' last name initials), previously modified version with added soil moisture (BTA-θ) and the extended version in this study with CO 2 concentration (BTA-Ca) were tested for daily ET simulation at the Dinghushan forest ecosystem in south China. The results show that eddy covariance based forest ET was well simulated by the models, while the two modified models outperformed the original one. Despite the limited improvement of accuracy, the BTA-Ca model is more superior and meaningful regarding the physical mechanisms. Moreover, we evaluated the biophysical responses of ET and GPP to the major environmental factors, which shows that ET and GPP responded similarly to environmental changes with close linkages between the two. This study proposed a Ca stress function and provides an effective way of coupling water and carbon exchange processes for ET simulations, which can be applied for forest water budget assessment under climate change. [ABSTRACT FROM AUTHOR]

Using the daily precipitation and temperature data at 153 stations in the karst area of Southwest China from 1959 to 2009, the long-term change characteristics of wet and dry climatic conditions are analyzed by the Mann–Kendall test. This study shows that: 1) the annual average temperature has increased at 88% of the stations with an average rate of + 0.16 °C/10-year. This increase rate is greater than + 0.30 °C/10-year in the southeastern, northeastern, and western parts of the study region. Very warm days and abnormally warm days were seen increased at 47% and 63% of the stations, respectively. Very cool days and abnormally cool days in a year have reduced at respectively 94% and 95% of the stations; 2) no clear change trend was found for average annual precipitation over the entire area as a whole, but regional and seasonal changes were quite obvious. The annual total precipitation has decreased by − 22.5 mm/10-year in the central part but increased by + 8.9 mm/10-year in the western part of the region, and summer and winter seemed to become wetter while spring and autumn became drier. Although the number of rainy days in a year has decreased at almost 53% of the total stations with an average rate of − 3.9 days/10-year over the entire area, the number of extremely heavy rainy days has increased by + 0.2, + 0.4 and + 0.4 days/10-year in the southern Sichuan Province, the central part of Yunnan Province, and the northeastern Hubei Province, respectively; and 3) the extreme drought became more serious. The consecutive dry days has increased significantly at nearly 46% of the stations, especially in the western Guangxi Autonomous Region, the southern Guizhou Province, and the eastern Chongqing Municipality. The extreme drought remained at a high frequency at the beginning of the 21st century. It has shifted gradually from the eastern and western parts to the south-central part which is characterized by medium-high rocky desertification. Hopefully findings from this study will help for a better understanding of the impacts to some eminent geological hazards such as rocky desertification, increased frequency of drought and storms, and landslides in recent years. [ABSTRACT FROM AUTHOR]

This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological models and climate downscaling models. The performance and weight distributions of five model ensembles were thoroughly compared, including simple equal-weight averaging, BMA, and REAs optimizing mean (REA-mean), maximum (REA-max), and minimum (REA-min) monthly runoff. The results suggest that REA and BMA both can synthesize individual models' diverse skills with comparable reliability, despite of their different averaging strategies and assumptions. While BMA weighs candidate models by their predictive skills in the baseline period, REA also forces the model ensembles to approximate a convergent projection towards the long-term future. The type of incorporation of the uncertain future climate in REA weighting criteria, as well as the differences in parameter estimation (i.e., the expectation maximization (EM) algorithm in BMA and the Markov Chain Monte Carlo sampling method in REA), tend to cause larger uncertainty ranges in the weight distributions of REA ensembles. Moreover, our results show that different averaging objectives could cause much larger discrepancy than that induced by different weighting criteria or parameter estimation algorithms. Among the three REA ensembles, REA-max most resembled BMA because the EM algorithm of BMA converges to the minimum aggregated error, and thus emphasize the simulation of high flows. REA-min achieved better performance in terms of inter-annual temporal pattern, yet at the cost of compromising accuracy in capturing mean behaviors. Caution should be taken to strike a balance among runoff features of interest. [ABSTRACT FROM AUTHOR]

