Cooperative Observer Research Articles

Vegetation geo-climatic zonation in the rocky mountains, Northern Utah, USA

We developed a vegetation geo-climatic zonation incorporating the zonal concept, gradient and discriminant analysis in Wasatch Range, northern Utah, USA. Mountainous forest ecosystems were sampled and described by vegetation, physiographic features and soil properties. The Snowpack Telemetry and National Weather Service Cooperative Observer Program weather station networks were used to approximate the climate of sample plots. We analysed vegetation and environmental data using clustering, ordination, classification, and ANOVA techniques to reveal environmental gradients affecting a broad vegetation pattern and discriminate these gradients. The specific objective was to assess and classify the response of the complex vegetation to those environmental factors operating at a coarse-scale climatic level. Ordination revealed the dominant role of regional, altitude-based climate in the area. Based on vegetation physiognomy, represented by five tree species, climatic data and taxonomic classification of zonal soils, we identified two vegetation geo-climatic zones: (1) a montane zone, with Rocky Mountain juniper and Douglas-fir; and (2) a subalpine zone, with Engelmann spruce and subalpine fir as climatic climax species. Aspen was excluded from the zonation due to its great ecological amplitude. We found significant differences between the zones in regional climate and landform geomorphology/soils. Regional climate was represented by elevation, precipitation, and air and soil temperatures; and geomorphology by soil types. This coarse-scale vegetation geo-climatic zonation provides a framework for a comprehensive ecosystem survey, which is missing in the central Rocky Mountains of the United States. The vegetation-geo-climatic zonation represents a conceptual improvement on earlier classifications. This framework explicitly accounts for the influence of the physical environment on the distribution of vegetation within a complex landscape typical of the central Rocky Mountains and in mountain ranges elsewhere.

Role of extreme snowfall events in interannual variability of snowfall accumulation in the western United States

AbstractWater resources in the western United States are contingent on interannual variations in snowpack. Interannual snowpack variability has been attributed to large‐scale climate patterns including the El Niño‐Southern Oscillation (ENSO), however, the contribution of snowfall frequency and extreme snowfall events to this variability are less well quantified. Long‐term records from Snowpack Telemetry and Cooperative Observer Program stations in the 11 western states were used to investigate these relationships by considering the number of snowfall days and snowfall water equivalent (SFE) of extreme snowfall events. The top decile of snowfall events contributed 20–38% of annual SFE, depending on the region. An average of 65% and 69% of the interannual variability in annual SFE was explained by snowfall days and SFE of top decile snowfall events, respectively, with extreme events being a more significant predictor at most stations. The latitudinal dipole in SFE during ENSO phases results from changes in snowfall frequency and extreme events. In the Pacific Northwest, above normal SFE during La Niña winters was a product of both larger contributions from extremes and more snowfall days, while below normal SFE during El Niño winters was primarily associated with a substantial reduction in extremes. Conversely, annual SFE during ENSO phases in the mountains of Arizona was more closely linked to fluctuations in snowfall days than extremes. Results indicate the importance of extreme snowfall events in shaping interannual variability in water resources and suggest that improved predictive ability may inform better water resource management now and in the coming decades.

Effects of El Niño Southern Oscillation on the space–time variability of Agricultural Reference Index for Drought in midlatitudes

