Our research team is involved in several projects that seek to integrate the science-based prediction models of flood-causing events such as hurricanes with the decision-making models for critical infrastructure resilience. To this end, we use the state-of-the-art hydrological models such as WRF-Hydro and ADCIRC to simulate potential realizations of inland and coastal flooding events caused by tropical storms. We use these simulations to generate statistically sound scenarios to populate the inputs of several resilience-based decision making models, all developed using the state-of-the-art scenario-based stochastic and robust optimization methodologies. We identify three time lines where these models can be used to improve the quality of decision making processes: (1) Short-term preemptive resource allocation (preparedness) just before impending tropical storms, (2) Mid-term hardening and resilience investment strategies (mitigation) within a multi-season horizon considering multitudes of potential storms, and (3) Long-term resilience investment and infrastructure design strategy development considering potentially increasing flooding risks due to climate change and sea level rise. We present the overall framework that our team developed relying on the team’s in-progress work, particularly for the short- and mid-term prediction-optimization models. We use two specific infrastructures as examples to instantiate our models: (1) Evacuation of patients from healthcare facilities (hospitals and nursing homes), and (2) Substation hardening and preparation for power grids. To create realistic, high-resolution case studies, we consider historical and synthetic storms that impact actual healthcare facilities and power grid for Texas.

Extinction within the Paleozoic era has been studied in the past, but there still lacks a comprehensive understanding of how extinction risk changed throughout it. Our research project aims to bridge this gap by exploring extinction risk in relation to major Paleozoic phyla and ecological characteristics. Using R, we analyzed the Stanford Earth Body Size dataset, which includes extensive data (n=8816) on Paleozoic marine animals. In Step 1, regression coefficients were formed, indicating whether being in one of the 6 phyla in each period of the Paleozoic era conferred greater or less extinction risk. In Step 2, the examined ecological characteristics included ocean acidification resilience, feeding patterns, body volume, length, surface area, motility, tiering, circulatory systems, and respiratory organ type. In Step 3, 6 binomial machine learning models were created using the traits from Step 2 to determine whether an individual genus went extinct in a particular period. Our Step 1 results confirm that within these timeframes, while certain phyla have greater extinction risk, extinction risk was not uniform across these groups. Our Step 2 results show certain traits provided advantages and disadvantages for an organism’s extinction risk. One interesting pattern was that the only consistently non-significant traits were body length, area, and volume. Likewise with Step 1, extinction risk for each ecological characteristic varied across the Paleozoic. Finally, in Step 3, the results were largely successful. Most of the six models had an accuracy above 80% with the highest being 92% in the Cambrian. The areas under the Precision-Recall and the Receiver Operating Characteristic Curves were all in the acceptable (<0.6) range, demonstrating that the model has low false positive/ negative rates and is able to distinguish between what trait indicates extinction or survival for each period. Our research project identified phyla at risk of extinction in each period of the Paleozoic, determined which natural traits incited greater extinction risk, and demonstrated machine learning models trained on fossil descriptors can predict when an individual genus became extinct. Our results confirmed that extinction risk is not consistently dependent on a singular factor nor is it constant across every period of the Paleozoic era.

The author provides stable and safe designs of coastal and river banks using a new structural concept which stably keeps frame structure inside of the embankments, and therefore protects the backside of lowland areas, where more than ten millions of people live and social investment facilities are distributed. The projects can proceed under the framework of Public Private Partnership (PPP). This new proposal simplifies the design of multi water hazards from the probabilistic collapse risks to deterministic planning for human lives and accumulated social public facilities around coastal mega cities such as Tokyo and Nagoya in Japan. Those Metropolitan citizens experienced historical Kanto earthquake in 1923 and largest Tone-River flooding in 1947. On the other hand, heaviest storm surge in Japan by Typhoon Isewan in 1959 around Nagoya City in the Ise Bay facing Pacific Ocean. These regions distribute in flat low elevation areas for conveniences of foreign trade with port facilities, industries, and traffic infrastructures including hub airports. Mean while, those coastal districts and citizens sometimes remains in legendary collapse risks, such as gigantic fires or pandemic infection diseases nowadays along with the catastrophic destructions by M8 to M9 earthquakes, inundation and river flooding against social frameworks to keep human cultural livelihoods in the hazardous era of this modern earth of Gaia.

