Increasing irrigation efficiency on turfgrass could help reduce water consumption on large turf facilities. Two experiments were conducted using perennial ryegrass (Lolium perenne (L.) Derby) to evaluate the potential of using remote sensing to estimate turf water status, predict daily evapotranspiration (ET), and estimate turf biomass. In the first experiment a crop water stress index, utilizing remotely sensed canopy temperature, were used to schedule irrigations on 6 of 10 drainage lysimeters. Three of the remaining lysimeters were irrigated used on meteorological estimates of ET calculated using a modified Penman equation. The results of this experiment were inconclusive due to inconsistent lysimeter drainage characteristics. The second experiment was conducted on a turf green with multiple heights to evaluate the potential for using canopy radiance to estimate turf biomass. These results showed that turf biomass could be estimated from a vegetative index (Red Ratio = Near Infrared/Red radiance) obtained through measurements of canopy radiance (r2 = 0.91).

No description available.

No description available.

Effective management of saline soils requires rapid, reliable methods of monitoring both soil water content (theta) and salt concentration, the latter measured in terms of electrical conductivity (sigma). This thesis examines estimation of theta, and bulk soil sigma (sigmaa) and soil water sigma (sigmaw), using time domain reflectometry (TDR). Calibration experiments were conducted in soil columns and in a vineyard that were irrigated with saline water. Within a theta range of 0.025--0.490 m3/m3, the correlation between TDR theta (thetaTDR) and gravimetrically determined theta (theta g) was high (r2 = 0.979 in soil columns, r2 = 0.836 in the field). The error of estimate of thetaTDR was 0.020 m3/m3 or less. Field thetaTDR estimates were sensitive to high salinity (sigmaw > 10 dS/m). Using a dual pathway parallel conductance (DPPC) model, sigma a was derived from sigmaw of saturated paste extracts and theta g. The correlation of TDR sigmaa to DPPC sigmaa was good in the laboratory (r2 = 0.915), and moderate in the field (r2 = 0.791), indicating a functional relationship between sigmaw and theta and TDR sigmaa. Models, to estimate sigmaw, were built by regression between paste extract sigma w and TDR sigmaa and thetaTDR. In a sigma w range of 3.0--23.4 dS/m in the columns, error of estimate of sigma w was small at 1.50 dS/m (+/-12.4% relative error range). In a sigma w range of 2.2--25.2 dS/m in the field, error of estimate of sigma w was 3.37 dS/m (+/-37% relative error range), which was significantly higher than the acceptable error range of +/-10%. ANOVA tests indicated that both TDR sigmaa and sigmaw·theta changed significantly with respect to the same sources of variance. Error in field estimates of sigmaw was associated with effects of salinity on thetaTDR and variability of soil conditions, particularly with respect to depth and time of sampling.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1987. / Bibliography: leaves [179]-185. / Photocopy. / Microfiche. / xiv, 185 leaves, bound ill. 29 cm

Drought is the most costly hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring and forecasting skills, this study presents a hybrid system with a combination of remotely sensed data assimilation based on particle filtering and a probabilistic drought forecasting model. Besides the proposed drought monitoring system through land data assimilation, another novel aspect of this dissertation is to seek the use of data assimilation to quantify land initial condition uncertainty rather than relying entirely on the hydrologic model or the land surface model to generate a single deterministic initial condition. Monthly to seasonal drought forecasting products are generated using the updated initial conditions. The computational complexity of the distributed data assimilation system required a modular parallel particle filtering framework which was developed and allowed for a large ensemble size in particle filtering implementation. The application of the proposed system is demonstrated with two case studies at the regional (Columbia River Basin) and the Conterminous United States. Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves drought monitoring and forecasting skills. These results also show how sensitive the seasonal drought forecasting skill is to the initial conditions, which can lead to better facilitation of the state/federal drought preparation and response actions.

No description available.

No description available.

Soil moisture information can be used accurately in determining the timing and amount of irrigation applied to plants. Pan and Pan et al. proposed a robust and simple daily diagnostic equation for estimating daily soil moisture. The diagnostic equation evaluates the relationship between the soil moisture loss function and the summation weighted average of precipitation. The loss function uses the sinusoidal wave function which employs day of the year (DOY) to evaluate the seasonal variation in soil moisture loss for a given year. This was incorporated into the daily diagnostic equation to estimate the daily soil moisture for a location. Solar radiation is an energy source that drives the energy and water exchanges between vegetation and the atmosphere (i.e., evapotranspiration), and thus impacts the soil moisture dry-down. In this paper, two parameters (the actual solar radiation and the clear sky solar radiation) are introduced into loss function coefficient to improve the estimation of soil moisture. After the Introduction of the solar radiation data into soil moisture loss function, a slight improvement was observed in the estimated daily soil moisture. Pan observed that generally the correlation coefficient between the estimated and the observed soil moisture is above 0.75 and the root mean square error is below 5.0 (%v/v). The introduction solar radiation data (i.e. clear sky solar radiation and actual solar) improve the correlation coefficient average for all the sites evaluated by 0.03 when the root mean square error is generally below 4.5(%v/v) for the entire root zone.

An instrument (differential flow water meter) to measure the water vapor concentration in stack gases was developed. This is intended for use as a standard reference as well as a practical method for the determination of the moisture content of stack gases from wood stoves. The accuracy of the instrument was tested by generating gas mixtures with known water vapor content and comparing the measured concentrations with the actual values. Several tests were made under actual operating conditions, i.e., testing the water vapor concentration of stack gases from a wood stove under different firing conditions. The accuracy of the results was further checked by weighing the condensed catch and comparing the measured and predicted values. For each of the tests a wet and dry bulb technique was also used to measure the stack gas moisture content. A comparison of the results obtained using these two methods and the WHA (Wood Heating Alliance) standard method was also done. The results show that the wet and dry bulb method overpredicts the moisture content as compared to the differential flow water meter. / M.S.