Development is well-advanced for the next version of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG), labeled Version 07. IMERG is a key output of the U.S. GPM Science Team, and V07 will be the second generation in which data from both the Tropical Rainfall Measuring Mission (TRMM) and GPM projects are combined into a single, uniformly processed record, currently starting in June 2000. This presentation will show several examples of successes and challenges in V06, and use these to illuminate the upgrades that have been pursued for V07. For example, the V06 IMERG near-real-time products (Early and Late Runs) show regional biases because they do not have climatological calibration (despite the documentation), and this will be done in V07. As well, the time series of precipitation-rate histograms shows a seam in the transition from TRMM calibration to GPM Core Observatory calibration at the start of June 2014. V07 will benefit from better continuity in the input calibration datasets across that boundary. A third issue is that the Kalman filter used in IMERG a) introduces a variable amount of smoothing, and b) depends on relatively simple measures of input data quality. Both of these are revisited in V07. We will report the status of IMERG Version 07 processing as of the conference time, and introduce some topics that are being considered for the future, including improved uncertainty estimates, addition of sub-monthly gauge information, and strategies for incorporating precipitation estimates from multiple, relatively short-lived small satellites.

Flood depth grids from U.S. Federal Emergency Management Agency (FEMA) provide model-output estimates of the depth of water that can, on average, be expected to occur at various return periods for localized areas. However, use of these depth grids can be limited by spurious data and an insufficient number of return periods for certain planning applications. This research proposes a new method for estimating flood depth grids to address these shortcomings. The Gumbel distribution is used to characterize the flood depth-return period relationship for grid cells for which the data are plausible. Then the Gumbel parameters of slope (α) and intercept (u) are used to project flood elevations for extreme return periods for which an entire area can be assumed to be submerged. Spatial interpolation methods are then used to impute the flood elevations for spurious or missing grid cells. Then, the flood depth is recomputed from the flood elevations, once they are re-calculated at the shorter return periods. Validation of this technique for a Metairie, Louisiana, U.S.A. study area suggests that the cokriging spatial interpolation technique provides the most suitable estimates of flood depth, provided that the FEMA-generated model output is assumed to provide the “correct” results. These methods may assist engineers, developers, planners, and others in mitigating the world’s most widespread and expensive natural hazard.

A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current continental cropland extent maps of Africa are either inaccurate, have too coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland extent map for the African continent is therefore recognized as a gap in the current crop monitoring services. Using Digital Earth Africa’s Open Data Cube platform, and working in conjunction with multiple regional African geospatial institutions, we co-develop a 10 metre resolution cropland extent map over the African continent using a Random Forest machine learning classifier and an annual time-series of Sentinel-2 satellite images. Members of the regional African geospatial institutions (RCMRD, OSS, Afrigist, AGRHYMET, and NADMO) were instrumental in defining the specifications of the product, in developing and implementing a continental scale reference data collection strategy, and assisted with iterative model building. The cropland extent map comes packaged with three layers: a pixel-based classification, a pixel-based cropland probability layer, and an object-based segmentation filtered classification. All the components of Digital Earth Africa’s cropland extent map: models, reference data, code, and results are open source and freely available online through Digital Earth Africa’s mapping and analysis platforms. A fuller description of the dataset, including methods, the validation results, and how to access the different datasets can be seen on the DE Africa user guide: https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.

