Identifying Changes in Trends of Summer Air Temperatures of the USA High Plains

Change in climate variables, especially air temperature, can substantially impact water availability, use, management, allocation, and projections for rural and urban applications. This study presents analyses for detecting summer air temperature change by investigating trends of two separate climate-periods in the USA High Plains. Two trend periods, the reference period (1895–1930) and the warming period (1971–2006), were investigated using parametric and nonparametric methods. During the reference period, minimum air temperature (Tmin) was statistically stationary at a nonsignificant increasing rate of 0.02°C/year. However, from early 1970s, Tmin increased at a significant rate of 0.02°C/year. The maximum air temperature (Tmax) had a weaker warming signal than Tmin during the reference period. During the warming period, Tmax had a cooling trend at a nonsignificant rate of −0.004°C/year. About 22% of the High Plains had significant warming trends before 1930. Compared to the summers before 1930, the summer temperatures of the High Palins since the 1970s increased, on average, by 0.86°C. Overall, parametric methods lead to the conclusion that 50% of the study area experienced a significant warming trend in Tmin. In comparison, nonparametric methods indicated that 94% of the study area experienced a warming trend. Overall, in recent decades, summer average temperatures in the High Plains have been warming as compared to the early twentieth-century decades, and the warming is most likely driven primarily by increasing nighttime Tmin

The Effect of Land Cover/Land Use Changes on the Regional Climate of the USA High Plains

We present the detection of the signatures of land use/land cover (LULC) changes on the regional climate of the US High Plains. We used the normalized difference vegetation index (NDVI) as a proxy of LULC changes and atmospheric CO2 concentrations as a proxy of greenhouse gases. An enhanced signal processing procedure was developed to detect the signatures of LULC changes by integrating autoregression and moving average (ARMA) modeling and optimal fingerprinting technique. The results, which are representative of the average spatial signatures of climate response to LULC change forcing on the regional climate of the High Plains during the 26 years of the study period (1981–2006), show a significant cooling effect on the regional temperatures during the summer season. The cooling effect was attributed to probable evaporative cooling originating from the increasing extensive irrigation in the region. The external forcing of atmospheric CO2 was included in the study to suppress the radiative warming effect of greenhouse gases, thus, enhancing the LULC change signal. The results show that the greenhouse gas radiative warming effect in the region is significant, but weak, compared to the LULC change signal. The study demonstrates the regional climatic impact of anthropogenic induced atmospheric-biosphere interaction attributed to LULC change, which is an additional and important climate forcing in addition to greenhouse gas radiative forcing in High Plains region

Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration

Extreme high environmental temperatures produce a variety of consequences for wildlife, including mass die-offs. Heat waves are increasing in frequency, intensity, and extent, and are projected to increase further under climate change. However, the spatial and temporal dynamics of die-off risk are poorly understood. Here, we examine the effects of heat waves on evaporative water loss (EWL) and survival in five desert passerine birds across the southwestern United States using a combination of physiological data, mechanistically informed models, and hourly geospatial temperature data. We ask how rates of EWL vary with temperature across species; how frequently, over what areas, and how rapidly lethal dehydration occurs; how EWL and die-off risk vary with body mass; and how die-off risk is affected by climate warming. We find that smaller-bodied passerines are subject to higher rates of mass-specific EWL than larger-bodied counterparts and thus encounter potentially lethal conditions much more frequently, over shorter daily intervals, and over larger geographic areas. Warming by 4 °C greatly expands the extent, frequency, and intensity of dehydration risk, and introduces new threats for larger passerine birds, particularly those with limited geographic ranges. Our models reveal that increasing air temperatures and heat wave occurrence will potentially have important impacts on the water balance, daily activity, and geographic distribution of arid-zone birds. Impacts may be exacerbated by chronic effects and interactions with other environmental changes. This work underscores the importance of acute risks of high temperatures, particularly for small-bodied species, and suggests conservation of thermal refugia and water sources

IDENTIFYING CHANGES IN CLIMATIC TRENDS AND THE FINGERPRINTS OF LANDUSE AND LANDCOVER CHANGES IN THE HIGH PLAINS OF THE USA