• The drought projection uncertainty in SMILEs mainly arises from internal variability. • Model uncertainty is the major uncertainty source of drought projection in CMIP6. • Model (scenario) uncertainty dominates the near-term (long-term) drought projection. • The reducible model uncertainty is mainly distributed in tropical regions. Reliable projection of future drought conditions is critical for informing adaptation in response to future climate change. However, the uncertainty associated with drought projection impedes the precise estimation of drought risk and consequently efficient adaptation strategy. To better understand the role of uncertainty in drought projection, this study distinguished the total uncertainty of drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty by variance analysis using realizations from the Single Model Initial-condition Large Ensembles (SMILEs) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that SMILEs differ from CMIP6 mainly in the magnitude of internal variability and model uncertainty. The contribution of internal variability to total drought projection uncertainty in SMILEs (31–56 %) is greater than that in CMIP6 (9–21 %), while model uncertainty of SMILEs (36–41 %) is nearly half that of CMIP6 (67–76 %). The estimation of total uncertainty in drought projection is comparable between SMILEs and CMIP6, indicating that total uncertainty attains a minimum in the mid-21st century (the 2060 s for SMILEs and 2050 s for CMIP6). In addition, SMILEs and CMIP6 consistently show that model uncertainty is dominant in tropical regions, and scenario uncertainty is the main uncertainty contributor in western North America, eastern South America, the Mediterranean, and southern Australia. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mild impact of climate extremes on vegetation was found for Hu Line transect in China. • Latitudinal and altitudinal patterns of vegetation responses to climate were analyzed. • Non-woody plants in the mid-to-high latitudes were more sensitive to climate extremes. • Productivity above 45°N increased with drought severity due to favorable environment. • Policy making suggestions were proposed with emphasis on the identified hotspots. Climate projections infer increasing frequency of extreme events in the future which would alter terrestrial hydrologic functioning and ecosystem services. Understanding the responses of vegetation to extreme events can provide prerequisite knowledge to mitigate climate change impacts. The relationships between climate extremes and vegetation phenology and productivity were examined for a transect along the Hu Line across China during 1988–2014. Results show that high frequency and severity of droughts existed in the mid-latitude regions where happened to be mostly occupied by grassland and cropland with relatively low productivity compared to trees. Prolonged growing seasons (4.3 days/decade) resulted mostly from advanced green-up were observed over the transect. No strong impact of extreme droughts on vegetation was observed for any plant functional types, due to the timing of drought occurrences primarily in pre- and post-growing seasons while water supply was likely not so limited during growth indicated by a drought index. Non-woody plants in the mid-to-high latitudes (37-54°N) were identified more sensitive to climate extremes than woody plants in the low latitudes. Meanwhile, grassland in the relatively low-elevation areas (<1000 m.a.s.l) was more responsive than that in the high-elevation areas. Productivity above 45°N increased with drought severity attributable to an intensified favorable environment during frequent and mild droughts with warmer temperature and increased solar radiation. The results may suggest the generally relative stability of the ecosystems along the Hu Line, however, with further deteriorating climate, the stable relationships between climate and ecosystems may be shifted, likely starting from low-elevation grassland. [ABSTRACT FROM AUTHOR]

Highlights • Natural/anthropogenic water connections among US watersheds were investigated. • Highly water-stressed areas may expand from 14% to 18% by 2070–2099. • Regional water connectivity could buffer the potential stress escalations. • Future changes may aggravate upstream-downstream water conflicts. Abstract Surface water supply for a watershed relies on local water generated from precipitation and water connections with other watersheds. These connections are confined by topography and infrastructure, and respond diversely to stressors such as climate change, population growth, increasing energy and water demands. This study presents an integrative simulation and evaluation framework that incorporates the natural and anthropogenic water connections (i.e., stream flows, inter-basin water transfers, water withdrawals and return flows) among the 2099 8-digit Hydrologic Unit Code (HUC-8) watersheds across the conterminous United States. The framework is then applied to investigate the potential impacts of changes in climate and water use on regional water availability and water stress (the ratio of demand to supply). Our projections suggest that highly water-stressed areas may expand from 14% to 18% and the stressed population would increase from 19% to 24% by 2070–2099. Climate-change mitigation practices (e.g., energy structure reform, technology innovation) could largely offset these trends by reducing demand and enhancing supply. At the watershed scale, the spatially inhomogeneous responses to future changes suggest that regional water connectivity could significantly buffer the potential stress escalations due to the redistribution of water resources and diverse changes in consumptive uses and water supplies in different source areas. However, the detrimental future changes (e.g., depleting river discharges, larger demands of water withdrawal) may aggravate conflicts over water rights among regions and challenge our current water infrastructure system. This study provides new insights into the critical role of regional water connectivity in water supply security, and highlights the increasing need for integrated monitoring and management of water resources at various spatial levels in a changing world. [ABSTRACT FROM AUTHOR]

• WUE trends were determined by the WUE magnitude, followed by atmospheric CO2 concentration and N deposition. • Individualized effects of five driving factors on WUE were quantified through machine learning. • Precipitation was found to shape most of the spatial patterns of WUE. • WUE was increased by increased atmospheric CO 2 concentration, but WUE growth rate declined with increased CO 2 growth rate. • WUE and N deposition exhibited positive or negative correlations depending on the different nutrient constraints on vegetation. The responses of ecosystem water use efficiency (WUE) to environmental perturbations are non-linearly dependent on how strongly coupled are water and carbon cycling. Here, we evaluate the structure of these non-linearities using two high-fidelity explainable machine learning (XML) models to disentangle the confounding effects of the air temperature (Ta), precipitation (PR), downward shortwave radiation (SR), atmospheric CO 2 concentration (Ca), and nitrogen deposition (Ndep) on WUE. The findings suggest that mean annual PR plays a dominant role in shaping the spatial pattern of WUE. Spatially, WUE responded positively to PR and Ca but negatively to Ta and SR , while Ndep had little impact on WUE. For temporal trends, the spatial pattern of the mean annual WUE determined the spatial pattern of the WUE trend. Furthermore, the spatial patterns of the Ndep and Ca trends also contributed substantially to the spatial pattern of the WUE trend. Notably, the WUE and Ca trends exhibited negative correlations, while the WUE and Ndep trends showed positive or negative correlations depending on the different nutrient constraints on vegetation. The WUE growth rate responded negatively to SR in both spatial patterns and temporal trends. Additionally, using the trend of 0.077 K yr−1 and 0 mm yr−1 as the threshold, the trends of Ta and PR shifted from positive to negative relationships on WUE growth. Our results help identify key sensitivities and thresholds in WUE to environmental controls over space and time. [ABSTRACT FROM AUTHOR]