Terrestrial primary productivity plays a pivotal role as a forcing factor of atmospheric CO2 and drives biospheric carbon dynamics. India is one of the largest GHGs emitters, yet less is understood in carbon cycling in terrestrial ecosystems. Here we explored the trend and magnitude of gross and net productivities of India for the last two decades (2000 – 2019) by integrating satellite observation from MODIS, remote sensing-based CASA model and twenty DGVMs from the TRENDY ensemble. Preliminary results exhibited a unimodal response across the data products with an overall positive trend and a declining decadal trend for 2010 – 2019. Alongside, the SPEI drought severity index across various ecological zones indicated India was more positively sensitive to wet span than the dry. We found that the ecosystems were drastically shifting their nature to C source with a positive trend in the productivities and were mediated by the changing climate. The analysis also revealed the increasing decadal amplitude of GPP by 0.0884 Pg C/Year, NBP by 0.0096 Pg C/Year, NEP by 0.0195 Pg C/Year, NPP by 0.0448 Pg C/Year and NEE by 0.0161 Pg C/Year. CASA underestimated the magnitudes but with the temporal synchronisation of the ensemble. Seasonal variability across the agro-ecological zones was more sensitive and was an offset for the declining productivities in the primaeval forests of India. The monsoon season contributed to the interannual variability of India. Higher uncertainty in productivities was observed in the high greening areas, whereas it contradicted NBP by reflecting a stable trend. Our results underscore the nature of C variability in the terrestrial ecosystems of India; and, they indicate that C release has reacted stronger than the C uptake, which was substantially inferred from NEE across the ecological zones.

The Interstellar Boundary Explorer (IBEX) mission has shown that variations in the ENA flux from the outer heliosphere are associated with the solar cycle and longer-term variations in the solar wind. In particular, there is a good correlation between the dynamic pressure of the outbound solar wind and variations in the later-observed IBEX ENA flux. The time difference between observations of the outbound solar wind and the heliospheric ENAs with which they correlate ranges from approximately two to six years or more, depending on ENA energy and look direction. This time difference can be used as a means of “sounding” the heliosheath, that is, finding the average distance to the ENA source region in a particular direction. We apply this method to build a three-dimensional map of the heliosphere. We use IBEX ENA data collected over a complete solar cycle, from 2009 through 2019, corrected for survival probability to the inner heliosphere. We divide the data into 56 “macro-pixels” covering the entire sky, and as each point in the sky is sampled once every six months, this gives us a time series of 22 points per macro-pixel on which to time-correlate. Consistent with prior studies and heliospheric models, we find that the shortest distance to the heliopause dHP is slightly south of the nose direction (dHP ~ 110 – 120 au), with a flaring toward the flanks and poles (dHP ~ 160 – 180 au). The heliosphere extends at least ~350 au tailward, which is the distance limit of the technique.

Groundwater is critical in sustaining streamflow, especially in mountain catchments, because of its ability to supply baseflow in the absence of precipitation. In water-limited arid and semi-arid mountain environments, the need to characterize groundwater recharge and discharge has grown in tandem with demands to effectively manage current and future water resources. However, studying groundwater is challenging in complex terrain due to limited field measurements. Nearly a decade of monitoring data collection at Gordon Gulch in the Colorado Front Range provides a unique opportunity to study such an environment. The field data is used to parameterize and calibrate a groundwater flow model (MODFLOW-NWT). Model results reveal spatial and temporal patterns in groundwater recharge and discharge to the stream. Groundwater is recharged primarily by one to two recharge events each year, driven by spring snowmelt and rain. The majority of groundwater recharge occurs in upper Gordon Gulch and is stored in saprolite and weathered bedrock. Groundwater is discharged to the stream via long, deep flowpaths sourced from upper Gordon Gulch and short, shallow flowpaths from soil and saprolite in lower Gordon Gulch. Using Gordon Gulch as a case study, this model and data analysis contribute to a larger effort to understand and constrain the mechanisms driving groundwater recharge and groundwater-stream exchanges in semi-arid, montane environments.

