Non-stationary Climate Research Articles

Amplified threat of tropical cyclones to US offshore wind energy in a changing climate

The vulnerability of US offshore wind energy to tropical cyclones is a pressing concern, particularly along the Atlantic and Gulf Coasts, key areas for offshore wind energy development. Assessing the impact of projected climate change on tropical cyclones, and consequently on offshore wind resources, is thus critical for effective risk management. Herein, we investigate the evolving risk to offshore wind turbines posed by Atlantic tropical cyclones in a non-stationary climate using a synthetic tropical cyclone model. Integrated with climate model simulations, projections show widespread increases in tropical cyclone exposure, with historical 20-year storms occurring every ~12.7 years on average, increasing in intensity by about 9.3 ms−1. Subsequent fragility analysis reveals that the probabilities of turbine yielding and buckling from a 20-year tropical cyclone could increase by about 37% and 13%, respectively, with regional increases reaching up to 51%. These findings carry substantial implications for the operation and future expansion of offshore wind farms.

Explainable deep learning insights into the history and future of net primary productivity in China

Net primary productivity (NPP) is a crucial component of the terrestrial carbon cycle and plays a crucial role in assessing ecological security. Although machine learning models have been widely used in research on ecosystem modelling, however, the performance of deep learning models with interpretability in predicting NPP are still very limited. In this study, we developed an interpretable deep learning predictive model to uncover the historical drivers and future changes of NPP in China using remote sensing data, climate data and topography. The results show that more than three quarters (88 %) of China’s regional NPP have shown an increasing trend over the past 20 years, with an overall trend of 2.46 gC/m2/yr. This extensive range of dynamic processes is a nonlinear response to climatic conditions and topographic factors. The constructed convolutional neural network (CNN) model showed good predictive skill for historical NPP, and revealed that temperature is the main controlling factor, highlighting its importance in vegetation growth in China. Compared to the historical period (2001–2020), in the context of future climate change, the risk of carbon sinks decreasing in China is highly likely in the imminent near term (2021–2039), especially under high emission scenarios. These results suggest that interpretable deep learning method is a useful tool for revealing vegetation productivity drivers and estimating vegetation productivity under a nonstationary climate background.

LRFD methodology for river embankments against non-stationary flooding under climate change

Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

AbstractEffective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Partial Duration Series of Wet and Dry Years Can Improve Flood Estimates in the Context of a Nonstationary Climate and Anthropogenic Disturbances

Partial Duration Series of Wet and Dry Years Can Improve Flood Estimates in the Context of a Nonstationary Climate and Anthropogenic Disturbances

Impacts of climate timescale on the stability of trait-environment relationships.

Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait-environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait-environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub-optimal climate timescales reduces the accuracy of the resulting trait-environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long-term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait-environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.

On the identification of seasonal trends, dependency and driving forces of precipitation and vertically integrated vapour transport over Northeast India

AbstractThe vertically integrated horizontal vapour transport (IVT) accompanied by westerlies and southwesterlies mainly contributes as a distant source of moisture to the seasonal precipitation (PR). The moisture transport and PR patterns across the eastern Himalayas (Sikkim, northern Sub‐Himalayan West Bengal and Arunachal Pradesh) are strongly modulated by the orographic setup, local land‐atmospheric factors and teleconnection with climate indices of the Indian and Pacific oceans. This study elaborates on the changing patterns of PR and IVT, changes in IVT contribution to the seasonal PR under the nonstationary framework and evaluates the nonlinear dependency on local and distant drivers with piecewise regression model. Asymmetries in significant seasonal trends are conspicuous due to multi‐altitude interactions. Strong declining trend over Assam and Arunachal Pradesh is evident for PR with concurrent rising trend over the Meghalaya plateau. The IVT signals show a significant decline in post‐monsoon (October and November) and winter (December to February) across Assam Plain. The increase in mean IVT contributes significantly to seasonal PR. Significant decline in the contribution of heavy IVT to winter PR is observed over Manipur, Mizoram, Tripura and part of Assam. Surface 2 m temperature and net thermal radiation exhibit contrasting influence on seasonal PR. IVT shows mainly positive association with surface 2 m dew point temperature and latent heat flux across the eastern Himalayas. NINO3.4 index exhibits strong negative influence on post‐monsoon PR. The Pacific Decadal Oscillation is strongly associated with summer (March to May) PR. Monsoon (June to September) IVT is negatively influenced by the Pacific Decadal Oscillation over the Sub‐Himalayan West Bengal, Gangetic West Bengal, Bihar and Jharkhand. The Southern Oscillation Index is negatively linked with summer IVT. The combination of local driving forces mainly controls the seasonal PR and IVT across all seasons. Besides, distant drivers are prominent in monsoon and post‐monsoon. This study reveals insights into recent changes of PR and IVT, their contribution to seasonal water resources in the eastern Himalayas and the controlling strength of local land‐atmosphere feedback mechanism and distant land‐ocean coupling drivers under nonstationary climate.

