EFFECTS OF CLIMATIC CHANGE ON THE THORNTHWAITE MOISTURE INDEX1

ABSTRACT: The Thornthwaite moisture index is a useful indicator of the supply of water (precipitation) in an area relative to the demand for water under prevailing climatic conditions (potential evapotranspiration). This study examines the effects of changes in climate (temperature and precipitation) on the Thornthwaite moisture index in the conterminous United States. Estimates of changes in mean annual temperature and precipitation for doubled-atmospheric CO₂ conditions derived from three general circulation models (GCMs) are used to study the response of the moisture index under steady-state doubled-CO₂ conditions. Results indicate that temperature and precipitation changes under doubled-CO₂ conditions generally will cause the Thornthwaite moisture index to decrease, implying a drier climate for most of the United States. The pattern of expected decrease is consistent among the three GCMs, although the amount of decrease depends on which GCM climatic-change scenario is used. Results also suggest that changes in the moisture index are related mainly to changes in the mean annual potential evapotranspiration as a result of changes in the mean annual temperature, rather than to changes in the mean annual precipitation.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Volatilization and degradation of soil-applied dimethylselenide

Dimethylselenide (DMSe) is a highly volatile gas that is produced by indigenous microorganisms in seleniferous soils and sediments; however, little is known about the soil conditions that affect the persistence of DMSe and its transport to the atmosphere. In this study we investigated the effect of moisture content, temperature, and organic amendments on the degradation of soil-applied DMSe. The degradation of DMSe was entirely a result of biological mechanisms, but changes in temperature (20-40 degrees C) and soil moisture content (30-70% of the maximum water holding capacity) had little influence on the degradation rate. In contrast, amending soil with either 1% casein or gluten (by weight) had an inhibitory effect on the degradation of DMSe. After 18 d, 2.1 times more DMSe was present in the casein-amended soil and 2.6 times more DMSe was present in the gluten-amended soil. The transport of DMSe in packed soil columns was also investigated. Increasing the depth to soil surface was found to significantly decrease the amount of DMSe transported to the air. After 6 d, 57% of DMSe injected 10 cm below the soil surface was volatilized. At an injection depth of 20 cm the cumulative emissions were reduced by 38% and at 30 cm the cumulative emissions were reduced by 51%. In columns containing 1% casein or gluten in the top 5 cm of soil the cumulative loss of DMSe was about 9% higher than in unamended soil. Increasing our understanding of the soil conditions that influence the gaseous diffusion of DMSe should help in determining the feasibility of using Se volatilization as a remediation technique.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

Effects of moisture and temperature on carbon and nitrogen mineralization in mine tailings mixed with sewage sludge

Digested sewage sludge mixed with copper mine tailings was incubated for 3 mo at 16 combinations of temperature (-1, 5, 10, and 15 degrees C) and soil moisture content (2, 8, 14, and 24% by weight). Carbon dioxide evolution and net N mineralization were measured at increasing time intervals. A two compartment first-order kinetic model (refractory and labile C) was fitted to the time series of measured CO2 fluxes using nonlinear regression analysis. The dependencies of the rate constants on moisture and temperature could be well described by log-linear functions. The estimated Q10 value (the factor by which the rate is increased as temperature is increased 10 degrees C) was 2.55. Within the range of temperature and moisture considered here, which correspond to conditions occurring naturally in Sweden, CO2 evolution was more strongly controlled by moisture than by temperature. Less mineral N accumulated during the experiment at the lowest moisture or temperature. However, the dependency of net N mineralization on moisture and temperature in the remaining treatments was less clear than for C evolution, presumably due to denitrification at the higher temperatures and moisture contents. Nitrate was formed after around 2 wk but only at 10 and 15 degrees C.     