​​We believe that the future of Earth Observation (EO) is in fusion, harmonization, and interoperability of satellite imagery. Intensified monitoring leads to better understanding of land use and reduction of maintenance costs for all Land Cover products. RapidAI4EO is an initiative that aims to establish the foundations for the next generation of Copernicus Land Monitoring Service (CLMS) products. The goal is to provide intensified monitoring of Land Use (LU), Land Cover (LC) changes at a much higher spatial resolution and temporal cadence than is possible today. Key objectives are to explore, evaluate, and quantify state of the art deep learning algorithms and methodologies that leverage three meter, daily time series, in conjunction with higher spectral resolution Sentinel-2 imagery.  Consortium Partners: The RapidAI4EO projects brings together Planet Labs PBC, the operator of the world’s largest fleet of Earth-imaging satellites and the recognized leader of the CubeSat revolution, VITO, the main production center of the Copernicus Global Land Service, Vision Impulse, a recent spin-off of German Research Center for Artificial Intelligence (DFKI, the largest research center for Artificial Intelligence in the world and one of the two European NVIDIA AI Labs), the International Institute for Applied Systems Analysis (IIASA) whose Center for Earth Observation and Citizen Science (EOCS) devises new approaches and technologies to collect data on land cover and land use, and Serco Italia, a worldwide service provider to governments, international agencies and industries, and operator of the ONDA DIAS platform. The objectives of RapidAI4EO are: 1) the creation and release of the most comprehensive spatiotemporal EO training sets ever produced for machine learning;  2) the development and implementation of novel AI solutions for continuous change detection that leverage these data sets; 3)the ability to drive frequent temporal updates of the Corine Land Cover (CLC) product; and 4)to demonstrate improved LULC mapping using harmonized Sentinel-2 and very high resolution, high cadence data streams.

NASA designated Reiner Gamma (RG) as the landing site for the first Payloads and Research Investigations on the Surface of the Moon (PRISM) delivery (dubbed PRISM-1a). Reiner Gamma is home to a magnetic anomaly, a region of magnetized crustal rocks. The RG magnetic anomaly is co-located with the type example of a class of irregular high-reflectance markings known as lunar swirls. RG is an ideal location to study how local magnetic fields change the interaction of an airless body with the solar wind, producing stand-off regions that are described as mini-magnetospheres. The Lunar Vertex mission, selected by NASA for PRISM-1a, has the following major goals: 1) Investigate the origin of lunar magnetic anomalies; 2) Determine the structure of the mini-magnetosphere that forms over the RG magnetic anomaly; 3) Investigate the origin of lunar swirls; and 4) Evaluate the importance of micrometeoroid bombardment vs. ion/electron exposure in the space weathering of silicate regolith. The mission goals will be accomplished by the following payload elements. The lander suite includes: The Vertex Camera Array (VCA), a set of fixed-mounted cameras. VCA images will be used to (a) survey landing site geology, and (b) perform photometric modeling to yield information on regolith characteristics. The Vector Magnetometer-Lander (VML) is a fluxgate magnetometer. VML will operate during descent and once on the surface to measure the in-situ magnetic field. Sophisticated gradiometry allows for separation of the natural field from that of the lander. The Magnetic Anomaly Plasma Spectrometer (MAPS) is a plasma analyzer that measures the energy, flux, and direction of ions and electrons. The lander will deploy a rover that conducts a traverse reaching ≥500 m distance, obtaining spatially distributed measurements at locations outside the zone disturbed by the lander rocket exhaust. The rover will carry two instruments: The Vector Magnetometer-Rover (VMR) is an array of miniature COTS magnetometers to measure the surface field. The Rover Multispectral Microscope (RMM) will collect images in the wavelength range ~0.34–1.0 um. RMM will reveal the composition, texture, and particle-size distribution of the regolith.

Subaqueous dunes are fascinating morphological features that exist in diverse environments such as the deep sea, continental shelves and inland streams or rivers. Due to their rhythmic and oftentimes very frequent occurrence along the predominant flow direction, the analysis of these bedforms is usually assigned to bedform identification tools. Such algorithms automatically determine crest and trough locations and calculate dune dimensions accordingly. Over the last years, the number of these tools has notably increased with specialized methodologies for every environment and bedform scale. Although many of them are readily available to interested researchers, there may be uncertainty as to which method should be applied in view of a specific research question. As authors of some of the most recent bedform identification tools, we have started to systematically compare our approaches by analyzing an agreed set of diverse bathymetric data. The bed features assessed in this context range from river dunes formed under unidirectional flow over tidally constrained compound dunes to bed elevation profiles gained from flume experiments. The resulting dune characteristics, which each scientist obtained by applying his/her particular algorithm, are thereupon contrasted in a qualitative and quantitative manner uncovering the similarities and differences between individual methodologies. Our preliminary results suggest a strong influence of the original focus of each algorithm and therefore corroborate the need for systematic comparison. In the next step, the gained insights will be used to find and explain the optimum fields of application and, in the end, provide user-oriented guidelines that may support the bedform community in deciding which identification tool should be used for what purpose.

