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USGS2025

Advancements in satellite observations of inland and coastal waters: Building towards a global validation network

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide suggestions for future field campaigns to improve coordination with remote sensing data collection and to ensure that field data is well suited for use in model or algorithm development.

Remote Sensing↗

USGS2022

Advances in the study and understanding of groundwater discharge to surface water

Groundwater discharge is vitally important for maintaining or restoring valuable ecosystems in surface water and at the underlying groundwater-surface-water ecotone . Detecting and quantifying groundwater discharge is challenging because rates of flow can be very small and difficult to measure, exchange is commonly highly heterogeneous both in space and time, and surface-water hydrodynamics can influence the exchange and hinder measurements . Fortunately, a growing number of methods developed during the last several decades has led to advancements in our capabilities to identify and quantify groundwater discharge to surface water, including better use of seepage meters , application of tracers such as heat or isotopes , and improved groundwater-modeling capabilities . This progress has led to coalescence in characterizing the complex mix of hydrological, biological, and chemical processes that occur at the groundwater-surface water interface , along with relevant societal effects . Still, many uncertainties and assumptions show an incomplete knowledge of these processes, including the lack of studies in many regions of the world, insufficient sharing of practical methodologies between scientific disciplines , incomplete understanding of processes and parameters specific to the sediment-water interface , and challenges associated with measuring exchange at multiple scales of time and space.

Water↗

USGS2023

Integration of distributed streamflow measurement metadata for improved water resource decision-making

Streamflow data are critical for monitoring and managing water resources, yet there are significant spatial gaps in our federal monitoring networks with biases toward large perennial rivers. In some cases, streamflow monitoring exists in these spatial gaps, but information about these monitoring locations is challenging to obtain. Here, we present a streamflow catalog for the United States Pacific Northwest that includes current and historical streamflow monitoring location information obtained from 32 organizations (other than the U.S. Geological Survey), which includes 2661 continuous streamflow gaging locations (22% are currently active) and 30,557 discrete streamflow measurements. A stakeholder advisory board with representatives from organizations that operate streamflow monitoring networks identified metadata requirements and provided feedback on the Streamflow Data Catalog user interface. Engagement with the water resources community through this effort highlighted challenges that water professionals face in collecting and managing streamflow data so that data are findable, accessible, interoperable, and reusable (FAIR). Over 60% of the streamflow monitoring locations in the Streamflow Data Catalog are not available online and are thus not findable through web search engines. Providing organizations technical assistance with standard measurement procedures, metadata collection, and web accessibility could substantially increase the availability and utility of streamflow information to water resources communities.

Water↗

USGS2016

A long-term study of ecological impacts of river channelization on the population of an endangered fish: Lessons learned for assessment and restoration

Projects to assess environmental impact or restoration success in rivers focus on project-specific questions but can also provide valuable insights for future projects. Both restoration actions and impact assessments can become “adaptive” by using the knowledge gained from long-term monitoring and analysis to revise the actions, monitoring, conceptual model, or interpretation of findings so that subsequent actions or assessments are better informed. Assessments of impact or restoration success are especially challenging when the indicators of interest are imperiled species and/or the impacts being addressed are complex. From 1997 to 2015, we worked closely with two federal agencies to monitor habitat availability for and population density of Roanoke logperch ( Percina rex ), an endangered fish, in a 24-km-long segment of the upper Roanoke River, VA. We primarily used a Before-After-Control-Impact analytical framework to assess potential impacts of a river channelization project on the P. rex population. In this paper, we summarize how our extensive monitoring facilitated the evolution of our (a) conceptual understanding of the ecosystem and fish population dynamics; (b) choices of ecological indicators and analytical tools; and (c) conclusions regarding the magnitude, mechanisms, and significance of observed impacts. Our experience with this case study taught us important lessons about how to adaptively develop and conduct a monitoring program, which we believe are broadly applicable to assessments of environmental impact and restoration success in other rivers. In particular, we learned that (a) pre-treatment planning can enhance monitoring effectiveness, help avoid unforeseen pitfalls, and lead to more robust conclusions; (b) developing adaptable conceptual and analytical models early was crucial to organizing our knowledge, guiding our study design, and analyzing our data; (c) catchment-wide processes that we did not monitor, or initially consider, had profound implications for interpreting our findings; and (d) using multiple analytical frameworks, with varying assumptions, led to clearer interpretation of findings than the use of a single framework alone. Broader integration of these guiding principles into monitoring studies, though potentially challenging, could lead to more scientifically defensible assessments of project effects.

