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At least 1,441 records · Page 80Linked to original sources
USGS2001

A spatially referenced regression model (SPARROW) for suspended sediment in streams of the Conterminous U.S.

Suspended sediment has long been recognized as an important contaminant affecting water resources. Besides its direct role in determining water clarity, bridge scour and reservoir storage, sediment serves as a vehicle for the transport of many binding contaminants, including nutrients, trace metals, semi-volatile organic compounds, a nd numerous pesticides (U.S. Environmental Protection Agency, 2000a). Recent efforts to addr ess water-quality concerns through the Total Maximum Daily Load (TMDL) process have iden tified sediment as the single most prevalent cause of impairment in the Nation’s streams a nd rivers (U.S. Environmental Protection Agency, 2000b). Moreover, sediment has been identified as a medium for the tran sport and sequestration of organic carbon, playing a potentia lly important role in understa nding sources and sinks in the global carbon budget (Stallard, 1998). A comprehensive understanding of sediment fate a nd transport is considered essential to the design and implementation of effective plans for sediment management (Osterkamp and others, 1998, U.S. General Accounting Office, 1990). An exte nsive literature addr essing the problem of quantifying sediment transport has produced a nu mber of methods for estimating its flux (see Cohn, 1995, and Robertson and Roerish, 1999, for us eful surveys). The accuracy of these methods is compromised by uncertainty in the concentration measurements and by the highly episodic nature of sediment movement, particul arly when the methods are applied to smaller basins. However, for annual or decadal flux es timates, the methods are generally reliable if calibrated with extended periods of data (Robertson and Roerish, 1999). A substantial literature also supports the Universal Soil Loss Equation (U SLE) (Soil Conservation Service, 1983), an engineering method for estimating sheet and rill erosion, although the empirical credentials of the USLE have recently been questioned (Tri mble and Crosson, 2000). Conversely, relatively little direct evidence is available concerning the fate of sediment. The common practice of quantifying sediment fate with a sediment deliv ery ratio, estimated from a simple empirical relation with upstream basin area, does not artic ulate the relative importance of individual storage sites within a basin (Wolman, 1977). Rates of sediment deposition in reservoirs and flood plains can be determined from empirical measurement s , but only a limited number of sites have been monitored, and net rates of deposition or loss from other potential sinks and sources is largely unknown (Stallard, 1998). In particular, little is known about how much sediment loss from fields ultimately makes its way to stream channels, and how much sediment is subsequently stored in or lost from th e streambed (Meade and Parker, 1985, Trimble and Crosson, 2000). This paper reports on recent progress made to a ddress empirically the question of sediment fate and transport on a national scale. The model pres ented here is based on the SPAtially Referenced Regression On Watershed attr ibutes (SPARROW) methodology, fi rst used to estimate the distribution of nutrients in str eams and rivers of the United Stat es, and subsequently shown to describe land and stream processes affecting the delivery of nutrients (Smith and others, 1997, Alexander and others, 2000, Preston and Brakeb ill, 1999). The model makes use of numerous spatial datasets, available at the national level, to explain long-term sediment water-quality conditions in major streams and rivers throughou t the United States. Sediment sources are identified using sediment erosion rates from the National Resources I nventory (NRI) (Natural Resources Conservation Service, 2000) and apportioned over the landscape according to 30- meter resolution land-use information from th e National Land Cover Data set (NLCD) (U.S. Geological Survey, 2000a). More than 76,000 reservoirs from the National Inventory of Dams (NID) (U.S. Army Corps of Engin eers, 1996) are identified as pot ential sediment sinks. Other, non-anthropogenic sources and sinks are identified using soil in formation from the State Soil Survey Geographic (STATSGO) data base (Schwarz and Alexander, 1995) and spatial coverages representing surficial rock t ype and vegetative cover. The SPA RROW model empirically relates these diverse spatial datasets to estimates of long-term, mean annual sediment flux computed from concentration and flow measurements co llected over the period 1985 -95 from more than 400 monitoring stations maintained by the Na tional Stream Quality Accounting Network (Alexander and others, 1998), the National Wa ter Quality Assessment Program, and U.S. Geological Survey District offices (Turcios and Gray, in press). Th e calibrated model is used to estimate sediment flux for over 60,000 stream segments included in the River Reach File 1 (RF1) stream network (Alexander and others, 1999). SPARROW uses statis tical methods to calibrate a simple, structural model of riverine water quality, one that imposes mass ba lance in accounting for changes in contaminant flux. As applied here, the mass-balance approach facilitates the interpretation of model results in terms of physical processes affecting sediment transport, and makes possible the estimation of various rates of sediment generation and loss associated with stream channels and features of the landscape. The statistical approach provides a basi s for assessing the error of these inferred rates and of the error in extrapolated estimates of sediment flux made for streams in the RF1 network. An important implication of the holistic modeling approach adopted in this analysis is that estimates of sediment production and loss ar e based on, and therefore consistent with, measurements of in-stream flux. Other ancillary information, such as direct measurements of long-term sediment storage and release from rese rvoirs (Steffen, 1996), is incorporated into the analysis by specifying additional equations expl aining these ancillary variables. The imposition of cross-equation constraints affords this info rmation a statistically consistent weight in explaining in-stream sediment flux. Thus, the me thodology described here represents a general framework for synthesizing a wide spectrum of available information relevant to the understanding of sediment fate and transport.