Economically optimal hydropower development with uncertain climate change

Climate change affects the planning, construction, operation and management of hydropower plants in various ways. Evaluating hydropower generation with nonstationary climate changes is important for planning electric power infrastructure development. For a new hydropower plant with climate change, specifying too large a reservoir may result in excessive investment if the future becomes drier, while too small a reservoir may cause insufficient utilization of hydropower resources. Whether to build a new hydropower station at once or build it dynamically in stages over time is challenged by uncertainty in future conditions. This study proposes a planning method for developing a flexible hydropower construction schedule to adapt to long-term uncertain hydrology with climate changes. Projected uncertain nonstationary hydrology scenarios were developed first, and Bayes’ theorem is used to update the probability of each scenario based on observed hydrologic conditions over time. A profit maximization model integrates stochastic dynamic programming and linear programming for a cascade hydropower system to evaluate hydropower plant expansion and how to build it dynamically with uncertain nonstationary climate conditions. In this, stochastic dynamic programming evaluates and prescribes inter-stage reservoir upgrades and linear programming prescribes inner-stage hydroelectric power generation. The case study of Jinsha cascade hydropower system in China’s Yunnan province illustrates the method. The intensity and trend of climate change effects on streamflow directly affect the optimized results. If streamflow decreases in the future, the best strategy is to build a small reservoir initially, without future changes. If streamflow increases in the future, it is better to build a small reservoir first, and gradually increase capacity according to observed changes in streamflow and economies of scale in capacity construction. If there is no clear trend in streamflow change, it is also better to build a small reservoir at first, then decide whether to expand the reservoir according to future observed changes in streamflow. This method with uncertain nonstationary climate change and upgrading adaptively with observed hydrology usually produced better results than optimization for a deterministic nonstationary hydrology.

Agricultural drought severity in NE Italy: Variability, bias, and future scenarios

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.

Post-water political-economics

ABSTRACT Existing concepts of water political economy focus on political bargaining within relatively static institutional and climactic environments. This article adds ‘post’ to water political economy to explore how non-stationary climate interacts with endogenous political institutions to create and magnify policy and behavioural uncertainties. These post-water political economy ideas are then applied to food security to explore potential future scenarios. The implications are terrible for poor people living in water-scarce, food-importing countries, but not much better for everyone else.

Tracing Time-Varying Characteristics of Meteorological Drought through Nonstationary Joint Deficit Index

Abstract Standardized precipitation index (SPI) is one of the frequently used meteorological drought indices. However, the time-varying characteristics observed in the historical precipitation data questions the reliability of SPI and motivated the development of nonstationary SPI. To overcome some of the limitations in the existing nonstationary drought indices, a new framework for drought index is proposed, incorporating the temporal dynamics in the precipitation. The proposed drought index is developed by coupling the joint deficit index with the extended time sliding window–based nonstationary modeling (TSW-NSM). The proposed nonstationary joint deficit index (NJDI) detects the signature of nonstationarity in the distribution parameter and models both long-term (i.e., trend) and short-term (i.e., step-change) temporal dynamics of distribution parameters. The efficacy of NJDI is demonstrated by employing it to identify the meteorological drought-prone areas over India. The changes observed in the distribution parameter of rainfall series reveal an increasing number of dry days in recent decades all over India, except the northeast. Comparison of NJDI and stationary joint deficit index (JDI) reveals that JDI overestimates drought when frequent severe dry events are clustered and underestimates when these events are scattered, which indicates that the traditional index is biased toward the lowest magnitude of precipitation while classifying the drought. Moreover, NJDI could closely capture historical droughts and their spatial variations, thereby reflecting the temporal dynamics of rainfall series and the changes in the pattern of dry events over India. NJDI proves to be a potentially reliable index for drought monitoring in a nonstationary climate. Significance Statement Drought is one of the most severe natural disasters and is expected to intensify under a warming climate. There has been progress on developing newer methodologies to characterize drought severity under the changing climate. However, some limitations remain in capturing the temporal changes of the precipitation, especially the nonstationarity in variance (rapid increases in extreme precipitation versus the average precipitation). We propose an extension to the time sliding window approach to capture the nonstationarity in variance and mean while incorporating short-term (step-change) and long-term (trend) temporal dynamics. We apply the methodology to a subcontinent-sized heterogeneous country of India with gridded rainfall dataset (0.25° × 0.25°, 1901–2013) from the India Meteorological Department. We find that the majority of grids exhibit negative and positive trends in shape and scale parameters, respectively, which ultimately leads to an increase in the number of drier events, whereas a contradictory pattern is exhibited in the northwest Indian region (decrease in the number of dry days). The proposed index gives a potential framework for drought monitoring under the changing climate and can be extended to develop a multivariate nonstationary joint deficit index by incorporating other hydrological variables (e.g., soil moisture, diurnal temperature range, streamflow).

