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Results for “Integrated Crop Management”

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USGS2001

Water quality in the eastern Iowa basins

This article summarizes major findings about nutrients in surface and groundwater in the eastern Iowa basins (see map) between 1996 and 1998. The data were collected as part of the U.S. Geological Survey (USGS) National Water-Quality Assessment Program (NAWQA). Water quality is discussed in terms of local and regional issues and compared with conditions found in all 36 National NAWQA study areas assessed to date. Findings are explained in the context of selected national U.S. Environmental Protection Agency (EPA) benchmarks, such as those for drinking water quality and the protection of aquatic organisms. The Eastern Iowa Basins Study Unit includes the Wapsipinicon, Cedar, Iowa, and Skunk River basins and covers approximately 19,500 square miles in eastern Iowa and southern Minnesota. More than 90 percent of the land in the study unit is used for agricultural purposes. Forested areas account for only 4 percent of the land area.

Iowa, Minnesota↗

USGS2024

The LTAR cropland Common Experiment at Lower Chesapeake Bay

The Lower Chesapeake Bay (LCB) Long-Term Agroecosystem Research (LTAR) Common Experiment (CE) located in Beltsville, MD, focuses on research of concern to producers of the major regional crops, which are corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], wheat ( Triticum aestivum L.), and various forage species. Livestock production in the region includes broiler and laying chickens ( Gallus gallus domesticus L.) and dairy and beef cattle ( Bos taurus L.). The LCB region is among the most heavily populated in the United States. Urban development pressure is high for both farms and natural areas. The need to restore Chesapeake Bay water quality is a major influence on regional agricultural practices. Conservation practices such as cover cropping, no-till agriculture, and nutrient management planning are more common in the region compared to nationally. However, farmers still face management challenges implementing practices that address water quality and the rise of herbicide-resistant weeds. Researchers at the LCB site recognize the need to protect the Chesapeake and Delaware Bays and maintain farmer profitability. The LCB CE compares a 3-year crop rotation system featuring alternative crop management (cover crop intensification, crop rotation diversification, and integrated weed management [IWM]) with a prevailing 2-year system (no cover crops and no IWM), both under continuous no-tillage, to identify the optimal balance to promote the sustainability of regional cropping systems. The LTAR LCB site provides data-driven tools and solutions to support farmers in the mid-Atlantic region.

Delaware, Maryland, New Jersey, Pennsylvania, Virg↗

USGS2017

Anticoagulant rodenticides and wildlife: Concluding remarks

Rodents are known to affect human society globally in various adverse ways, resulting in a widespread demand for their continuous control. Anticoagulant rodenticides (ARs) have been, and currently remain, the cornerstone of rodent control throughout the world. Although alternative control methods exist, they are generally less effective. ARs work by affecting vitamin K metabolism, thereby preventing the activation of blood clotting factors and eventual coagulopathy. Since ARs are non-selective, their undoubted benefits for rodent control have to be balanced against the environmental risks that these compounds pose. Although they have been used for decades, pharmacokinetic and toxicokinetic data are mainly available for laboratory mammals and have concentrated on acute effects. Limited information is available on chronic exposure scenarios and for wildlife species. Important gaps exist in our understanding of the large inter- and intra-species differences in sensitivity to ARs, especially for non-target species, and in our knowledge about the occurrence and importance of sub-lethal effects in wildlife. It is clear that mere presence of AR residues in the body tissues may not indicate the occurrence of effects, although unequivocal assessment of effects under field conditions is difficult. Ante-mortem symptoms, like lethargy, subdued behaviour and unresponsiveness are generally not very specific as is true for more generic post-mortem observations (e.g. pallor of the mucous membranes or occurrence of haemorrhages). It is only by combining ante or post-mortem data with information on exposure that effects in the field may be confirmed. We do know however that a wide variety of non-target species are directly exposed to ARs. Secondary exposure in predators is also widespread although there is limited information on whether this exposure causes actual effects. Exposure is driven by ecological factors and is context specific with respect to spatial habitat configuration and bait placement. Another key factor that affects the interaction between ARs and wildlife is the development of resistance in target species. The development of resistance has resulted in higher use of SGARs, thereby increasing the potential of non-target and secondary exposure. AR use has increasingly become more strictly regulated, increasing the need for alternatives. Alternatives are available, including non-anticoagulant rodenticides, but these may also pose significant risk to environmental organisms, humans and pets. There are also various mitigation measures that can be implemented when using ARs, including bait protection, pulsed baiting at the onset of infestation, restricting use by non-professionals, and avoiding use in areas of high non-target density. Reduction in secondary exposure may result from e.g. non-chemical control, habitat management, and, in agricultural habitats, the use of lure crops and supplemental feeding. Such Integrated Pest Management (IPM) may not only reduce non-target exposure but also benefit resistance management. Barriers to adopt IPM approaches however, include the perception that they do not work or too slowly and are more laborious, expensive and time consuming. It is therefore important that the expectations of stakeholders are considered and managed. Nevertheless, further development of alternatives and IPM measures is essential, so the key research priority related to rodent control may ultimately be to address the lack of scientific assessment of the effectiveness of both specific AR mitigation measures and of IPM approaches to rodent control.