Since 2017, the education and public engagement team at the Lunar and Planetary Institute has conducted seminars for planetary scientists (including early career scientists and planetary engineers) interested in engaging public audiences. Sharing Planetary Science seminars address some of the challenges in effectively interacting with audiences. Seminars include current educational research and incorporate the participants’ knowledge and experiences in engaging public audiences. Sessions include invited speakers with expertise in science education, science writing, journalism, communicating with policy makers, and social media. The seminars are hour-long sessions, on over a dozen topics including understanding audience needs, common planetary misconceptions, engaging culturally diverse audiences, and more. While those conducted in 2017 were limited to in-person participants, by 2019 scientists were welcome to join online. Resources, powerpoints, and recordings are available online, along with information about upcoming seminars, at www.lpi.usra.edu/education/scientist-engagement. The number of participants has increased over the past 18 months. Post-session surveys have been conducted after each seminar; participants find the seminars useful and interesting. This poster will include further details of these professional development sessions, which topics and aspects of the seminars are most popular, and the findings of the evaluation to date.

The Underground Gas Storage (UGS) project of Castor, Spain, was strategically conceived to guarantee the Spanish gas demand during 50 days. Yet, the project did not enter into operation because it was cancelled as a result of a sequence of felt earthquakes induced after cushion gas injection. The project cancellation implied an investment compensation to the operating company that may cost up to 4.73 billion euros to Spanish citizens. The sequence contained the three largest earthquakes (M4.08, M4.01 and M3.97) ever induced by any of the more than 640 UGS facilities around the world. The largest earthquakes were induced some 20 days after stopping injection, which lasted for 15 days. The focal depth of these earthquakes was between 4 to 10 km, far deeper than the 1.7 km injection depth. To understand the causes of this induced seismicity, we have performed coupled two-phase flow and geomechanical numerical simulations and we have employed Okada’s solution to analyze the shear slip stress transfer. We analyzed four seismicity-inducing mechanisms (pore pressure build-up, stress transfer, destabilizing buoyancy, and recovery of pressure drops that ensure transient stability in regions that are mechanically destabilized after a microseism). We found that the onset of seismicity was induced by gas injection, which reactivated the critically stressed Amposta fault through pore pressure buildup and buoyancy. The Amposta fault, a mature fault bounding the storage formation, crept, accumulating aseismic slip. Destabilization of the fault continued even after the stop of injection because of the permanent effect of buoyancy caused by the low density of the injected gas. The progressive accumulation of slip perturbed the stress around the rupture area of the Amposta fault and eventually reactivated a critically stressed unmapped fault located in the crystalline basement. Once this deep fault was reactivated, the sequence of earthquakes was induced by shear slip stress transfer, with transient slip-driven pore pressure changes likely controlling the delay between earthquakes. We contend that an analysis of fault stability prior to gas injection would have identified the high risk of inducing seismicity at Castor.

Patagonia is one of the key places to study the interaction of plate tectonics and mantle flow patterns with geological processes. This part of the continent is shaped by the northward migration of the Chile Triple Junction, currently marked by subduction of the Chilean spreading ridge at latitude 46oS, opening a slab window beneath Southern Patagonia. The idea of slab window was hypothesized to explain the volcanic gap between north Patagonia and the southern part of the peninsula. The analysis of volcanic rock composition shows the transition between a domain with the signature of slab melt (metasomatized MORB) and a domain with no slab signature (OIB source mantle). Along the Pacific coast, other slab windows were suggested in Central America, California and North Cordillera. The analysis of uplifted terranes and seismic imaging tried to constrain the geometry of these slab windows and map the mantle flow pattern that controls the present-day surface expression (topography, volcanism distribution). From a limited seismic coverage, early studies mapped the Patagonian slab window from body wave tomography and shear wave splitting. The recent deployment of a temporary seismic array from 2018 to 2021 and the Chilean seismic networks fills the data gap between the seismically active northern part of Patagonia and the more poorly studied southern part. This presentation will show the results of our recent seismic studies in Patagonia and help constrain the geodynamical processes associated with the slab window. From the analysis of SKS and similar core phases, we determine the pattern of azimuthal seismic anisotropy resulting from the mantle flow pattern beneath South America. Fast splitting directions are generally NE-SW throughout most of Southern Patagonia, similar to the pattern of large-scale azimuthal seismic anisotropy from global and regional surface wave models. However, between 46oS - 48oS, we observe large splitting values and an E-W direction showing the effect of the slab edge. This is consistent with models of rapid upper mantle flow from the Pacific around the southern edge of the Nazca slab. Seismic imaging using receiver functions and Rayleigh waves from earthquakes and ambient noise show very low upper mantle velocities and an absence of mantle lithosphere in this region, suggesting the lithosphere has been thermally eroded by the dynamics of the slab window. We will also show and discuss preliminary results of a body wave tomographic analysis of the same seismic station dataset.

