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USGS2010

Relations between groundwater levels and anthropogenic and meteorological stressors at selected sites in east-central Florida, 1995-2007

Multivariate linear regression analyses were used to define the relations of water levels in the Upper Floridan aquifer (UFA) and surficial aquifer system (SAS) to anthropogenic and meteorological stressors between 1995 and 2007 at two monitoring well sites (Charlotte Street and Lake Oliver) in east-central Florida. Anthropogenic stressors of interest included municipal and agricultural groundwater withdrawals, and application of reclaimed-water to rapid-infiltration basins (source of aquifer recharge). Meteorological stressors included precipitation and potential evapotranspiration. Overall, anthropogenic and meteorological stressors accounted for about 40 to 89 percent of the variance in UFA and SAS groundwater levels and water-level changes. While mean monthly water levels were better correlated with monthly stressor values, changes in UFA and SAS water levels were better correlated with changes in stressor values. Water levels and water-level changes were influenced by system persistence as the moving-averaged values of both stressor types, which accounted for the influence of the previous month(s) conditions, consistently yielded higher adjusted coefficients of determination (R2 adj) values than did single monthly values. While monthly water-level changes tend to be influenced equally with both stressors across the hydrologically averaged 13-year period, changes were more influenced by one stressor or the other seasonally and during extended wet and dry periods. Seasonally, UFA water-level changes tended to be more influenced by anthropogenic stressors than by meteorological stressors, while changes in SAS water levels tended to be more influenced by meteorological stressors. During extended dry periods (12 months or greater), changes in UFA water levels at Charlotte Street were more affected by anthropogenic stressors than by meteorological stressors, while changes in SAS levels were more affected by meteorological stressors. At Lake Oliver, changes in both UFA and SAS water levels were better correlated with meteorological stressors for all but the wet period between April 1995 and April 1996. Interestingly, changes in both UFA and SAS water levels at Charlotte Street were also better correlated with anthropogenic stressors during a similar wet period between April 1995 and June 1996 when substantive reductions in groundwater withdrawals resulted in appreciable recovery of both UFA and SAS water levels. The regional effects of anthropogenic stressors had limited influence on water-level changes at Charlotte Street and virtually no influence on changes at Lake Oliver. When regressed against the 2.2 Mgal/d (million gallons per day) of municipal withdrawals located within 2 miles of the Charlotte Street site, water-level changes were influenced solely by precipitation and potential evapotranspiration. At a radius of 2.5 miles, however, where cumulative withdrawals totaled about 9.5 Mgal/d, water-level changes were equally influenced by both anthropogenic and meteorological stressors. Withdrawals located at distances of greater than 3 miles from this site had no appreciable effect on relations between water-level changes and these stressors. At Lake Oliver, changes in UFA water levels were equally influenced by both stressors regardless of distance, while changes in SAS levels were more influenced by meteorological stressors at all distances.

Scientific Investigations Report↗

USGS2016

GOES-derived fog and low cloud indices for coastal north and central California ecological analyses

Fog and low cloud cover (FLCC) changes the water, energy, and nutrient flux of coastal ecosystems. Easy-to-use FLCC data are needed to quantify the impacts of FLC on ecosystem dynamics during hot, dry Mediterranean climate summers. FLCC indices were generated from 26,000 hourly night and day FLCC maps derived from Geostationary Environmental Operational Satellite (GOES) data for June, July, August, and September, 1999- 2009 for coastal California, latitude 34.50°N, south of Monterey Bay, to latitude 41.95°N, north of Crescent City. Monthly FLCC average hours per day (h/d) range from < 2 to 18. Average FLCC over the ocean increases from north (9 h/d) to south (14 h/d) whereas FLCC over land is reversed. Over land, FLCC is highest where land juts into the prevailing NW winds and is lowest in the lee of major capes. FLCC advects furthest inland through low-lying NW ocean-facing valleys. At night hours of FLCC is higher more frequently on land than over the ocean. Interannual FLCC coefficient of variation shows long term geographic stability strongly associated with landform position. Contours delineating homogeneous zones of FLCC, derived from average decadal h/d FLCC, provide data to refine the commonly used term ‘fog belt.’ FLCC indices are available for download from the California Landscape Conservation Cooperative Climate Commons website. FLCC indices can be used to improve analyses of biogeographic and bioclimatic species distribution models, meteorological mechanisms driving FLCC patterns, ecohydrological investigations of evapotranspiration, solar energy feasibility studies, agricultural irrigation demand and viticultural ripening models.

