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The 3rd ARTMIP Workshop What: Over 30 participants from multiple universities and research insititutions met to discuss new results from the Atmospheric River Tracking Method Intercomparison Project. Where: Lawrence Berkeley National Lab, Berkeley, CA, USA When: 16-18 October 2019
Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry.
In mid-April 2024, the United Arab Emirates (UAE) and the surrounding Gulf region experienced unprecedented rainfall and catastrophic flooding, causing widespread damage, loss of life, and significant economic costs. During the 3-day period from 15 to 17 April, rainfall in the UAE exceeded 100 mm in the hardest-hit areas, with more than 170% of the typical annual precipitation recorded in just 72 h. The event was associated with exceptionally strong integrated water vapor transport (IVT), driven by a persistent low pressure system and a focused corridor of moisture transport. This environment, combined with favorable dynamical and convective conditions, triggered intense thunderstorms and widespread flooding. This article examines the role of an atmospheric river (AR) in the April 2024 extreme precipitation event, emphasizing the contribution of extreme IVT to preconditioning and amplifying the heavy rainfall. While this event has previously been described primarily in terms of a mesoscale convective system (MCS) and potential vorticity (PV) streamers, here we document and illustrate its close relationship to a record-breaking moisture transport corridor. This AR-based perspective highlights how large-scale moisture-transport frameworks can complement synoptic and mesoscale analyses in understanding extreme rainfall in arid and semiarid regions such as the UAE and the Gulf. The event serves as a stark example of the vulnerabilities faced by arid regions, where atmospheric conditions conducive to extreme flooding may become more frequent in the future.
Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and es-pecially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer pro?lers, six radar wind pro?lers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical pro?les of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the ?eld project. Characteristics of these events are described, including statistics of the wind speed distributions and pro?les, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.
The frequency of North Pacific atmospheric rivers (ARs) affects water supply and flood risk over western North America. Thus, understanding factors that affect the variability of landfalling AR frequency is of scientific and societal importance. This study aims at identifying the sources of the moisture for North Pacific ARs and assessing how different modes of variability modulate these sources. To this end, the sources and variability of the background divergent component of the integrated moisture flux (DIVT) in ARs are identified using MERRA reanalysis. It is shown that in the boreal winter, this background DIVT in ARs is related to the outflow from the subsidence over the subtropics which transports moisture northward, while in summer it is related to the Asian monsoon and it transports moisture northwestward. This leads to a seasonal northwest/southeast movement of the AR frequency climatology. At the intra-seasonal scale, propagation of the Madden-Julian Oscillation introduces an anti-clockwise rotation of the background DIVT, with northward transport in phases 1 and 2, westward in 3 and 4, southward in 5 and 6 and eastward in 7 and 8, making landfall over the west coast of North America most likely during the last two phases. Similarly, El Niño Southern Oscillation variability also affects the frequency of ARs through modulation of the westerly background DIVT, favoring landfall over the US west coast during strong El Niño phases. It is shown that in general the likelihood of AR landfall over the western US is correlated with the zonal background DIVT over northeastern Pacific.
The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.
Abstract The Lower Mississippi River basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here, we study the impacts of land-cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface–subsurface hydrologic model, Energy Exascale Earth System Model (E3SM) Land Model coupled with the three-dimensional ParFlow subsurface flow model (ELM-ParFlow), we simulate the effects of land-cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land-cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land-cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land-use, land-cover, and hydrologic processes in coastal regions.
Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s -1 and RMSEs of 2-3 m s -1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Finally, measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.
The 300 Area is adjacent to the Columbia River on the southeastern end of Hanford, approximately one mile north of the city of Richland. Uranium fuel fabrication took place in the 300 Area from 1943 to 1988. In this chapter, the historical waste releases that occurred within the 300 Area of the Hanford Site, and specifically the resulting uranium plume, will be used as a case study. There are 3 different operable units (OU) that were established in the 300 Area for cleanup purposes: The 300-FF-1 OU, 300-FF-2 OU and the 300-FF-5 groundwater OU. To determine the location of contaminant plumes within the Hanford Site’s 300 Area, several puzzle pieces must be put together using multiple resources. The first piece of the puzzle is determining the location and chemistry of waste disposed of in the area. Records beginning in the 1940’s can help piece this information together, and the known waste sites and waste disposed of are an example of that process knowledge. Characterization of the subsurface and groundwater is important to determine where the contaminants are now, and what form they are in. This information can be used to design a remediation strategy, as well as to inform modeling efforts to predict where the contaminants will move to next.
Total nitrogen leaching rates were calculated over the Mississippi Atchafalaya River Basin (MARB) using an integrated economic-biophysical modeling approach. Land allocation for corn production and total nitrogen application rates were calculated for crop reporting districts using the Biofuel and Environmental Policy Analysis Model (BEPAM) for 5 RFS2 policy scenarios. These were used as input in the Integrated BIosphere Simulator-Agricultural Version (Agro-IBIS) and the Terrestrial Hydrologic Model with Biogeochemistry (THMB) to calculate the nitrogen loss. Land allocation and total nitrogen application simulations were simulated for the period 2016-2030 for 303 crop reporting districts (https://www.nass.usda.gov/Data_and_Statistics/County_Data_Files/Frequently_Asked_Questions/county_list.txt). The final 2030 values are reported here. Both are stored in csv files. Units for land allocation are million ha and nitrogen application are million kg. The nitrogen leaching rates were modeled with a spatial resolution of 5' x 5' using the North American Datum of 1983 projection and stored in NetCDF files. The 30-year average is calculated over the last 30 years of the 45 years being simulated. Leaching rates are calculated in kg-N/ha.
