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At least 1,135 records · Page 63Linked to original sources
USGS2015

Integrated climate and land use change scenarios for California rangeland ecosystem services: wildlife habitat, soil carbon, and water supply

Context In addition to biodiversity conservation, California rangelands generate multiple ecosystem services including livestock production, drinking and irrigation water, and carbon sequestration. California rangeland ecosystems have experienced substantial conversion to residential land use and more intensive agriculture. Objectives To understand the potential impacts to rangeland ecosystem services, we developed six spatially explicit (250 m) climate/land use change scenarios for the Central Valley of California and surrounding foothills consistent with three Intergovernmental Panel on Climate Change emission scenario narratives. Methods We quantified baseline and projected change in wildlife habitat, soil organic carbon (SOC), and water supply (recharge and runoff). For six case study watersheds we quantified the interactions of future development and changing climate on recharge, runoff and streamflow, and precipitation thresholds where dominant watershed hydrological processes shift through analysis of covariance. Results The scenarios show that across the region, habitat loss is expected to occur predominantly in grasslands, primarily due to future development (up to a 37 % decline by 2100), however habitat loss in priority conservation errors will likely be due to cropland and hay/pasture expansion (up to 40 % by 2100). Grasslands in the region contain approximately 100 teragrams SOC in the top 20 cm, and up to 39 % of this SOC is subject to conversion by 2100. In dryer periods recharge processes typically dominate runoff. Future development lowers the precipitation value at which recharge processes dominate runoff, and combined with periods of drought, reduces the opportunity for recharge, especially on deep soils. Conclusion Results support the need for climate-smart land use planning that takes recharge areas into account, which will provide opportunities for water storage in dry years. Given projections for agriculture, more modeling is needed on feedbacks between agricultural expansion on rangelands and water supply.

California↗
USGS2022

Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region

Context Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments. Objectives This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog ( Rana pipiens ) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation. Methods Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity. Results STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation. Conclusion While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.

North Dakota↗
USGS2019

Preface—Evaluating the response of critical zone processes to human impacts with sediment source fingerprinting

1) Background: Critical Zone Processes in the Anthropocene The Earth’s Critical Zone encompasses a suite of interconnected processes in the near-surface lithosphere, pedosphere, biosphere, atmosphere, and hydrosphere (Brantley et al., 2007; Lin, 2010) (Fig. 1). Processes and interactions both within and between these various Critical Zone components supports life-sustaining ecosystem services and resources that establish the foundation for humanity (NRC, 2001). This includes the formation production of fertile soils, flourishing vegetation, productive rivers, lakes and oceans, and our life-sustaining atmosphere (Gaillardet, 2014; Guo and Lin, 2016). Rapid population growth, land use intensification, and global environmental change are disturbing many of these fundamental Critical Zone processes. More than half of the Earth’s terrestrial surface is now impacted by anthropogenic activities (e.g., clearing, grazing, plowing, mining, and logging) (Hooke et al., 2012; Richter and Mobley, 2009). These changes are so widespread and pervasive that the great acceleration of socioeconomic development that occurred around 1950 (Fig. 2) has been recommended to delineate the dawn of the Anthropocene (Waters et al., 2016). Although the utility of adopting and delineating the Anthropocene as the current epoch is subject to debate (Crutzen, 2002; Ruddiman et al., 2015; Smith and Zeder, 2013), the concept effectively highlights both the nature and the extent of our global impact on Earth’s Critical Zone. Soil forming processes and ecosystem services provided by the pedosphere are central to the Critical Zone (Banwart et al., 2011; Lin, 2010). Many of these processes have been disturbed by the agricultural intensification that coincided with the great acceleration resulting in unsustainable land use practices now outpacing soil formation processes (Brantley et al., 2007). As agricultural landscapes now cover an area equivalent to what was scoured during the last glacial maximum (Amundson et al., 2007), the broad-scale intensification of anthropogenic activities has resulted in significant on- and off-site impacts. On-site, soil loss has resulted in decreases in soil fertility and agricultural yields (Ladha et al., 2009) threatening the ability to feed the world’s growing population (Brantley et al., 2007). Off-site, the excess delivery of particulate matter downstream is degrading riverine, lacustrine, and estuarine ecosystems (Bilotta and Brazier, 2008; Clark, 1985; Owens et al., 2005). The challenge, as noted by Brantley et al., (2007), is that despite our society having over 10,000 years of experience working with soils, our conceptual and quantitative models remain inadequate at predicting Critical Zone dynamics under current conditions. Notwithstanding growing pressure for improved environmental management, we still have a limited capacity to predict changes in the Critical Zone in response to anthropogenic activities owing to the multiple spatial and temporal scales at which these complex processes and feedbacks are manifest. As river basin systems are impacted by many of these processes, a deep understanding of soil-sediment continuum dynamics may provide a valuable framework for evaluating the disturbance response of Critical Zone processes. Understanding these processes may also provide land and resource managers with the information necessary to manage both the on-site and off-site effects of accelerated soil erosion.