Human activities such as conversion of natural ecosystem to croplands and urban-centers, deforestation and afforestation impact biophysical properties of land surface such as albedo, energy balance, and surface roughness. Alterations in these properties affect the heat and moisture exchanges between the land surface and atmospheric boundary layer. The objectives of this research were; (i) to quantitatively identify the High plains’ regional climate change in temperatures over the period 1895 to 2006, (ii) detect the signatures of anthropogenic forcing of LULC changes on the regional climate change of the High Plains, and (iii) examine the trends in evolving regional latent heat flux under the changing climate during the past thirty years. We investigated the regional climate change by comparing two trend periods, the reference period (1895 – 1930) and the warming period (1971 – 2006), using the base period as 1935 – 1965. For the objective (ii) the study developed an enhanced signal processing procedure to maximize the signal to noise ratio by introducing a pre-filtering technique of ARMA modeling, before applying the optimal fingerprinting technique to detect the signals of LULC change. For the objective (iii), we estimated ETc using the widely accepted two-step approach. We developed a linear model to estimate spatial crop coefficient (Kc) from AVHRR-based NDVI. The Kc estimates were used to adjust spatial ETo estimates, thereby yielding spatial ETc estimates that are representative of the summer latent heat fluxes of years 1981 to 2006. The results from the study show that, the overall warming trend in the High Plains was about 0.11oC/decade. The minimum temperature had the strongest warming at a rate of 0.19oC/decade. Due to LULC changes attributed to increase in irrigation application and vegetation surfaces, more surface energy in summer is being redistributed into latent heat flux. Therefore, there is a significant influence of evaporative cooling on regional temperatures during summer season. As a result, the greenhouse warming effect in the region is being surpassed

AVHRR-NDVI-based crop coefficients for analyzing long-term trends in evapotranspiration in relation to changing climate in the U.S. High Plains

Studies in regions of extensive irrigation practices have revealed a significant influence of evaporative cooling on regional temperatures as a result of surface energy redistribution during evaporation. In the U.S. High Plains, maximum temperatures during the last quarter of the 20th century have been decreasing. We investigated the trends in evapotranspiration (ET or latent heat) fluxes originating from increasing irrigation practices in the High Plains region from 1981 to 2008. We estimated actual ET (ETc) over the entire High Plains from the spatial crop coefficients (Kc) and spatial reference (potential) ET (ETref). We proposed and validated a global linear relation between Kc and advanced very high resolution radiometer-based normalized difference vegetation index. Our results show an increase in ETc trends over the region in the last three decades. The study shows that the increase in ETc flux was not in principal from increased atmospheric evaporative demand. Rather, the increase in ETc was due to significant increase in irrigated surfaces. The increase in ETc fluxes is likely a manifestation of increased redistribution of surface energy into latent heat and less partitioning into the sensible heat. We investigated the evolution of full canopy cover vegetation (normalized difference vegetation index \u3e0.70) in relation to the maximum temperature anomalies during the study period. Results revealed a significant negative correlation between the two variables. These results appear to demonstrate that there is a regional evaporative cooling signal due to extensive irrigation practices, which impacts regional temperatures during the summer seasons

