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At least 775 records · Page 43Linked to original sources

USGS2006

Modeling physical and biogeochemical controls over carbon accumulation in a boreal forest soil

Boreal soils are important to the global C cycle owing to large C stocks, repeated disturbance from fire, and the potential for permafrost thaw to expose previously stable, buried C. To evaluate the primary mechanisms responsible for both short- and long-term C accumulation in boreal soils, we developed a multi-isotope (12,14C) Soil C model with dynamic soil layers that develop through time as soil organic matter burns and reaccumulates. We then evaluated the mechanisms that control organic matter turnover in boreal regions including carbon input rates, substrate recalcitrance, soil moisture and temperature, and the presence of historical permafrost to assess the importance of these factors in boreal C accumulation. Results indicate that total C accumulation is controlled by the rate of carbon input, decomposition rates, and the presence of historical permafrost. However, unlike more temperate ecosystems, one of the key mechanisms involved in C preservation in boreal soils examined here is the cooling of subsurface soil layers as soil depth increases rather than increasing recalcitrance in subsurface soils. The propagation of the 14C bomb spike into soils also illustrates the importance of historical permafrost and twentieth century warming in contemporary boreal soil respiration fluxes. Both 14C and total C simulation data also strongly suggest that boreal SOM need not be recalcitrant to accumulate; the strong role of soil temperature controls on boreal C accumulation at our modeling test site in Manitoba, Canada, indicates that carbon in the deep organic soil horizons is probably relatively labile and thus subject to perturbations that result from changing climatic conditions in the future. Copyright 2006 by the American Geophysical Union.

Journal of Geophysical Research G: Biogeosciences↗

USGS2006

Holocene history of drift ice in the northern North Atlantic: Evidence for different spatial and temporal modes

We present new high-resolution proxy data for the Holocene history of drift ice off Iceland based on the mineralogy of the <2-mm sediment fraction using quantitative X-ray diffraction. These new data, bolstered by a comparison with published proxy records, point to a long-term increasing trend in drift ice input into the North Atlantic from 6 to 5 ka toward the present day at sites influenced by the cold east Greenland Current. This feature reflects the late Holocene Neoglacial or cooling period recorded in ice cores and further terrestrial archives on Greenland. In contrast, a decrease in drift ice during the same period is recorded at sites underlying the North Atlantic Drift, which may reflect a warming of this region. The results document that Holocene changes in iceberg rafting and sea ice advection did not occur uniformly across the North Atlantic. Centennial-scale climate variability in the North Atlantic region over the last ???4 kyr is linked to the observed changes in drift ice input. Increased drift ice may have played a role in the increase of cold intervals during the late Holocene, e.g., the Little Ice Age cooling. Copyright 2006 by the American Geophysical Union.

Paleoceanography↗

USGS2009

Hydrologic support of carbon dioxide flux revealed by whole-lake carbon budgets

Freshwater lakes are an important component of the global carbon cycle through both organic carbon (OC) sequestration and carbon dioxide (CO 2 ) emission. Most lakes have a net annual loss of CO 2 to the atmosphere and substantial current evidence suggests that biologic mineralization of allochthonous OC maintains this flux. Because net CO 2 flux to the atmosphere implies net mineralization of OC within the lake ecosystem, it is also commonly assumed that net annual CO 2 emission indicates negative net ecosystem production (NEP). We explored the relationship between atmospheric CO 2 emission and NEP in two lakes known to have contrasting hydrologie characteristics and net CO 2 emission. We calculated NEP for calendar year 2004 using whole-lake OC and inorganic carbon (IC) budgets, NEP oc and NEP IC , respectively, and compared the resulting values to measured annual CO 2 flux from the lakes. In both lakes, NEP Ic and NEP Ic were positive, indicating net autotrophy. Therefore CO 2 emission from these lakes was apparently not supported by mineralization of allochthonous organic material. In both lakes, hydrologie CO 2 inputs, as well as CO 2 evolved from netcalcite precipitation, could account for the net CO 2 emission. NEP calculated from diel CO 2 measurements was also affected by hydrologie inputs of CO 2 . These results indicate that CO 2 emission and positive NEP may coincide in lakes, especially in carbonate terrain, and that all potential geologic, biogeochemical, and hydrologie sources of CO 2 need to be accounted for when using CO 2 concentrations to infer lake NEP. Copyright 2009 by the American Geophysical Union.