Based on the observational dataset SA-OBS and model outputs from the Community Earth System Model Large Ensemble project, this study investigates heatwaves in Southeast Asia in the current and future warmer climate. A heatwave is detected when the daily maximum temperature exceeds the 90th percentile threshold at each grid for at least three consecutive days. Three characteristics describing the frequency, duration, and amplitude of heatwaves are examined, including the sum of heatwave days per year (HWF) satisfying the heatwave definition, the length of the longest yearly heatwave event (HWD), and the hottest amplitude of the hottest yearly heatwave event (HWA). Results indicate that increased global warming is associated with substantial changes in heatwave characteristics over Southeast Asia, with more frequent heatwaves, longer heatwave duration, and higher extreme temperatures. The increase in HWA has a linear growth against global warming levels with distinct regional differences between the Maritime Continent and the Indochina Peninsula due to their different heat content of lower atmospheric boundaries. In contrast, those in HWF and HWD have nonlinear growth characteristics. The projected warmer future tends to be associated with a higher risk ratio value with the occurrence of rarer extreme heatwaves relative to the current climate. These results reiterate the potential risks of extreme regional heatwaves if global warming is unrestricted.

The increasing amount of Synthetic Aperture Radar (SAR) satellites orbiting the Earth, their increasing time and space resolution, the variety of wavelengths, polarizations and looking geometries, the shortening of data availability latency and the lengthening of archive databases offer unprecedented opportunities for Earth observation and hazard monitoring. The downside is that it brings new challenges for processing that huge amount of data and for making the results quickly analyzable. To fully benefit from these advances in SAR, it requires efficient data processing infrastructure (optimized for processing speed, storage usage and security), efficient data visualization, and standardization of the final products for easy ingestion by conventional analysis tools. We present here the InSAR Mass processing Toolbox for Multidimensional time series (MasTer), which can combine any type of SAR data to produce unsupervised vertical and horizontal ground deformation time series. MasTer is optimized to automatically download SAR data, select the appropriate interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is made available. The incremental architecture allows updating the time series within the shortest time possible (typically a few hours) as soon as a new SAR image is provided. Several steps are self-evaluating to ensure robust and reliable processing. Moreover, recent methodological improvement consists in the computation of a coherence proxy to guide the pair selection optimization balancing the use of each image as primary and secondary image during the differential interferometric (DInSAR) processing. Such a pair selection increases both the processing efficiency and the signal-to-noise ratio of the time series. MasTer also allows the production of time series of coherences or SAR amplitude images, which can be used e.g. for land use monitoring or geomorphological changes detection. The capabilities and performances of MasTer will be illustrated with several examples. Software and manual are available upon request from the authors.

The IGS contribution to ITRF2020, based on the third IGS reprocessing campaign (repro3), comprises daily position estimates for 1905 globally distributed GNSS stations. As an essential step of the ITRF2020 preparation, a detailed analysis of the IGS repro3 station position time series has been carried out, including identification of offsets, modeling of post-seismic displacements, confrontation with deformation predicted by geophysical loading models, and characterization of systematic and random errors. This presentation covers the different aspects of this analysis, the methods used and the lessons learned.