Agriculture is an economic activity directly affected by drought. With the constant increase of food demand and the need for high efficiency in food production, drought effects gain attention. The Agricultural Reference Index for Drought (ARID) was developed to quantify drought and better understand its effect on agriculture. ARID values range from 0 to 1 where zero is transpiration occurring at potential rate and one is full water deficit. El Niño Southern Oscillation (ENSO) is the main factor of climate variability around the world and affects the climate of two states located in midlatitudes, Florida, USA and Rio Grande do Sul, Brazil. An historical assessment of the relationship between ARID and ENSO will assist with establishing the role played by ENSO in the seasonal variation of soil moisture, which is critical to drought forecasting based on ENSO. The main objectives of this study were to investigate ARID's temporal and spatial variability in Florida and Rio Grande do Sul, and provide an assessment of ENSO-induced potential anomalies in ARID. Daily ARID values in Florida were calculated using data obtained from the National Weather Service COOP (Cooperative Observer Program) weather stations. In Rio Grande do Sul, ARID was calculated using daily weather data from the Instituto Nacional de Meteorologia (INMET) weather stations, Brazil. Daily ARID values were compiled into monthly averages and categorized according to ENSO phase based on the Multivariate ENSO Index (MEI). Results showed that typical ARID values for both regions vary throughout the year. The highest values were observed in the warm season due to increased evapotranspiration. During this period, ARID values varied from 0.3 to 0.8 in Florida and 0.2 to 0.7 in Rio Grande do Sul. In the cold period, South Florida had the highest ARID values, varying from 0.3 to 0.7, while for the rest of state and Rio Grande do Sul the values varied from 0 to 0.5. ENSO showed stronger influence from November to March in Florida, and November and May in Rio Grande do Sul. For both regions, El Niño caused ARID values below normal, indicating higher soil moisture while during La Niña the values were above normal. This study contributes to establish historical ARID records across the southeastern United States and southern Brazil, and can assist farmers to adjust management practices according to expected water stress conditions.

Varied Diagnosis of the Observed Surface Temperature Trends in the Southeast United States

Abstract This paper diagnoses the temperature trends in maximum (Tmax) and minimum temperatures (Tmin) over a selection of 65 stations spread over the southeast United States (SEUS) from three observed datasets. They are the Cooperative Observer network program (COOP), the COOP data corrected for documented shifts in time of observation (COOP1), and the COOP data additionally corrected for documented changes in instrumentation (COOP2). These 65 stations have been isolated for having the three observed datasets for the same time period from 1948 to 2009. The authors’ comparisons suggest that COOP2 displays stronger warming (cooling) trends in Tmax (Tmin) compared with COOP1 in all four seasons. This is consistent with the expectation from the bias correction applied for the instrument change. In comparison, the differences between COOP and COOP2 are relatively larger. In the spring, summer, and fall seasons, the median Tmax trend is warming in COOP2 while it is cooling in COOP. In the winter season, the median trends of Tmax in the two datasets are positive, but their magnitudes are substantially different. Similarly, in the winter, summer, and fall seasons, the warming trend in Tmin in COOP is contrary to the cooling trend in COOP2. In the spring season, the median trend in Tmin is comparable between the two datasets. COOP2 shows the relationship of trends in Tmin, with the extent of urbanization in these 65 stations, to be statistically significant and to be consistent with expectations from theory in contrast to the COOP data.

Contributions of Lake-Effect Periods to the Cool-Season Hydroclimate of the Great Salt Lake Basin

AbstractAlthough smaller lakes are known to produce lake-effect precipitation, their influence on the precipitation climatology of lake-effect regions remains poorly documented. This study examines the contribution of lake-effect periods (LEPs) to the 1998–2009 cool-season (16 September–15 May) hydroclimate in the region surrounding the Great Salt Lake, a meso-β-scale hypersaline lake in northern Utah. LEPs are identified subjectively from radar imagery, with precipitation (snow water equivalent) quantified through the disaggregation of daily (i.e., 24 h) Cooperative Observer Program (COOP) and Snowpack Telemetry (SNOTEL) observations using radar-derived precipitation estimates. An evaluation at valley and mountain stations with reliable hourly precipitation gauge observations demonstrates that the disaggregation method works well for estimating precipitation during LEPs. During the study period, LEPs account for up to 8.4% of the total cool-season precipitation in the Great Salt Lake basin, with the largest contribution to the south and east of the Great Salt Lake. The mean monthly distribution of LEP precipitation is bimodal, with a primary maximum from October to November and a secondary maximum from March to April. LEP precipitation is highly variable between cool seasons and is strongly influenced by a small number of intense events. For example, at a lowland (mountain) station in the lake-effect-precipitation belt southeast of the Great Salt Lake, just 12 (13) events produce 50% of the LEP precipitation. Although these results suggest that LEPs contribute modestly to the hydroclimate of the Great Salt Lake basin, infrequent but intense events have a profound impact during some cool seasons.

Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records

An assessment quantifying the impact of urbanization on temperature trends from the U.S. Historical Climatology Network (USHCN) is described. Stations were first classified as urban and nonurban (rural) using four different proxy measures of urbanity. Trends from the two station types were then compared using a pairing method that controls for differences in instrument type and via spatial gridding to account for the uneven distribution of stations. The comparisons reveal systematic differences between the raw (unadjusted) urban and rural temperature trends throughout the USHCN period of record according to all four urban classifications. According to these classifications, urbanization accounts for 14–21% of the rise in unadjusted minimum temperatures since 1895 and 6–9% since 1960. The USHCN version 2 homogenization process effectively removes this urban signal such that it becomes insignificant during the last 50–80 years. In contrast, prior to 1930, only about half of the urban signal is removed. Accordingly, the National Aeronautics and Space Administration Goddard Institute for Space Studies urban‐correction procedure has essentially no impact on USHCN version 2 trends since 1930, but effectively removes the residual urban‐rural temperature trend differences for years before 1930 according to all four urban proxy classifications. Finally, an evaluation of the homogenization of USHCN temperature series using subsets of rural‐only and urban‐only reference series from the larger U.S. Cooperative Observer (Coop) Network suggests that the composition of Coop stations surrounding USHCN stations is sufficiently “rural” to limit the aliasing of urban heat island signals onto USHCN version 2 temperature trends during homogenization.

The Use of a High-Resolution Standardized Precipitation Index for Drought Monitoring and Assessment

AbstractA high-resolution drought-monitoring tool was developed to assess drought on multiple time scales using the standardized precipitation index (SPI). Daily precipitation data at 4-km resolution are obtained from the Advanced Hydrologic Prediction Service multisensor precipitation estimates (MPE) and are aggregated on several time scales. Daily station precipitation data available from the Cooperative Observer Program (COOP) provide the historical context for the MPE precipitation data. Pearson type-III distribution parameters were interpolated to the 4-km grid on the basis of a regional frequency analysis of the COOP stations and L-moment ratios of the precipitation data. The resulting high-resolution SPI data can be used as guidance for the U.S. Drought Monitor at the subcounty scale in areas where local precipitation is the primary driver of drought. The temporal flexibility and spatial resolution of the drought-monitoring tool are used to illustrate the onset, intensity, and termination of the 2008–09 Texas drought, and the tool is shown to provide better county- and subcounty-scale information than do gauge-based products.

A Climatology of 500-hPa Closed Lows and Associated Precipitation in the Northeastern United States

AbstractTemporal trends in precipitation associated with 500-hPa low pressure systems are assessed for consistency with overall precipitation trends in the northeastern United States. Increases in precipitation associated with closed upper lows can be attributed to an increase in the number of closed lows and/or an increase in the average precipitation occurring in association with closed lows. A climatological description of 500-hPa closed lows was developed, and precipitation was analyzed using data from the U.S. Cooperative Observer Program. Closed 500-hPa lows were identified within a rectangular geographic region bounded by 35° and 50°N and 65° and 90°W. Precipitation observations within 1.25° of latitude and 1.25° of longitude of the closed-low center were attributed to the closed low. Statistical testing procedures were conducted to evaluate whether a long-term trend existed in the closed-low frequency, the average precipitation occurring near closed lows, or the percentage of >2.54-cm precipitation observations associated with the lows. Regional trends (1948–2007) in the frequency of closed lows were evident, and a statistically significant increase in average precipitation near closed lows was found. Likewise, the percentage of precipitation totals in excess of 2.54 cm (and separately 5.08 cm) showed a statistically significant increase with time. In both cases, recent (2007) values were nearly 50% higher than the comparable value in 1948. Increases were particularly evident during the winter months. These trends are consistent with increases in tropospheric water vapor due to increased global mean temperature.

Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule‐based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high‐elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower‐elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse‐scale AMSR‐E SWE and fine‐scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR‐E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root‐mean‐square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR‐E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR‐E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near‐complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts.

Evaluation of Satellite‐Based, Modeled‐Derived Daily Solar Radiation Data for the Continental United States

Decision support tools for agriculture often require meteorological data as inputs, but data availability and quality are often problematic. Difficulties arise with daily solar radiation (SRAD) because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. NASA's Prediction of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project estimates SRAD based on satellite observations and atmospheric parameters obtained from satellite observations and assimilation models. These data are available for a global 1° × 1° coordinate grid. The SRAD can also be generated from atmospheric attenuation of extraterrestrial radiation (Q0). We compared daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (observed values of daily solar radiation, SRADOB) and values estimated by Weather Generator for Solar Radiation (WGENR) generator. Two sources of air temperature and precipitation records provided inputs to WGENR: the stations reporting solar data and the NOAA Cooperative Observer Program (COOP) stations. The resulting data were identified as solar radiation valaues obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from the stations providing values of observed values of daily solar radiation (SRADWG) and solar radiation values obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from NOAA COOP stations (SRADCO), respectively. Values of SRADNP for individual grid cells consistently showed higher correlations (typically 0.85–0.95) with SRADOB than did SRADWG or SRADCO. Mean values of SRADOB, SRADWG, and SRADNP for a grid cell usually were within 1 MJ m−2 d−1 of each other, but NASA/POWER values averaged 1.1 MJ m−2 d−1 lower than SRADOB. This bias increased at lower latitudes and during summer months and is partially explained by assumptions about ambient aerosol properties. The NASA/POWER solar data are a promising resource for studies requiring realistic accounting of historic variation.

A Real-Time Gridded Crop Model for Assessing Spatial Drought Stress on Crops in the Southeastern United States

AbstractThe severity of drought has many implications for society. Its impacts on rain-fed agriculture are especially direct, however. The southeastern United States, with substantial rain-fed agriculture and large variability in growing-season precipitation, is especially vulnerable to drought. As commodity markets, drought assistance programs, and crop insurance have matured, more advanced information is needed on the evolution and impacts of drought. So far many new drought products and indices have been developed. These products generally do not include spatial details needed in the Southeast or do not include the physiological state of the crop, however. Here, a new type of drought measure is described that incorporates high-resolution physical inputs into a crop model (corn) that evolves based on the physical–biophysical conditions. The inputs include relatively high resolution (as compared with standard surface or NOAA Cooperative Observer Program data) (5 km) radar-derived precipitation, satellite-derived insolation, and temperature analyses. The system (referred to as CropRT for gridded crop real time) is run in real time under script control to provide daily maps of crop evolution and stress. Examples of the results from the system are provided for the 2008–10 growing seasons. Plots of daily crop water stress show small subcounty-scale variations in stress and the rapid change in stress over time. Depictions of final crop yield in comparison with seasonal average stress are provided.

A New Homogenized Climate Division Precipitation Dataset for Analysis of Climate Variability and Climate Change

AbstractA new homogeneous climate division monthly precipitation dataset [based on full network estimated precipitation (FNEP)] was created as an alternative to the National Climatic Data Center (NCDC) climate division dataset. These alternative climate division monthly precipitation values were estimated using an equal-weighted average of Cooperative Observer Program stations that contained serially complete time series. Missing station observations were estimated by a procedure that was optimized through testing on U.S. Historical Climate Network stations. Inhomogeneities in the NCDC dataset arise from two principal causes. The pre-1931 estimation of NCDC climate division monthly precipitation from statewide averages led to a significant time series discontinuity in several climate divisions. From 1931 to the present, NCDC climate division averages have been calculated from a subset of available station data within each climate division, and temporal changes in the location of available stations have caused artificial changes in the time series. The FNEP climate division dataset is recommended over the NCDC dataset for studies involving climate trends or long-term climate variability. According to the FNEP data, the 1895–2009 linear precipitation trend is positive across most of the United States, and trends exceed 10% per century across the southern plains and the Corn Belt. Remaining inhomogeneities from changes in gauge technology and station location may be responsible for an artificial trend of 1%–3% per century.