Book chapter↗

USGS2002

Integrated management of waterbirds: Beyond the conventional

Integrated waterbird management over the past few decades has implicitly referred to methods for managing wetlands that usually attempt to enhance habitat for taxonomic groups such as shorebirds and wading birds, in addition to waterfowl, the traditional focus group. Here I describe five elements of integration in management: taxonomic, spatial, temporal, population and habitat, and multiple-use management objectives. Spatial integration simply expands the scale of management concern. Rather than emphasizing management on a very limited number of impoundments or wetlands in small refuges or wildlife management areas, the vision is beginning to shift to connectivity within larger landscapes on the order of many square kilometers as telemetry data on daily and seasonal movements for many species become available. Temporal integration refers to the potential for either simultaneous management for waterbirds and commercial 'crops' (e.g., crayfish and rice) or for temporally-staggered management such as row crop production in spring-summer growing seasons and waterbird management on fallow fields in the non-growing (winter) season. Integrating population dynamics with habitats has become a major research focus over the past decade. Identifying which wetlands are ?sources? or ?sinks? for specific populations provides managers with critical information about effective management. Further, the applications of spatially explicit population models place heavy demands on researchers to identify use patterns for breeding and dispersing individuals by age, sex, and reproductive class. Population viability analysis models require much the same information. Finally, multiple-use management integration refers to trying to optimize the uses of wetlands, when only one (perhaps secondary) use may include waterbird management. Depending upon the ownership and primary land use of a particular parcel of land containing wetlands and/or water bodies, managing for waterbirds may be an ?easy sell? (e.g., public natural resource lands) or a very contentious one, where wetlands are created for industrial, aquaculture or urban uses. In the latter case, careful planning and implementation require broad stakeholder participation and education.