Journal of Soils and Sediments↗
USGS2009

Relations between sinkhole density and anthropogenic contaminants in selected carbonate aquifers in the eastern United States

The relation between sinkhole density and water quality was investigated in seven selected carbonate aquifers in the eastern United States. Sinkhole density for these aquifers was grouped into high (>25 sinkholes/100 km 2 ), medium (1–25 sinkholes/100 km 2 ), or low (<1 sinkhole/100 km 2 ) categories using a geographical information system that included four independent databases covering parts of Alabama, Florida, Missouri, Pennsylvania, and Tennessee. Field measurements and concentrations of major ions, nitrate, and selected pesticides in samples from 451 wells and 70 springs were included in the water-quality database. Data were collected as a part of the US Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program. Areas with high and medium sinkhole density had the greatest well depths and depths to water, the lowest concentrations of total dissolved solids and bicarbonate, the highest concentrations of dissolved oxygen, and the lowest partial pressure of CO 2 compared to areas with low sinkhole density. These chemical indicators are consistent conceptually with a conduit-flow-dominated system in areas with a high density of sinkholes and a diffuse-flow-dominated system in areas with a low density of sinkholes. Higher cave density and spring discharge in Pennsylvania also support the concept that the high sinkhole density areas are dominated by conduit-flow systems. Concentrations of nitrate-N were significantly higher ( p < 0.05) in areas with high and medium sinkhole density than in low sinkhole-density areas; when accounting for the variations in land use near the sampling sites, the high sinkhole-density area still had higher concentrations of nitrate-N than the low sinkhole-density area. Detection frequencies of atrazine, simazine, metolachlor, prometon, and the atrazine degradate deethylatrazine indicated a pattern similar to nitrate; highest pesticide detections were associated with high sinkhole-density areas. These patterns generally persisted when analyzing the detection frequency by land-use groups, particularly for agricultural land-use areas where pesticide use would be expected to be higher and more uniform areally compared to urban and forested areas. Although areas with agricultural land use and a high sinkhole density were most vulnerable (median nitrate-N concentration was 3.7 mg/L, 11% of samples exceeded 10 mg/L, and had the highest frequencies of pesticide detection), areas with agricultural land use and low sinkhole density still were vulnerable to contamination (median nitrate-N concentration was 1.5 mg/L, 8% of samples exceeded 10 mg/L, and had some of the highest frequencies of detections of pesticides). This may be due in part to incomplete or missing data regarding karst features (such as buried sinkholes, low-permeability material in bottom of sinkholes) that do not show up at the scales used for regional mapping and to inconsistent methods among states in karst feature delineation.