Assessment of Extreme Quantitative Precipitation Forecasts and Development of Regional Extreme Event Thresholds Using Data from HMT-2006 and COOP Observers

Abstract Extreme precipitation events, and the quantitative precipitation forecasts (QPFs) associated with them, are examined. The study uses data from the Hydrometeorology Testbed (HMT), which conducted its first field study in California during the 2005/06 cool season. National Weather Service River Forecast Center (NWS RFC) gridded QPFs for 24-h periods at 24-h (day 1), 48-h (day 2), and 72-h (day 3) forecast lead times plus 24-h quantitative precipitation estimates (QPEs) from sites in California (CA) and Oregon–Washington (OR–WA) are used. During the 172-day period studied, some sites received more than 254 cm (100 in.) of precipitation. The winter season produced many extreme precipitation events, including 90 instances when a site received more than 7.6 cm (3.0 in.) of precipitation in 24 h (i.e., an “event”) and 17 events that exceeded 12.7 cm (24 h)−1 [5.0 in. (24 h)−1]. For the 90 extreme events {>7.6 cm (24 h)−1 [3.0 in. (24 h)−1]}, almost 90% of all the 270 QPFs (days 1–3) were biased low, increasingly so with greater lead time. Of the 17 observed events exceeding 12.7 cm (24 h)−1 [5.0 in. (24 h)−1], only 1 of those events was predicted to be that extreme. Almost all of the extreme events correlated with the presence of atmospheric river conditions. Total seasonal QPF biases for all events {i.e., ≥0.025 cm (24 h)−1 [0.01 in. (24 h)−1]} were sensitive to local geography and were generally biased low in the California–Nevada River Forecast Center (CNRFC) region and high in the Northwest River Forecast Center (NWRFC) domain. The low bias in CA QPFs improved with shorter forecast lead time and worsened for extreme events. Differences were also noted between the CNRFC and NWRFC in terms of QPF and the frequency of extreme events. A key finding from this study is that there were more precipitation events >7.6 cm (24 h)−1 [3.0 in. (24 h)−1] in CA than in OR–WA. Examination of 422 Cooperative Observer Program (COOP) sites in the NWRFC domain and 400 in the CNRFC domain found that the thresholds for the top 1% and top 0.1% of precipitation events were 7.6 cm (24 h)−1 [3.0 in. (24 h)−1] and 14.2 cm (24 h)−1 [5.6 in. (24 h)−1] or greater for the CNRFC and only 5.1 cm (24 h)−1 [2.0 in. (24 h)−1] and 9.4 cm (24 h)−1 [3.7 in. (24 h)−1] for the NWRFC, respectively. Similar analyses for all NWS RFCs showed that the threshold for the top 1% of events varies from ∼3.8 cm (24 h)−1 [1.5 in. (24 h)−1] in the Colorado Basin River Forecast Center (CBRFC) to ∼5.1 cm (24 h)−1 [3.0 in. (24 h)−1] in the northern tier of RFCs and ∼7.6 cm (24 h)−1 [3.0 in. (24 h)−1] in both the southern tier and the CNRFC. It is recommended that NWS QPF performance in the future be assessed for extreme events using these thresholds.

Multisensor Precipitation Reanalysis

Abstract Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

Comparison of Rain Gauge Measurements in the Mid-Atlantic Region

Abstract A comparative study of daily and monthly rainfall between research and operational gauges was conducted at the mid-Atlantic region. Fifty research tipping-bucket gauges were deployed to 20 sites where each site had dual or triple gauges. The gauges were in place to validate the National Aeronautics and Space Administration’s newly developed polarimetric radar rainfall estimate. For logistic purposes, these research gauges were collocated with operational gauges and were operated over a year at each site. Therefore, this is an experimental study, which involves a mixture of one to five sites of seven operational gauge networks. A very good to excellent agreement between the two collocated research gauges at daily time scale raised the authors’ confidence to consider them as a reference before comparing with the operational gauges. Among operational networks, the National Weather Service’s (NWS) Automated Surface Observing Systems (ASOS) weighing bucket and the Climate Reference Network and Forest Services tipping-bucket gauges demonstrated high performance for both daily and monthly rainfall, while the Federal Aviation Administration’s Automated Weather Observing Systems (AWOS) tipping-bucket gauges performed poorly. Among the other networks, the ASOS tipping-bucket and Cooperative observer program’s stick gauges seemed to be reliable for monthly rainfall, but not always for daily rainfall. The Virginia Agricultural Experimental Station (VAES) tipping-bucket gauges, on the other hand, had a mixture of high and low performance for daily and monthly rainfall. Unlike other gauge networks, VAES gauges were in place for long-term research applications.