Book chapter↗

USGS2018

Agriculture

Agricultural production is a fundamental activity conducted on 45% of the U.S. land area, 55% of Mexico’s land area, and 7% of Canada’s land area (World Bank 2016). Because of this vast spatial extent and the strong role that land management plays in how agricultural ecosystems function, agricultural lands and activities represent a large portion of the North American carbon budget. Accordingly, improved quantification of the agricultural carbon cycle, new trends in agriculture, and added opportunities for emissions reductions provide a critical foundation for considering the relationships between agriculture and carbon cycling at local, regional, continental, and global scales. More than 145 countries have specifically included agriculture in their targets and actions for mitigating climate change (FAO 2016), and agriculture has featured particularly prominently in recent target and action commitments made by developing countries to reduce greenhouse gas (GHG) emissions (Richards et al., 2015). Conversion of vast native forest and prairie to agriculture across North America between 1860 and 1960 resulted in carbon dioxide (CO2) fluxes to the atmosphere from biota and soils that exceeded those from fossil fuel emissions over the same period (Houghton et al., 1983). Correspondingly, soil organic carbon (SOC) declined in many soils during the 50 years following conversion from native ecosystems to production agriculture (Huggins et al., 1998; Janzen et al., 1998; Slobodian et al., 2002). Crop yields and corresponding above- and belowground biomass have steadily increased since the 1930s due to genetic and management innovations, which provide more organic input from which to build SOC ( Johnson et al., 2006; Hatfield and Walthall 2015). This, coupled with improved input-use efficiencies may reduce GHG-emissions per unit yield (GHG intensity), with additional improvements possible through management optimization (Grassini and Cassman 2012; Pittelkow et al., 2015). Options include reducing tillage, integrating perennials onto the landscape, reducing or eliminating bare-fallow land (i.e., land without living plants), adding cover crops, and enrolling lands in conservation easement programs. These options, originally proposed to control erosion, have potential co-benefits in terms of increased soil health, plant productivity, and soil carbon stabilization (Lehman et al., 2015). Conversely, returning lands previously enrolled in conservation easements (e.g., the Conservation Reserve Program [CRP] and other land set-aside efforts) to row-crop production, tillage, or aggressive harvesting of crop residues all risk degrading soil quality and exacerbating SOC loss. Of note is that the net results of land use and land management practices in an agricultural setting vary according to many factors, such as crop or production system type, soil type, climate, and the collection of practices at any given site. For example, many traditional practices followed by Indigenous people on tribal lands are based on an integrated approach to natural resource management and response to environmental change that may provide agricultural options uniquely suited to varied environmental settings (see Ch. 7: Tribal Lands, p. 303). Agricultural land in the United States totaled 408.2 million hectares (ha) in 2014, of which 251 million ha were in permanent meadows and pastures, 152.2 million ha were in arable land, and 2.6 million ha were in permanent crops (FAOSTAT 2016). Compared with the distribution in 2007, these numbers reflect a 4.7 million ha decline in total agricultural lands, driven by declines in arable land and permanent crops but partially offset by a modest increase in permanent meadows and pastures. Although arable lands have been declining, the combined acreage of the four major crops (corn, wheat, soybeans, and cotton) has risen slightly, with increases in land planted in corn and soybeans and decreases in cotton and wheat (see Figure 5.1, p. 232). Despite the overall slight decline in agricultural land area, the value of U.S. agricultural production rose over the past decade as a result of increased production efficiency and higher prices (USDA 2017a; see also www.ers.usda.gov). Canada has about 65 million ha of agricultural land, of which about 46 million ha are arable, accounting for only about 7% of the country’s total land area (FAOSTAT 2017). Prominent crops on Canada’s arable lands include cereals (e.g., wheat, barley, and maize), oilseeds (e.g., canola and soybeans), and pulses (e.g., peas and lentils). Natural and seeded pastures available for grazing in Canada make up about 20 million ha (Legesse et al., 2016). Agricultural land in Mexico makes up 107 million ha, of which 23 million ha are arable land, 2.7 million ha are permanent crops, and 81 million ha are permanent meadows and pastures (FAOSTAT 2017). Mexico’s major crops are fruits, corn, grains, vegetables, and sugarcane.

Report↗

USGS2025

Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions

Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses ( n = 797), legumes ( n = 264), and brassicas ( n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses ( R 2 = 0.51 – 0.64) and legumes ( R 2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses ( R 2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models ( R 2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha −1 for legumes and ∼4000 kg ha −1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled ( R 2 = 0.58 – 0.61) and ACS-214-estimated biomass ( R 2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.

Alabama, Florida, Indiana, Iowa, Kansas, Maryland,↗

USGS2014

Mercury cycling in agricultural and managed wetlands: a synthesis of methylmercury production, hydrologic export, and bioaccumulation from an integrated field study