Conterminous United States↗
USGS2021

Using ALOS-2 synthetic aperture radar (SAR) and interferometric SAR to detect landslides on the mountainous island of Pohnpei, Federated States of Micronesia

This study was undertaken by the U.S. Geological Survey to assess the detectability of landslides in the densely forested and mountainous island of Pohnpei in the Federated States of Micronesia. The study used existing field-observed land-cover changes and landslides visible on Google Earth (GE) images. A limited number of ALOS-2 PALSAR-2 L-band synthetic aperture radar (SAR) images were collected on two adjacent orbit paths before and after an intense rainfall event that affected Pohnpei in mid-March 2018. Similar sets of images were collected in 2019 and 2020. Low coherence throughout the island interior eliminated use of phase-change products, and change analysis identified no landslide features as having formed in 2019 or 2020. The assessment of red-green-blue image composites and application of the log-ratio method to the 2018 ground-range SAR images identified 5 of the 11 landslides observed on the GE images. Visual comparisons of the co-event and post-event coherence image products detected 9 of the 11 landslides observed on the GE images. Combined, the ground-based SAR and interferometric SAR coherence change detections overcame high temporal and spatial decorrelations, identified all but one landslide visible in the GE comparison, and included substantial redundancy. The robustness of the landslide detection indicates that an increased collection frequency of L-band images could support systematic monitoring of land-cover change on Pohnpei at the scale reported in this study.

Island of Pohnpei↗
USGS2015

Summary of hydrologic modeling for the Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER)

The Water Availability Tool for Environmental Resources (WATER) is a decision support system for the nontidal part of the Delaware River Basin that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. In order to quantify the uncertainty associated with these simulations, however, streamflow and the associated hydroclimatic variables of potential evapotranspiration, actual evapotranspiration, and snow accumulation and snowmelt must be simulated and compared to long-term, daily observations from sites. This report details model development and optimization, statistical evaluation of simulations for 57 basins ranging from 2 to 930 km 2 and 11.0 to 99.5 percent forested cover, and how this statistical evaluation of daily streamflow relates to simulating environmental changes and management decisions that are best examined at monthly time steps normalized over multiple decades. The decision support system provides a database of historical spatial and climatic data for simulating streamflow for 2001–11, in addition to land-cover and general circulation model forecasts that focus on 2030 and 2060. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that were parameterized by using three hydrologic response units: forested, agricultural, and developed land cover. This integration enables the regional hydrologic modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model.

Delaware, Maryland, New Jersey, New York, Pennsylv↗
USGS2021

Precipitation-driven flood-inundation mapping of the Little Blue River at Grandview, Missouri

The U.S. Geological Survey (USGS), in cooperation with the City of Grandview, Missouri, assessed flooding of the Little Blue River at Grandview resulting from varying precipitation magnitudes and durations and expected land-cover changes. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.5-mile reach of the Little Blue River and tributaries within and adjacent to the city. A hydrologic model of the upper Little Blue River Basin and a hydraulic model of a selected study reach of the Little Blue River and tributaries were constructed to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rain events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to construct a steady-state hydraulic model to map resulting areas of flood inundation. Both models were calibrated to the May 28, 2020, high-flow event that produced a peak streamflow approximating a 10-percent annual exceedance probability (10-year flood-frequency recurrence interval) at the Little Blue River at Grandview streamgage (USGS station 06893750). The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 1- to 8-hour durations and ranging from a 100- to 0.2-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 17.0 feet (ft), or near bankfull, to 23.0 ft, or a stage exceeding the 0.2-percent annual exceedance interval flood, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 1-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the reference USGS streamgage. Four scenarios were developed with the HEC–HMS hydrologic model: (1) current (2016) land cover, normal antecedent soil-moisture conditions; (2) current land cover, wet antecedent soil-moisture conditions; (3) future land cover, normal antecedent soil-moisture conditions; and (4) future land cover, wet antecedent soil-moisture conditions. The future land-cover condition was estimated based on anticipated development in the basin. All precipitation scenarios were input into each of the four land-cover antecedent moisture conditions and then assigned to a resulting flood-inundation map based on the generated peak flow and corresponding stage at the reference streamgage.

