Spatial assessment and redesign of a groundwater quality monitoring network using entropy theory, Gaza Strip, Palestine

Using entropy theory, a methodology was developed for the evaluation and redesign of groundwater quality monitoring wells in the Gaza Strip in Palestine. Essential to the methodology is the development of a Transinformation Model (TM) which yields the amount of information transfer and the dependency between the wells as a function of distance. The TM parameters, such as the minimum transinformation and the range, were employed for evaluating the network which revealed that most of the distances between wells were less than the range. It also indicated that a high percentage of redundant information existed in the network. Therefore, the network was reduced by superimposing a square pattern over the monitored area and selecting one well per square block in a stratified pattern. The methodology was tested using the chloride data collected from 1972–2000 from 417 groundwater quality monitoring wells in the Gaza Strip. The number of the groundwater quality monitoring wells in the Gaza Strip was reduced by 53%, while there was 26% redundant information based on the minimum existing distance between wells. This methodology is meant to help monitor the groundwater quality (salinity) in the Gaza Strip.     

Operational feasibility of neural-network-based radar rainfall estimation

An operational radar rainfall estimation system based on the adaptive radial basis function (RBF) neural network is developed. During the process of training and cross validation, the rainfall estimation was computed only at the gauge locations. Once the training is done, the radar rainfall estimation based on neural networks is applied to the full coverage area of the radar. Such large-scale application of the rainfall estimate poses several questions in the context of operational applications. This letter addresses two of those questions, namely: 1) the feasibility of adaptively updating RBF neural network models on a daily basis and 2) the ability of neural network radar rainfall estimation at high spatial resolution within reasonable and practical time frame for operational applications. Using the datasets collected by WSR-88D radar located in Melbourne, FL, it is demonstrated that radar-based rainfall estimation using an adaptive RBF neural network is feasible. The results show that 73% of overnight updating for the RBF neural network can be completed within 2 h, and the estimation over an area of 100 km/spl times/100 km can be generated within the time frame (a few tens of seconds-150 s), which is much smaller than the average radar volume scan time.     

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.     

A GIS numerical framework to study the process basis of scaling statistics in river networks

A new geographic information system (GIS) numerical framework (NF), called CUENCAS, for flows in river networks is presented. The networks are extracted from digital elevation models (DEMs). The program automatically partitions a basin into hillslopes and channel links that are required to correspond to these features in an actual terrain. To investigate the appropriate DEM resolution for this correspondence, we take a high-resolution DEM at 10-m pixel size, and create DEMs at eight different resolutions in increments of 10 m by averaging. The extracted networks from 10-30 m remain about the same, even though there is a tenfold reduction in the number of pixels. By contrast, the extracted networks show increasing distortions of the original network from 40-90 m DEMs. We show the presence of statistical self-similarity (scaling) in the probability distributions of drainage areas in a Horton-Strahler framework using CUENCAS. The NF for flows takes advantage of the hillslope-link decomposition of an actual terrain and specifies mass and momentum balance equations and physical parameterizations at this scale. These equations are numerically solved. An application of NF is given to test different physical assumptions that produce statistical self-similarity in spatial peak flow statistics in a Horton-Strahler framework.     

Estimation of ship velocities from MODIS and OCM

Detection of ships and their tracks in the atmosphere from satellites was earlier demonstrated by Porch, Noone, and Kaufman, among others. In this letter, we have gone one step further to estimate the ship speed and direction by locating them and their tracks from multisatellite imagery. Exhausts from the ships create streaks of clouds in the atmosphere that help identify the same ship from two satellites. Ship velocities are estimated from displacements of ships. We have used optical sensors data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Color Monitor (OCM) to demonstrate this technique. Estimated velocities of ships are within the expected range. Application of this approach has general interest to the navy, coast guards, shipping corporations, commercial ship owners, and fishermen. More satellite observations can be used to continuously monitor the ship velocities.     

Eddies and planetary waves in the Central Arabian Sea

Eddies and planetary waves are identified as one of the important factors that control the dynamics of the Arabian Sea. During 10–14 January 1990, Ignat, Paulyuchenkov (USSR ship) conducted an experiment in the central Arabian Sea and of late TOPEX/POSEIDON satellites collected data on sea surface height (SSH) anomalies of the Arabian Sea. These data sets give an opportunity to understand the characteristic of eddies and planetary waves in this region during winter. The geostrophic flow revealed three anticyclonic and two cyclonic eddies of diameters ranging from 75 to more than 150 km from surface to subsurface levels. Current speeds around different eddies were maximum at surface and varied from 9 cm/s to 25 cm/s (at the middle point between the center and periphery). The occurrence of eddies were further investigated with the TOPEX/POSEIDON altimetry for the years 1993–97. The analysis revealed multiple eddies of diameter 100 to 550 km occur every year with maximum number of eddies during 1997 and minimum during 1995. The calculated speed varied between 8–30 cm/s around various eddies. Longitude-Time plots showed annual Rossby waves generating at the eastern Arabian Sea and propagating westwards with a phase speed of ~ 10 cm/s along 16° N. Further, it was observed that these waves arrived in the study area by January. In addition, another positive anomaly of SSH was found generating at the western Arabian Sea simultaneously and extended up to the study region by April–June. Time series of SSH at selected locations along 16°N revealed many small-scale oscillations and their spatial variability. These oscillations were delineated using the FFT analysis. Other than the Rossby wave, the major components at the study region were 40–60 and 26–32 day oscillations. The implications of these long period waves associated with eddies are discussed.     

Simulating the effect of Nitrogen application on Wheat Yield by linking remotely sensed measurements with wtgrows simulation model

A field experiment was conducted on wheat at New Delhi with five treatments of Nitrogen (N) fertilizer application (0, 30, 60, 90 and 120 kgha^-1). Relationship has been established between observed leaf area index (LAI) and remotely sensed vegetation indices. These relationships are inverted and used for predicting LAI from vegetation indices on different days after sowing. The “re-initialization” strategy is implemented in model WTGROWS in which initial conditions of model are changed so that the model simulated LAI match remote sensing predicted LAI. The model performance with re-initialization has been evaluated by comparing the simulated grain yield and total above-ground dry matter (TDM) values with the actual observations. The results show that in-season re-initialization is effective in model course correction by improving the simulated results of yield and TDM for different N treatments even though the model was run with no N stress condition. Model re-initialization at different days shows that the closer is the day of re-initialization to crop anthesis the more effective is model course correction. Also, the treatment showing maximum error in yield simulation without re-initialization shows maximum reduction in error by re-initialization. The approach shows that the remote sensing inputs can substitute for some of the inputs or errors in inputs required by crop models for yield prediction.