Journal of Geophysical Research G: Biogeosciences↗

USGS2009

Nitrogen attenuation of terrestrial carbon cycle response to global environmental factors

Nitrogen cycle dynamics have the capacity to attenuate the magnitude of global terrestrial carbon sinks and sources driven by CO 2 fertilization and changes in climate. In this study, two versions of the terrestrial carbon and nitrogen cycle components of the Integrated Science Assessment Model (ISAM) are used to evaluate how variation in nitrogen availability influences terrestrial carbon sinks and sources in response to changes over the 20th century in global environmental factors including atmospheric CO 2 concentration, nitrogen inputs, temperature, precipitation and land use. The two versions of ISAM vary in their treatment of nitrogen availability: ISAM-NC has a terrestrial carbon cycle model coupled to a fully dynamic nitrogen cycle while ISAM-C has an identical carbon cycle model but nitrogen availability is always in sufficient supply. Overall, the two versions of the model estimate approximately the same amount of global mean carbon uptake over the 20th century. However, comparisons of results of ISAM-NC relative to ISAM-C reveal that nitrogen dynamics: (1) reduced the 1990s carbon sink associated with increasing atmospheric CO 2 by 0.53 PgC yr −1 (1 Pg = 1015g), (2) reduced the 1990s carbon source associated with changes in temperature and precipitation of 0.34 PgC yr −1 in the 1990s, (3) an enhanced sink associated with nitrogen inputs by 0.26 PgC yr −1 , and (4) enhanced the 1990s carbon source associated with changes in land use by 0.08 PgC yr −1 in the 1990s. These effects of nitrogen limitation influenced the spatial distribution of the estimated exchange of CO 2 with greater sink activity in high latitudes associated with climate effects and a smaller sink of CO 2 in the southeastern United States caused by N limitation associated with both CO 2 fertilization and forest regrowth. These results indicate that the dynamics of nitrogen availability are important to consider in assessing the spatial distribution and temporal dynamics of terrestrial carbon sources and sinks.

Global Biogeochemical Cycles↗

USGS2012

Probabilistic prediction of barrier-island response to hurricanes

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

Florida↗

USGS2012

Factors controlling nitrate fluxes in groundwater in agricultural areas

The impact of agricultural chemicals on groundwater quality depends on the interactions of biogeochemical and hydrologic factors. To identify key processes affecting distribution of agricultural nitrate in groundwater, a parsimonious transport model was applied at 14 sites across the U.S. Simulated vertical profiles of NO 3 - , N 2 from denitrification, O 2 , Cl - , and environmental tracers of groundwater age were matched to observations by adjusting the parameters for recharge rate, unsaturated zone travel time, fractions of N and Cl - inputs leached to groundwater, O 2 reduction rate, O 2 threshold for denitrification, and denitrification rate. Model results revealed important interactions among biogeochemical and physical factors. Chloride fluxes decreased between the land surface and water table possibly because of Cl - exports in harvested crops (averaging 22% of land-surface Cl - inputs). Modeled zero-order rates of O 2 reduction and denitrification were correlated. Denitrification rates at depth commonly exceeded overlying O 2 reduction rates, likely because shallow geologic sources of reactive electron donors had been depleted. Projections indicated continued downward migration of NO 3 - fronts at sites with denitrification rates <0.25 mg-N L -1 yr -1 . The steady state depth of NO 3 - depended to a similar degree on application rate, leaching fraction, recharge, and NO 3 - and O 2 reaction rates. Steady state total mass in each aquifer depended primarily on the N application rate. In addition to managing application rates at land surface, efficient water use may reduce the depth and mass of N in groundwater because lower recharge was associated with lower N fraction leached. Management actions to reduce N leaching could be targeted over aquifers with high-recharge and low-denitrification rates.

Water Resources Research↗

USGS2013

A Bayesian method to rank different model forecasts of the same volcanic ash cloud: Chapter 24

Volcanic eruptions often spew fine ash high into the atmosphere, where it is carried downwind, forming long ash clouds that disrupt air traffic and pose a hazard to air travel. To mitigate such hazards, the community studying ash hazards must assess risk of ash ingestion for any flight path and provide robust and accurate forecasts of volcanic ash dispersal. We provide a quantitative and objective method to evaluate the efficacy of ash dispersal estimates from different models, using Bayes theorem to assess the predictions that each model makes about ash dispersal. We incorporate model and measurement uncertainty and produce a posterior probability for model input parameters. The integral of the posterior over all possible combinations of model inputs determines the evidence for each model and is used to compare models. We compare two different types of transport models, an Eulerian model (Ash3d) and a Langrangian model (PUFF), as applied to the 2010 eruptions of Eyjafjallajökull volcano in Iceland. The evidence for each model benefits from common physical characteristics of ash dispersal from an eruption column and provides a measure of how well each model forecasts cloud transport. Given the complexity of the wind fields, we find that the differences between these models depend upon the differences in the way the models disperse ash into the wind from the source plume. With continued observation, the accuracy of the estimates made by each model increases, increasing the efficacy of each model’s ability to simulate ash dispersal.