Substantial progress on machine learning (ML) models and graphical processing units (GPUs) has stimulated emerging research in applications of ML to earth science. As snow is a vital component of the global hydroclimate system, precise snowpack prediction is of considerable value for science and society. In this work, we have trained three different ML models (LSTM, CNN and Attention) to predict daily snow water equivalent (SWE) with both dynamic and static features in the Western Contiguous United States from Snow Telemetry (SNOTEL) observations. Dynamic features include precipitation, minimum and maximum temperature, minimum and maximum relative humidity, specific humidity, solar radiation and wind velocity. Static features are latitude, longitude, elevation, diurnal anisotropic heating (DAH) index and topographic radiative aspect (TRASP) index. This choice of features allows us to produce high-resolution maps of regional SWE for a given set of input meteorological conditions. The importance and the sensitivity of input variables will be tested by several explainable AI methods including feature permutation and integrated gradient. The ML-based dataset is further up-sampled and compared with the 4km gridded SWE dataset from the National Snow & Ice Data Center (NSIDC), which is from a physical-based model. Future SWE estimates are also produced under climate conditions projected by CMIP class models, along with associated uncertainty estimates based on our sensitivity analysis. The ML models are demonstrated to be a fast and accurate method of producing high-resolution SWE estimates with minimal computing power.

Eddy Covariance method has been actively used by expert micrometeorologists for over 30 years, covering 2155 stationary locations globally, and numerous mobile campaigns over land and water surfaces. Latest measurement technologies and automated processing software are rapidly expanding the use of the method to non-micrometeorological research. Regulatory and commercial uses of the method also increase year-by-year. Despite widening adoption of the method, academic investigators outside the area of micrometeorology and the majority of non-academic investigators are still not familiar enough with the proper implementation of the method required for conducting high-quality, reliable, traceable, and defensible measurements in their respective areas of interest. Although data collection and processing are now automated, the method still requires significant care to correctly design the experiment, set up the site, organize and analyze the large amount of data. Efforts of the flux networks (e.g., FluxNet, Ameriflux, Asiaflux, ICOS, NEON, OzFlux, etc.) have led to major progress in the standardization of the method. The project-specific workflow, however, is difficult to unify because various experimental sites and purposes of studies demand different treatments, and site-, measurement- and purpose-specific approaches. To address this situation, step-by-step instructions were created to introduce a novice to general principles, requirements, applications, processing and analysis steps of the conventional Eddy Covariance technique in the form of the free electronic resource, a 660-page textbook titled “Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications”. The explanations are provided using easy-to-understand illustrations and real-life examples, and text is written in a non-technical language to be practically useful to those new to this field. Information is provided on theory of the method (including the state of methodology, basic derivations, practical formulations, major assumptions, sources of errors, error treatments, etc.), practical workflow (e.g., experiment design, implementation, data processing, quality control and analysis), data sharing and flux stations networking, key alternative methods, and the most frequently overlooked details.

Buoyant hydraulic fractures occur in nature as magmatic dikes and sills. In industrial applications like well stimulation, the emergence of buoyant fractures is undesirable and often limited by the injected volume and/or variation of in-situ stress. This class of tensile fractures is governed by a buoyancy force resulting from the density contrast between the surrounding solid and the fracturing fluid. We focus here on fluid releases from a point source in an impermeable elastic media with homogeneous rock and fluid properties. The resulting buoyant force is thus constant. We combine scaling arguments and planar 3D hydraulic fracture growth simulations [1] to fully understand the emergence as well as the different propagation regimes of buoyant fractures. For a continuous release, a family of solutions dependent on a dimensionless-viscosity Mkˆ exists. In the limit of large toughness (Mkˆ≪ 1), we retrieve a finger-like shape [2]. The stable breadth of the tail is generally akin to the PKN approximation presented in [2]. The limit of a viscosity-dominated buoyant fracture (Mkˆ≫ 1) has no stabilized breadth and exhibits a teardrop shape. For the case of a finite fluid volume release, a dimensionless buoyancy Bk¯ controls if a buoyant fracture emerges (Bk¯≥ 1) or stops and remains at depth (Bk¯<1). For a finite release, a single large-time solution corresponding to the solution of [2] exists. Detailed characterization of the fracture evolution requires separation between the cases where the buoyant transition occurs during or after the release (see attached Fig. 1). For natural configurations, the emerging buoyant fractures are typically viscosity-dominated, which may explain the reported discrepancy between field and laboratory measurements of rock fracture toughness. Representative values of industrial single-entry hydraulic fracturing treatments lead to buoyant fractures under homogeneous conditions, which indicate the critical importance of stress and material heterogeneities in the containment of buoyant fractures at depth. [1] Haseeb Zia and Brice Lecampion. Pyfrac: A planar 3d hydraulic fracture simulator. Computer Physics Communications, page 107368, 2020. [2] L.N. Germanovich, D. I. Garagash, Murdoch, L., and Robinowitz M. Gravity-driven hydraulic fractures. In AGU Fall meeting, 2014.

