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Jennifer Rover

Publications and source records attributed to Jennifer Rover.

13 recordsLinked to original sources
USGS2022

Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA

We analyzed impacts of interannual disturbance on the water balance of watersheds in different forested ecosystem case studies across the United States from 1985 to 2016 using a remotely sensed long-term land cover monitoring record (U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science products), gridded precipitation and evaporation data, and streamgaging data using paired watersheds (high and low disturbance). LCMAP products were used to quantify the timing and degree of interannual disturbance and to gain a better understanding of how land cover change affects the water balance of disturbed watersheds. In this paper, we present how LCMAP science products can be used to improve knowledge for hydrologic modeling, climate research, and forest management. Anthropogenic influences (e.g., dams and irrigation diversions) often minimize the impacts of land cover change on water balance dynamics when compared to interannual fluctuations of hydroclimatic events (e.g., drought and flooding). Our findings show that each watershed exhibits a complex suite of influences involving climate variables and other factors that affect each of their water balances differently when land cover change occurs. In this study, forests within arid to semi-arid climates experience greater water balance effects from land cover change than watersheds where water is less limited.

Land↗
USGS2022

Vulnerable waters are essential to watershed resilience

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Ecosystems↗
USGS2020

Land change monitoring, assessment, and projection

There is a pressing need to monitor and understand the rapid land change happening around the world. The U.S. Geological Survey is developing a new capability, called Land Change Monitoring, Assessment, and Projection (LCMAP), to innovate the understanding of land change. This capability is the Earth Resources Observation and Science Center's foundation for an integrated U.S. Geological Survey-wide land change science framework. LCMAP supports the development of consistent data and land cover products spanning large geographic extents, over extended periods, and at a higher frequency than in the past. LCMAP provides solutions to the science and management communities’ growing need for an improved understanding of the fundamental drivers of land change, the consequences of change in human and natural systems, and feedbacks associated with land change processes.

Fact Sheet↗
USGS2019

Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a >50% probability of obtaining weekly clear observations.

Minnesota↗
USGS2019

Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska

Lakes in boreal lowlands cycle carbon and supply an important source of freshwater for wildlife and migratory waterfowl. The abundance and distribution of these lakes are supported, in part, by permafrost distribution, which is subject to change. Relationships between permafrost thaw and lake dynamics remain poorly known in most boreal regions. Here, new airborne electromagnetic (AEM) data collected during June 2010 and February 2016 were used to constrain deep permafrost distribution. AEM data were coupled with Landsat-derived lake surface-area data from 1979 through 2011 to inform temporal lake behavior changes in the 35 500- km 2 Yukon Flats ecoregion of Alaska. Together, over 1500 km of AEM data, and roughly 30 years of Landsat data were used to explore processes that drive lake dynamics across a variety of permafrost thaw states not possible in studies conducted with satellite imagery or field measurements alone. Clustered time-series data identified lakes with similar temporal dynamics. Clusters possessed similarities in lake permanence (i.e. ephemeral versus perennial), subsurface permafrost distribution, and proximity to rivers and streams. Of the clustered lakes, ~66% are inferred to have at least intermittent connectivity with other surface-water features, ~19% are inferred to have shallow subsurface connectivity to other surface water features that served as a low-pass filter for hydroclimatic fluctuations, and ~15% appear to be isolated by surrounding permafrost (i.e. no connectivity). Integrated analysis of AEM and Landsat data reveals a progression from relatively synchronous lake dynamics among disconnected lakes in the most spatially continuous, thick permafrost to quite high spatiotemporal heterogeneity in lake behavior among variably-connected lakes in regions with notably less continuous permafrost. Variability can be explained by the preferential development of thawed permeable gravel pathways for lateral water redistribution in this area. The general spatial progression in permafrost thaw state and lake area behavior may be extended to the temporal dimension. However, extensive permafrost thaw, beyond what is currently observed, is expected to promote ubiquitous subsurface connectivity, eventually evolving to a state of increased lake synchronicity.

Alaska↗
USGS2018

An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

International Journal of Remote Sensing↗
USGS2018

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll- a (chl- a ) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl- a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl- a concentrations in lakes ( R 2 value of 0.66; root mean square error value of 8.9 µg l −1 ). Using this model, we forecast the spatial and temporal variability of chl- a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.

North Carolina↗
USGS2018

Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska

A methodology based on general hydraulic relations for rivers has been developed to estimate the discharge (flow rate) of rivers using satellite remote sensing observations. The estimates of discharge, flow depth, and flow velocity are derived from remotely observed water surface area, water surface slope, and water surface height, and demonstrated for two reaches of the Yukon River in Alaska, at Eagle (reach length 34.7 km) and near Stevens Village (reach length 38.3 km). The method is based on fundamental equations of hydraulic flow resistance in rivers, including the Manning equation and the Prandtl-von Karman universal velocity distribution equation. The method employs some new hydraulic relations to help define flow resistance and height of the zero flow boundary in the channel. Estimates are made both with and without calibration. The water surface area of the river reach is measured by using a provisional version of the U.S. Geological Survey (USGS) Landsat based product named Dynamic Surface Water Extent (DSWE). The water surface height and slope measurements require a self-consistent datum, and are derived from observations from the Jason-2 satellite altimeter mission. At both reach locations, the Jason-2 radar altimeter non-winter heights consistently tracked the stage recorded at USGS streamgages with a standard deviation of differences (error) during the non-winter periods of less than 7%. Part of the error may be due to differences in the gage and altimeter crossing locations with respect to the range of stage change and the response to changes in discharge at the upstream and downstream locations. For the non-winter periods, the radar derived slope estimates (mean = 0.0003) were constant over the mission lifetime, and in agreement with previously measured USGS water surface slopes and slopes determined from USGS topographic maps. The accuracy of the mean of the uncalibrated daily estimates of discharge varied between reaches, ranging from 13% near Stevens Village (N = 90) to −21% at Eagle (N = 246) based on the absolute error, and 5% to −6% based on the error of the log of the estimates. Calibrating to the mean of USGS daily discharge estimates from the streamflow rating for the same period of record at each streamgage resulted in mean absolute errors ranging from 1% to 2%, and log errors ranging from 1% or less. The error pattern of the estimates shows that without calibration, even though the mean is well simulated, the high and low end values over the range of estimates may have significant bias.