The supercontinent Gondwana broke up 200 Myrs ago in smaller continents known today as South America, Africa, India, Australia, Antarctica. In the last twenty years, extensive petrological, geochemical, and geophysical studies were done in Australia and South Africa. Still, models of the post-Gondwana lithospheric mantle evolution are constrained by sparse geochemical analysis and/or numerical modelling. Access to different scale geophysical datasets allow applying joint inversion framework to better constrain the present-day physical state of cratonic lithosphere. Here we use a 3D multi-observable inversion method based on a probabilistic (Bayesian) formalism. This approach constrains the present-day thermochemical structure (temperature and major element composition) of the mantle using the sensitivity of multi-geophysical datasets within a thermodynamically consistent Bayesian framework, solved with state-of-the-art Markov Chain Monte Carlo algorithms. This presentation will show recent thermochemical tomography of central-southern Africa, Antarctica, South America, and Australia at a resolution of 1°x1°. We will show new thermal lithospheric thickness and the average chemical composition of the lithospheric mantle maps beneath these Gondwana terranes. We will discuss the evolution of cratonic lithosphere derived from our modelling and previously suggested by comparing it with external observables (xenolith/xenocrysts thermo-barometry analysis, hotspot tracks reconstruction, volcanism). This contribution will address the following questions: 1. How thick, cold and depleted is the cratonic lithosphere beneath Gondwana terranes? 2. How did mantle plumes affect the thermochemical structure in the last 200 Myrs? 3. What can geophysical data and paleo reconstruction tell us about the evolution of the cratonic lithosphere?

Geothermal heat flux (GHF) is an important basal boundary condition for models of ice sheet dynamics, but is poorly constrained by conventional borehole-based estimates. The accuracy and uncertainties associated with geophysical proxy-based GHF models are contingent upon reliable models of heat production and thermal conductivity that are difficult to constrain. In this study, we examine (1) the statistical distribution of these thermal properties to gain insight into the Antarctic crust, (2) revaluate GHF estimates using these new constraints, and (3) identify areas where a lack of knowledge still hampers our ability to produce accurate models of GHF. Our approach centers on developing statistical models for the thermal properties based on global and regional distributions from global geochemical datasets. We then calibrate our heat production models of Antarctica using a combination of exposed Antarctic terranes, conjugate terranes and crustal tomography models of Antarctica. Regions where exposures and conjugate terranes are not accessible, we use a terrane model of Antarctica to reduce uncertainty. We then estimate GHF for different proxy-based temperature models using our thermal property calibrations. We find more diversity in GHF models derived from geophysical-based proxies when using a standardized crustal property model than predicted by the original. We suggest that there is still much to learn about the thermal state of Antarctica, which requires further improvements in predictors of thermal properties, especially their variation with depth.

Temporary seismic network deployments are quite common both in land and offshore. The acquired data have significantly helped improve our understanding of earthquake processes and internal structure of the Earth. However, some temporary stations, especially these all-in-one units without external GPS timing system, suffer from incorrect timing record and thus pose a challenge to fully utilize the valuable data. To inspect and fix such time problems, ambient noise cross-correlation function (NCCF) is widely adopted by using daily waveforms. However, it is difficult to identify short-term time drift after stacking the NCCF output for several days to months. To detect such clock errors, travel times of local and distant earthquakes are utilized along with NCCF. We apply such a strategy on an Ocean Bottom Seismograph (OBS) dataset from southern Mariana subduction zone and a dataset from a temporary dense network from Weiyuan shale gas field, Sichuan, China. By inspecting travel times from local and distant events, we identify a very short-term clock drift (~25 sec) on the OBS data that was not detectable using NCCF only. To overcome the problem, short segments (3, 6, 12 hours) of daily wavefrom data is inspected as clock errors become stable within the selected segments. In addition, the data quality is carefully inspected with impact of different interstation distance and period band on NCCF. In particular, we find that the 6-hour segment with a period band of 2-5 sec is able to detect and correct short term changes, including linear drift. For the dense array data, we observe that NCCF symmetry is well-preserved for short interstation distance (within 1 km) but becomes distorted for larger interstation distances. Therefore, we split our dense array (79 stations) into 16 groups with a maximum interstation distance of 500 meters and 1-2 sec period band was selected after testing. Short data segments improve the time-drift detection efficiency in NCCF results, which is consistent for both local and distant events. In a nutshell, the carefull selection of data length and NCCF parameters can be helpful to identify and correct the time drift errors of temporary seismic stations.