Rainfall data for the Savannah River Site (SRS) has been historically measured by rain gauges. These instruments serve as ground truth for most climatological and weather applications; however, gauge measurements are prone to errors or biases under certain weather conditions. Rainfall estimates from radar reflectivity values have been developed and improved over the years and serve as an alternative or supplement for gauge measurements. This study compares measurements from tipping bucket rain gauges with rainfall estimates from the NOAA NWS WSR-88D Level III hourly rainfall product and the Multi-Radar Multi-Sensor (MRMS) gauge-corrected precipitation estimate. Results show good agreement between radar derived amounts and ground measurements, with MRMS values showing correlation coefficients of 0.60-0.95 and RMSE values of less than 1.0 cm (0.4 in). The WSR-88D Level III estimates result in correlation coefficients of 0.46-0.86 and RMSE values of less than 1.3 cm (0.5 in). Few outliers are observed for each data pair and are evaluated against precipitation classification products (WSR-88D Hybrid Hydrometeor Classification product and MRMS Precipitation Flag product). The rain gauges used in this study are not part of the Hydrometeorological Automated Data System (HADS) network used to correct the MRMS estimates and therefore, results of this work provide an independent validation of the MRMS gauge correction scheme.
This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.
This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON
This dataset comprises paired environmental and genomic data for soil samples collected across meander-bound floodplains G (ERMG), L (ERML) and Z (ERMZ). A total of 96 soil samples (32 per floodplain) from the ~ 10-25 cm layer were collected in September 2015, during base flow conditions. An additional 32 soil samples were collected from ERML in September 2016, including three samples from a depth just above the water table. A subset of samples (14) collected in 2016 were used for metatranscriptomics as well as metagenomics. The data package includes: 1) A spread sheet with sample names, location and depth by meander-bound floodplain. 2) Total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TC = TOC + TIC), total nitrogen (TN), and the TOC to TN ratio (TOC:N) from 2015 subsamples. 3) Soil chemistry for 2016 subsamples. 4) A spread sheet with elevation, Easting, Northing, and topographic position index (TPI) for each site sampled in 2015. 5) A kml file, which can be viewed in Google Earth, with the locations of the 96 soil sampling sites along the East River Watershed Floodplain in Colorado. Metagenomics and metatranscriptomics data, as well as reconstructed genomes, are available elsewhere (see data package methods and bioRxiv preprint for links to the data). 6) Two sets of reconstructed genomes with overlapping members (see bioRxiv preprint for description and associated data). Metagenomes and metatranscriptomes are available elsewhere (see data package methods and bioRxiv preprint). To view tar.gz files, the following software can be used: https://www.gnu.org/software/tar/, https://www.7-zip.org/.
Soil moisture, temperature and electrical conductivity have been monitored with 5TE sensors (Decagon Inc.) located at 10 and 50 cm depth at 4 different locations along the northeast-facing slope at the Lower Montane site in the East River Watershed, Colorado from Oct 2016 to Oct 2018. The purpose of this data is to inform hydrobiogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). The data is reported in 4 csv files labeled their by location identifiers (ER-SMN1-5) and location metadata is provided in SM_loc.csv. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.
This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells with Location ID's ER-DOW (alias DO1West), ER-DOE (alias DO2East), ER-MBA1 (alias M1Bend1), ER-MBA2 (alias M1Bend2), ER-UPW (alias UP1West), ER-UPM (alias UP2), ER-UPE (alias UP3East). Another dataset contains the data from wells with Location ID's ER-CPA1 to ER-CPA6. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 8 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.
This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6. Another dataset contains the data from wells at nearby Locations. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 7 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests. The publication year was updated from "2020" to "2025" to reflect the revised version of this dataset.
This dataset includes sapflux and stable water isotopes of soil water and xylem water for aspen, fir and spruce trees along the Snodgrass Mountain transect in the East River Watershed, Colorado USA. The purpose of generating this dataset was to understand: (1) the total flux of water being used by trees in the watershed, (2) separate the component of transpiration that was derived from recent precipitation vs. older water such as winter precipitation of groundwater and (3) understand how total water use and water sources for the trees varies between species and position on a hillslope. The data were collected from May 2019 until October 2022. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. The xylem water isotope data were collected approximately weekly from each of the trees instrumented with sap flux. The stems were collected and the water extracted using classic cryogenic methods. Isotope measurements were done using a Picarro 2140i analyzer. We provide an estimate of the Seasonal Origin Index for each measurements following Allen et al., 2019 (10.5194/hess-23-1199-2019) where values of -1 equate to trees relying on winter precipitation and +1 tree relying on summer precipitation. We also provide the simultaneous sap flux for each isotope measurement when this data was available. This is an update to an earlier data repository with the same name that only included 2019 data. The new dataset was posted in January 2023 and is inclusive of the original 2019 data but now includes 2021 and 2022 data. Please note there was an error in the units of transpiration in the original dataset. It was incorrectly listed as mm h-1 when it should have been kg h-1. This change was made in May 2024.