On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables

The 1‐D and single layer combination‐based energy balance Penman‐Monteith (PM) model has limitations in practical application due to the lack of canopy resistance (rc) data for different vegetation surfaces. rc could be estimated by inversion of the PM model if the actual evapotranspiration (ETa) rate is known, but this approach has its own set of issues. Instead, an empirical method of estimating rc is suggested in this study. We investigated the relationships between primary micrometeorological parameters and rc and developed seven models to estimate rc for a nonstressed maize canopy on an hourly time step using a generalized‐linear modeling approach. The most complex rc model uses net radiation (Rn), air temperature (Ta), vapor pressure deficit (VPD), relative humidity (RH), wind speed at 3 m (u3), aerodynamic resistance (ra), leaf area index (LAI), and solar zenith angle (Q). The simplest model requires Rn, Ta, and RH. We present the practical implementation of all models via experimental validation using scaled up rc data obtained from the dynamic diffusion porometer‐measured leaf stomatal resistance through an extensive field campaign in 2006. For further validation, we estimated ETa by solving the PM model using the modeled rc from all seven models and compared the PM ETa estimates with the Bowen ratio energy balance system (BREBS)‐measured ETa for an independent data set in 2005. The relationships between hourly rc versus Ta, RH, VPD, Rn, incoming shortwave radiation (Rs), u3, wind direction, LAI, Q, and ra were presented and discussed. We demonstrated the negative impact of exclusion of LAI when modeling rc, whereas exclusion of ra and Q did not impact the performance of the rc models. Compared to the calibration results, the validation root mean square difference between observed and modeled rc increased by 5 s m−1 for all rc models developed, ranging from 9.9 s m−1 for the most complex model to 22.8 s m−1 for the simplest model, as compared with the observed rc. The validation r2 values were close to 0.70 for all models, and the modeling efficiency ranged from 0.61 for the most complex model to −1.09 for the simplest model. There was a strong agreement between the BREBSmeasured and the PM‐estimated ETa using modeled rc. These findings can aid in the selection of a suitable model based on the availability and quality of the input data to predict rc for one‐step application of the PM model to estimate ETa for a nonstressed maize canopy

Transferability of jarvis-type models developed and re-parameterized for maize to estimate stomatal resistance of soybean: analyses on model calibration, validation, performance, sensitivity, and elasticity

In a previous study by the same authors, a new modified Jarvis model (NMJ-model) was developed, calibrated, and validated to estimate stomatal resistance (rs) for maize canopy on an hourly time step. The NMJ-model’s unique subfunctions, different from the original Jarvis model (J-model), include a photosynthetic photon flux density (PPFD)-rs response subfunction developed from field measurements and a new physical term, Aexp(1/LAI), where A is the minimum stomatal resistance and LAI is the green leaf area index, to account for the influence of canopy development on rs, especially during partial canopy stage in the early season and in late-season stage during leaf aging and senescence. This study evaluated the transferability of the J-model and NMJ-models that were re-parameterized and calibrated for maize canopy to estimate soybean rs. Due to the differences in physiological and photosynthetic pathway differences between the two crops, the rs response to the same environmental variables, i.e., PPFD, vapor pressure deficit (VPD), and air temperature (Ta), were substantially different. Thus, this study demonstrated the inherent limitation in applying the Jarvis-type models that were calibrated for maize to soybean without re-calibration. Maize-calibrated models performed poorly in estimating soybean rs, with the coefficient of determination (r2) ranging from 0.30 to 0.38 and the root mean square difference (RMSD) between the estimated and measured rs ranging from 94.4 to 166 s m-1. The J-model and NMJ model were re-calibrated by parameter optimization method for soybean. The J-model calibrated well; however, the validation had poor performance results. The NMJ-model had a good calibration, resulting in a good r2 (0.71) and a small RMSD (13.7 s m-1). The NMJ-model validation produced superior results to the J-model, explaining more than 80% of the variation in the measured rs (RMSD = 38.4 s m-1). These results show the robustness and practical accuracy of the NMJ model in estimating rs over different canopies if well calibrated for a specific crop. In terms of sensitivity and elasticity analyses, among all parameters, rs estimates were most sensitive to uncertainties introduced in parameter a1 of the PPFD subfunction due to its exponential impact on rs in the NMJ-model. Therefore, for accurate estimates of rs, uncertainties in parameter a1 should not exceed the range of -2% and 2% so that the error in estimated rs is kept between -3.5% and 3.6%. The study observed that the relative change in rs due to uncertainties in parameters a2 and a3 of the VPD subfunction was a linear function and less sensitive than the PPFD subfunction. The sensitivity of rs to uncertainties in temperature subfunction parameters (a4 and a5) was higher than that of VPD subfunction parameters, but less than that of PPFD subfunction parameters. The uncertainty in parameters a4 and a5 should range within -10% and 10%, and the calibration of these parameters should be determined with greater precision as compared with the VPD subfunction parameters. The study confirmed that the addition of the rs_min and the Aexp(1/LAI) terms, which were not accounted for in the original J-model, improved the model accuracy for estimating soybean rs