The Oak Openings Region of Northwest Ohio has a unique shallow sandy aquifer that is responsible for the wet prairie ecosystem above it. However, groundwater flux and contaminant transport within the 1 – 3 m thick sandy aquifer and a potential flow exchange with the deeper carbonate aquifer in the post glacial regional aquifer system are not well understood. In this study, integrated geophysical methods involving electrical resistivity tomography (ERT) and ground penetrating radar (GPR) are co-located to delineate the sandy aquifer unit at the Stranahan Arboretum and the Sandhill Crane Wetland sites in Toledo and Swanton, Ohio. Parallel ERT profiles were acquired using a SuperSting R8 resistivity meter with a dipole-dipole configuration and unit electrode spacing of 1 m while the GPR profiles were acquired using a PulseEKKO Pro 250 MHz radar system. Additionally, we obtained soil samples extending to a depth of 2.5 m at six locations on three of the profiles at each site. The sand samples were analyzed for their grainsize and to estimate the hydraulic conductivity (K) of the aquifer. Multiple slug tests were also used to estimate the variation in K. We found that the sandy aquifer is somewhat disconnected at the Stranahan Arboretum, with the thickest lenses around 10 - 40 m on the ERT profiles while a continuous and thicker sand sequence is observed at the Sandhill Crane site. The sandy aquifer is underlain by clay-rich silt and glacial till respectively who’s hydraulic leakance controls potential vertical fluxes. The average grain size of the sands was between 0.285-0.33 mm, suggesting fine to medium-grained sands. The average K ranged from 2 × 10-4 to 9 × 10-4 m/s, with generally larger K values found in sands sampled from the thickest lenses. Overall, the correlation of higher K values within thicker sand lenses suggests that in these areas, groundwater would be able to flow more easily, and the aquifer could be more easily contaminated than thinner, less connected sand units. We hope to continue this research and improve K estimates and conceptual models to help devise better plans to protect the groundwater resources and ecosystems of the OOR.

Seagrasses provide a variety of ecosystem services, including sediment stabilization, sediment accretion, improving water quality through trapping suspended sediments, and storing carbon and nutrients. Sharp declines in eelgrass (Zostera marina) abundance at South Slough have prompted efforts to understand eelgrass stressors in the estuary. Sediment characteristics including organic matter, carbon content, grain size distribution, bulk density, and porosity were investigated along with eelgrass metrics at several sites along the estuarine salinity gradient. These data were used to study the sediment characteristics of intertidal eelgrass beds in the South Slough estuary, how characteristics vary between sites along the salinity gradient, and to determine the relationship of sediment characteristics and eelgrass abundance. Results show that eelgrass was present at sites characterized by high percent sand and low percent silt/clay, low porosity and high bulk density, and low organic matter and carbon content. Eelgrass was absent from sites characterized by low percent sand and high percent silt/clay, high porosity and low bulk density, and high organic matter and carbon content. These results suggest that fine sediment may be one stressor contributing to changes in intertidal eelgrass distribution in the South Slough estuary. This study can help inform eelgrass restoration efforts in South Slough, especially regarding habitat suitability and site selection.

Accurate sub-seasonal to seasonal (S2S) weather forecast between 2 weeks and several months is crucial to making informed decisions regarding changes in the risk of extreme weather events, resource management, agriculture, forestry, public health, energy, etc. However, significant gaps exist between the needs of society and what forecasters can produce, especially over longer lead times. Using three goodness-of-fit metrics, this study examined the ability of the SOMs-generated CFSv2 to forecast the correct (observed) circulation pattern, as opposed to the actual observed gridded field over a 90-day forecast period. Mean sea-level pressure (MSLP), near-surface wind (wnd10m), 850-mb temperature (t850), and 700-mb geopotential heights (z700) from the North American Regional Reanalysis were used to categorize the synoptic-scale circulation for three regions (East, West, and Gulf) across North America from January 1979 – December 2016. Expectedly, forecast skill generally decreased from the first day down to the skill of climatology (after 10 -15 days) and also varied regionally, seasonally, and between variables. The forecasts for the winter and summer seasons outperformed others, while t850 and z700 forecasts outperformed wnd10m, except in the west region. More importantly, this study found that the SOMs-generated CFSv2 forecasts improve upon the skill of the raw CFSv2 forecast near the one 1 – 2 weeks lead time. This study thus demonstrates the potential utility of a SOMs-based forecasting method in medium-range weather forecasts.

