Kenneth G. Hubbard

Impact of Irrigation on Midsummer Surface Fluxes and Temperature under Dry Synoptic Conditions: A Regional Atmospheric Model Study of the U.S. High Plains

The impact of irrigation on the surface energy budget in the U.S. high plains is investigated. Four 15-day simulations were conducted: one using a 1997 satellite-derived estimate of farmland acreage under irrigation in Nebraska (control run), two using the Olson Global Ecosystem (OGE) vegetation dataset (OGE wet run and OGE dry run), and the fourth with the Kuchler vegetation dataset (natural vegetation run) as lower boundary conditions in the Colorado State University Regional Atmospheric Modeling System (RAMS). In the control and OGE wet simulations, the topsoil in the irrigated locations, up to a depth of 0.2 m, was saturated at 0000 UTC each day for the duration of the experiment (1‐15 July 1997). In the other two runs, the soil was allowed to dry out, except when replenished naturally by rainfall. Identical observed atmospheric conditions were used along the lateral boundary in all four cases. The area-averaged model-derived quantities for the grid centered over Nebraska indicate significant differences in the surface energy fluxes between the control (irrigated) and the ‘‘dry’’ simulations. For example, a 36% increase in the surface latent heat flux and a 2.6 8C elevation in dewpoint temperature between the control run and the OGE dry run is shown. Surface sensible heat flux of the control run was 15% less and the near-ground temperature was 1.28C less compared to the OGE dry run. The differences between the control run and the natural vegetation run were similar but amplified compared to the control run‐OGE dry run comparisons. Results of statistical analyses of long-term (1921‐2000) surface temperature data from two sites representing locations of extensive irrigated and nonirrigated land uses appear to support model results presented herein of an irrigationrelated cooling in surface temperature. Growing season monthly mean and monthly mean maximum temperature data for the irrigated site indicate a steady decreasing trend in contrast to an increasing trend at the nonirrigated site.

Impacts of land use/land cover change on climate and future research priorities.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Application of geostatistics to evaluate partial weather station networks

Abstract Climatic data are an essential input for the determination of crop water requirements. The density and location of weather stations are the important design variables for obtaining the required degree of accuracy of weather data. The planning of weather station networks should include economic considerations, and a mixture of full and partial weather stations could be a cost-effective alternative. A ‘full’ weather station is defined here as one in which all the weather variables used in the modified Penman equation are measured, and a ‘partial’ weather station is one in which some, but not all, weather variables are measured. The accuracy of reference evapotranspiration (Etr) estimates for sites located some distance from surrounding stations is dependent on measurement error, error of the estimation equation, and interpolation error. The interpolation error is affected by the spatial correlation structure of weather variables and method of interpolation. A case-study data set of 2 years of daily climatic data (1989–1990) from 17 stations in the states of Nebraska, Kansas, and Colorado was used to compare alternative network designs and interpolation methods. Root mean squared interpolation error (RMSIE) values were the criteria for evaluating Etr estimates and network performance. The kriging method gave the lowest RMSIE, followed by the inverse distance square method and the inverse distance method. Co-kriging improved the estimates still further. For a given level of performance, a mixture of full and partial weather stations would be more economical than full stations only.

Spatial variability of daily weather variables in the high plains of the USA

Abstract Confidence in network measurements is more than a question of sensor performance. Measurements represent conditions at a station and are also used to infer conditions between station sites. Site measurements are incorporated into basinwide and regionwide values as well. The confidence associated with these applications is network dependent and a function of the variability imposed on the network by the atmosphere. This study was undertaken in the High Plains of the United States to determine the spatial variability of daily measurements taken in a multivariable network. The fraction of variation explained in a given weather variable at one site by that weather variable at a second site is characterized in terms of the distance of separation. It is shown that 1 year of data is not sufficient to characterize the seasonal patterns in spatial variability. To explain more than 90% of the variation in maximum temperatures between sites, a spacing of 60 km is sufficient on a year-round basis. Minimum temperature, relative humidity, solar radiation, and evapotranspiration require closer spacing (~30 km) to achieve this criterion while wind and soil temperature require 10 and 20 km, respectively. Spacing of precipitation gauges, for this criterion, would be less than 5 km. All results are specific to the High Plains study area. Seasonality of specific variables at this latitude is suggested as the underlying cause for observed differences in spatial variability from month to month.