Missouri↗
USGS2014

Evaluating the effects of land use on headwater wetland amphibian assemblages in coastal Alabama

Anthropogenic land use is known to impact aquatic ecosystems in several ways, including increased frequency and intensity of floods, stream channel incision, sedimentation, and loss of microtopography. Amphibians are susceptible to changes in wetland and surrounding habitats. This study evaluated amphibian assemblages of fifteen headwater slope wetlands in coastal Alabama across a gradient of land uses. Amphibians were surveyed on a seasonal basis and land use was delineated within wetland watersheds and within a 200-m buffer surrounding each wetland. Amphibian presence/absence and land use data were used to develop species occupancy models. Both urban and agricultural land use were shown to influence amphibian occurrence. Species richness ranged from five to ten species across sites; however, five species only occurred in wetlands surrounded by forested lands. Many species were detected more frequently on these wetlands compared to wetlands surrounded by urban or mixed land uses. Occupancy models showed Acris gryllus was negatively associated with the amount of agriculture within a buffer around the wetland. Hyla squirella , Lithobates clamitans , and L. sphenocephalus were positively associated with agricultural land within a watershed. Anaxyrus terrestris and the non-native Eleutherodactylus planirostris were positively associated with the amount of impervious surface area within the wetland buffer.

Alabama↗
USGS1995

Uraniferous waters of the Arkansas River valley, Colorado, U.S.A.: A function of geology and land use

The effect of local geology and land-use practices on dissolved U was investigated by analysis of surface water and some springs in the Arkansas River valley of southeastern Colorado. Water samples were collected during a 2 week period in April, 1991. The rate of increase of U concentration with distance downriver increased markedly as the river flowed from predominantly undeveloped lands underlain by igneous and metamorphic rocks to agriculturally developed lands underlain by marine shale and limestone. An additional abrupt increase in dissolved U was observed along the section of river where discharge is often greatly reduced because of extensive diversions for irrigation and where remaining flow is largely composed of irrigation return water. Dissolved U in this last section of river and in most of its tributaries exceeded the proposed U.S. drinking water standard of 20 μ/L In water samples collected from agricultural areas dissolved U showed strong positive correlation with major dissolved constituents Na, Ca, Mg, and SO 4 that increase as a result of sulfate mineral dissolution and clay mineral ion-exchange reactions in weathered shale bedrock and shaley soils. Highly soluble minor and trace elements Cl, Li, B, Sr, and Se that are not subject to strong sorptive uptake or precipitation in this setting also correlated positively with U. These combined observations indicate that natural leaching of U-bearing shale bedrock and derivative soils, additional leaching of rock and soil by irrigation water, and evaporative concentration in a semi-arid climate can produce concentrations of dissolved U in surface water and shallow ground water that may threaten nearby drinking water supplies. Other agriculturally developed areas of the semi-arid Western U.S. with similar geology are likely to contain high concentrations of U in irrigation drain water.

Colorado↗
USGS2014

Mobilization of selenium from the Mancos Shale and associated soils in the lower Uncompahgre River Basin, Colorado

This study investigates processes controlling mobilization of selenium in the lower part of the Uncompahgre River Basin in western Colorado. Selenium occurs naturally in the underlying Mancos Shale and is leached to groundwater and surface water by limited natural runoff, agricultural and domestic irrigation, and leakage from irrigation canals. Soil and sediment samples from the study area were tested using sequential extractions to identify the forms of selenium present in solid phases. Selenium speciation was characterized for nonirrigated and irrigated soils from an agricultural site and sediments from a wetland formed by a leaking canal. In nonirrigated areas, selenium was present in highly soluble sodium salts and gypsum. In irrigated soils, soluble forms of selenium were depleted and most selenium was associated with organic matter that was stable under near-surface weathering conditions. Laboratory leaching experiments and geochemical modeling confirm that selenium primarily is released to groundwater and surface water by dissolution of highly soluble selenium-bearing salts and gypsum present in soils and bedrock. Rates of selenium dissolution determined from column leachate experiments indicate that selenium is released most rapidly when water is applied to previously nonirrigated soils and sediment. High concentrations of extractable nitrate also were found in nonirrigated soils and bedrock that appear to be partially derived from weathered organic matter from the shale rather than from agricultural sources. Once selenium is mobilized, dissolved nitrate derived from natural sources appears to inhibit the reduction of dissolved selenium leading to elevated concentrations of selenium in groundwater. A conceptual model of selenium weathering is presented and used to explain seasonal variations in the surface-water chemistry of Loutzenhizer Arroyo, a major tributary contributor of selenium to the lower Uncompahgre River.