A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting

Extreme streamflow is a key indicator of flood risk, and quantifying the changes in its distribution under non-stationary climate conditions is key to mitigating the impact of flooding events. We propose a non-stationary process mixture model (NPMM) for annual streamflow maxima over the central US (CUS) which uses downscaled climate model precipitation projections to forecast extremal streamflow. Spatial dependence for the model is specified as a convex combination of transformed Gaussian and max-stable processes, indexed by a weight parameter which identifies the asymptotic regime of the process. The weight parameter is modeled as a function of the annual precipitation for each of the two hydrologic regions within the CUS, introducing spatio-temporal non-stationarity within the model. The NPMM is flexible with desirable tail dependence properties, but yields an intractable likelihood. To address this, we embed a neural network within a density regression model which is used to learn a synthetic likelihood function using simulations from the NPMM with different parameter settings. Our model is fitted using observational data for 1972–2021, and inference carried out in a Bayesian framework. The two regions within the CUS are estimated to be in different asymptotic regimes based on the posterior distribution of the weight parameter. Annual streamflow maxima estimates based on global climate models for two representative climate pathway scenarios suggest an overall increase in the frequency and magnitude of extreme streamflow for 2006–2035 compared to the historical period of 1972–2005.

The resistance and resilience of Great Plains ecoregion boundaries to the 1930s drought as a lens to future dynamics

AbstractPotential natural vegetation (PNV) remains an important concept in ecology by providing the basis for comparing current or future conditions and trends in vegetation with historic conditions. However, nonstationary climate and land‐use‐driven deviations from baseline conditions challenge the validity of PNV at local scales or for locations that are especially sensitive to environmental changes, such as ecotonal boundaries between ecosystem types. Our goal was to identify the historic boundaries between major grassland types in the Central Great Plains (CGP) of North America (tallgrass prairie and northern and southern mixed‐grass prairies) by examining the environmental drivers of primary production prior to, during, and after the decades‐long drought in the 1930s. We sought to explore differences among grassland types in resistance and resilience to drought, and to evaluate legacy effects resulting from spatially explicit processes during drought. We used county‐level cropland production data from 1926 to 1948 as a proxy for native grass production data. We regressed production data against a suite of models containing variables associated with climate, soils, land use, and drought indices prior to, during, and after drought. We used a dimension reduction approach to identify the locations of grassland boundaries in multivariate space defined by each model. Our results show that climate and soil factors predicted grass production prior to the drought, yet additional land use and erosion factors were needed to explain production in both drought and post‐drought. Grassland responses were most variable along ecotonal boundaries. Tallgrass prairie exhibited stronger production responses to precipitation and temperature compared with northern and southern mixed‐grass prairies during all periods. Tallgrass prairie counties along the ecotone with mixed‐grass prairies were more negatively impacted by soil loss from abandoned cropland during the drought compared with other tallgrass prairie counties. As the intensity and frequency of drought are predicted to increase, landscape‐scale variables and spatially explicit processes that govern novel production responses will need to be accounted for in explaining heterogeneity in grassland responses.

Stochastic modelling of non-stationary environmental loads for reliability analysis under the changing climate

In recent years, a rapid pace of climate change is becoming evident by a marked increase in the frequency and intensity of weather extremes, and this trend is expected to continue with an increase in global warming for decades to come. The paper presents the non-homogeneous Poisson process as a basis to model an environmental load process induced by non-stationary climate conditions. The distribution of maximum load generated by a non-stationary process is derived, which explicitly accounts for the loading frequency and intensity amplification over time. Using this distribution, structural reliability can be computed for any given time interval in future. The paper shows that traditional measures like the return period, extreme percentiles, and the annual probability of failure, would vary with time under the changing climate. Examples presented in the paper show that a modest amplification of the load magnitude has a drastic negative effect on reliability than that of the frequency of loading. Probabilistic models presented in this paper will be useful in revising currently used “stationary” design codes and ensuring a consistent level of safety in the non-stationary climate.