Water↗

USGS2023

Evaluating the institutional and ecological effects of invasive species prevention policy: A case study from the U.S. Fish and Wildlife Service

Wildlife and natural resource institutions play key roles in invasive species monitoring and management. Paradoxically, the extensive fieldwork undertaken by these institutions and their partners may result in the inadvertent movement and spread of invasive species within and between sensitive ecosystems. In this work, we consider the potential effects of internal guidelines and policies designed to prevent the spread of invasive species by the field activities of management institutions and their partners. Such policies could be high-leverage tools for invasive species prevention, however, as large, complex organizations seek to implement policies to limit the spread of invasive species, it may be challenging to accommodate the wide diversity of potential invasion pathways and mitigation efforts facilitated via the programs, activities, and ecosystems they manage. Prevention policies may also be met with resistance due to the costs of implementation unless concrete benefits can be demonstrated. Assessing and communicating the effects of prevention policies could motivate improved implementation and adherence by institutional units and partners and could help inform adaptive policy changes. However, assessing the effectiveness of invasive species prevention presents a unique set of challenges, including incomplete data on invasive species distribution and pathways, that make it difficult to measure the effects of prevention efforts. In this work, we present a conceptual framework for evaluating institutional policies for invasive species prevention. We describe a flexible, multifaceted approach that considers policy implementation and adherence as well as ecological outcomes. We discuss potential application of this framework using a policy recently implemented by the Pacific Region of the U.S. Fish and Wildlife Service to prevent the introduction and spread of invasive species by service personnel and partners during field activities as a case study.

Management of Biological Invasions↗

USGS2022

Southeast Utah Group climate and drought adaptation report: Exposure and perennial grass sensitivity

National Park Service (NPS) managers face growing challenges resulting from the effects of climate change. In particular, as temperatures rise in coming decades, natural resource management in the western United States must cope with expectations for elevated severity and frequency of droughts. These challenges are particularly pronounced for vegetation managers in dryland environments. Developing adaptive strategies requires specific information about the expected magnitude of change in climate and drought conditions as well as insights into how those changes will affect important vegetation resources. This report describes research focused on Southeast Utah Group (SEUG) park units designed to provide information about exposure and sensitivity of perennial grasses to aridification. Analyses at larger regional scales are also reported for context and comparison. This report is a product of an ongoing climate adaptation collaboration between the U.S. Geological Survey (USGS), NPS, and Northern Arizona University. The study it summarizes contributes quantitative ingredients for vulnerability assessments that are needed in the Climate-Smart Conservation framework. As such, the results informed a series of climate adaptation workshops conducted between 2018 and 2021 for Colorado Plateau scientists and managers. This is a giant step forward in science-informed management. The information in this report can be used to craft management strategies that can be implemented at the right place and time for individual species of concern.

Colorado, Utah↗

USGS2015

Assessing transmissible spongiform encephalopathy species barriers with an in vitro prion protein conversion assay

Studies to understanding interspecies transmission of transmissible spongiform encephalopathies (TSEs, prion diseases) are challenging in that they typically rely upon lengthy and costly in vivo animal challenge studies. A number of in vitro assays have been developed to aid in measuring prion species barriers, thereby reducing animal use and providing quicker results than animal bioassays. Here, we present the protocol for a rapid in vitro prion conversion assay called the conversion efficiency ratio (CER) assay. In this assay cellular prion protein (PrP C ) from an uninfected host brain is denatured at both pH 7.4 and 3.5 to produce two substrates. When the pH 7.4 substrate is incubated with TSE agent, the amount of PrP C that converts to a proteinase K (PK)-resistant state is modulated by the original host’s species barrier to the TSE agent. In contrast, PrP C in the pH 3.5 substrate is misfolded by any TSE agent. By comparing the amount of PK-resistant prion protein in the two substrates, an assessment of the host’s species barrier can be made. We show that the CER assay correctly predicts known prion species barriers of laboratory mice and, as an example, show some preliminary results suggesting that bobcats ( Lynx rufus ) may be susceptible to white-tailed deer ( Odocoileus virginianus ) chronic wasting disease agent.