Cattle feedlot soil moisture and manure content: I. Impacts on greenhouse gases, odor compounds, nitrogen losses, and dust

Beef cattle feedlots face serious environmental challenges associated with manure management, including greenhouse gas, odor, NH3, and dust emissions. Conditions affecting emissions are poorly characterized, but likely relate to the variability of feedlot surface moisture and manure contents, which affect microbial processes. Odor compounds, greenhouse gases, nitrogen losses, and dust potential were monitored at six moisture contents (0.11, 0.25, 0.43, 0.67, 1.00, and 1.50 g H2O g(-1) dry matter [DM]) in three artificial feedlot soil mixtures containing 50, 250, and 750 g manure kg(-1) total (manure + soil) DM over a two-week period. Moisture addition produced three microbial metabolisms: inactive, aerobic, and fermentative at low, moderate, and high moisture, respectively. Manure content acted to modulate the effect of moisture and enhanced some microbial processes. Greenhouse gas (CO2, N2O, and CH4) emissions were dynamic at moderate to high moisture. Malodorous volatile fatty acid (VFA) compounds did not accumulate in any treatments, but their persistence and volatility varied depending on pH and aerobic metabolism. Starch was the dominant substrate fueling both aerobic and fermentative metabolism. Nitrogen losses were observed in all metabolically active treatments; however, there was evidence for limited microbial nitrogen uptake. Finally, potential dust production was observed below defined moisture thresholds, which were related to manure content of the soil. Managing feedlot surface moisture within a narrow moisture range (0.2-0.4 g H2O g(-1) DM) and minimizing the accumulation of manure produced the optimum conditions that minimized the environmental impact from cattle feedlot production.     

SATELLITE MICROWAVE OBSERVATIONS OF SOIL MOISTURE VARIATIONS1

ABSTRACT: Results from studies in the Illinois-Indiana and Texas-Oklahoma areas indicate that satellite microwave observations at the 1.55 cm wavelength are responsive to relative moisture variations in the near surface layer of the soil. Because significant vegetation cover absorbs the 1.55 cm microwave emission from the soil, the target area must be predominately bare soil or low density vegetation cover for meaningful measurements to result. The 25 km resolution of the satellite sensor limits application of the microwave techniques to large areas such as watersheds or agricultural districts rather than individual fields. In general, at 1.55 cm. there is an inverse relationship between microwave brightness temperature and changes in soil moisture levels (as indicated by antecedent rainfall) in agricultural regions before the planting of crops or during the early growing season when vegetation cover is sparse. Even early season observations should be of great value in deciding on the time and type of crop planting and for initial irrigation scheduling when the root zone is still in close proximity to the surface.     

消化/剩余污泥水分扩散通量的研究

在传递理论的基础上,研究了干燥温度、污泥厚度及其种类3个主要因素对污泥水分扩散的影响,干燥温度设为30℃、40℃和50℃,污泥厚度设为1.3 cm、2.6 cm和3.9 cm,分别考察了消化和剩余两种污泥的干燥性能。利用实验数据获得两种污泥的有效质扩散系数,采用因次分析法建立了它们的水分扩散通量的准则关系式,通过实验研究两种污泥的水分扩散通量随干基含湿量的变化规律,并获得了两种污泥的水分扩散通量的经验公式。实验结果显示3.9 cm厚的消化污泥在40℃条件下的干燥效果最好。     

CLASSIFYING STORM RUNOFF POTENTIAL WITH PASSIVE MICROWAVE MEASUREMENTS1

ABSTRACT: The average microwave temperature of the watershed surface as detected by an airborne Passive Microwave Imaging Scanner (PMIS) was compared with the measured Soil Conservation Service (SCS) watershed storm runoff coefficient (CN). Previous laboratory work suggested that microwave response to the watershed surface is influenced by some of the same surface characteristics that affect runoff, i.e., soil moisture, surface roughness, vegetative cover, and soil texture. In order to field test and develop relations between runoff potentfal and microwave response, several highly instrumented watersheds of approximately 1.5 to 17 km2 were scanned under wet- and dry-soil conditions in April and June 1973. The polarized (horizontal and vertical) scans at 2.8 cm wavelength provided the data base from which other values were calculated. The best relationship between runoff coefficients (CN) and PMIS temperatures was observed when horizontally polarized temperatures from the near-dormant, early-growing season flight were used. Lower SCS runoff coefficients seem to be correlated with the cross-polarized response under dry watershed conditions late in the growing season and the difference in horizontal polarized response between wet conditions early in the growing season and dry conditions late in the growing season. To apply the results, the relationships need to be verified further.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