Earth and Space Science↗

USGS2024

Synoptic analysis and WRF-Chem model simulation of dust events in the southwestern United States

Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.

southwestern United States↗

USGS1990

Effects of surface coal mining and reclamation on the geohydrology of six small watersheds in West-Central Indiana

Six small watersheds in west-central Indiana were selected for study of the hydrologic effects of surface coal mining and reclamation. The watersheds include mined and reclaimed, mined and unreclaimed, and unmined agricultural land uses and are each less than 3 square miles in area. Surface-water, ground-water, and meteorologic data for the 1981 and 1982 water years were used to describe and compare hydrologic systems of the six watersheds and to identify hydrologic effects of mining and reclamation. Discharge at the unreclaimed watersheds was continuous during the study period, whereas discharge at the other watersheds was intermittent and more variable. Peak discharges were greater at the agricultural watersheds than at the unreclaimed watersheds, primarily because of large final-cut lakes in the unreclaimed watersheds. Annual runoff was greatest at the unreclaimed watersheds, intermediate at the agricultural watersheds, and least at the reclaimed watersheds. Hydrologic effects of mining were identified by comparing the hydrologic systems at mined and unreclaimed watersheds with those at unmined agricultural watersheds. These comparisons indicate that surface coal mining without reclamation can increase base flow, annual runoff, and ground-water recharge to the bedrock; reduce peak flow rates and variation in flow; lower the water table in upland areas; change the relation between surface- and ground-water divides; and create numerous, local flow systems in the shallow ground water. Hydrologic effects of reclamation were identified by comparing the hydrologic systems at mined and reclaimed watersheds with those at mined and unreclaimed watersheds. Reclamation can decrease base flow, annual runoff, and recharge to the bedrock; increase peak flow rates, variation in flow, and the response to thunderstorms; reestablish the premining relation between surface- and ground-water divides; and create fewer local flow systems in the shallow ground water. Hydrologic effects of mining and reclamation were identified by comparing the hydrologic systems at mined and reclaimed watersheds with those at unmined agricultural watersheds. The presence or absence of a large final-cut lake in the reclaimed watershed greatly influences the hydrologic systems and the effects of mining and reclamation. Surface coal mining and reclamation can decrease base flow, annual runoff, and peak flow rates; increase the variability of flow and recharge to the bedrock; reestablish the premining relation between surface- and ground-water divides; and lower the water table in upland areas.

Indiana↗

USGS2009

An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS Enhanced Vegetation Index and ground measurements of ET. II. Application to the lower Colorado River, U.S.

Large quantities of water are consumed by irrigated crops and riparian vegetation in western U.S. irrigation districts. Remote sensing methods for estimating evaporative water losses by soil and vegetation (evapotranspiration, ET) over wide river stretches are needed to allocate water for agricultural and environmental needs. We used the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite to scale ET over agricultural and riparian areas along the Lower Colorado River in the southwestern U.S., using a linear regression equation between ET of riparian plants and alfalfa measured on the ground, and meteorological and remote sensing data, with an error or uncertainty of about 20%. The algorithm was applied to irrigation districts and riparian areas from Lake Mead to the U.S./Mexico border. The results for agricultural crops were similar to results produced by crop coefficients developed for the irrigation districts along the river. However, riparian ET was only half as great as crop coefficient estimates set by expert opinion, equal to about 40% of reference crop evapotranspiration. Based on reported acreages in 2007, agricultural crops (146,473 ha) consumed 2.2 × 10 9 m 3 yr −1 of water. All riparian shrubs and trees (47,014 ha) consumed 3.8 × 10 8 m 3 yr −1 , of which saltcedar, the dominant riparian shrub (25,044 ha), consumed 1.8 × 10 8 m 3 yr −1 , about 1% of the annual flow of the river. This method could supplement existing protocols for estimating ET by providing an estimate based on the actual state of the canopy as determined by frequent-return satellite data.

Arizona, California, Nevada↗

USGS2006

Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change?