Effects of the resolution of soil dataset and precipitation dataset on SWAT2005 streamflow calibration parameters and simulation accuracy

The resultant calibration parameter values and simulation accuracy of hydrologic models such as the 2005 Soil and Water Assessment Tool (SWAT2005) depend on how well spatial input parameters describe the characteristics of the study area. The objectives of this study were to (1) investigate the effect of soils dataset resolution (State Soil Geographic Database and Soil Survey Geographic Database) on SWAT2005 streamflow simulation performance and calibration parameters using four precipitation datasets and (2) determine the best combination of soil and precipitation datasets for the Cobb Creek, Lake Creek, and Willow Creek subwatersheds within the Fort Cobb Reservoir Experimental watershed, Oklahoma. SWAT2005 was calibrated and validated for streamflow for the three subwatersheds using the State Soil Geographic Database and the Soil Survey Geographic Database for each of the four available precipitation datasets with different spatial resolutions. The four sources of rainfall data included the National Weather Service's network of Cooperative Observer Program weather stations, statewide Oklahoma Mesonet, USDA Agricultural Research Service's weather station network (MICRONET), and National Weather Service Next Generation Radar (NEXRAD) precipitation estimates. The model performance was assessed using the Nash-Sutcliffe efficiency coefficient and percent bias statistics. During both the calibration and validation periods, there were no significant differences in the model monthly performance statistics between the higher resolution Soil Survey Geographic Database and the lower resolution State Soil Geographic Database across subwatersheds, irrespective of the rainfall dataset used. However, the model performed better when the NEXRAD and MICRONET precipitation datasets were used. There were slight to large differences in the resultant calibration parameter values depending on the calibration parameter, the precipitation data used, and the subwatershed. Large differences in the simulated surface runoff and deep aquifer recharge due to soils dataset resolution could lead to significant differences in the simulated water quality components such as sediments and nutrients. This is important because significant differences in simulated sediments and/or nutrients could lead to significantly different outcomes in terms of the impacts of a given conservation practice for studies like the Conservation Effects Assessment Project. Due to the lack of measured data to validate the simulated water balance components, it was recommended to use both the fine and coarse resolution soil datasets in combination with the finer spatial resolution precipitation datasets and the simulated water balance components of interest reported as a range.

Spatial Interpolation of Climate Variables in Nebraska

Temperature and rainfall are important climatological parameters, and knowledge of their temporal and spatial patterns is useful for researchers working in many disciplines. In this study, spatial interpolation techniques were implemented in a Geographic Information System (GIS) to study the spatial variability of climate variables (maximum air temperature, minimum air temperature, and seasonal and annual rainfall) in Nebraska. Thirty years (1971-2000) of climate data (average monthly maximum and minimum temperatures and rainfall) from 215 National Weather Service Cooperative Observer Network (COOP) weather stations distributed throughout Nebraska and surrounding states were used in the analyses. Literature suggests that there is no single preferred method of interpolation, and the selection of interpolation method is usually based on the available data, desired level of accuracy, and available resources. We analyzed three different commonly used interpolation methods (inverse distance weighted, spline, and kriging) and evaluated their performance. Overall, the summary of all statistical parameters showed no significant difference between interpolation techniques in predicting the spatial variability in 30-year climate normals. Investigation of interpolation errors at individual weather stations agreed with summary statistics. Spatial variability, in this instance, is likely smoothed due to long-term averaging of the data (30 years), resulting in similar errors for all the interpolation techniques. Subjective assessment of maps for all climate variables showed that the kriging method produced smoother maps compared to spline and inverse distance weighted. Considering the degree to which accurate spatial interpolation could be accomplished with relative ease and less bias, the spline method proves the better option.

