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Brian D. Wardlow

Publications and source records attributed to Brian D. Wardlow.

13 recordsLinked to original sources
USGS2023

Using seasonal climate scenarios in the ForageAhead annual forage production model for early drought impact assessment

High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within-season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950–2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19-year period analysis (2000–2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass-Cast). This method can be made available in a user-friendly automated system that can be used by livestock producers and rangeland managers to inform within-season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.

Ecosphere↗
USGS2021

Monitoring climate impacts on annual forage production across U.S. semi-arid grasslands

The ecosystem performance approach, used in a previously published case study focusing on the Nebraska Sandhills, proved to minimize impacts of non-climatic factors (e.g., overgrazing, fire, pests) on the remotely-sensed signal of seasonal vegetation greenness resulting in a better attribution of its changes to climate variability. The current study validates the applicability of this approach for assessment of seasonal and interannual climate impacts on forage production in the western United States semi-arid grasslands. Using a piecewise regression tree model, we developed the Expected Ecosystem Performance (EEP), a proxy for annual forage production that reflects climatic influences while minimizing impacts of management and disturbances. The EEP model establishes relations between seasonal climate, site-specific growth potential, and long-term growth variability to capture changes in the growing season greenness measured via a time-integrated Normalized Difference Vegetation Index (NDVI) observed using a Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting 19 years of EEP were converted to expected biomass (EB, kg ha −1 year −1 ) using a newly-developed relation with the Soil Survey Geographic Database range production data (R 2 = 0.7). Results were compared to ground-observed biomass datasets collected by the U.S. Department of Agriculture and University of Nebraska-Lincoln (R 2 = 0.67). This study illustrated that this approach is transferable to other semi-arid and arid grasslands and can be used for creating timely, post-season forage production assessments. When combined with seasonal climate predictions, it can provide within-season estimates of annual forage production that can serve as a basis for more informed adaptive decision making by livestock producers and land managers.

Remote Sensing↗
USGS2021

Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Kansas↗
USGS2021

Simulated atmospheric response to four projected land-use land-cover change scenarios for 2050 in the north-central United States

Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg −1 ). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.

Nebraska, South Dakota↗
USGS2019

Monitoring drought impact on annual forage production in semi-arid grasslands: A case study of Nebraska sandhills

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha −1 *yr −1 , was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R 2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses. View Full-Text

Nebraska↗
USGS2018

Priority questions in multidisciplinary drought research

Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors ranging from individual projects to global initiatives therefore require prioritization. Here, we present 60 priority questions for optimizing future drought research. This topical catalogue reflects the experience of 65 scholars from 21 countries and almost 20 fields of research in both natural sciences and the humanities. The set of drought-related questions primarily covers drought monitoring, impacts, forecasting, climatology, adaptation, as well as planning and policy. The questions highlight the increasingly important role of remote sensing techniques in drought monitoring, importance of drought forecasting and understanding the relationships between drought parameters and drought impacts, but also challenges of drought adaptation and preparedness policies.

Climate Research↗
USGS2016

Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought

This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS). Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4 wk), whereas corn conditions were more closely related to longer-range (8–12 wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.

Agricultural and Forest Meteorology↗
USGS2015

Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

Journal of Applied Meteorology and Climatology↗
USGS2012

Remote sensing of evapotranspiration for operational drought monitoring using principles of water and energy balance

Evapotranspiration (ET) is an important component of the hydrologic budget because it režects the exchange of mass and energy between the soil-water-vegetation system and the atmosphere. Prevailing weather conditions inžuence potential or reference ET through variables such as radiation, temperature, wind, and relativity humidity. In addition to these weather variables, actual ET (ETa) is also affected by land cover type and condition, as well as soil moisture. The dependence of ETa on land cover and soil moisture, and its direct relationship with carbon dioxide assimilation in plants, makes it an important variable for monitoring drought, crop yield, and biomass-a critical capability for decision makers interested in food security, grain markets, water allocation, and carbon sequestration (Bastiaanssen et al., 2005).

Book chapter↗
USGS2012

Future opportunities and challenges in remote sensing of drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Book chapter↗
USGS2008

Assessment of 2006 and 2007 drought patterns in the vegetation drought response index across Nebraska

The Vegetation Drought Response Index (VegDRI) is a hybrid geospatial drought indicator and monitoring tool that provides timely drought severity information with relatively higher spatial resolution (1-km2) than the traditional drought monitoring maps. The VegDRI model integrates climate-based drought index data, satellite-based vegetation index information, and several biophysical parameters. During the 2008 growing season, VegDRI was produced in near-real time for 22 states in the central and western United States. Coverage will expand across the conterminous United States in 2009. Validating the results of large-area, operational monitoring tools such as VegDRI requires extensive ground truth information across space and time and to date, only a limited number of assessments of this index have been conducted. This study initiates a comprehensive assessment of historical VegDRI for two summer row crops identified using crop and irrigation masks in an effort to better characterize the accuracy and performance of this index for agricultural drought monitoring. The assessment evaluated drought severity information provided by VegDRI for Nebraska in 2006 and 2007, which represented drought and non-drought conditions over much of the state in those years, respectively. Spatial and statistical comparisons of VegDRI and U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) crop yield data were conducted for both years. For this initial study, comparisons were restricted to Nebraska’s two predominant crops, corn and soybeans. Irrigated and non-irrigated row crops were analyzed separately. Preliminary results showed that non-irrigated corn yields had a stronger relationship to late-season VegDRI than non-irrigated soybeans. Moderate drought impacts in 2006 were also highlighted by this analysis.

Conference Paper↗