A procedure is described to construct time series of regional surface temperatures and is then applied to interior central California stations to test the hypothesis that century-scale trend differences between irrigated and nonirrigated regions may be identified. The procedure requires documentation of every point in time at which a discontinuity in a station record may have occurred through (a) the examination of metadata forms (e.g., station moves) and (b) simple statistical tests. From this "homogeneous segments" of temperature records for each station are defined. Biases are determined for each segment relative to all others through a method employing mathematical graph theory. The debiased segments are then merged, forming a complete regional time series. Time series of daily maximum and minimum temperatures for stations in the irrigated San Joaquin Valley (Valley) and nearby nonirrigated Sierra Nevada (Sierra) were generated for 1910-2003. Results show that twentieth-century Valley minimum temperatures are warming at a highly significant rate in all seasons, being greatest in summer and fall (> +0.25??C decade-1). The Valley trend of annual mean temperatures is +0.07?? ?? 0.07??C decade-1. Sierra summer and fall minimum temperatures appear to be cooling, but at a less significant rate, while the trend of annual mean Sierra temperatures is an unremarkable -0.02?? ?? 0.10??C decade-1. A working hypothesis is that the relative positive trends in Valley minus Sierra minima (>0.4??C decade-1 for summer and fall) are related to the altered surface environment brought about by the growth of irrigated agriculture, essentially changing a high-albedo desert into a darker, moister, vegetated plain. ?? 2006 American Meteorological Society.

Journal of Climate↗

USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗

USGS2023

A global long-term daily reanalysis of reference evapotranspiration for drought and food-security monitoring

NOAA has developed a global reference evapotranspiration (ET 0 ) reanalysis using the UN Food and Agriculture Organization formulation (FAO-56) of the Penman-Monteith equation forced by MERRA phase 2 (MERRA2) meteorological and radiative drivers. The NOAA ET 0 reanalysis is provided daily from January 1, 1980 to the near-present at a resolution of 0.5° latitude × 0.625° longitude. The reanalysis is verified against station data across southern Africa, a region presenting both significant challenges regarding hydroclimatic variability and observational quantity and quality and significant potential benefits to food-insecure populations. These data are generated from observations from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network. We further verified globally against spatially distributed ET 0 derived from two reanalyses–the Global Data Assimilation System (GDAS) and Princeton Global Forcing (PGF)–and these verifications produced similar results, yet demonstrated wide regional and seasonal differences. We also present cases that verify the operational applicability of the reanalysis in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts.

Nature Scientific Data↗

USGS1988

Thematic mapping from satellite imagery

This report, produced between 1984 and 1987 in a bilingual edition (English and French), provides a wealth of information on a wide variety of cartographic applications which are being developed to make effective use of new data that is now being collected by Earth observation satellites. At a time when natural resource development and environmental problems have to be taken into account in their entirety, satellites provide an exceptional means of evaluating, synthesizing and creating geographic information. The production of new images, such as the Landsat Thematic Mapper, the SPOT High Resolution Visible Sensor and the adoption meteorological images such as AVHRR sensors on NOAA satellites, which have been extended to thematic domains such as agriculture and glaciology, have led to many new developments as well as problems for the cartographer. These problems are numerous and varied and recur frequently depending on the images and themes being processed e.g. how to select the graphic specification of maps and legends and account for classification accuracy; how to introduce the minimum of topographical data into an image in order to supply users with an adequate geographic reference; when using a physical terrain image, on which, by definition there are no `blanks' and where no details have been eliminated, how to overcome conceptual difficulties e.g. the side by side placing of different semiological information; the interpretation levels left to reader; achieving a balance between objectivity and readability. Obviously the cartographers role is of prime importance in solving these problems. In order to illustrate the way in which this information is presented, the report provides samples from 33 thematic applications taken from 13 different categories, including agriculture, town planning and water resources. Each application is provided with a descriptive note both in English and French indicating the objective, method and results obtained. This volume provides an important record of current developments in thematic mapping from satellite imagery and should be essential reference for all cartographers.

Book↗

USGS2021

OpenET: Filling a critical data gap in water management for the western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

western United States↗

USGS2021

Characterization of water use and water balance for the croplands of Kansas using satellite, climate, and irrigation data

Kansas is one of the most productive agricultural states in the United States, where agricultural irrigation is a primary user of underground and surface water. Because of low precipitation and declining groundwater levels in western and central Kansas, sustainable management of irrigation water resources is a critical issue in the agricultural productivity of the state. The objective of this study is to analyze and characterize the water use and water balance in the croplands of Kansas using satellite observations, meteorological data, and in situ irrigation water use records. We used actual evapotranspiration ( ETa ), precipitation, soil moisture, and irrigation water use to calculate water balance for Kansas in 2015 at scales of counties, climatic divisions, and groundwater management districts (GMD). The Operational Simplified Surface Energy Balance model was implemented to estimate 30-m resolution ETa . Results showed that the seasonal (May – September) precipitation, soil water storage change, and ETa are 528 mm, 80 mm, and 555 mm, respectively, on average of all croplands in the state. The annual net irrigation water consumption was 293 mm for irrigated croplands, indicating that irrigation water constitutes an substantial portion of the water supply in the state. The total volumetric irrigation water use was 3.24 km 3 for all croplands within five GMDs in western and south-central Kansas, while only 0.38 km 3 was outside of GMDs. The multiple regression models of ETa against precipitation and irrigation water use were statistically significant with R 2 values of 0.71 and 0.87, respectively, at county and climate division scales. Regression models also indicated a higher rate of ETa response to irrigation water use than that to precipitation. Our study demonstrated the spatial patterns of crop water use and water balance in Kansas, which could provide useful information for management of irrigation agriculture and water resources for the state.

