Identifying and Evaluating a Suitable Index for Agricultural Drought Monitoring in the Texas High Plains

Drought is a complex and highly destructive natural phenomenon that affects portions of the United States almost every year, and severe water deficiencies can often become catastrophic for agricultural production. Evapotranspiration (ET) by crops is an important component in the agricultural water budget; thus, it is advantageous to include ET in agricultural drought monitoring. The main objectives of this study were to (1) conduct a literature review of drought indices with a focus to identify a simple but simultaneously adequate drought index for monitoring agricultural drought in a semiarid region and (2) using the identified drought index method, develop and evaluate time series of that drought index for the Texas High Plains. Based on the literature review, the Standardized Precipitation‐Evapotranspiration Index (SPEI) was found to satisfy identified constraints for assessing agricultural drought. However, the SPEI was revised by replacing reference ET with potential crop ET to better represent actual water demand. Data from the Texas High Plains Evapotranspiration network was used to calculate SPEIs for the major irrigated crops. Trends and magnitudes of crop‐specific, time‐series SPEIs followed crop water demand patterns for summer crops. Such an observation suggests that a modified SPEI is an appropriate index to monitor agricultural drought for summer crops, but it was found to not account for soil water stored during the summer fallow period for winter wheat.     

Heterogeneous Traffic Induced Effects on Vertical Dispersion Parameter in the Near Field of Roadways - a Wind Tunnel Study

The varying traffic parameters such as traffic volume, speed, shape and size, and terrain roughness conditions play a vital role on dispersion of pollutants in the near field of roadways. Simulation experiments were carried out in the Environmental Wind Tunnel (EWT) to evaluate the traffic induced effects on vertical dispersion parameter (σ _z ) for heterogeneous traffic conditions in the near field of roadways for evaluating the effect of variations in traffic volume, terrain roughness condition and approaching wind direction. The model vehicle movement system was fabricated and made operational in the EWT, which allowed the variation in traffic volume, speed and wind road inclination. Sixty-six hydrocarbon tracer experiments were performed to evaluate σ _z in the near field of roadways for variable traffic volume, three terrain roughness conditions and two approaching wind directions (i.e., 90° and 60°). The values of σ _z for heterogeneous traffic conditions were found to be higher for low roughness conditions in comparison to other two higher roughness conditions for various traffic volumes and approaching wind directions. This revised version was published online in July 2006 with corrections to the Cover Date.     

Detecting Extinction Risk from Climate Change by IUCN Red List Criteria

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow‐acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short‐lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions. Detección del Riesgo de Extinción a partir del Cambio Climático por medio del Criterio de la Lista Roja de la UICNKeith et al.     

Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach

The increasing availability of multi‐scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model‐assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the “hot” and “cold” reference conditions. The SSEBop model was used for computing ET for 12 years (2000‐2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000‐2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.     

Fire Management,Managed Relocation,and Land Conservation Options for Long‐Lived Obligate Seeding Plants under Global Changes in Climate,Urbanization, and Fire Regime

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long‐lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire‐prone ecosystems, including the biodiversity hotspots of Mediterranean‐type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long‐lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land‐use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land‐use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored. Manejo de Incendios, Reubicación Administrada y Opciones de Conservación de Suelo para Plantas de Vida Larga con Sembrado Obligado bajo los Cambios Globales en el Clima, la Urbanización y el Régimen de Incendios     

Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.     

Water quality modeling of fertilizer management impacts on nitrate losses in tile drains at the field scale

Nitrate losses from subsurface tile drained row cropland in the Upper Midwest U.S. contribute to hypoxia in the Gulf of Mexico. Strategies are needed to reduce nitrate losses to the Mississippi River. This paper evaluates the effect of fertilizer rate and timing on nitrate losses in two (East and West) commercial row crop fields located in south-central Minnesota. The Agricultural Drainage and Pesticide Transport (ADAPT) model was calibrated and validated for monthly subsurface tile drain flow and nitrate losses for a period of 1999-2003. Good agreement was found between observed and predicted tile drain flow and nitrate losses during the calibration period, with Nash-Sutcliffe modeling efficiencies of 0.75 and 0.56, respectively. Better agreements were observed for the validation period. The calibrated model was then used to evaluate the effects of rate and timing of fertilizer application on nitrate losses with a 50-yr climatic record (1954-2003). Significant reductions in nitrate losses were predicted by reducing fertilizer application rates and changing timing. A 13% reduction in nitrate losses was predicted when fall fertilizer application rate was reduced from 180 to 123 kg/ha. A further 9% reduction in nitrate losses can be achieved when switching from fall to spring application. Larger reductions in nitrate losses would require changes in fertilizer rate and timing, as well as other practices such as changing tile drain spacings and/or depths, fall cover cropping, or conversion of crop land to pasture.     

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.