Journal of Visualized Experiments↗

USGS2021

Tools for increasing visual encounter probabilities for invasive species removal: A case study of brown treesnakes

Early detection and rapid response (EDRR) are essential to identifying and decisively responding to the introduction or spread of an invasive species, thus avoiding population establishment and improving the probability of achieving eradication. However, detection can be challenging at the onset of a species invasion as low population densities can reduce detection probability and conceal the true extent of the situation until the species is well established. This is doubly challenging if the invading species displays cryptic behavior or is nocturnal, thus further limiting opportunities for its discovery. Survey methods that maximize a searcher’s ability to detect an incipient population are therefore critical for successful EDRR. Brown treesnakes (Boiga irregularis) on Guåhan are a classic cautionary example of the dangers of not detecting an invasion early on, and the risk of their introduction to other islands within the Marianas, Hawai’i and beyond remains. Nocturnal visual surveys are known to detect brown treesnakes of all sizes and are the primary detection tool used by the Brown Treesnake Rapid Response Team, but detection probability remains low in complex forest habitats. As such, we investigated the use of two potential enhancements to nocturnal visual surveys – a live mouse lure and spray scent attractant – that may create hotspots of increased detection probability during surveys. We found that, while brown treesnake detection probabilities were low for all surveys, visual surveys conducted on transects with live mouse lures resulted in detection probabilities that were 1.3 times higher than on transects without live mouse lures. Conversely, the spray scent attractant did not increase the probability of detecting brown treesnakes compared to transects without scent, and in fact had detection probabilities that were 0.66 times lower, though the reasons for this phenomenon are unclear. Unlike scent attractants, live mouse lures likely provide both visual and olfactory cues that attract brown treesnakes to transects and thus provide more opportunities to detect and capture them. These enhancements were trialed on Guåhan, where prey populations are depressed. It remains unclear whether live mouse lures will be as effective for EDRR applications in prey-rich settings.

Guam↗

USGS2023

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons ( Python molurus bivittatus ) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Florida↗

USGS2015

Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change

State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine ( Pinus albicaulis ). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae ), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

AIMS Environmental Science↗

USGS2020

Dendrochronology of a rare long-lived mediterranean shrub

Ceanothus verrucosus (CEVE) is a globally rare, long-lived, chaparral shrub endemic to coastal southern California (CA) and northern Mexico. There is concern for CEVE persistence because of habitat loss, fire, and climate change, yet little is known about basic features of the plant, including whether it contains annual rings, plant age, and climate–growth response. Growth-ring analysis was challenging because of semi-ring-porous structure, false, and missing rings. We successfully crossdated CEVE annual rings, primarily from Cabrillo National Monument, CA, using a nearby Pinus torreyana chronology. The oldest living individual had 116 rings; the oldest inner-ring date was 1873; and most of the plants established between 1894 and 1905, all older than previous estimates. CEVE mortality occurred during a dry period from the late 1940s through the early 1960s. Correlations between age and stem measurements were weak to moderate (r = 0.10 to 0.56) posing challenges for field-based estimates of plant ages, which are important for population modeling. Variability in CEVE ring width had a strong positive correlation with prior cool-season (October–April) precipitation, yet 2- to 7-day warm-season precipitation events were recorded as rare false rings in multiple years, indicating extreme plasticity in cambial phenology and growth response to moisture.

California↗

USGS2017

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Advances in Statistical Climatology, Meteorology a↗

USGS2025

Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems

The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.

Biogeosciences↗

USGS2026

Assessing the prevalence, timing, and rapidity of transitions between hydrological extremes and their relation to meteorological extremes in the conterminous United States

Rapid shifts between droughts and floods, termed hydrological whiplash, challenge water management, yet their timing and drivers remain poorly understood at continental scales. While drought-to-flood (DtF) transitions have received growing attention, flood-to-drought (FtD) transitions — though rarer — pose distinct operational challenges that are less well characterized. These wet-to-dry shifts can disrupt post-flood recovery, strain warm-season water demands, and create compounding risks for infrastructure and water quality. We analyzed daily streamflow records from 3,219 USGS streamgages (1981-2024) to characterize both DtF and FtD transitions across CONUS, with particular emphasis on understanding why these transitions are not symmetric inverses of each other. We test a wide variety of hydrological extreme transition definitions to examine the sensitivity of the number of transitions identified and their rapidity. Additionally, we identify a subset of transitions that may be impactful based on the maximum change in percentile magnitude during a transition. DtF transitions are faster than FtD transitions, and short-term (<= 30-days) transitions in both directions are concentrated in the Northeast, Northwest, and Rocky Mountains regions. Short-term DtF transitions are additionally concentrated in southern California and along the line from North Dakota down to Texas where precipitation approximately equals potential evapotranspiration. We find direction-specific controls: snow-dominated, urban, regulated, and minimally disturbed basins show the most frequent impactful DtF transitions, while regulated basins are most prone to impactful FtD transitions. Rapid and impactful transitions make up a substantially larger fraction of DtF transitions than FtD transitions across nearly all basin types. A key finding is that hydrological and meteorological whiplash rarely coincide: only 19-24% of hydrological extreme transitions co-occur with hydroclimate whiplash, revealing that basin storage, regulation, and routing processes create a fundamental decoupling between climate forcing and streamflow response. Our findings highlight the need to better understand quick hydrological transitions under increasing hydroclimatic volatility, particularly the understudied FtD direction, and the mechanisms by which anthropogenic modifications reshape the hydrological whiplash risk.