Colorado↗
USGS2008

Seasonal temperature responses to land-use change in the western United States

In the western United States, more than 79 000??km2 has been converted to irrigated agriculture and urban areas. These changes have the potential to alter surface temperature by modifying the energy budget at the land-atmosphere interface. This study reports the seasonally varying temperature responses of four regional climate models (RCMs) - RSM, RegCM3, MM5-CLM3, and DRCM - to conversion of potential natural vegetation to modern land-cover and land-use over a 1-year period. Three of the RCMs supplemented soil moisture, producing large decreases in the August mean (- 1.4 to - 3.1????C) and maximum (- 2.9 to - 6.1????C) 2-m air temperatures where natural vegetation was converted to irrigated agriculture. Conversion to irrigated agriculture also resulted in large increases in relative humidity (9% to 36% absolute change). Modeled changes in the August minimum 2-m air temperature were not as pronounced or consistent across the models. Converting natural vegetation to urban land-cover produced less pronounced temperature effects in all models, with the magnitude of the effect dependent upon the preexisting vegetation type and urban parameterizations. Overall, the RCM results indicate that the temperature impacts of land-use change are most pronounced during the summer months, when surface heating is strongest and differences in surface soil moisture between irrigated land and natural vegetation are largest. ?? 2007 Elsevier B.V. All rights reserved.

Global and Planetary Change↗
USGS2020

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗
USGS2021

Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Michigan↗
USGS2022

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km 2 ) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

Chesapeake Bay Watershed↗
USGS2008

Effects of climate and land management change on streamflow in the driftless area of Wisconsin

Baseflow and precipitation in the Kickapoo River Watershed, located in the Driftless Area of Wisconsin, exhibit a step increase around 1970, similar to minimum and median flows in many other central and eastern USA streams. Potential effects on streamflow due to climatic and land management changes were evaluated by comparing volumetric changes in the hydrologic budget before and after 1970. Increases in precipitation do not fully account for the increase in baseflow, which appears to be offset by a volumetric decrease in stormflow. This suggests that factors that influence the partitioning of precipitation into overland runoff or infiltration have changed. A transition from relatively more intensive to relatively less intensive agricultural land use is generally associated with higher infiltration rates, and likely influences partitioning of flow. Changes in agricultural land management practices in the Driftless Area, which began in the mid-1930s, do not coincide with the abrupt increase in baseflow around 1970. Instead, the timing of hydrologic change appears to coincide with changes in precipitation, whereas the magnitude of the change in baseflow and stormflow was likely amplified by changes in agricultural land management. ?? 2008 Elsevier B.V. All rights reserved.

Journal of Hydrology↗
USGS2009

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service-Southeast Watershed Research Lab (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2-4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity.

Journal of Hydrology↗
USGS2009

A simple approach to distinguish land-use and climate-change effects on watershed hydrology

Impacts of climate change on watershed hydrology are subtle compared to cycles of drought and surplus precipitation (PPT), and difficult to separate from effects of land-use change. In the US Midwest, increasing baseflow has been more attributed to increased annual cropping than climate change. The agricultural changes have led to increased fertilizer use and nutrient losses, contributing to Gulf of Mexico hypoxia. In a 25-yr, small-watershed experiment in Iowa, when annual hydrologic budgets were accrued between droughts, a coupled water-energy budget (ecohydrologic) analysis showed effects of tillage and climate on hydrology could be distinguished. The fraction of PPT discharged increased with conservation tillage and time. However, unsatisfied evaporative demand (PET - Hargreaves method) increased under conservation tillage, but decreased with time. A conceptual model was developed and a similar analysis conducted on long-term (>1920s) records from four large, agricultural Midwest watersheds underlain by fine-grained tills. At least three of four watersheds showed decreases in PET, and increases in PPT, discharge, baseflow and PPT:PET ratios (p < 0.10). An analysis of covariance showed the fraction of precipitation discharged increased, while unsatisfied evaporative demand decreased with time among the four watersheds (p < 0.001). Within watersheds, agricultural changes were associated with ecohydrologic shifts that affected timing and significance, but not direction, of these trends. Thus, an ecohydrologic concept derived from small-watershed research, when regionally applied, suggests climate change has increased discharge from Midwest watersheds, especially since the 1970s. By inference, climate change has increased susceptibility of nutrients to water transport, exacerbating Gulf of Mexico hypoxia.