Geophysical Monograph↗

USGS2012

A robust method to forecast volcanic ash clouds

Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an efficient means to assess all of the hazards associated with these ash clouds.

Journal of Geophysical Research D: Atmospheres↗

USGS2020

Geographic and oceanographic influences on ferromanganese crust composition along a Pacific Ocean meridional transect, 14N to 14S

The major controls on the variability of ferromanganese (FeMn) crust composition have been generally described over the past 40 years; however, most compilation studies lack quantitative statistics and are limited to a small region of several seamounts or compare FeMn crusts from disparate areas of the global oceans. This study provides the ﬁrst detailed research to address the geographic and oceanographic controls of FeMn crust composition from a line of seamounts across 30° of latitude in the west central Paciﬁc. Element concentrations from the uppermost layer (<15 mm) of 57 FeMn crusts were evaluated for statistically signiﬁcant variance and correlation with a variety of oceanographic and geographic parameters. Manganese, Co, Ni, Mo, and Zn concentrations in crusts in this region are highly anticorrelated with seawater oxygen concentrations, suggesting oxygen as the dominant controlling factor for these elements. Iron instead correlates with water depth, which we attribute to increased carbonate ion concentration with increasing water depth. Silicon and Al content in crusts demonstrate a potential meridional variance of detrital inputs and sources in the region. Iron, Ba, and Mg are enriched in FeMn crusts below the equatorial upwelling zone which is related to biological productivity. Fluctuations in the four oceanographic and geographic parameters, seawater oxygen content, detrital input, surface productivity, and deep sources of iron, are robustly recorded by FeMn crusts. Modern measurements of these primary parameters, as well as paleoceanographic reconstructions, can be used to deﬁne regions of interest for FeMn crust exploration.

Geochemistry, Geophysics, Geosystems↗

USGS2020

Alkalinity in tidal tributaries of the Chesapeake Bay

Despite the important role of alkalinity in estuarine carbon cycling, the seasonal and decadal variability of alkalinity, particularly within multiple tidal tributaries of the same estuary, is poorly understood. Here we analyze more than 26,000 alkalinity measurements, mostly from the 1980s and 1990s, in the major tidal tributaries of the Chesapeake Bay, a large, coastal-plain estuary of eastern North America. The long-term means of alkalinity in tidal-fresh waters vary by a factor of 6 among seven tidal tributaries, reflecting the alkalinity of non-tidal rivers draining to these estuaries. At 25 stations, mostly in the Potomac River Estuary, we find significant long-term increasing trends that exceed the trends in the non-tidal rivers upstream of those stations. Box model calculations in the Potomac River Estuary indicate that the main cause of the estuarine trends is a declining alkalinity sink. The magnitude of this sink is consistent with a simple model of calcification by the invasive bivalve Corbicula fluminea. More generally, in tidal tributaries fed by high-alkalinity non-tidal rivers, alkalinity is consumed, with sinks ranging from 8 to 27% of the upstream input. In contrast, tidal tributaries that are fed by low-alkalinity non-tidal rivers have sources of alkalinity amounting to 34 to 171% of the upstream input. For a single estuarine system, the Chesapeake Bay has diverse alkalinity dynamics and can thus serve as a laboratory for studying the numerous processes influencing alkalinity among the world’s estuaries.