Changes in glacier terminus position have been implicated as one of the primary drivers of the rapid changes in glacier dynamics observed across the globe in the last two decades. Iceberg calving exerts a critical control on the terminus position of the vast majority of marine-terminating glaciers, yet calving is relatively poorly understood due to the inherent difficulties in collecting observations of a stochastic process in a dangerous setting. Time-lapse camera and satellite observations suggest that the style of iceberg calving can vary tremendously in both space and time depending on the physical properties of the terminus, ranging from the detachment of giant tabular icebergs every few decades from Antarctic’s floating ice shelves to the growlers produced nearly daily from serac topples along Alaska’s coast. Here we extract quantitative metrics on the relative importance of calving driven by branching and uncorrelated fractures through application of fragmentation theory to iceberg size distributions extracted from high-resolution digital elevation models for 17 fjords around Greenland. We find that iceberg size distributions typically deviate from the widely-assumed power-law form for icebergs with surface areas >0.05 km^2, with fewer icebergs than predicted by the power-law for larger sizes. Icebergs larger than ~0.1 km^2 primarily calve as the result of full-thickness penetration of uncorrelated fractures (i.e., as tabular icebergs). Although the dataset is temporally sparse for the majority of the study sites, the data suggest that iceberg formation via branching fractures reaches a seasonal peak in summer, when icebergs up to ~0.1 km^2 follow power-law distributions. These data provide a novel means to assess the accuracy of iceberg calving models and potentially to constrain the physical characteristics of termini susceptible to the marine ice cliff instability mechanism.

Global atmospheric ‘storm-resolving’ models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real-geography coarse-grid model (FV3GFS with a 200 km grid) to be closer to those of a 40-day reference simulation using X-SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea-surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse-grid model without machine learning corrections has too little cloud, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine-grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hours of skill at 3-7 day leads and time-mean precipitation patterns are improved 30% by applying the ML correction. Adding machine-learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time-mean upper tropospheric temperature and zonal wind patterns thereafter. The figure shows maps of 30-day time-mean precipitation pattern difference from the fine-grid reference for prognostic simulations: (a) 200 km baseline(no machine learning correction) (b) Using random forest correction and (c) neural net correction for temperature. humidity and surface radiation corrections. RMSE is the root mean squared precipitation difference from the reference, which is 30% less for the two machine-learning corrected simulations compared to the baseline. (d) Bar charts of the land-mean, ocean-mean and global-mean precipitation biases for these three configurations, showing the machine-learning corrected simulations remove a high bias of land surface precipitation in the baseline simulation.

Coastal aquifers act as a major host of seawater-groundwater interaction and play an important role in modulating the marine elemental budget. Calcium stable isotopes (δ44/40Ca) have been used to identify mass dependent isotope fractionation processes such as carbonate dissolution and precipitation in a range of geological reservoirs that has major implications in constraining global geochemical cycles. However, there is limited Ca isotope data from coastal aquifers globally. Here we report δ44/40Ca values of groundwater collected in 2017-18 from multiple locations and depths from the Bakkhali delta front, Sundarbans, India. The sampling depth varied between 14 m below ground level (m bgl) and 333 m bgl and the salinity of the groundwater samples range from 1-25 ppt. The salinity of the water samples decreases with increasing depth indicating greater seawater incursion from the Bay of Bengal at shallower depths. Variable amounts of mixing of freshwater and seawater is also supported by Sr and Ca concentrations which vary between 1.6-62.8 μmol/l and 0.29-8.92 mmol/l, respectively, and show progressively lower concentrations with depth. The δ44/40Ca values of dissolved phase in groundwater samples (relative to NIST SRM 915a) were measured using a 43Ca-48Ca Double Spike TIMS technique at Centre for Earth Sciences, Indian Institute Science, Bangalore. The δ44/40Ca values of the groundwater samples show significant variability between 1.52-2.28‰ (2SD ~0.1‰) with several samples showing δ44/40Ca values higher than modern seawater (~1.88‰). Low δ44/40Ca values, mostly in deeper groundwater samples, is consistent with higher proportions of freshwater input. Samples showing high δ44/40Ca values are mostly from shallower depths and likely reflect carbonate precipitation which is consistent with high Sr/Ca (~8.13-12.26) in samples from 30-42 m depth. The high δ44/40Ca in groundwater samples from the Ganges-Brahmaputra delta could explain the high δ44/40Ca values reported in water samples from the Bay of Bengal1. [1] Chakrabarti et al., Goldschmidt Boston, 2018 Abstract.