With seasonal wetting and drying, and high biological productivity, agricultural wetlands (rice paddies) may enhance the conversion of inorganic mercury (Hg(II)) to methylmercury (MeHg), the more toxic, organic form that biomagnifies through food webs. Yet, the net balance of MeHg sources and sinks in seasonal wetland environments is poorly understood because it requires an annual, integrated assessment across biota, sediment, and water components. We examined a suite of wetlands managed for rice crops or wildlife during 2007–2008 in California's Central Valley, in an area affected by Hg contamination from historic mining practices. Hydrologic management of agricultural wetlands for rice, wild rice, or fallowed — drying for field preparation and harvest, and flooding for crop growth and post-harvest rice straw decay — led to pronounced seasonality in sediment and aqueous MeHg concentrations that were up to 95-fold higher than those measured concurrently in adjacent, non-agricultural permanently-flooded and seasonally-flooded wetlands. Flooding promoted microbial MeHg production in surface sediment of all wetlands, but extended water residence time appeared to preferentially enhance MeHg degradation and storage. When incoming MeHg loads were elevated, individual fields often served as a MeHg sink, rather than a source. Slow, horizontal flow of shallow water in the agricultural wetlands led to increased importance of vertical hydrologic fluxes, including evapoconcentration of surface water MeHg and transpiration-driven advection into the root zone, promoting temporary soil storage of MeHg. Although this hydrology limited MeHg export from wetlands, it also increased MeHg exposure to resident fish via greater in situ aqueous MeHg concentrations. Our results suggest that the combined traits of agricultural wetlands — slow-moving shallow water, manipulated flooding and drying, abundant labile plant matter, and management for wildlife — may enhance microbial methylation of Hg(II) and MeHg exposure to local biota, as well as export to downstream habitats during uncontrolled winter-flow events.

California↗

USGS2019

Integrated hydrologic modeling of the Salinas River, California, for sustainable water management

The Salinas River is the largest river in California’s Central Coast region. Groundwater resources of the Salinas River basin are used to meet water supply needs, including crop irrigation and municipal water supply. Two large multipurpose reservoirs also supply irrigation and municipal water uses. Historical imbalances between supply and demand have resulted in sinking groundwater levels, seawater intrusion, regulatory actions on pumping, adjudication, and requirements for minimum in-stream fish flows. Present needs include finding replacement water supplies and improving watershed management to comply with legal mandates, adapt to future climate variability and landuse conversions, and improve environmental conditions. The Salinas Valley Integrated Hydrologic Model (SVIHM) was developed to help water managers evaluate and adjust to projected impacts on water supplies and demands in the Salinas Valley watershed caused by changes in land use, population, and climate. The SVIHM includes four modeling components: (1) the Basin Characterization Model (BCM), (2) the Hydrologic Simulation Program – FORTRAN (HSPF), (3) MODFLOW - One Water Hydrologic Model (MF-OWHM), and (4) the Surface Water Operations (SWO) package. The BCM and HSPF components compose the Salinas Valley Watershed Model (SVWM). The 4,530 square-mile (mi2) SVWM domain encompasses the entire Salinas River watershed, as well as coastal drainages adjacent to the Salinas River outflow, and includes two separate and connected HSPF model domains, the 2,540 mi2 upper Salinas River and the 1,990 mi2 lower Salinas River models. SVWM (1) simulates the water budget for the entire Salinas River basin containing both the SVIHM domain as well as the mountainous terrain of the tributary headwater areas not included in the SVIHM; and (2) was used to develop the 148 boundary inflows for the SVIHM. Simulated evapotranspiration (ET) is the largest component of the water budget after precipitation, with a 71-year average basin-wide ET of 13.9 in/yr, compared to the basin-wide average precipitation of 18.4 in/yr. Simulated ET ranges from 15 to 29 in/yr along the western side of the SVWM to less than 10 in/yr throughout the valley floor and in the southeast part of the Salinas River watershed. The simulated total 71-year average inflow to the SVIHM was 890 ft3/sec (about 640,000 acre-feet per year), with the highest average inflow of 270 ft3/sec simulated for the Nacimiento River; whereas, the simulated 71-year average streamflow at the mouth of the Salinas River was only about 190 ft3/sec, indicating that most of the streamflow generated in the Salinas River basin is lost to channel seepage. The lack of sustained baseflow causes streamflow to be highly sensitive to the temporal variability in precipitation, especially during the drier periods, and this increases the importance of developing adequate reservoir management, flow augmentation, and conjunctive water use scenarios for potential future drought periods and potentially increased temporal variability in precipitation.