Journal of Hydrology↗
USGS2012

Land change variability and human-environment dynamics in the United States Great Plains

Land use and land cover changes have complex linkages to climate variability and change, biophysical resources, and socioeconomic driving forces. To assess these land change dynamics and their causes in the Great Plains, we compare and contrast contemporary changes across 16 ecoregions using Landsat satellite data and statistical analysis. Large-area change analysis of agricultural regions is often hampered by change detection error and the tendency for land conversions to occur at the local-scale. To facilitate a regional-scale analysis, a statistical sampling design of randomly selected 10 km × 10 km blocks is used to efficiently identify the types and rates of land conversions for four time intervals between 1973 and 2000, stratified by relatively homogenous ecoregions. Nearly 8% of the overall Great Plains region underwent land-use and land-cover change during the study period, with a substantial amount of ecoregion variability that ranged from less than 2% to greater than 13%. Agricultural land cover declined by more than 2% overall, with variability contingent on the differential characteristics of regional human–environment systems. A large part of the Great Plains is in relatively stable land cover. However, other land systems with significant biophysical and climate limitations for agriculture have high rates of land change when pushed by economic, policy, technology, or climate forcing factors. The results indicate the regionally based potential for land cover to persist or fluctuate as land uses are adapted to spatially and temporally variable forcing factors.

Colorado;Iowa;Kansas;Minnesota;Missouri;Montana;Ne↗
USGS2014

Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI

Agricultural production capacity contributes to food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by snowmelt. Because of the high contribution of irrigated crops (> 80%) to total agricultural production, knowing the spatial distribution and year-to-year variability in irrigated areas is imperative to monitoring food security for the country. We used 16-day composites of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to create 23-point time series for each year from 2000 through 2013. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm to map irrigated areas in Afghanistan for the last 14 years. In the absence of ground reference irrigated area information, we evaluated these maps with the irrigated areas classified from multiple snapshots of the landscape during the growing season from Landsat 5 optical and thermal sensor images. We were able to identify irrigated areas using Landsat imagery by selecting as irrigated those areas with Landsat-derived NDVI greater than 0.30–0.45, depending on the date of the Landsat image and surface temperature less than or equal to 310 Kelvin (36.9 ° C). Due to the availability of Landsat images, we were able to compare with the MODIS-derived maps for four years: 2000, 2009, 2010, and 2011. The irrigated areas derived from Landsat agreed well r 2 = 0.91 with the irrigated areas derived from MODIS, providing confidence in the MODIS NDVI threshold approach. The maps portrayed a highly dynamic irrigated agriculture practice in Afghanistan, where the amount of irrigated area was largely determined by the availability of surface water, especially snowmelt, and varied by as much as 30% between water surplus and water deficit years. During the past 14 years, 2001, 2004, and 2008 showed the lowest levels of irrigated area (~ 1.5 million hectares), attesting to the severe drought conditions in those years, whereas 2009, 2012 and 2013 registered the largest irrigated area (~ 2.5 million hectares) due to record snowpack and snowmelt in the region. The model holds promise the ability to provide near-real-time (by the end of the growing seasons) estimates of irrigated area, which are beneficial for food security monitoring as well as subsequent decision making for the country. While the model is developed for Afghanistan, it can be adopted with appropriate adjustments in the derived threshold values to map irrigated areas elsewhere.

Remote Sensing of Environment↗
USGS2020

A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery

Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Maryland↗