Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.

Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly-used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that ESM simulated runoff-ratio and soil-moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor-pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.

The predicted Antarctic contribution to global-mean sea-level rise is one of the most uncertain among all major sources. Partly this is because of instability mechanisms of the ice flow over deep basins. Errors in bedrock topography can substantially impact the projected resilience of glaciers against such instabilities. Here we analyze the Pine Island Glacier topography to derive a statistical model representation. Our model allows for inhomogeneous and spatially dependent uncertainties and avoids unnecessary smoothing from spatial averaging or interpolation. A set of topography realizations is generated representing our best estimate of the topographic uncertainty in ice sheet model simulations. The bedrock uncertainty alone creates a 5% to 25% uncertainty in the predicted sea level rise contribution at year 2100, depending on friction law and climate forcing. Pine Island Glacier simulations on this new set are consistent with simulations on the BedMachine reference topography but diverge from Bedmap2 simulations.

Increasing ice flux from glaciers retreating over deepening bed topography has been implicated in the recent acceleration of mass loss from the Greenland and Antarctic ice sheets. We show in observations that some glaciers have remained at peaks in bed topography without retreating despite enduring significant changes in climate. Observations also indicate that some glaciers which persist at bed peaks undergo sudden retreat years or decades after the onset of local ocean or atmospheric warming. Using model simulations, we show that glacier persistence may lead to two very different futures: one where glaciers persist at bed peaks indefinitely, and another where glaciers retreat from the bed peak suddenly without a concurrent climate forcing. However, it is difficult to distinguish which of these two futures will occur from current observations. We conclude that inferring glacier stability from observations of persistence obscures our true commitment to future sea-level rise under climate change.

The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor re-used. Given the commonalities between existing process-based hydrological models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific datasets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global, continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling’‘ (CWARHM; pronounced “swarm”).

The isotopic composition of dissolved oxygen offers a family of potentially unique tracers of respiration and transport in the subsurface ocean. Uncertainties in transport parameters and isotopic fractionation factors, however, have limited the strength of the constraints offered by 18O/16O and 17O/16O ratios in dissolved oxygen. In particular, puzzlingly low 17O/16O ratios observed for some low-oxygen samples have been difficult to explain. To improve our understanding of oxygen cycling in the ocean’s interior, we investigated the systematics of oxygen isotopologues in the subsurface Pacific using new data and a 2-D isotopologue-enabled isopycnal reaction-transport model. We measured 18O/16O and 17O/16O ratios, as well as the “clumped” 18O18O isotopologue in the northeast Pacific, and compared the results to previously published data. We find that transport and respiration rates constrained by O2 concentrations in the oligotrophic Pacific yield good measurement-model agreement across all O2 isotopologues only when using a recently reported set of respiratory isotopologue fractionation factors that differ from those most often used for oxygen cycling in the ocean. These fractionation factors imply that an elevated proportion of 17O compared to 18O in dissolved oxygen―i.e., its triple-oxygen isotope composition―does not uniquely reflect gross primary productivity and mixing. For all oxygen isotopologues, transport, respiration, and photosynthesis comprise important parts of their respective budgets. Mechanisms of oxygen removal in the subsurface ocean are discussed.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than +/-0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI dataset excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original dataset. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

Alkalinization of natural waters by the dissolution of natural or artificial minerals is a promising solution to sequester atmospheric CO$_2$ and counteract acidification. Here we address the alkalinization carbon capture efficiency (ACCE) by deriving an analytical factor that quantifies the increase in dissolved inorganic carbon in the water due to variations in alkalinity. We show that ACCE strongly depends on the water pH, with a sharp transition from minimum to maximum in a narrow interval of pH values. We also compare ACCE in surface freshwater and seawater and discuss potential bounds for ACCE in the soil water. Finally, we present two applications of ACCE. The first is a local application to 156 lakes in an acid-sensitive region, highlighting the great sensitivity of ACCE to the lake pH. The second is a global application to the surface ocean, revealing a latitudinal pattern of ACCE driven by differences in temperature and salinity.