California↗

USGS2024

Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale

Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence-only data from various sources with detection/non-detection data from structured surveys, we estimated the distribution and expected abundance of three difficult-to-monitor mammals of management concern across New York State, namely, coyotes ( Canis latrans ), bobcats ( Lynx rufus ) and black bears ( Ursus americanus ). Three distinct landscape-scale camera trap surveys provided detection/non-detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence-only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications . Our integrated modelling method allows for managers to use citizen sightings combined with detection/non-detection surveys to estimate robust indices of abundance for both high- and low-density, and wide-spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad-scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

New York↗

USGS2024

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods The objective of this study was to quantify cash and cover crop residue nitrogen and carbon traits using partial least squares regression models and a combination of 1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected ASD spectra (n = 296) of 11 cover crop species and three cash crop species, and 2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 – 0.98; RMSE = 0.24 – 4.25%) and results suggest that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 – 0.75; RMSE = 2.71 – 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38 – 1.85%). PRISMA imagery models were also impacted by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

Maryland↗

USGS2026

The impacts of cover crop biomass on satellite-based detectability of cover crops in Maryland

Cover crop adoption in the U.S. has increased over the past decades, increasing the need to quantify their performance and environmental benefits. While remote sensing (RS)-based approaches for detecting cover crop presence have been developed, there has been limited research on how cover crops with varied biomass and management practices influence detectability. Using unique field-level cover crop presence and biomass datasets in Maryland, U.S., we investigated how RS-based detectability changes for cover crops with varied aboveground biomass, planting, and termination dates. Specifically, we proposed a time-integrated satellite-based greenness feature from Harmonized Landsat-8 and Sentinel-2 (HLS) time series from 2017 to 2021 to estimate biomass of cover crops and evaluated their detectability using a phenology-based cover crop detection framework. The impacts of cover crop planting and termination dates on cover crop biomass were also analyzed. Our results demonstrated that Normalized Difference Vegetation Index (NDVI) estimated biomass with higher accuracy compared to other vegetation indices, and the time-integrated model estimated biomass with higher accuracy than the single-date “snapshot” linear model (R2 from 0.53 to 0.66 and RMSE from 922 kg/ha to 747 kg/ha). While the snapshot models were species sensitive, the time-integrated models showed strong robustness across different species. Detectability increased with cover crop biomass, as detected cover crops averaged 963.3 ±719.5 kg/ha compared to 297.2 ± 209.0 kg/ha for non-detected cover crops. Detection accuracy reached 96.1% for fields exceeding 500 kg/ha, compared with an overall accuracy of 62.7%. Earlier planting and later termination increased biomass and detectability, with biomass rising by 4.14 kg/ha/day (p < 0.01). This study demonstrates how management practices affect cover crop biomass and detectability via satellite time series and provides insights that can inform management of cover crops and monitoring of their effects on agroecosystems.

Maryland↗

USGS2020

One-Water Hydrologic Flow Model: A MODFLOW based conjunctive-use simulation software