Temporal variations in frost‐free season in the United States: 1895–2000

[1] A newly available data set of daily temperature observations was used to study the temporal variability of the frost-free season, based on an inclusive 0°C threshold, for 1895-2000 in the conterminous United States. A national average time series of the length of the frost-free season is characterized by 3 distinct regimes. The period prior to 1930 was notable for decreasing frost-free season length from 1895 to a minimum around 1910, followed by a marked increase in length of about 1 week from 1910 to 1930. During 1930-1980, frost-free season length was near the period average with relatively little decadal-scale variability. Since 1980, frost-free season length has increased by about 1 week. The national average increase in frost-free season length from the beginning to the end of the 20th Century is about 2 weeks. Frost-free season length has increased much more in the western U.S. than in the eastern U.S.

Interception and use efficiency of light in winter wheat under different nitrogen regimes

In an identical experiment conducted at Mandan (ND), Manhattan (KS) and Lubbock (TX), the influence of the environment and nitrogen (N) fertility upon light interception efficiency (ei) and light use efficiency (ec) of winter wheat (Triticum aestivum L.) were examined using remotely sensed canopy reflectance data to estimate ei. Treatments consisted of two cultivars, four levels of applied N and three levels of irrigation. Increased N application resulted in increased ei, with only secondary effects on ec. Whole season values of ec did not differ significantly between sites or between crops grown under different N regimes. However, ec did change through the season, increasing from an average of 1.5 during the double ridge-to-terminal spikelet stage to an average of 3.8 during the terminal spikelet-to-anthesis stage and finally decreasing to an average of 3.1 during the anthesis-to-soft dough stage. These changes in ec corresponded to changes in the mean temperatures for each growth period.

Documentation of Uncertainties and Biases Associated with Surface Temperature Measurement Sites for Climate Change Assessment

The objective of this research is to determine whether poorly sited long-term surface temperature monitoring sites have been adjusted in order to provide spatially representative independent data for use in regional and global surface temperature analyses. We present detailed analyses that demonstrate the lack of independence of the poorly sited data when they are adjusted using the homogenization procedures employed in past studies, as well as discuss the uncertainties associated with undocumented station moves. We use simulation and mathematics to determine the effect of trend on station adjustments and the associated effect of trend in the reference series on the trend of the adjusted station. We also compare data before and after adjustment to the reanalysis data, and we discuss the effect of land use changes on the uncertainty of measurement. A major conclusion of our analysis is that there are large uncertainties associated with the surface temperature trends from the poorly sited stations. Moreover...

Performance of Quality Assurance Procedures for an Applied Climate Information System

Abstract Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of ±1°C for temperature, not too different from the sensor field accuracy. The study...

The potential effects of climate change on summer season dairy cattle milk production and reproduction

The potential direct effects of possible global warming on summer season dairy production and reproduction were evaluated for the United States and Europe. Algorithms used for milk production and conception rate were previously developed and validated. Three widely known global circulation models (GISS, GFDL, and UKMO) were used to represent possible scenarios of future climate. Milk production and conception rate declines were highest under the UKMO model scenario and lowest under the GISS model scenario. Predicted declines for the GCM scenarios are generally higher than either ‘1 year in 10’ probability-based declines or declines based on the abnormally hot summer of 1980 in the United States. The greatest declines (about 10% for the GISS and GFDL scenarios, and about 20% for the UKMO scenario) in the United States are predicted to occur in the Southeast and the Southwest. Substantial declines (up to 35%) in conception rates were also predicted in many locations, particularly the eastern and southern United States. These areas correspond to areas of high dairy cattle concentration. They already have relatively large summer season milk production declines resulting from normally hot conditions. Thus, the actual impacts of increased production declines may be greater in other areas, which are not accustomed to large summer season declines and therefore may require more extensive mitigation measures.

Simulating sensitivity of soil moisture and evapotranspiration under heterogeneous soils and land uses

Abstract Soil moisture (SM) plays an important role in land surface and atmospheric interactions. It modifies energy balance at the surface and the rate of water cycling between the land and atmosphere. In this paper we provide a sensitivity assessment of SM and ET for heterogeneous soil physical properties and for three land uses including irrigated maize, rainfed maize, and grass at a climatological time-scale by using a water balance model. Not surprisingly, the study finds increased soil water content in the root zone throughout the year under irrigated farming. Soil water depletes to its lowest level under rainfed maize cultivation. We find a ‘land use’ effect as high as 36 percent of annual total evapotranspiration, under irrigated maize compared to rainfed maize and grass, respectively. Sensitivity analyses consisting of comparative simulations using the model show that soil characteristics, like water holding capacity, influence SM in the root zone and affect seasonal total ET estimates at the climatological time-scale. This ‘soils’ effect is smaller than the ‘land use’ effect associated with irrigation but, it is a source of consistent bias for both SM and ET estimates. The ‘climate’ effect basically masks the ‘soils’ effect under wet conditions. These results lead us to conclude that appropriate representation of land use, soils, and climate are necessary to accurately represent the water and energy balance in real landscapes.