Despite extensive research into the transport and fate of oceanic microplastics (MP, <5mm in size), there is comparatively little focus on river systems considered to be pathways for these contaminants. The Savannah River, forming the border between Georgia and South Carolina, provides a unique location to study MP pollution along a variably industrialized river system terminating in the Atlantic Ocean. We investigated spatial variations in MP concentrations along the Savannah River to better understand their transport and deposition in rural to highly developed fluvial systems. Samples of riverbank sediment and suspended particles captured by a <80μm plankton net were collected along a 115 km reach of the river extending from just below the Strom Thurmond Dam to 25km downstream of Augusta, GA. Laboratory MP separation followed NOAA guidelines with a heavy liquid float-sink separation technique and wet peroxide oxidation treatment. Visually identified MPs were counted and photographed using a stereomicroscope; a subset of particles from each sample were examined using a Horiba XploRa Plus confocal microscope system. Average MP concentrations were measured at 3.1 (range: 1.5-4.6) particles/cubic meter in water and 16.8 (range: 6.2-27.4) particles/kg sediment and primarily composed of polyester fibers and polypropylene pellets. Comparison of MP concentrations between sediment samples from the upper bank and water margin suggests that MP particle deposition is dependent on river stage. Preliminary results further indicate that there is no observable relationship between increasing drainage area and MP concentration, suggesting that concentration may be dependent on localized anthropogenic sources rather than cumulative upstream contributions. Measured concentrations of MP in bank sediment in the upper reaches of the Savannah River are an order-of magnitude less than published concentrations at the river’s mouth collected over the same sampled cross-sectional area, suggesting tidal action exerts a significant control on MP pollution in coastal and near coastal areas. Future work will focus on quantifying the predicted role of tidally dominated systems in concentrating microplastics around river mouths and identifying river reaches with highly concentrated MP particles for targeted remediation.

The role of anthropogenic carbon dioxide (CO2) in global warming is confusing. Experts predict that changes in ocean level and atmospheric temperature will increase considerably in distant future. On the other hand, loss of ices in the World is already dramatic and has increased over the recent years. Anthropogenic CO2-related greenhouse effects may be responsible for the global warming; however ice imbalance remains to be explained in more details. We previously showed that estimated anthropogenic heat released between 1994 and 2017 was energetic enough to have caused the melting of a large part of the global ice lost during the same period. To complement this finding, the present work suggests that water on Earth behaves as a refrigerant and manages solar heat and anthropogenic heat similarly. It is also shown that the combustion of fossil hydrocarbons is releasing a huge amount of water stored for millions years in fossil hydrocarbon sources of energy. As anthropogenic heat is no longer negligible, minimizing CO2 production may not be enough to control climate perturbations. Hydrogen is regarded as a climate-friendly alternative source of energy. The last part suggests that heat-cycle assessment from cradle to grave should be used in addition to life cycle assessment to compare hydrogen with other sources of energy in the search for ways to minimize anthropogenic heat release and its impact on climate changes.

Few studies have utilized machine learning techniques to predict or understand the Madden-Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here we present a simple framework for real-time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real-time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ~17 days in October-March and ~10 days in April-September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks is highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena.

The cryptocurrency sector is increasingly integrated into the global financial system. The world’s transition to a digital economy, facilitated by major technological breakthroughs, has several benefits. But as the demand for exchanging and investing in digital currencies is growing , the world must pay careful attention to the hidden and overlooked environmental impacts of this growth. The dramatic increase in the price of Bitcoin (BTC) over the last year and the resulting global race for BTC mining is turning the cryptocurrency market turning into one of the world’s leading polluting sectors. Yet, our knowledge about the environmental footprints of mining BTC is very limited. To address this hap, this study provides the first estimates of the carbon, water and land footprints of BTC mining around the world.

Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording the total solar irradiance (TSI). Some of these measurements last for more than a decade. It is then mandatory to merge them to obtain a seamless record whose duration exceeds that of the individual instruments. Climate models can be better validated using such long TSI records which can also help provide stronger constraints on past climate reconstructions (e.g.,back to the Maunder minimum). We propose a 3-stepmethod based on data fusion, including a stochastic noise model to take into account short and long-term correlations. Compared with previous products, the difference in terms of mean value over the whole time series and at the various solar minima are below 0.2W/m2. Next, we model the frequency spectrum of this 41-year TSI composite time series with a Generalized Gauss-Markov model to help describing an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is∼−0.009±0.010 W/(m2.yr) for the period 1980-2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these com-posite time series is mostly an artefact due to the coloured noise.