PRIESTLEY‐TAYLOR ALPHA COEFFICIENT: VARIABILITY AND RELATIONSHIP TO NDVI IN ARCTIC TUNDRA LANDSCAPES1

ABSTRACT: Average daily values of the Priestley‐Taylor coefficient (a) were calculated for two eddy covariance (flux) tower sites with contrasting vegetation, soil moisture, and temperature characteristics on the North Slope of Alaska over the 1994 and 1995 growing seasons. Because variations in a have been shown to be associated with changes in vegetation, soil moisture, and meteorological conditions in Arctic ecosystems, we hypothesized that a values would be significantly different between sites. Since variations in the normalized difference vegetation index (NDVI) follow patterns of vegetation community composition and state that are largely controlled by moisture and temperature gradients on the North Slope of Alaska, we hypothesized that temporal variations in a respond to these same conditions and thus co‐vary with NDVI. Significant differences in a values were found between the two sites in 1994 under average precipitation conditions. However, in 1995, when precipitation conditions were above average, no significant difference was found. Overall, the variations in a over the two growing seasons showed little relationship to the seasonal progression of the regional NDVI. The only significant relationship was found at the drier, upland study site.     

A numerical model for drying of spherical object in an indirect type solar dryer and estimating the drying time at different moisture level and air temperature

A numerical model for simultaneous heat and mass transfer was developed for solar drying of spherical objects and the object considered is green peas. Solar collector outlet temperature is assumed as drying chamber temperature and justified through energy balance equations. Assumptions are imposed on heat and mass transfer governing equations without losing the physics of the problem. Discretization is performed by finite difference method with implicit scheme. To generalize, the governing equation and boundary conditions are non-dimensionalized. The set of finite difference equations was solved by Tridiagonal Matrix Algorithm and a computer code in MATLAB was developed to solve them. The drying curves showed two stages of drying, initial, and secondary drying stage. At all drying temperatures and drying time, the center moisture was maximum and it was minimum at the boundary. A percentage of 85.67 surface moisture content and 25.33% center moisture was eliminated in the first 1 hr at 348 K. The product should be dried up to 7.45, 4.74, and 3.74 hr at air drying temperatures of 318, 333, and 348 K respectively, to maintain 10% of the product’s initial moisture content. The result is compared with the experimental result from literature and they are found to be in good agreement.     

PRECIPITATION DIFFERENCES AMONGST GCMs USED FOR THE U.S. NATIONAL ASSESSMENT1

ABSTRACT: Two general circulation models (GCMs) used in the U.S. national assessment of the potential consequences of climate variability and change (CGCM1 and HadCM2) show a large increase in precipitation in the future over the southwestern U.S., particularly during winter. This precipitation increase is an extension of a larger region of increased precipitation in the Pacific Ocean off the west coast of North America that is associated with a deepened and southward-shifted Aleutian Low, a weaker subtropical high, and warmer sea surface temperatures (SSTs). The models differ in their simulation of precipitation anomalies over the southeastern U.S., with CGCM1 showing drier conditions and HadCM2 showing wetter conditions in the future. While both models show decreased frequency of Atlantic storms, consistent with decreased meridional and land/sea temperature gradients, the more coastal position of the storm track in CGCM1 results in less precipitation than modern along the eastern seaboard of the U.S. During summer, differences in land surface models within the two GCMs sometimes lead to differences in soil moisture that feed back to the precipitation over land due to available moisture.     

ESTIMATION OF SOIL MOISTURE FROM SEASAT SAR DATA1

ABSTRACT A study was made to determine if Seasat Synthetic Aperture Radar (SAR) data could be used to make practical estimates of soil moisture. Extensive ground measurements were collected at two primary sites near Guymon, Oklahoma, and Sublette, Kansas. The relative sensitivity of the SAR to differences in soil moisture, tillage roughness, and vegetation was determined. To validate the effects detected in the SAR data, an airborne scatterometer with a similar wavelength was flown repeatedly over the Guymon site. Soil moisture variations in the surface 2 cm and surface 15 cm of fields with bare soil, milo and alfalfa produce similar responses in the scattering coefficient from both systems. Roughness due to tillage in row crops produced as much as 12–15 dB increase in the scatterometer return. Most agricultural vegetation was effectively penetrated by the L-band frequencies; however, corn produced an exceptionally high radar return either standing or after combine harvesting. When corn had ripened, there was some evidence that tillage roughness could be detected through the canopy. Moderate tillage roughness produced by grain drill furrows caused over 12 dB increase in return when row directions changed from parallel to perpendicular with respect to the SAR look direction. Dramatic increases in return occurred when vegetation surfaces were wet. Increased radar returns from tillage roughness, some vegetation and wet vegetation surfaces, all dyanmic in nature, were significant and may limit the practical estimation of soil moisture from the radar data.     