Missouri↗
USGS2006

Assimilation of snow covered area information into hydrologic and land-surface models

This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy. ?? 2005 Elsevier Ltd. All rights reserved.

Advances in Water Resources↗
USGS1993

Analysis of urban regions using AVHRR thermal infrared data

Using 1-km AVHRR satellite data, relative temperature difference caused by conductivity and inertia were used to distinguish urban and non urban land covers. AVHRR data that were composited on a biweekly basis and distributed by the EROS Data Center in Sioux Falls, South Dakota, were used for the classification process. These composited images are based on the maximum normalized different vegetation index (NDVI) of each pixel during the 2-week period using channels 1 and 2. The resultant images are nearly cloud-free and reduce the need for extensive reclassification processing. Because of the physiographic differences between the Eastern and Western United States, the initial study was limited to the eastern half of the United States. In the East, the time of maximum difference between the urban surfaces and the vegetated non urban areas is the peak greenness period in late summer. A composite image of the Eastern United States for the 2-weel time period from August 30-Septmeber 16, 1991, was used for the extraction of the urban areas. Two channels of thermal data (channels 3 and 4) normalized for regional temperature differences and a composited NDVI image were classified using conventional image processing techniques. The results compare favorably with other large-scale urban area delineations.

Pecora 12 Symposium↗
USGS2013

The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio

Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables. A source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers. The degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas. Area-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range. Differences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models. The use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.

Ohio↗
USGS2011

Phreatophytic land-cover map of the northern and central Great Basin Ecoregion: California, Idaho, Nevada, Utah, Oregon, and Wyoming

Increasing water use and changing climate in the Great Basin of the western United States are likely affecting the distribution of phreatophytic vegetation in the region. Phreatophytic plant communities that depend on groundwater are susceptible to natural and anthropogenic changes to hydrologic flow systems. The purpose of this report is to document the methods used to create the accompanying map that delineates areas of the Great Basin that have the greatest potential to support phreatophytic vegetation. Several data sets were used to develop the data displayed on the map, including Shrub Map (a land-cover data set derived from the Regional Gap Analysis Program) and Gap Analysis Program (GAP) data sets for California and Wyoming. In addition, the analysis used the surface landforms from the U.S. Geological Survey (USGS) Global Ecosystems Mapping Project data to delineate regions of the study area based on topographic relief that are most favorable to support phreatophytic vegetation. Using spatial analysis techniques in a GIS, phreatophytic vegetation classes identified within Shrub Map and GAP were selected and compared to the spatial distribution of selected landforms in the study area to delineate areas of phreatophyte vegetation. Results were compared to more detailed studies conducted in selected areas. A general qualitative description of the data and the limitations of the base data determined that these results provide a regional overview but are not intended for localized studies or as a substitute for detailed field analysis. The map is intended as a decision-support aide for land managers to better understand, anticipate, and respond to ecosystem changes in the Great Basin.

Scientific Investigations Map↗
USGS2015

Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010

Groundwater recharge is one of the most difficult components of a water budget to ascertain, yet is an important boundary condition necessary for the quantification of water resources. In Minnesota, improved estimates of recharge are necessary because approximately 75 percent of drinking water and 90 percent of agricultural irrigation water in Minnesota are supplied from groundwater. The water that is withdrawn must be supplied by some combination of (1) increased recharge, (2) decreased discharge to streams, lakes, and other surface-water bodies, and (3) removal of water that was stored in the system. Recent pressure on groundwater resources has highlighted the need to provide more accurate recharge estimates for various tools that can assess the sustainability of long-term water use. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency, used the Soil-Water-Balance model to calculate gridded estimates of potential groundwater recharge across Minnesota for 1996‒2010 at a 1-kilometer (0.621-mile) resolution. The potential groundwater recharge estimates calculated for Minnesota from the Soil-Water Balance model included gridded values (1-kilometer resolution) of annual mean estimates (that is, the means for individual years from 1996 through 2010) and mean annual estimates (that is, the mean for the 15-year period 1996−2010). The Soil-Water-Balance model uses a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated on a daily basis. A key advantage of this approach includes the use of commonly available geographic information system data layers that incorporate land cover, soil properties, and daily meteorological data to produce temporally and spatially variable gridded estimates of potential recharge. The Soil-Water-Balance model was calibrated by using a combination of parameter estimation techniques, making manual adjustments of model parameters, and using parameter values from previously published Soil-Water-Balance models. Each calibration simulation compared the potential recharge estimate from the model against base-flow estimates derived from three separate hydrograph separation techniques. A total of 35 Minnesota watersheds were selected for the model calibration. Meteorological data necessary for the model included daily precipitation, minimum daily temperature, and maximum daily temperature. All of the meteorological data were provided by the Daymet dataset, which included daily continuous surfaces of key climatological data. Land-cover data were provided by the 2001 and 2006 National Land Cover Database: the 2001 classification was used from 1994 through 2003, and the 2006 classification was used from 2004 through 2010. Soil data used in the model included hydrologic soils group and the available soil-water capacity. These soil data were obtained from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) database and the State Soil Geographic (STATSGO) database. The statewide mean annual potential recharge rate from 1996–2010 was 4.9 inches per year. Potential recharge estimates increased from west to east across Minnesota. The mean annual potential recharge estimates across Minnesota at a 1-km resolution for the overall simulation period (1996–2010) ranged from less than 0.1 to 17.8 inches per year. Some of the lowest potential recharge rates for the simulation period were in the Red River of the North Basin of northwestern Minnesota, and generally were between 1.0 and 1.5 inches per year. The highest potential recharge rates were in northeastern Minnesota and the Anoka Sand Plain in central Minnesota. Eighty-eight percent of the potential recharge rates (by grid cell) were between 2 and 8 inches per year from 1996–2010. Only about 3 percent of all the potential recharge estimates (by grid cell) were less than 2 inches per year, and 9 percent of estimates were greater than 8 inches per year. On an annual basis, however, potential recharge rates were as high as 27.2 inches per year. The highest annual mean recharge estimate across the State was for 2010, and the lowest mean recharge estimate was for 2003. Although precipitation variability partially explained the annual differences in potential recharge estimates, precipitation alone did not account for these differences, and other factors such as antecedent moisture conditions likely were important. Also, because precipitation gradients across the State can vary from year to year, the dominant land-cover class and hydrologic soil group combinations for a particular region had a large effect on the resulting potential recharge value. During 1996–2010, April had the greatest monthly mean potential recharge compared to all other months, accounting for a mean of 30 percent of annual potential recharge in this single month.

Minnesota↗
USGS2001

Monthly fractional green vegetation cover associated with land cover classes of the conterminous USA

The land cover classes developed under the coordination of the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) have been analyzed for a study area that includes the Conterminous United States and portions of Mexico and Canada. The 1-km resolution data have been analyzed to produce a gridded data set that includes within each 20-km grid cell: 1) the three most dominant land cover classes, 2) the fractional area associated with each of the three dominant classes, and 3) the fractional area covered by water. Additionally, the monthly fraction of green vegetation cover (fgreen) associated with each of the three dominant land cover classes per grid cell was derived from a 5-year climatology of 1-km resolution NOAA-AVHRR data. The variables derived in this study provide a potential improvement over the use of monthly fgreen linked to a single land cover class per model grid cell.

Geophysical Research Letters↗
USGS2019

Simulating land cover change impacts on groundwater recharge under selected climate projections, Maui, Hawaiʻi