Alaska↗
USGS2016

Controls on the geochemical evolution of Prairie Pothole Region lakes and wetlands over decadal time scales

One hundred sixty-seven Prairie Pothole lakes, ponds and wetlands (largely lakes) previously analyzed chemically during the late 1960’s and early to mid-1970’s were resampled and reanalyzed in 2011–2012. The two sampling periods differed climatically. The earlier sampling took place during normal to slightly dry conditions, whereas the latter occurred during and immediately following exceptionally wet conditions. As reported previously in Mushet et al. ( 2015 ), the dominant effect was expansion of the area of these lakes and dilution of their major ions. However, within that context, there were significant differences in the evolutionary pathways of major ions. To establish these pathways, we employed the inverse modeling computer code NetpathXL. This code takes the initial and final lake composition and, using mass balance constrained by the composition of diluting waters, and input and output of phases, calculates plausible geochemical evolution pathways. Despite the fact that in most cases major ions decreased, a subset of the lakes had an increase in SO 4 2− . This distinction is significant because SO 4 2− is the dominant anion in a majority of Prairie Pothole Region wetlands and lakes. For lakes with decreasing SO 4 2− , the proportion of original lake water required for mass balance was subordinate to rainwater and/or overland flow. In contrast, lakes with increasing SO 4 2− between the two sampling episodes tended to be dominated by original lake water. This suite of lakes tended to be smaller and have lower initial SO 4 2− concentrations such that inputs of sulfur from dissolution of the minerals gypsum or pyrite had a significant impact on the final sulfur concentration given the lower dilution factors. Thus, our study provides context for how Prairie Pothole Region water bodies evolve geochemically as climate changes. Because wetland geochemistry in turn controls the ecology of these water bodies, this research contributes to the prediction of the impact of climate change on this important complex of ecosystems.

North Dakota↗
USGS2014

Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data

Wetlands provide ecosystem goods and services vitally important to humans. Land managers and policymakers working to conserve wetlands require regularly updated information on the statuses of wetlands across the landscape. However, wetlands are challenging to map remotely with high accuracy and consistency. We investigated the use of multitemporal polarimetric synthetic aperture radar (SAR) data acquired with Canada’s Radarsat-2 system to track within-season changes in wetland vegetation and surface water. We speculated, a priori , how temporal and morphological traits of different types of wetland vegetation should respond over a growing season with respect to four energy-scattering mechanisms. We used ground-based monitoring data and other ancillary information to assess the limits and consistency of the SAR data for tracking seasonal changes in wetlands. We found the traits of different types of vertical emergent wetland vegetation were detected well with the SAR data and corresponded with our anticipated backscatter responses. We also found using data from Landsat’s optical/infrared sensors in conjunction with SAR data helped remove confusion of wetland features with upland grasslands. These results suggest SAR data can provide useful monitoring information on the statuses of wetlands over time.

Water↗
USGS2013

Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA

[1] Linkages between permafrost distribution and lake surface-area changes in cold regions have not been previously examined over a large scale because of the paucity of subsurface permafrost information. Here, a first large-scale examination of these linkages is made over a 5150 km 2 area of Yukon Flats, Alaska, USA, by evaluating the relationship between lake surface-area changes during 1979–2009, derived from Landsat satellite data, and sublacustrine groundwater flow-path connectivity inferred from a pioneering, airborne geophysical survey of permafrost. The results suggest that the shallow (few tens of meters) thaw state of permafrost has more influence than deeper permafrost conditions on the evolving water budgets of lakes on a multidecadal time scale. In the region studied, these key shallow aquifers have high hydraulic conductivity and great spatial variability in thaw state, making groundwater flow and associated lake level evolution particularly sensitive to climate change owing to the close proximity of these aquifers to the atmosphere.

Alaska↗
USGS2012

The regional abundance and size distribution of lakes and reservoirs in the United States and implication for estimates of global lake extent

We analyzed complete geospatial data for the 3.5 million lakes and reservoirs larger than 0.001 km 2 , with a combined surface area of 131,000 km 2 , in the contiguous United States (excluding the Laurentian Great Lakes) and identified their regional distribution characteristics. For Alaska, we also analyzed (1) incomplete data that suggest that the state contains 1–2.5 million lakes larger than 0.001 km 2 covering over 50,000 km 2 and (2) localized high-resolution (5 m) data that suggest that the number of very small water bodies (< 0.001 km 2 ) may be comparable with the number of lakes > 0.001 km 2 in some areas. The Pareto distribution cannot accurately describe the lake abundance-size relationship across the entire size spectrum, and extrapolation of this density function to small size classes has likely resulted in the overestimation of the number of small lakes in the world. While small water bodies dominate in terms of numbers, they are not numerous enough to dominate in terms of surface area, as has been previously suggested. Extending our results to the global scale suggests that there are on the order of 64 million water bodies larger than 0.001 km 2 in the world, with a total surface area of approximately 3.8 million km 2 .

Limnology and Oceanography↗