EGUSphere↗

USGS2025

Global methane budget 2000-2020

Understanding and quantifying the global methane (CH 4 ) budget is important for assessing realistic pathways to mitigate climate change. CH 4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO 2 ), and both emissions and atmospheric concentrations of CH 4 have continued to increase since 2007 after a temporary pause. The relative importance of CH 4 emissions compared to those of CO 2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH 4 sources and from the uncertain magnitude and temporal change in the destruction of CH 4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH 4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH 4 budget, integrating results of top-down CH 4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH 4 yr −1 ). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH 4 yr −1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH 4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH 4 yr −1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH 4 yr −1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH 4 yr −1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH 4 yr −1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH 4 yr −1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH 4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH 4 yr −1 ) larger global emissions (669 Tg CH 4 yr −1 , range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH 4 yr −1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH 4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH 4 yr −1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty ( ∼ 25 %). The total sink of CH 4 is estimated at 633 [507–796] Tg CH 4 yr −1 by the bottom-up approaches and at 554 [550–567] Tg CH 4 yr −1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH 4 yr −1 in the top-down budget and 211 [195–231] Tg CH 4 yr −1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH 4 yr −1 in the top-down budget and 120 [117–125] Tg CH 4 yr −1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH 4 yr −1 in the top-down budget and 28 [21–39] Tg CH 4 yr −1 in the bottom-up budget. We identify five major priorities for improving the CH 4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH 4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH 4 observations at local (e.g. FLUXNET-CH 4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

Earth System Science Data↗

USGS2012

A multi-source satellite data approach for modelling Lake Turkana water level: Calibration and validation using satellite altimetry data

Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellite-driven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004–2009). Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1–2 m. The lake level fluctuated in the range up to 4 m between the years 1998–2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance model for (i) quantitative assessment of the impact of basin developmental activities on lake levels and for (ii) forecasting lake level changes and their impact on fisheries. From this study, we suggest that globally available satellite altimetry data provide a unique opportunity for calibration and validation of hydrologic models in ungauged basins.

Lake Turkana↗

USGS2023

Automated mapping of culverts, bridges, and dams

Accurate maps of built structures around stream channels, such as dams, culverts, and bridges, are vital in monitoring infrastructure, risk management, and hydrologic modeling. Hydrologic modeling is essential for research and decisionmaking related to infrastructure and development planning, emergency management, ecology, and developing hydrographic data. Technological advances in remote sensing afford increasingly fine-scale elevation data, such as the U.S. Geological Survey 1-meter digital elevation models (DEMs), that can accurately model the Earth’s surface characteristics and related hydrologic dynamics. A long-standing challenge in flow modeling is the presence of built structures in an elevation model that resist flow in a way that does not reflect actual dynamics, such as culverts, bridges, and dams. This challenge is exacerbated in fine-scale elevation data as more built structures are resolved. Here we present a test of the extensibility of a culvert and dam detection workflow, culvert-net (CN). CN was developed using a large dataset of field-validated culverts, bridges, and dam locations for Alexander County, North Carolina, USA, supplemented by manual review and identification of additional features. In this workflow, the CN model is tested on a new study area in western Michigan, USA, where culverts and associated hydrography have recently been manually compiled.

Conference Paper↗

USGS2018

Streams do work: Measuring the work of low-order streams on the landscape using point clouds

The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for hydrologic modeling, ecosystem research, and boundary monitoring. High resolution digital elevation models derived from lidar data have shown promise in low order stream modeling yet forested high relief landscapes and low relief agricultural areas remain challenging. Here we present early results from research analyzing lidar point clouds to identify features and patterns that may be used in low-order stream identification and classification in challenging geographic conditions. This work has identified characteristics derived from point clouds that correlate with the presence of streams and stream heads and show promise for mapping small streams. In low topographic relief agricultural areas, cross sections collected at regular intervals along drainage channels extracted as 3D lines show a significant jump in value and variance of profile curvature standard deviation at stream heads. In high relief areas, observations show potential for stream mapping by identifying trends in riparian zone structure. Lidar return point density from riparian vegetation under 30 feet tall dips in the vicinity of intermittent stream heads. Also seen is an increase in point density above 60 feet downstream of stream heads. The trends found here likely reflect a change in vegetation structure relative to the presence of streams.

Illinois, Iowa, North Carolina↗