The U.S. Geological Survey’s (USGS) Modular Ground-Water Flow Model (MODFLOW-2005) is a computer program that simulates groundwater flow by using finite differences. The MODFLOW-2005 framework uses a modular design that allows for the easy development and incorporation of new features called processes and packages that work with or modify inputs to the groundwater-flow equation. A process solves a flow equation or set of equations. For example, the central part of MODFLOW is the groundwater-flow process that solves the groundwater-flow equation; the surface-water routing process is an additional process that solves the surface-water flow equation. Packages are code related to the groundwater-flow process. For example, the subsidence package modifies the groundwater-flow process by including aquifer compaction effects on flow. With the development of new packages and processes, the MODFLOW-2005 base framework diverged into multiple independent versions designed for specific simulation needs. This divergence limited each independent MODFLOW release to its specific purpose, so that there was no longer a single, comprehensive, general-purpose hydraulic-simulation framework. The MODFLOW One-Water Hydrologic Flow Model (MF-OWHM, also informally known as OneWater) is an integrated hydrologic flow model that combines multiple MODFLOW-2005 variants in one cohesive simulation software; changes were made to enable multiple capabilities in one code. This fusion of the MODFLOW-2005 versions resulted in a simulation software that can be used to address and analyze a wide class of conjunctive-use, water-management, water-food-security, and climate-crop-water scenarios. As a second core version of MODFLOW-2005, MF-OWHM maintains backward compatibility with existing MODFLOW-2005 versions, with features that include the following: Process-based simulation. Saturated groundwater flow (three-dimensional). Surface-water flow (one- and two-dimensional). Stream and river flow. Lake and reservoir storage. Landscape simulation and irrigated agriculture. Land-use and crop simulation. Root uptake of groundwater. Actual evapotranspiration. Estimated irrigation demand. Reservoir operations. Aquifer compaction and subsidence by vertical model-grid deformation. Seawater intrusion by a sharp-interface assumption. Karst-aquifer and fractured-bedrock flow. Turbulent and laminar-pipe network flow. Unsaturated groundwater flow (one-dimensional). Internal linkages among the processes that couple hydraulic head, flow, and deformation. Redesigned code for faster simulation, increased user-input options, easier model updates, and more robust error reporting than in previous models. MF-OWHM is a MODFLOW-2005 based integrated hydrologic model that can simulate and analyze varying environmental conditions to allow for the evaluation of management options from many components of human and natural water movement through a physically based, supply and demand framework. The term “integrated,” in the context of this report, refers to the tight coupling of groundwater flow, surface-water flow, landscape processes, aquifer compaction and subsidence, reservoir operations, and conduit (karst) flow. Another benefit of this integrated hydrologic model is that models developed to run by MODFLOW-2005, MODFLOW-NWT, MODFLOW-CFP, or MODFLOW-FMP can also be simulated with MF-OWHM. At the time of this report’s publication, MF-OWHM version 2 (MF-OWHM2) does not include a direct internal simulation of snowmelt, advanced mountainous watershed rainfall-runoff simulation, detailed shallow soil-moisture accounting, or atmospheric moisture content. Atmospheric moisture may be accounted for indirectly by, optionally, specifying a pan-evaporation rate, reference evapotranspiration, and precipitation. These features are not included to ensure that simulation runtime remains short enough to enable the use of automated methods of calibrating model parameters to field observations, which typically require many simulation model runs. The MF-OWHM approach is to include as much detail as possible to simulate hydrological processes, providing the simulation runtimes remain reasonable enough to allow for robust parameter estimation and model calibration. To represent both natural and human-influenced flow, MF-OWHM integrates physically based flow processes derived from MODFLOW-2005 in a supply and demand framework. From this integration, the physically based movement of groundwater, surface water, imported water, and precipitation serve as supply to meet consumptive demands associated with irrigated and non-irrigated agriculture, natural vegetation, and urban water uses. Water consumption is determined by balancing the available water supply with water demand, leading to the concept of a demand-driven, supply-constrained simulation. The MF-OWHM Supply-and-Demand Framework is especially useful for the analysis of agricultural water use, where there are often few data available to describe changes in land-use through time, such as crop type and distribution, and the associated changes in groundwater pumpage. This framework attempts to satisfy each land-use water demand with available water supplies—that is, groundwater uptake, precipitation, and irrigation. An option provided in MF-OWHM2 is to automatically increase groundwater pumping for irrigation, which often is unknown, by the calculated residual between demand and the other available sources of supply. From large- to small-scale applications, the physically based supply and demand framework provides key capabilities for simulating and analyzing historical, current, and future conjunctive-use of surface water and groundwater. To achieve the physically based supply and demand framework, the MODFLOW-2005 standard of no inter-package and -process communication was relaxed for MF-OWHM2. Traditional MODFLOW simulation models required that all packages and processes interact through the groundwater-flow equation or by removing the water flow from the simulation domain. For example, the MODFLOW-2005 representation of a groundwater well extracts water from the groundwater-flow equation (by subtraction) and removes it from the simulation domain. This feature is available in the MF-OWHM framework, but options have been added to allow the specification of a use or destination of pumped groundwater within the model domain, for example, it can be used for irrigation, managed aquifer recharge, or return-flow to streams.

Techniques and Methods↗

USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗

USGS2024

Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions

Study region The Mississippi Alluvial Plain (MAP) in the United States (US). Study focus Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km 3 /year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R 2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.