Maryland, Virginia↗

USGS2021

STEPS: Slip time earthquake path simulations applied to the San Andreas and Toe Jam Hill Faults to redefine geologic slip rate uncertainty

Geologic slip rates are a time-averaged measurement of fault displacement calculated over hundreds to million-year time scales and are a primary input for probabilistic seismic hazard analyses, which forecast expected ground shaking in future earthquakes. Despite their utility for seismic hazard calculations, longer-term geologic slip rates represent a time-averaged measure of the tempo of strain release and do not measure variability across earthquake cycles. We have developed a numerical approach called STEPS (Slip Time Earthquake Path Simulations), which is built upon field-based observations and explicitly incorporates realistic variations in displacement per event and variability in the recurrence interval between earthquakes. The STEPS approach, which simulates strain release through time, relies on representing earthquake cycles as stairsteps, rather than straight-line paths, connecting per earthquake time-displacement coordinates. We simulate earthquake histories based on these input constraints using two examples: the Carrizo section of the San Andreas fault and the Toe Jam Hill fault of the Seattle fault zone. We find that modeled slip rate distributions agree with slip rates reported for the sites of interest by the original investigators, while providing a slip rate distribution that reflects the variability of earthquake frequency and displacement. The STEPS approach provides an estimate of fault slip rate uncertainty based on a synthetic suite of plausible time-displacement paths resulting from individual earthquakes, rather than measurement uncertainties associated with offset features. When considering this simulated earthquake behavior between measurements, the uncertainty associated with earthquake paths is greater than that calculated by the long-term rate.

California, Washington↗

USGS2021

Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt

Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic-terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high-frequency oxygen, temperature, and carbon dioxide (CO 2 ) data to infer watershed connectivity in Boulder Creek, a mid-sized (1160 km 2 ) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients ( K 600 ) were paired with high-frequency, in-situ dissolved CO 2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated ( ρ = −0.72, p ≪ 0.001) during the non-snowmelt period and NEP was frequently negative. Mean F CO2 during the non-snowmelt period was approximately 302 (±171) mmol C m −2 d −1 and was primarily supported by watershed CO 2 inputs. During snowmelt, GPP and ER were not significantly correlated ( ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non-snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher F CO2 (843 ± 338 mmol C m −2 d −1 ) and greater allochthonous CO 2 inputs than during non-snowmelt periods, emphasizing the effects of seasonal differences in aquatic-terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.

Colorado↗

USGS2021

Climatic controls on soil carbon accumulation and loss in a dryland ecosystems

Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. To address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO 2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO 2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.

Utah↗

USGS2022

Hydrologic and landscape controls on dissolved organic matter composition across western North American Arctic lakes

Northern high-latitude lakes are hotspots for cycling dissolved organic carbon (DOC) inputs from allochthonous sources to the atmosphere. However, the spatial distribution of lake dissolved organic matter (DOM) is largely unknown across Arctic-boreal regions with respect to the surrounding landscape. We expand on regional studies of northern high-latitude DOM composition by integrating DOC concentrations, optical properties, and molecular-level characterization from lakes spanning the Canadian Taiga to the Alaskan Tundra. Lakes were sampled during the summer from July to early September to capture the growing season. DOM became more optically processed and molecular-level aromaticity increased northward across the Canadian Shield to the southern Arctic and from interior Alaska to the Tundra, suggesting relatively greater DOM incorporation from allochthonous sources. Using water isotopes (δ 18 O-H 2 O), we report a weak overall trend of increasing DOC and decreasing aromaticity in lakes that were hydrologically isolated from the landscape and enriched in δ 18 O-H 2 O, while within-region trends were stronger and varied depending on the landscape. Finally, DOC correlated weakly with chromophoric dissolved organic matter (CDOM) across the study sites, suggesting that autochthonous and photobleached DOM were a major component of the DOC in these regions; however, some of the northernmost and wetland-dominated lakes followed pan-Arctic riverine DOC-CDOM relationships, indicating strong contributions from allochthonous inputs. As many lakes across the North American Arctic are experiencing changes in temperature and precipitation, we expect the proportions of allochthonous and autochthonous DOM to respond with aquatic optical browning with greater landscape connectivity and more internally produced DOM in hydrologically isolated lakes.

Global Biogeochemical Cycles↗

USGS2023

Predicting daily river chlorophyll concentrations at a continental scale

Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a . Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.

Water Resources Research↗

USGS2023

Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

Delaware River Basin↗

USGS2024

Adaptation and Response in Drylands (ARID): Community insights for scoping a NASA terrestrial ecology field campaign in drylands

Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at-risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi-year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end-user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end-users and scientists. We also summarize insights gained from hundreds of follow-up activities, including from a tribal-engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements.

Earth's Future↗

USGS2024

Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm

Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We used data from 58 diverse and globally distributed temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. Across these diverse systems, and given relatively limited inputs, model predictions of primary production were highly correlated with observed values derived from high-frequency sensor data. Our analysis provides a model structure, including calibrated parameter estimates, that may be broadly useful for understanding current and future patterns in lake primary production.

JGR Biogeosciences↗