Cloud bottom altitude determination from a satellite

The letter is devoted to the introduction of a new technique to derive the cloud bottom height (CBH) from satellite measurements of the cloud reflectance in the oxygen A-band. The information on the cloud top height needed in the retrieval of the CBH must be obtained from separate measurements to insure small biases in the retrieved CBH. Such measurements can be performed by a space-based lidar.     

Effects of detailed soil spatial information on watershed modeling across different model scales

Hydro-ecological modelers often use spatial variation of soil information derived from conventional soil surveys in simulation of hydro-ecological processes over watersheds at mesoscale (10–100 km²). Conventional soil surveys are not designed to provide the same level of spatial detail as terrain and vegetation inputs derived from digital terrain analysis and remote sensing techniques. Soil property layers derived from conventional soil surveys are often incompatible with detailed terrain and remotely sensed data due to their difference in scales. The objective of this research is to examine the effect of scale incompatibility between soil information and the detailed digital terrain data and remotely sensed information by comparing simulations of watershed processes based on the conventional soil map and those simulations based on detailed soil information across different simulation scales. The detailed soil spatial information was derived using a GIS (geographical information system), expert knowledge, and fuzzy logic based predictive mapping approach (Soil Land Inference Model, SoLIM). The Regional Hydro-Ecological Simulation System (RHESSys) is used to simulate two watershed processes: net photosynthesis and stream flow. The difference between simulation based on the conventional soil map and that based on the detailed predictive soil map at a given simulation scale is perceived to be the effect of scale incompatibility between conventional soil data and the rest of the (more detailed) data layers at that scale. Two modeling approaches were taken in this study: the lumped parameter approach and the distributed parameter approach. The results over two small watersheds indicate that the effect does not necessarily always increase or decrease as the simulation scale becomes finer or coarser. For a given watershed there seems to be a fixed scale at which the effect is consistently low for the simulated processes with both the lumped parameter approach and the distributed parameter approach.     

Comparison of ERS-SAR and spot data for sugar beet crop cover assessment

The present work was aimed to compare the abilities of radar and optical satellite data to estimate crop canopy cover, which is a key component of productivity estimates. Three ERS-1 SAR images were obtained of East Anglia (UK) in 1995 and one ERS-2 SAR image in 1996. The images covered a study area around the IACR Brooms Barn Sugar Beet Research Institute. Field data comprising radiometric and biophysical measurements of the crop canopy were collected in two fields from June 22 to August 3, 1995 to coincide with ERS-1 SAR overpass dates. In 1996, field data were collected in two fields from June 11 to July 29 on a weekly basis. A previously calibrated version of the water cloud model was inverted to estimate Leaf Area Index (LAI) from ERS-1 and ERS-2 SAR backscatter and soil moisture samples. Canopy cover was estimated from the radar-estimated LAI using a standard exponential relationship that has a well-established coefficient for sugar beet. Radio-metrically and atmospherically corrected data from three SPOT images in 1995 and one SPOT image in 1996 were used to calculate the Optimised Soil Adjusted Vegetation Index (OSAVI), from which crop canopy cover was estimated using a relationship determined previously by canopy modelling. The crop cover values estimated by satellite were in good agreement with those measured on ground with the Parkinson radiometer. Radar data may be able to provide useful estimates of canopy cover for crop production modelling, especially in the case of loss of optical data due to cloud.     

Possible Effects of Global Warming on Agriculture and Water Resources in Saudi Arabia: Impacts and Responses

This study assesses the possible impact of climatic change on Saudi Arabia's agriculture and water supplies using climatic change scenarios from GCMs (General Circulation Models) and related research. The resulting assessment indicates that an increase in temperature and decrease in precipitation could have a major negative impact on agriculture and water supplies in Saudi Arabia. To find signs of climatic change in Saudi Arabia a preliminaryassessment of systematic changes in temperature and precipitation was made, based on the records of four Saudi weather stations. The analysis of this data, which dates back to 1961, shows no discernable signs of climatic change during the study period. Such data is, however, limited both spatially and temporally and cannot provide conclusive evidence to confirm climatic changes projected by GCMs. Nevertheless, in the light of recent climatic conditions and rapid population growth, Saudi decision-makers are urged to adopt a `no regret' policy. Ideally, such a policy would include measures to avoid future environmental or socioeconomic problems that may occur in the event of significant climatic change.     

Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results

Summary The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed “super sites”, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity. Received November 18, 2001 Revised December 28, 2001     

Low-frequency oscillations in climatic and hydrological variables in southern South America’s tropical-subtropical regions

Summary The existence of low-frequency variability in climatic-hydrological-oceanic variables may be useful for long-term forecasting and climate modelling. By using long time series this paper attempts to identify large-scale quasi-cycles in the precipitation regimes of Northern Argentina, moisture advection from the Atlantic Ocean and the streamflow of the Paraná River. This work also shows the presence of coherent waves with long periodicity between the three series. As the three variables are estimated over different time intervals, the presence of waves in each variable is studied separately, to show they respond to the same process. The three variables show significant interdecadal variability at low frequency (22–26 years), which might be related to the ENSO cycle modulation and to the intensification or weakening of the South American Low Level Jet (SALLJ) and South Atlantic Convergence Zone (SACZ).