Evaluating the influence of anthropogenic emissions changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emissions changes. However, the ability of these widely-used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using two scenarios simulated by a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic emissions changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely-used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local and regional scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant emission input and quantify the degree to which emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the effectiveness of emissions reduction policies using statistical approaches.

El Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central-eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, by objectively optimizing the RO equations fit to observations, we develop an improved recharge index. (1) Te-variability is regressed out, building h_ind statistically-independent from Te. Capturing the pure recharge, h_ind reconciles usual indices. (2) The optimum is equatorial plus southwestern Pacific h_ind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using h_ind_eq+sw, the RO becomes more consistent with observations. h_ind_eq+sw is more relevant for ENSO operational diagnostics.

Cold temperatures limit nitrate-N load reductions of woodchip bioreactors in higher-latitude climates. This two-year, on-farm (Willmar, Minnesota, USA) study was conducted to determine whether field-scale nitrate-N removal of woodchip bioreactors can be improved by the addition of cold-adapted, locally isolated bacterial denitrifying strains (bioaugmentation) or dosing with a carbon (C) source (biostimulation). In Spring 2017, biostimulation removed 66% of the nitrate-N load, compared to 21% and 18% for bioaugmentation and control, respectively. The biostimulation nitrate-N removal rate (NRR) was also significantly greater, 15.0 g N m-1 d-1, versus 5.8 and 4.4 g N m-1 d-1, for bioaugmentation and control, respectively. Bioclogging of the biostimulation beds limited dosing for the remainder of the experiment; NRR was greater for biostimulation in Fall 2017, but in Spring 2018 there were no differences among treatments. Carbon dosing did not increase outflow dissolved organic C concentration. The abundance of one of the inoculated strains, Cellulomonas sp. strain WB94, increased over time, while another, Microvirgula aerodenitrificans strain BE2.4, increased briefly, returning to background levels after 42 days. Eleven days after inoculation in Spring 2017, outflow nitrate-N concentrations of bioaugmentation were sporadically reduced compared to the control for two weeks but were insignificant over the study period. The study suggests that biostimulation and bioaugmentation are promising technologies to enhance nitrate removal during cold conditions. A means of controlling bioclogging is needed for biostimulation, and improved means of inoculation and maintaining abundance of introduced strains is needed for bioaugmentation. In conclusion, biostimulation showed greater potential than bioaugmentation for increasing nitrate removal in a woodchip bioreactor, whereas both methods need improvement before implementation at the field scale.

The Indus Fan, located in the Arabian Sea, contains the bulk of the sediment eroded from the Western Himalaya and Karakoram. Scientific drilling in the Laxmi Basin by the International Ocean Discovery Program (IODP) provides an erosional record from the Indus River drainage dating back to 10.8 Ma, and with a single sample from 15.5 Ma. We dated detrital zircon grains by U-Pb geochronology to reconstruct how erosion patterns changed through time. Long-term increases in detrital zircon U-Pb components of 750–1200 Ma and 1500–2300 Ma show increasing preferential erosion of the Himalaya relative to the Karakoram at 7.99–7.78 Ma and more consistently starting by 5.87 Ma. An increase in the contribution of 1500–2300 Ma zircons starting by 1.56 Ma indicates significant unroofing of the Inner Lesser Himalaya (ILH) by that time. The trend in zircon U-Pb age populations is consistent with bulk sediment Nd isotope data implies greater zircon fertility in Himalayan bedrock compared to the Karakoram and Transhimalaya. The initial change in spatial erosion patterns at 7.0–5.87 Ma occurred during a time of drying climate in the Indus foreland. The increase in ILH erosion postdates the onset of dry-wet glacial-interglacial cycles suggesting some role for climate control. However, erosion driven by rising topography in response to formation of the Lesser Himalayan thrust duplex, especially during the Pliocene may also be important. The influence of the Nanga Parbat Massif to the bulk sediment flux is modest, in contrast to the situation in the eastern Himalaya syntaxis.

The USA National Phenology Network was established in 2007 to address the conspicuous absence of widespread, standardized phenology monitoring in the United States. The aims of the Network are to collect, store, and share phenology data and information to support scientific discovery and understanding, decision-making, an appreciation for phenology, and equitable engagement within the Network. To support these aims, the Network launched Nature's Notebook, a rigorous plant and animal phenology monitoring program, in 2009. Since the launch of Nature's Notebook 13 years ago, participants in all 50 states and beyond have contributed over 26M records of plant and animal phenology. We review the breadth of scientific studies and applied management decisions that have utilized Nature's Notebook and the consequent data and consider how these findings might shape future efforts by the Network to grow phenology monitoring across the country.