Land Use Classification: A Surface Energy Balance and Vegetation Index Application to Map and Monitor Irrigated Lands

Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to classify and map the spatial distribution of irrigated and non-irrigated croplands. The purpose is to introduce a classification scheme applicable across a wide variation in regional climate and inter-growing seasonal precipitation. The rationale for climate and inter-growing seasonal adaptability is founded in the derivation and calibration of the scheme based on the wettest growing season. Therefore, the scheme becomes a more efficient classifier during normal and dry growing seasons. Using empirical distribution functions, two indices are derived from evapotranspiration fluxes and vegetation indices to contrast and classify irrigated croplands from non-irrigated. The synergy of the two indices increases the classification proficiency by adding another classifying layer which re-characterizes misclassified croplands by the base index. The scheme was applied to a region with wide climate variation and to multiple years of growing seasons. The results presented, in cross validation with groundtruth, show an accurate and consistent approach to classify irrigation with overall accuracy of 92.1%, applicable from humid to semi-arid climate, and from dry to normal and wet growing seasons

Recent spatiotemporal patterns in temperature extremes across conterminous United States

With a warming climate, understanding the physical dynamics of hot and cold extreme events has taken on increased importance for public health, infrastructure, ecosystems, food security, and other domains. Here we use a high-resolution spatial and temporal seamless gridded land surface forcing data set to provide an assessment of recent spatiotemporal patterns in temperature extremes over the conterminous United States (CONUS). We asked the following: (1) How are temperature extremes changing across the different regions of CONUS? (2) How do changes in extremes vary on seasonal, annual, and decadal scales? (3) How do changes in extremes relate to changes in mean conditions? And (4) do extremes relate to major modes of ocean-atmosphere variability? We derive a subset of the CLIMDEX extreme indices from the North American Land Data Assimilation phase 2 forcing data set. While there were warming trends in all indices, daytime temperature extremes warmed more than nighttime. Spring warming was the strongest and most extensive across CONUS, and summer experienced the strongest and most extensive decrease in cold extremes. Increase in winter warm extremes appeared weakening relative to the rapid 1950-1990 increase found in previous studies. The Northeast and Midwest experienced the most warming, while the Northwest and North Great Plains saw the least. We found changes in average temperatures were more associated with changes in cold extremes than warm extremes. Since 2006 there have been 5years when more than 5% of the U.S. experienced at least 90 warm days, something not observed in the previous 25years. The unusually warm first decade of 21st century could have been associated with the warm conditions of near El Nino-Southern Oscillation-neutral phase of the decade, and possibly amplified by anthropogenic forcing. The widespread, lengthy, and severe extreme hot events documented here during the past three decades underscore the need to implement thoughtful adaptation plans in the very near future, to the growing evidence of increasing warm extremes across United States

Evaluation of Valiantzas’ Simplified Forms of the FAO-56 Penman-Monteith Reference Evapotranspiration Model in a Humid Climate

The unavailability of some meteorological variables, especially solar radiation and wind speed, is the main constraint for reference evapotranspiration (ETo) estimation using the standard United Nations Food and Agriculture Organization (FAO) Penman–Monteith (FAO-PM) equation in most developing countries. The application of ETo methods with fewer input requirements is necessary under limited climatic data conditions. The FAO-PM method under limited data conditions and nine of Valiantzas’ equations were evaluated for daily ETo estimation in a humid climate in Uganda. The FAO-PM method with missing relative humidity data performed very well across Uganda, whereas using the long-term local wind speed average values in place of missing wind speed data resulted in inaccurate ETo estimates. Under missing solar radiation measurements, the FAO-PM method showed different performances relative to the locations. When more than one climatic variable is missing, the FAO-PM method yielded poor ETo estimates compared to the FAO-PM method with full climatic data. The performance of Valiantzas’ equations depends on data requirements: the more meteorological inputs, the higher the ETo accuracy