This project developed an integrated land cover/hydrological modeling framework using remote sensing and geographic information systems (GIS) data, stakeholder input, climate information and projections, and empirical data to estimate future groundwater recharge on the Island of Maui, Hawaiʻi, USA. End-of-century mean annual groundwater recharge was estimated under four future land cover scenarios: Future 1 (conservation-focused), Future 2 (status-quo), Future 3 (development-focused), and Future 4 (balanced conservation and development), and two downscaled climate projections: a coupled model intercomparison project (CMIP) phase 5 (CMIP5) representative concentration pathway (RCP) 8.5 “dry climate” future and a CMIP3 A1B “wet climate” future. Results were compared to recharge estimated using the 2017 baseline land cover to understand how changing land management and climate could influence groundwater recharge. Estimated recharge increased island-wide under all future land cover and climate combinations and was dominated by specific land cover transitions. For the dry future climate, recharge for land cover Futures 1 to 4 increased by 12%, 0.7%, 0.01%, and 11% relative to 2017 land cover conditions, respectively. Corresponding increases under the wet future climate were 10%, 0.9%, 0.6%, and 9.3%. Conversion from fallow/grassland to diversified agriculture increased irrigation, and therefore recharge. Above the cloud zone (610 m), conversion from grassland to native or alien forest led to increased fog interception, which increased recharge. The greatest changes to recharge occurred in Futures 1 and 4 in areas where irrigation increased, and where forest expanded within the cloud zone. Furthermore, new future urban expansion is currently slated for coastal areas that are already water-stressed and had low recharge projections. This study demonstrated that a spatially-explicit scenario planning process and modeling framework can communicate the possible consequences and tradeoffs of land cover change under a changing climate, and the outputs from this study serve as relevant tools for landscape-level management and interventions.

Hawaii↗
USGS2010

Indicators of streamflow alteration, habitat fragmentation, impervious cover, and water quality for Massachusetts stream basins

Massachusetts streams and stream basins have been subjected to a wide variety of human alterations since colonial times. These alterations include water withdrawals, treated wastewater discharges, construction of onsite septic systems and dams, forest clearing, and urbanization—all of which have the potential to affect streamflow regimes, water quality, and habitat integrity for fish and other aquatic biota. Indicators were developed to characterize these types of potential alteration for subbasins and groundwater contributing areas in Massachusetts. The potential alteration of streamflow by the combined effects of withdrawals and discharges was assessed under two water-use scenarios. Water-use scenario 1 incorporated publicly reported groundwater withdrawals and discharges, direct withdrawals from and discharges to streams, and estimated domestic-well withdrawals and septic-system discharges. Surface-water-reservoir withdrawals were excluded from this scenario. Water-use scenario 2 incorporated all the types of withdrawal and discharge included in scenario 1 as well as withdrawals from surface-water reservoirs—all on a long-term, mean annual basis. All withdrawal and discharge data were previously reported to the State for the 2000–2004 period, except domestic-well withdrawals and septic-system discharges, which were estimated for this study. The majority of the state’s subbasins and groundwater contributing areas were estimated to have relatively minor (less than 10 percent) alteration of streamflow under water-use scenario 1 (seasonally varying water use; no surface-water-reservoir withdrawals). However, about 12 percent of subbasins and groundwater contributing areas were estimated to have extensive alteration of streamflows (greater than 40 percent) in August; most of these basins were concentrated in the outer metropolitan Boston region. Potential surcharging of streamflow in August was most commonly indicated for main-stem river subbasins, although surcharging was also indicated for some smaller tributary subbasins. In the high-flow month of April, only 4.8 percent of subbasins and groundwater contributing areas had more than 10 percent potential flow alteration. A majority of the state’s subbasins and groundwater contributing areas were also indicated to have relatively minor alteration of streamflow under water-use scenario 2 (long-term average water use, including surface-water-reservoir withdrawals). Extensive alteration of mean annual flows was estimated for about 6 percent of the state’s subbasins and groundwater contributing areas. The majority of subbasins estimated to have extensive long-term flow alteration contained reservoirs that were specifically designed, constructed, and managed to supply drinking water to cities. Only a small number of subbasins and groundwater contributing areas (1 percent) were extensively surcharged on a long-term, mean annual basis. Because site-specific data concerning surface-water-reservoir storage dynamics and management practices are not available statewide, the seasonal effects of surface-water-reservoir withdrawals on downstream flows could not be assessed in this study. The impounded storage ratio (volume of impounded subbasin or groundwater-contributing-area storage divided by mean annual predevelopment outflow from the subbasin or contributing area, in units of days) indicates the potential for alteration of streamflow, sediment-transport, and temperature regimes by dams, independent of water use. Storage ratios were less than 1 day for 33 percent of the subbasins and groundwater contributing areas, greater than 1 month for about 40 percent of the cases, and greater than 1 year for 3.2 percent of the cases statewide. Dam density, an indicator of stream-habitat fragmentation by dams, averaged 1 dam for every 6.7 stream miles statewide. Many of these dams are not presently (2009) being managed. The highest dam densities were in portions of Worcester County and in the Plymouth-Carver region, respectively, reflecting the historical reliance of Massachusetts industry upon water power and agricultural water-management practices in southeastern Massachusetts. Impervious cover is a frequently used indicator of urban land use. About 33 percent of the state’s 1,429 subbasins and groundwater contributing areas are relatively undeveloped at the local scale, with a local impervious cover of less than 4 percent. About 18 percent of Massachusetts subbasins and contributing areas are highly developed, with a local impervious cover greater than 16 percent. The remaining 49 percent of subbasins and contributing areas have levels of urban development between these extremes (4 to 16 percent local impervious cover). Cumulative impervious cover, defined for the entire upstream area encompassed by each subbasin, shows a smaller range (0 to 55 percent) than local impervious cover. Both local and cumulative impervious cover were highest in metropolitan Boston and other urban centers. High elevated impervious-cover values were also found along major transportation corridors. The water-quality status of Massachusetts streams is assessed periodically by the Massachusetts Department of Environmental Protection pursuant to the requirements of the Federal Clean Water Act. Streams selected for assessment are commonly located in larger subbasins where some degree of impairment is expected. In the 72 percent of the state’s subbasins and groundwater contributing areas with assessed streams in 2002, more than 50 percent of the assessed stream miles were considered impaired. All of the assessed stream miles were considered impaired in 66 percent of the subbasins and groundwater contributing areas with assessed streams. Large streams, such as the main stems of rivers that make up most of the assessed stream miles, also are in many cases the receiving waters for treated wastewater discharges and for this reason may be more susceptible to water-quality impairments than smaller streams. Subbasins and contributing areas with large fractions of assessed stream miles that are listed as impaired are distributed across the state, but are more prevalent in eastern Massachusetts.