The Wayanad Schist Belt (WSB) located along the southern margin of the Western Dharwar Craton (WDC) is a small discontinuous schistose belt occur as pockets and patches within basement gneisses. WSB is oriented parallel to the E-W trending Moyar Shear Zone (MSZ) separating the WDC and Southern Granulite Terrane (SGT). The major rock types include metapelitic schists, metamafic schists, banded magnetite quartzite, pyroxene granulite, amphibolite, metapyroxenite, mylonite, hornblende-biotite schist/gneiss, micaceous quartzite etc. The fabrics in schistose rocks are defined mostly by the orientation of shape-preferred aggregates of biotite-chlorite-muscovite-sillimanite (in metapelites) and tremolite-talc-chlorite-actinolite (in metamafites). Whereas the gneissic fabric is defined by the quartz-plagioclase rich leucocratic layers and biotite-garnet-amphibole-pyroxene rich melanocratic layers. The WSB has undergone at least three phases of deformation. The most prominent fabric in WSB is the E-W to ESE-WNW trending sub-vertical foliations developed during the D3 deformation. Poles to the steep-dipping S3 foliations are WNW-ESE trending and the F3 folds are steeply plunging in nature. At the low strain domains of D3 deformation the early foliations are preserved. The N-S to NNW-SSE trending sub-vertical S2 fabric developed axial planar to moderately tight to open (F2) folds during D2 deformation. The early S1 foliations are rarely preserved in the interfolial domains of S2 foliation. In the regional scale, the poles to S1 fabrics lie on a well-defined girdle with NE-SW trending upright asymmetric folds. The U-Th-total Pb dating of texturally controlled monazites from WSB has yielded prominent age populations at around 700-850 Ma and 500-600 Ma with minor age peaks at 2.2-2.3 Ga, 2.4-2.6 Ga. The structural and chronological data suggest that, the deformation events in WSB are similar to that reported from the WDC. However, the D3 deformation is more prominent and all the earlier fabrics are truncated or brought into parallelism with the S3 fabric during D3 deformation. The 700-850 Ma and 500-600 Ma monazite growths post-tectonic with respect to the D3 deformation indicates that the Neoproterozoic accretionary events affected the whole SGT must have recrystallized the monazites present in the MSZ.

Recent advances in Earth observation data and computing ability create exciting opportunities for national and global studies of human impacts to water resources. But, with a lack of complete databases of artificial levees, there remains a need to better understand how artificial levees impact floodplain extent at regional and larger scales. Here, we estimate river-floodplain disconnection in the contiguous United States using an incomplete artificial levee database, machine learning algorithms, and hydrogeomorphic floodplain delineation models. We tested different topographic, land use, and spatial variables with different machine learning techniques in a case study of seven geographically diverse HUC8 basins before applying the technique at the national scale. We found that a parsimonious random forest model without topographic variables was 97% accurate. When applied to areas within a national 100-year hydrogeomorphic floodplain, the model indicated the potential for more than 180,000 km of undocumented artificial levees, meaning that the National Levee Database (NLD) is about 20% complete. More than 62% of potential levees are concentrated in the Upper and Lower Mississippi and Missouri basins. The stream order distribution of potential and NLD levees are similar; however, potential levees are primarily located along stream orders 3 and 6 while the NLD locations are along stream orders 2, 3 and 4. Using this, we explored the national impacts of artificial levees on floodplain extent by comparing two hydrogeomorphic floodplains based on (1) an unmodified USGS 1 arc second DEM and (2) a modified DEM with known and potential levees erased from the topography. We found that the overall impact of artificial levee removal was to shift the location of flooding. Over 30% of the CONUS 100-year floodplain was cultivated or developed land use.