The largest city in New Zealand, Auckland is home to roughly 1.5 million people -- one third of New Zealand's population. Here we assemble a bottom-up inventory of Auckland's fossil fuel carbon dioxide emissions from a variety of data sources. We use these emissions estimates in combination with the UrbanVPRM land surface model to estimate the net carbon balance of the region. This work is part of the larger CarbonWatch NZ project, which aims to produce estimates of New Zealand's net carbon balance quickly enough to assess and refine ongoing national efforts to reach carbon neutrality.

Financial institutions’ investment and lending portfolios could be affected by both physical climate risks stemming from impacts related to increasing temperatures, and from transition climate risks stemming from the economic consequences of the shift to a low-carbon economy. Here we present a consistent framework to explore near term (to 2030) transition risks and longer term (to 2050) physical risks, globally and in specific regions, for a range of plausible greenhouse gas emissions and associated temperature pathways, spanning 1.5-4oC levels of long-term warming. We draw on a technology-rich, regionally disaggregated Integrated Assessment Model representing energy system, agricultural and land-based greenhouse gas emissions, a reduced complexity climate model to simulate probabilistic global temperature changes over the 21st century, and a suite of impacts models to estimate regional climate-related physical hazards and impacts deriving from the temperature change pathways and their underlying socio-economics. We consider 11 scenarios to explore the dependence of risks on both temperature pathways, as well as socio-economic, technology and policy choices. This builds and expands on existing exercises such as the Network for Greening the Financial System (NGFS). By 2050, physical risks deriving from major heatwaves, agricultural drought, heat stress and crop duration reductions depend greatly on the temperature pathway. By 2030, transition risks most sensitive to temperature pathways stem from economy-wide mitigation costs, carbon price increases, fossil fuel demand reductions and potential stranding of carbon-intensive assets such as coal-fired power stations. The more stringent the mitigation action, the higher the abatement costs and sector-specific transition risks. However, such scenarios result in lower physical climate hazards throughout the century. Our study also explores multiple 2 deg C pathways which demonstrate that scenarios with similar longer-term physical risks could have very different near-term transition risks depending on technological, policy and socio-economic factors. As such, “a single scenario will not answer all questions”.

Climate warming is projected to increase the frequency of climate extremes. The physiological response of tree species to these changes has not been well characterized. Particularly, how hydraulic parameters may adjust with temperature and what are the impacts during a severe drought in mesic forest tree species have limited study. Further, the pattern of responses to drought and recovery has been linked with isohydry-anisohydry strategies, but we do not know if these strategies are still relevant in the hotter and drier future. To fill this knowledge gap, we built a factorial experiment with ambient (AT) and high temperature (HT; ambient +4.5 0C) for two growing seasons, with six weeks of the initial acclimation period, four weeks of drought, and six weeks of recovery during the first year on seven tree species. Throughout, we followed water relations, leaf gas exchange, stem water potential, and hydraulic conductivity. Seedlings were acclimated to prolonged HT by adjusting key hydraulic traits and increasing anisohydry strategies, but these traits were not coordinated, as some isohydric species had surprisingly high stem water potentials at 50% loss of xylem conductivity (P50) and some anisohydric species had negative safety margins. In addition to this, anisohydric species reduced stem water potential during drought and delayed recovery, while isohydric species experienced earlier reductions in photosynthesis and increased dieback at HT. Our findings highlight that hydraulic traits may acclimate to HT and reduce some of the additive effects during drought in anisohydric species, but hotter drought intensified soil and atmospheric drought and increased mortality in isohydric species. Thus, acclimation of hydraulic traits to HT may help anisohydric species to avoid some of the consequences but can’t compensate for the negative effects.