Evaluating the accuracy of VEMAP daily weather data for application in crop simulations on a regional scale

Weather plays a critical role in eco-environmental and agricultural systems. Limited availability of meteorological records often constrains the applications of simulation models and related decision support tools. The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) provides daily weather variables on a 0.5 latitude–longitude grid across the conterminous USA. Daily weather data from the VEMAP (1961–1990) for the state of Georgia were compared with data from 52 individual ground stations of the National Weather Service Cooperative Observer Program (COOP). Additionally, simulated crop grain yields of soybean ( Glycine max) were compared using the two data sources. Averaged daily maximum and minimum temperatures (Tmax and Tmin, respectively), solar radiation (SRAD), and precipitation (PPT) differed by 0.2 °C, −0.2 °C, 1.7 MJ m −2 d −1, and 0 mm, respectively. Mean absolute errors (MAEs) for Tmax, Tmin, SRAD, and PPT were 4.2 °C, 4.4 °C, 4.4 MJ m −2 d −1, and 6.1 mm, respectively, and root mean squared errors (RMSEs) for Tmax, Tmin, SRAD, and PPT were 5.5 °C, 5.9 °C, 5.8 MJ m −2 d −1, and 13.6 mm, respectively. Temperature differences were lowest during summer months. Simulations of grain yield using the two data sources were strongly correlated ( r = 0.68, p < 0.01). The MAE of grain yield was 552 kg ha −1. The RMSE of grain yield was 714 kg ha −1. Hybrid analyses indicated that the variation of simulated yield was mainly associated with the differences in rainfall. The results showed that the VEMAP daily weather data were able to be adequately applied to crop growth simulation at spatial and temporal scales, especially for long-term climate change research. Overall, the VEMAP weather data appears to be a promising source for crop growth modeling concerned with scale to 0.5° coordinate grid.

Contribution of tropical cyclones to extreme rainfall events in the southeastern United States

Extreme precipitation has been increasing in the United States over the past century. In light of the associated impacts and possible linkages to climate change, this topic has garnered a great deal of attention from the scientific community and general public. Because tropical cyclones are a common source of heavy rainfall in the southeastern United States, we examined the contribution of tropical cyclone precipitation relative to overall extreme precipitation from all weather systems combined. We used a surface observation network over the period 1972–2007, consisting of first‐order and Cooperative Observer Program weather stations. Furthermore, to account for precipitation that may be unmeasured by rain gauges because of windy conditions during tropical cyclones, we employed a wind‐corrected data set and the North American Regional Reanalysis. According to several metrics of extreme precipitation, we found that extreme precipitation from tropical cyclones has been increasing over the past few decades. Additionally, the contribution of tropical cyclone precipitation to overall extreme precipitation has been significantly increasing by approximately 5%–10% per decade in the southeastern Atlantic coastal states. We attribute this rise in tropical cyclone contribution to an increase in both the storm wetness (precipitation per storm) and storm frequency over the period of record. There is little evidence that changes in storm duration are responsible for the increase. As such, we believe that an important factor in accurately projecting changes in extreme precipitation rests on whether tropical cyclone activity is driven more by natural decadal oscillations or by large‐scale warming of the environment.

Parameterizing soil and weather inputs for crop simulation models using the VEMAP database

Soil and weather data are critical for the operation of crop simulation models. However, in many cases they are not readily available, especially applications at a regional or larger spatial scale. The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) provides massive quantities of geo-referenced soil and weather data variables on a half-degree latitude–longitude grid covering the conterminous USA. The VEMAP data were derived from a range of products and analyses, including ground observations, cluster analyses, kriging interpolation, and data assimilation. The objective of this study was to convert the soil and daily weather data of VEMAP into a format that can be used in the popular modeling software Decision Support System for Agrotechnology Transfer (DSSAT). Users can select appropriate soil or daily weather information for the area of interest. The conversion of the VEMAP data resulted in 5927 unique soil profiles and 3261 unique weather station files that encompass daily data from 1895 to 1993. To demonstrate the utility of this database in DSSAT, one representative county of the state of Georgia, USA was selected and a soybean simulation model was employed to simulate final yield using the extracted soil and daily weather data for the normal year (1961–1990). Meanwhile, the extracted daily weather data were compared with ground station observations from the National Weather Service Cooperative Observer Program (COOP). The paired t-test showed that no significant differences were found between the daily weather data and between simulated yields based on VEMAP and COOP weather data for the selected location. The outcome of this research showed that the VEMAP data can be used for crop model applications. However, further research is needed to assess the accuracy of the datasets for a variety of crops at a regional or national scale.