Intra-Annual Variability of the Surface Ablation of the Aldegondabreen Glacier (Spitsbergen)

The intra-annual variability of the surface ice ablation on the 5.5 km2 Aldegondabreen glacier (Spitsbergen Island, Barentsburg area) is presented. The ice ablation was measured during five seasons (2018–2022) at the two stakes, installed in the lower part of the glacier and at the index site, where the amount of ablation numerically coincides with the glacier-averaged value with the r = 0.99 agreement. The temporal resolution of the ice ablation data is uneven and varies from 3 to 45 days. To carry out the correlation analysis, meteorological data from the automated weather station located near the glacier terminus are used. The ice ablation rates, obtained after normalization for the number of days between stake readings, have a tight correlation with both the air temperature and the downwelling shortwave radiation flux for most of the seasons, in 2018–2021 (r = 0.71–0.99). Surface air temperature and short-wave radiation are closely related; the above estimates indicate the leading role of short-wave radiation in the summer ablation of the glacier in the period 2018–2021. The year 2022 became anomalous, as the correlation with the shortwave radiation significantly decreased (r = 0.21–0.34). The European heat wave of 2022, which also affected the Svalbard archipelago, interrupted the ordinary intra-annual variability of the air temperature, causing the unprecedented ice melt on Aldegondabreen in September. The predicted increase in frequency and intensity of the future heat waves will result in an increased role of turbulent fluxes in the surface energy balance of the low-elevated Svalbard glaciers. The article demonstrates how the empirically identified dependencies can change from season to season in a non-stationary climate.

Dendrochronology in the tropics using tree-rings of Pinus kesiya

The recent decade has witnessed considerable progress in the number of tree-ring studies using the tropical conifer taxa, Pinus kesiya. Several tree-ring networks have been established in less explored regions in Northeast India, Southwest China and Vietnam. The seasonal climate response of P. kesiya tree-rings has been examined and used to reconstruct temperature and soil moisture variability over the past century and augment the short instrumental records in South and Southeast Asia. In addition to standard approaches, the application of stable isotope, wood density, and blue intensity measurements indicates a significant development in P. kesiya studies. This review elaborates the future prospects of using multiple tree-ring parameters to establish discrete proxies besides tree-ring width. We recommend blue intensity as a cost-effective alternative to quantitative wood anatomy in tropical pines, and call for routine assessments of the temporal stability of climate-growth responses to identify and study potentially non-stationary climate signals. Efforts should be made towards developing extensive networks of long P. kesiya tree-ring chronologies to extend regional climate reconstructions.

Assessing hydrological performance for optimized integrated grey-green infrastructure in response to climate change based on shared socio-economic pathways

Integrated grey-green infrastructure (IGGI) is considered as a promising facility for mitigating urban flooding during extreme rainfall events. However, most IGGI strategies rely heavily on historical precipitation data, while ignoring the long-term effects of dynamic and non-stationary climate change. This study explored a novel approach to assess the hydrological performance under Shared Socio-economic Pathways (SSPs) based on long-term rainfall time series, facilitating the exploration of the optimal solution to urban runoff alleviation in response to climate change. A case study of Guangzhou, China, was applied for model validation. The results indicated that through non-stationarity analysis and stage division, the future time series of the three SSPs were identified with an abrupt point and divided into two stages. By assessing the hydrological performance of the optimal solutions at various historical and future stages, both fully centralized IGGI and decentralized IGGI are found vulnerable to long-term climate change. Based on a certain extent of trade-off between centralization and decentralization, an appropriate degree of decentralization was observed to significantly enhance the hydrological performance of the whole IGGI system, particularly for reducing peak flow following extreme rainfall events. The authors believe that the findings could provide a novel perspective on hydrological performance assessment for IGGI in response to non-stationary and multi-scenario climate change in urban catchments with high built-up density.

A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

AbstractProjections of nonstationary climate risks can vary considerably from one source to another, posing considerable communication and decision‐analytical challenges. One such challenge is how to present trade‐offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or treating all considered projections as equally likely. These approaches can underestimate risks, hide deep uncertainties, and are mostly silent on which assumptions drive decision‐relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade‐offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.