Massachusetts↗
USGS2018

Updates to the suspended sediment SPARROW model developed for western Oregon and northwestern California

A SPARROW (SPAtially Related Regressions On Watershed attributes) model that was previously developed for western Oregon and northwestern California was updated using advancements in the SPARROW software and refinements to the input data. As was the case for the original model calibration, the updated models used the NHD Plus Version 2 as a hydrologic framework and relied on the same estimates of long-term mean suspended-sediment loads and watershed attributes. The updated calibration results indicated that two different SPARROW models were possible—one model from which sediment sources were represented by local lithology and one from which sediment sources were represented by generalized land-cover classes; precipitation, catchment slope, wildfire disturbance, and sediment loss in impoundments were significantly correlated with suspended-sediment loads in both models. The updated models also included a method to compensate for the bias introduced by using total suspended solids to represent suspended sediment in the calibration dataset—a feature that was not available during the original model calibration. The effect of this feature was an overall increase in estimated suspended-sediment loads. Although the lithology- and the land-cover based models used different landscape properties to describe sediment sources, each could be useful in specific applications. The lithology-based model provides more accurate estimates of suspended-sediment load, but the land-cover based model allows water-quality managers to estimate how much in-stream suspended-sediment load originates in areas with extensive development compared to the load that originates in areas with relatively little human impact.

California, Oregon↗
USGS2014

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state ( n  = 1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes ( n  ≤ 75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.

California↗
USGS2011

Monitoring a boreal wildfire using multi-temporal Radarsat-1 intensity and coherence images

Twenty-five C-band Radarsat-1 synthetic aperture radar (SAR) images acquired from the summer of 2002 to the summer of 2005 are used to map a 2003 boreal wildfire (B346) in the Yukon Flats National Wildlife Refuge, Alaska under conditions of near-persistent cloud cover. Our analysis is primarily based on the 15 SAR scenes acquired during arctic growing seasons. The Radarsat-1 intensity data are used to map the onset and progression of the fire, and interferometric coherence images are used to qualify burn severity and monitor post-fire recovery. We base our analysis of the fire on three test sites, two from within the fire and one unburned site. The B346 fire increased backscattered intensity values for the two burn study sites by approximately 5–6 dB and substantially reduced coherence from background levels of approximately 0.8 in unburned background forested areas to approximately 0.2 in the burned area. Using ancillary vegetation information from the National Land Cover Database (NLCD) and information on burn severity from Normalized Burn Ratio (NBR) data, we conclude that burn site 2 was more severely burned than burn site 1 and that C-band interferometric coherence data are useful for mapping landscape changes due to fire. Differences in burn severity and topography are determined to be the likely reasons for the observed differences in post-fire intensity and coherence trends between burn sites.

Geomatics, Natural Hazards and Risk↗
USGS2014

On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates

Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

Remote Sensing↗
USGS2019

Characterizing crop water use dynamics in the Central Valley of California using landsat-derived evapotranspiration

Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use.

California↗