Kansas↗

USGS2022

Anthropogenic stressors compound climate impacts on inland lake dynamics: The case of Hamun Lakes

Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km 2 versus 410 km 2 ), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.

Science of the Total Envionrment↗

USGS2010

Vegetation index methods for estimating evapotranspiration by remote sensing

Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ETa) and meteorological data to project ETa over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ETa and measured ETa are in the range of 0.45–0.95, and root mean square errors are in the range of 10–30% of mean ETa values across biomes, similar to methods that use thermal infrared bands to estimate ETa and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.

Surveys in Geophysics↗

USGS2017

Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014

Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics. The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence. Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike ( Esox lucius ), walleye ( Sander vitreus ), and black crappie ( Pomoxis nigromaculatus ); examples of important warm-water fish include bluegill ( Lepomis macrochirus ), largemouth bass ( Micropterus salmoides ), and smallmouth bass ( Micropterus dolomieu ). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.

Minnesota↗

USGS2023

Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery

Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

Indiana, Michigan, Ohio, Wisconsin↗

USGS2015

Field measurement and analysis of climatic factors affecting dune mobility near Grand Falls on the Navajo Nation, southwestern United States

Aeolian sand covers extensive areas of the Navajo Nation in the southwestern United States. Much of this sand is currently stabilized by vegetation, although many drier parts of these Native lands also have active and partly active dunes. Current prolonged drought conditions that started in the mid-1990s are producing significant changes in dune mobility. Reactivation of regional aeolian deposits due to drought or increasing aridity from rising temperatures resulting from climate change could have serious consequences for human and animal populations, agriculture, grazing, and infrastructure. To understand and document the current and future potential for mobility, seasonally repeated surveys were used to track the location of multiple active barchan dunes. By utilizing Real-Time Kinematic GPS field surveys and simultaneously collecting in-situ meteorological data, it is possible to examine climatic parameters and seasonal variations that affect dune mobility and their relative influences. Through analysis of the recorded data, we examined the fit of various climate parameters, and demonstrate that under the current prolonged drought, wind power is the dominant factor controlling dune mobility.

Arizona↗

USGS2010

Distribution and trends in reference evapotranspiration in the North China plain

The distribution and trends in reference evapotranspiration (ET(o)) are extremely important to water resources planning for agriculture, and it is widely believed that rates of ET(o) will increase with global warming. This is a big concern in China, where water deficits are common in the North China Plain (NCP). In this study, Penman-Monteith reference evapotranspiration at 26 meteorological stations during 1961-2006 in and around the NCP was calculated. The temporal variations and spatial distribution of ET(o) were analyzed and the causes for the variations were discussed. The results showed that: (1) the NCP was divided into two climatic regions based on aridity values: a semiarid region that accounts for 69% of the area and subhumid regions that made of the remaining area; (2) over the entire NCP, the highest annual ET(o) occurred in the central and western areas and the lowest total ET(o) was observed in the east. Comparing the mean monthly ET(o) and annual ET(o) distributions, the high ET(o) values from May through July mainly determined the annual ET(o) distribution; (3) for the whole NCP, annual ET(o) showed a statistically significant decrease of 11.92 mm/decade over the 46 years of data collection in the NCP or approximately a 5% total decrease compared to the ET(o) values in 1961; (4) to determine which variable has the greatest effect on the decrease in ET(o), decadal changes were observed for daily values of maximum air temperature (+0.16 degrees C), minimum air temperature (+0.35 degrees C), net radiation (-0.13 MJ m(-2)), and mean wind speed (-0.09 m s(-1)). These results indicate that the decreasing net radiation and wind speed had a bigger impact on ET(o) rates than the increases observed by the maximum and minimum temperatures.

North China Plain↗

USGS2015

Modeling groundwater nitrate concentrations in private wells in Iowa

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square = 0.77) and was acceptable in the testing set (r-square = 0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.

Iowa↗