Environmental effects of soil property changes with off-road vehicle use

The effects of off-road vehicles (ORVs) on the physical and chemical properties of 6 soil series were measured at Hollister Hills State Vehicular Recreation Area in central California. Accelerated soil erosion and the alteration of surface strength, bulk density, soil moisture, temperature, and soil nutrients were quantified to gain an insight into the difficulty of revegetating altered, or modified, areas.Erosion is severe at Hollister Hills, particularly in coarse grained soils on steep slopes. Erosion displaced 0.5 and 3.0 metric tons per square meter on 2 trails on gravelly sandy loam, and 0.3 metric tons/m² from a trail on sandy loam. The surface strength and bulk density increased while the soil moisture decreased in gravelly sandy loam, coarse sandy loam, sandy loam, and clay. Clay loam had an increased surface strength with variably increased bulk density and no decrease in soil moisture. Diurnal temperature fluctuations increased and organic material and soil nutrients decreased in soil modified by vehicles.These property changes increase the erosion potential of the soil, impede germination of seedlings, and slow natural revegetation. Management methods in ORV-use areas should include planning trails by prior application of the universal soil loss equation and soil surveys, trail closure before complete loss of the soil mantle, and revegetation of closed areas.     

LANDSAT MID-INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL-MOISTURE ASSESSMENT1

ABSTRACT: Landsat satellite Thematic Mapper (TM) data were used to assess regional soil moisture conditions. The mid-infrared (MIR) data of TM band 7 were overlain onto four principal land-use categories (Agricultural/Irrigated, Urban/Clearings, Forest/ Wetlands, Water) using a geographic information system (GIS). M data were used to assess four qualitative surface soil-moisture conditions (water/very wet, wet, moist, and dry) within each land-use category of a 208,354 ha southwestern Florida study area. The MIR response was inversely related to the qualitative surface soil-moisture content. Integration of Landsat TM MIR data with land use through GIS appears to be a useful technique for high-resolution regional soil moisture assessment, and further research to reline this technique is recommended.     

POTENTIAL EFFECTS OF CLIMATE CHANGE ON SURFACE‐WATER QUALITY IN NORTH AMERICA1

ABSTRACT: Data from long‐term ecosystem monitoring and research stations in North America and results of simulations made with interpretive models indicate that changes in climate (precipitation and temperature) can have a significant effect on the quality of surface waters. Changes in water quality during storms, snowmelt, and periods of elevated air temperature or drought can cause conditions that exceed thresholds of ecosystem tolerance and, thus, lead to water‐quality degradation. If warming and changes in available moisture occur, water‐quality changes will likely first occur during episodes of climate‐induced stress, and in ecosystems where the factors controlling water quality are sensitive to climate variability. Continued climate stress would increase the frequency with which ecosystem thresholds are exceeded and thus lead to chronic water‐quality changes. Management strategies in a warmer climate will therefore be needed that are based on local ecological thresholds rather than annual median condition. Changes in land use alter biological, physical, and chemical processes in watersheds and thus significantly alter the quality of adjacent surface waters; these direct human‐caused changes complicate the interpretation of water‐quality changes resulting from changes in climate, and can be both mitigated and exacerbated by climate change. A rigorous strategy for integrated, long‐term monitoring of the ecological and human factors that control water quality is necessary to differentiate between actual and perceived climate effects, and to track the effectiveness of our environmental policies.     

PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

ASSESSMENT OF SAMPLING ERROR ASSOCIATED WITH SOIL MOISTURE ESTIMATION DESIGNS1

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two‐dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L‐band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively.