Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: Steven E. Hollinger x
- Refine by Access: All Content x
Abstract
Ten years of soil moisture measurements (biweekly from March through September and monthly during winter) within the top 1 m of soil at 17 grass-covered sites across Illinois are analyzed to provide a climatology of soil moisture for this important Midwest agricultural region. Sod moisture measurements were obtained with neutron probes that were calibrated for each site. Measurement errors are dependent upon the volumetric water content with errors less than 20 percent when soil moisture is above 10 percent of soil volume. Single point errors in moisture measurements from the top 1 m of soil range from 6 percent to 13 percent when volumetric soil moisture is 30 percent of soil volume. The average depletion in moisture between winter and summer over the 10-year period for the top 2 m of soil in Illinois was 72.3 mm. Three-quarters of this decrease occurred above 0.5 m and only 5 percent occurred between the 1.0-m and 2.0-m depths. The average moisture decrease between winter and summer during a wet year (1985) and a drought year (1988) in the top 2 m of soil was 64 percent and 204 percent of the average for the 10-year period, respectively. Seasonal means in soil moisture averaged for the state show the effects of different seasons and soil types on soil moisture. In the winter and spring a latitudinal gradient exists with the wetter soils in the southern part of the state. During summer and autumn there is a longitudinal gradient with the wetter soils in the eastern half of the state. The longitudinal gradient is closely associated with the depth of loess deposits. A north to south latitudinal gradient of soil moisture variability for the summer season is also evident in the 10 yr of records. A comparison of time series of soil moisture from sites with differing soil texture shows that a silty loam soil holds 2 to 3 times more water in the top 1 m than a loamy sand soil. Time series of soil moisture indicate that seasonal variations in water in the top 1 m at a grass-covered site was 1 to 2 times greater than at an adjacent nonvegetated site.
Abstract
Ten years of soil moisture measurements (biweekly from March through September and monthly during winter) within the top 1 m of soil at 17 grass-covered sites across Illinois are analyzed to provide a climatology of soil moisture for this important Midwest agricultural region. Sod moisture measurements were obtained with neutron probes that were calibrated for each site. Measurement errors are dependent upon the volumetric water content with errors less than 20 percent when soil moisture is above 10 percent of soil volume. Single point errors in moisture measurements from the top 1 m of soil range from 6 percent to 13 percent when volumetric soil moisture is 30 percent of soil volume. The average depletion in moisture between winter and summer over the 10-year period for the top 2 m of soil in Illinois was 72.3 mm. Three-quarters of this decrease occurred above 0.5 m and only 5 percent occurred between the 1.0-m and 2.0-m depths. The average moisture decrease between winter and summer during a wet year (1985) and a drought year (1988) in the top 2 m of soil was 64 percent and 204 percent of the average for the 10-year period, respectively. Seasonal means in soil moisture averaged for the state show the effects of different seasons and soil types on soil moisture. In the winter and spring a latitudinal gradient exists with the wetter soils in the southern part of the state. During summer and autumn there is a longitudinal gradient with the wetter soils in the eastern half of the state. The longitudinal gradient is closely associated with the depth of loess deposits. A north to south latitudinal gradient of soil moisture variability for the summer season is also evident in the 10 yr of records. A comparison of time series of soil moisture from sites with differing soil texture shows that a silty loam soil holds 2 to 3 times more water in the top 1 m than a loamy sand soil. Time series of soil moisture indicate that seasonal variations in water in the top 1 m at a grass-covered site was 1 to 2 times greater than at an adjacent nonvegetated site.
Abstract
An assessment was made of factors affecting the use of cloud seeding to increase summer (June-August) rainfall for improved corn and soybean yields in Illinois. Crop yields from a five-year agricultural field experiment involving nine levels of rain increases were compared with yields produced under natural rainfall. The sampled years (1987–91) included a wide range of summer weather conditions, including extremely hot and dry (1988) and very wet and cool (1990). Since the types of growing seasons sampled represented only 30% of all types, caution must be used in interpreting the results and applying them to other years.
Additional water of 10%, 25%, or 40% of each day's actual rainfall was applied after each rain. Additional water was also applied only to certain rains, depending on whether they were light, moderate, or heavy. The best treatment, based on performance in all years and considering both treated and untreated crops, was a 25% rain increase applied on days with moderate rain (2.5 mm–2.53 cm). However, it was only marginally better than the natural rainfall. The best treatment for soybeans alone, based on the average yields for 1987–90, was the natural, unmodified rainfall, whereas that for corn was 10%–40% increases only on heavy-rain days. In general, rain increases of 10% had little yield effect, and 40% increases applied in all years were found damaging in wetter years. However, in extremely dry summers, the 40% rain increases were the best for both crops. The best treatment in any given summer varied by the type and timing of rain conditions and crop. Selection of the best treatment to use in any summer would require the capability to predict the amount and timing of summer rainfall by 1 June.
Abstract
An assessment was made of factors affecting the use of cloud seeding to increase summer (June-August) rainfall for improved corn and soybean yields in Illinois. Crop yields from a five-year agricultural field experiment involving nine levels of rain increases were compared with yields produced under natural rainfall. The sampled years (1987–91) included a wide range of summer weather conditions, including extremely hot and dry (1988) and very wet and cool (1990). Since the types of growing seasons sampled represented only 30% of all types, caution must be used in interpreting the results and applying them to other years.
Additional water of 10%, 25%, or 40% of each day's actual rainfall was applied after each rain. Additional water was also applied only to certain rains, depending on whether they were light, moderate, or heavy. The best treatment, based on performance in all years and considering both treated and untreated crops, was a 25% rain increase applied on days with moderate rain (2.5 mm–2.53 cm). However, it was only marginally better than the natural rainfall. The best treatment for soybeans alone, based on the average yields for 1987–90, was the natural, unmodified rainfall, whereas that for corn was 10%–40% increases only on heavy-rain days. In general, rain increases of 10% had little yield effect, and 40% increases applied in all years were found damaging in wetter years. However, in extremely dry summers, the 40% rain increases were the best for both crops. The best treatment in any given summer varied by the type and timing of rain conditions and crop. Selection of the best treatment to use in any summer would require the capability to predict the amount and timing of summer rainfall by 1 June.
Abstract
This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997–99 and 2001–02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50–100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days > 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.
Abstract
This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997–99 and 2001–02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50–100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days > 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.
Abstract
Climate studies of precipitation have generally focused on daily or longer time scales of precipitation accumulation. The main objective of this work was to identify the precipitation characteristics of storms based on 15-min precipitation data, including storm total precipitation, storm duration, mean storm intensity, and maximum 15-min intensity. A group of precipitation characteristics was subjected to a cluster analysis that identified nine regions of the conterminous United States with homogeneous seasonal cycles of mean storm precipitation characteristics. Both mean and extreme statistics were derived for each characteristic and season for each zone. Continuous probability density functions were generated that appropriately fit the empirical distributions of storm total precipitation and maximum 15-min intensity. The storm characteristics, in turn, were a function of seasonal water availability from source regions, atmospheric water vapor capacity, and storm precipitation mechanism. This is the first time that such an extensive climatology of storm precipitation characteristics has been produced. A preliminary trend analysis of the 1972–2002 storm characteristic data by zone showed substantial changes that tended to be geographically coherent, with noteworthy differences between the western and eastern United States. The western United States displayed a trend toward decreasing storm total precipitation and storm duration in most seasons, while storm intensity increased. The eastern United States experienced a general pattern of increasing storm total precipitation and storm duration during winter, as well as a tendency for maximum 15-min precipitation intensity to increase.
Abstract
Climate studies of precipitation have generally focused on daily or longer time scales of precipitation accumulation. The main objective of this work was to identify the precipitation characteristics of storms based on 15-min precipitation data, including storm total precipitation, storm duration, mean storm intensity, and maximum 15-min intensity. A group of precipitation characteristics was subjected to a cluster analysis that identified nine regions of the conterminous United States with homogeneous seasonal cycles of mean storm precipitation characteristics. Both mean and extreme statistics were derived for each characteristic and season for each zone. Continuous probability density functions were generated that appropriately fit the empirical distributions of storm total precipitation and maximum 15-min intensity. The storm characteristics, in turn, were a function of seasonal water availability from source regions, atmospheric water vapor capacity, and storm precipitation mechanism. This is the first time that such an extensive climatology of storm precipitation characteristics has been produced. A preliminary trend analysis of the 1972–2002 storm characteristic data by zone showed substantial changes that tended to be geographically coherent, with noteworthy differences between the western and eastern United States. The western United States displayed a trend toward decreasing storm total precipitation and storm duration in most seasons, while storm intensity increased. The eastern United States experienced a general pattern of increasing storm total precipitation and storm duration during winter, as well as a tendency for maximum 15-min precipitation intensity to increase.
Abstract
Soil erosion is a major global challenge. An increased understanding of the mechanisms driving soil erosion, especially the storms that produce it, is vital to reducing the impact on agriculture and the environment. The objective of this work was to study the spatial distribution and time trends of the soil erosion characteristics of storms, including the maximum 30-min precipitation intensity (I 30), storm kinetic energy of the falling precipitation (KE), and the storm erosivity index (EI) using a long-term 15-min precipitation database. This is the first time that such an extensive climatology of soil erosion characteristics of storms has been produced. The highest mean I 30, KE, and EI values occurred in all seasons in the southeastern United States, while the lowest occurred predominantly in the interior west. The lowest mean I 30, KE, and EI values typically occurred in winter, and the highest occurred in summer. The exception to this was along the West Coast where winter storms exhibited the largest mean KE and EI values. Linear regression was used to identify trends in mean storm erosion characteristics for nine U.S. zones over the 31-yr study period. The south-central United States showed increases for all three storm characteristics for all four seasons. On the other hand, higher elevations along the West Coast showed strong decreases in all three storm characteristics across all seasons. The primary agricultural region in the central United States showed significant increases in fall and winter mean EI when there is less vegetative cover. These results underscore the need to update the storm climatology that is related to soil erosion on a regular basis to reflect changes over time.
Abstract
Soil erosion is a major global challenge. An increased understanding of the mechanisms driving soil erosion, especially the storms that produce it, is vital to reducing the impact on agriculture and the environment. The objective of this work was to study the spatial distribution and time trends of the soil erosion characteristics of storms, including the maximum 30-min precipitation intensity (I 30), storm kinetic energy of the falling precipitation (KE), and the storm erosivity index (EI) using a long-term 15-min precipitation database. This is the first time that such an extensive climatology of soil erosion characteristics of storms has been produced. The highest mean I 30, KE, and EI values occurred in all seasons in the southeastern United States, while the lowest occurred predominantly in the interior west. The lowest mean I 30, KE, and EI values typically occurred in winter, and the highest occurred in summer. The exception to this was along the West Coast where winter storms exhibited the largest mean KE and EI values. Linear regression was used to identify trends in mean storm erosion characteristics for nine U.S. zones over the 31-yr study period. The south-central United States showed increases for all three storm characteristics for all four seasons. On the other hand, higher elevations along the West Coast showed strong decreases in all three storm characteristics across all seasons. The primary agricultural region in the central United States showed significant increases in fall and winter mean EI when there is less vegetative cover. These results underscore the need to update the storm climatology that is related to soil erosion on a regular basis to reflect changes over time.
Abstract
The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. These results indicate that the production process examined has sufficient flexibility to utilize climate forecasts for specific production seasons but the value of those forecasts is sensitive to economic parameters as well as forecasts characteristics. Forecasts periods of greatest importance, as well as the relationships between forecast value, accuracy, and lead time, are evaluated.
Abstract
The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. These results indicate that the production process examined has sufficient flexibility to utilize climate forecasts for specific production seasons but the value of those forecasts is sensitive to economic parameters as well as forecasts characteristics. Forecasts periods of greatest importance, as well as the relationships between forecast value, accuracy, and lead time, are evaluated.
Effective responses by government agencies, businesses, and private industry to climate disasters such as the disastrous Mississippi River flood of 1993 hinge on the regional availability of diverse up-to-date weather, climate, and water information. In addition to the obvious need for accurate forecasts and warnings of severe weather and floods, other types of meteorologically based information can contribute to effective responses. Some examples of information requested during and after the 1993 flood include 1) hydroclimatic assessments of the magnitude of the event, 2) agricultural assessments of the impacts of heavy rains and flooding on corn and soybean production, and 3) probabilistic outlooks of the recurrence of flooding based on soil moisture conditions. Quick responses to these climate information needs necessitate 1) a real-time climate monitoring system, 2) physical models to assess effects and impacts, and 3) scientific expertise to address complex issues.
Effective responses by government agencies, businesses, and private industry to climate disasters such as the disastrous Mississippi River flood of 1993 hinge on the regional availability of diverse up-to-date weather, climate, and water information. In addition to the obvious need for accurate forecasts and warnings of severe weather and floods, other types of meteorologically based information can contribute to effective responses. Some examples of information requested during and after the 1993 flood include 1) hydroclimatic assessments of the magnitude of the event, 2) agricultural assessments of the impacts of heavy rains and flooding on corn and soybean production, and 3) probabilistic outlooks of the recurrence of flooding based on soil moisture conditions. Quick responses to these climate information needs necessitate 1) a real-time climate monitoring system, 2) physical models to assess effects and impacts, and 3) scientific expertise to address complex issues.
Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.
Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.