Journal of Hydrology Regional Studies↗

USGS2016

Tree Cover Mapping Tool—Documentation and user manual

The Tree Cover Mapping (TCM) tool was developed by scientists at the U.S. Geological Survey Earth Resources Observation and Science Center to allow a user to quickly map tree cover density over large areas using visual interpretation of high resolution imagery within a geographic information system interface. The TCM tool uses a systematic sample grid to produce maps of tree cover. The TCM tool allows the user to define sampling parameters to estimate tree cover within each sample unit. This mapping method generated the first on-farm tree cover maps of vast regions of Niger and Burkina Faso. The approach contributes to implementing integrated landscape management to scale up re-greening and restore degraded land in the drylands of Africa. The TCM tool is easy to operate, practical, and can be adapted to many other applications such as crop mapping, settlements mapping, or other features. This user manual provides step-by-step instructions for installing and using the tool, and creating tree cover maps. Familiarity with ArcMap tools and concepts is helpful for using the tool.

Open-File Report↗

USGS2020

Drought early warning and forecasting

Drought risk management involves three pillars: drought early warning, drought vulnerability and risk assessment, and drought preparedness, mitigation, and response. This book collects in one place a description of all the key components of the first pillar, and describes strategies for fitting these pieces together. The best modern drought early warning systems incorporate and integrate a broad array of environmental information sources: weather station observations, satellite imagery, land surface and crop model simulations, and weather and climate model forecasts, and analyze this information in context-relevant ways that take into account exposure and vulnerability. Drought Early Warning and Forecasting: Theory and Practice assembles a comprehensive overview of these components, providing examples drawn from the Famine Early Warning Systems Network and the United States Drought Monitor. This book simultaneously addresses the physical, social, and information management aspects of drought early warning, and informs readers about the tools, techniques, and conceptual models required to effectively identify, predict, and communicate potential drought-related disasters. This book is a key text for postgraduate scientists and graduate and advanced undergraduate students in hydrology, geography, earth sciences, meteorology, climatology, and environmental sciences programs. Professionals dealing with disaster management and drought forecasting will also find this book beneficial to their work.

Book↗

USGS2026

When best intentions are not enough - A collaboration between scientists, policymakers, and an agricultural community

Rocky Mountain National Park is a Congressionally mandated Clean Air Act Class I Area, but years of research have documented evidence of biogeochemical and ecological change caused by atmospheric deposition of nitrogen. A coalition of government agencies—National Park Service, the Environmental Protection Agency Region 8, and the State of Colorado—developed the 2007 Nitrogen Deposition Reduction Plan to reduce wet nitrogen deposition to the park from the baseline in 2006 by 50% by 2032. Fifty-four percent of wet nitrogen deposition is ammonium, primarily sourced from industrial cattle and dairy operations east of the park. An Agriculture Subcommittee, meeting with regulatory and management agencies and scientists since 2006, has explored voluntary options to reduce ammonia emissions. Ammonia emissions are unregulated in the U.S. Despite sustained efforts, neither ammonium deposition nor total inorganic nitrogen deposition have declined at the rate determined to reach the targeted value by 2032. In fact, ammonia is increasing over the Front Range agricultural area. Wet nitrogen deposition in 2024 was 2.81 kg N ha −1 yr −1 , 0.61 kg N ha −1 yr −1 higher than targeted in the Nitrogen Deposition Reduction Plan. The collaborative approach between agricultural producers, regulatory entities, and resource managers is not working, but not for lack of effort by all parties. Cattle process nitrogen inefficiently, and while management practices can somewhat reduce the proportion of ammonia emitted from feedlots, with more than one million head of cattle east of Rocky Mountain National Park, large-scale reductions are unlikely. New programs to reduce emissions of nitrogen oxides (46% of wet nitrogen deposition) may reduce nitrogen deposition that is changing park ecosystems. Improved integration of crop and livestock agriculture and valuation of the ecosystem services and economy of Rocky Mountain National Park alongside agricultural value are topics for further discussion. Implications : Nitrogen deposition in precipitation to Rocky Mountain National Park has changed natural ecosystems. To protect its Clean Air Act Class I status a 2007 Nitrogen Deposition Reduction Plan agreed upon by state and federal agencies set a goal to reduce wet nitrogen deposition to the Park by half by 2032. Industrial livestock operations are a large source of nitrogen; an Agriculture Subcommittee explored voluntary options to reduce emissions from cattle. Despite sustained efforts nitrogen deposition has not declined. Further reductions in industry and transportation emissions will be needed reduce nitrogen deposition to Rocky Mountain National Park.

Colorado↗

USGS2014

The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data

The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Project (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

Remote Sensing↗