The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long-term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content at the onset. Through a machine learning explainability method, we find the ANN is learning off-equatorial patterns of anomalous ocean heat content that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

Monitoring nighttime light (NTL) change enables us to quantitatively analyze the dynamic patterns of human activity and socioeconomic features. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) atmospheric- and Lunar-BRDF-corrected Black Marble product (VNP46A2) provides daily global nighttime radiances with high temporal consistency. However, timely and continuous monitoring of NTL changes based on the dense daily DNB time series is still lacking. In this study, we proposed a novel Viewing Zenith Angle (VZA) stratified COntinuous monitoring of Land Disturbance (COLD) algorithm (VZA-COLD) to detect NTL change at 15 arc-second spatial resolution with daily updating capability based on NASA’s Black Marble products. Specifically, we divided the clear observations into four VZA intervals (0–20°, 20°–40°, 40°–60°, and 0–60°) to mitigate the temporal variation of the NTL data caused by the combined angular effects of viewing geometry and the complex surface conditions (e.g., building heights, vegetation canopy covers, etc.). Single-term harmonic models were continuously estimated for new observations from each VZA interval, and by comparing the model predictions with the actual DNB observations, a unified set of NTL changes can be captured continuously among the different VZA intervals. The final NTL change maps were generated after excluding the consistent dark pixels. Results showed that the VZA-COLD algorithm reduced the DNB data temporal variations caused by disparities among different viewing angles and surface conditions, and successfully detected NTL changes for six globally distributed test sites with an overall accuracy of 99.71%, a user’s accuracy of 87.18%, and a producer’s accuracy of 68.88% for the NTL change category.

Atlantic Water (AW) is the largest reservoir of heat in the Arctic Ocean, isolated from the surface and sea-ice by a strong halocline. In recent years AW shoaling and warming are thought to have had an increased influence on sea-ice in the Eurasian Basin. In this study we analyse 59000 profiles from across the Arctic from the 1970s to 2018 to obtain an observationally-based pan-Arctic picture of the AW layer, and to quantify temporal and spatial changes. The potential temperature maximum of the AW (the AW core) is found to be an easily detectable, and generally effective metric for assessments of AW properties, although temporal trends in AW core properties do not always reflect those of the entire AW layer. The AW core cools and freshens along the AW advection pathway as the AW loses heat and salt through vertical mixing at its upper bound, as well as via likely interaction with cascading shelf flows. In contrast to the Eurasian Basin, where the AW warms (by approximately 0.7°C between 2002 and 2018) in a pulse-like fashion and has an increased influence on upper ocean heat content, AW in the Canadian Basin cools (by approximately 0.1°C between 2008 and 2018) and becomes more isolated from the surface due to the intensification of the Beaufort Gyre. These opposing AW trends in the Eurasian and Canadian Basins of the Arctic over the last 40 years suggest that AW in these two regions may evolve differently over the coming decades.

Wildfire is an important but understudied natural hazard. Research on wildfire, as with other natural hazards, is all too often conducted at a spatial scale that is too broad to identify local or even regional patterns. This study addresses these research gaps by examining the current and future wildfire risk, considering projections of population and property value, at the census-block level in Louisiana, a U.S. state with relatively dense population and abundant timber resources that would be vulnerable to loss from this hazard. Here wildfire risk is defined as the product of vulnerability to the hazard (which is itself defined as the product of burn probability, damage probability, and percent damaged) and exposure to the hazard, the latter of which is represented here by property value. Historical data (1992-2015) suggest that the highest risk is in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. Based on existing climate and environmental model output, this research assumes that wildfire will increase by 25 percent by 2050 in Louisiana from current values. When combined with projections of population and property value, it is determined that the geographic distribution of risk by 2050 will remain similar to that today-with highest risk in southwestern inland Louisiana and east-central Louisiana. However, the magnitude of risk will increase across the state, especially in those areas. These results will assist environmental planners in preparing for and mitigating a substantial hazard that often goes underestimated.

A short watercolor comic about the broken connection between humans and mountains. Funded by Sharing Science grants of the AGU, 2021.