River basin hydrosalinity modeling
ABSTRACT Tanji, K.K., (1981). River basin hydrosalinity modeling. Agric. Water Manage., 1981. Hydrologic-salinity modeling at the river sub-basin and basin spatial scales in the Western U.S.A. are evaluated. In the past two decades, significant advances have been made in basin hydrosalinity modeling. These models are providing increased understanding of the complexity of water and salt flows in large-scale systems. Most models, however, have been developed for specific problems and do not lend themselves for application to a wide variety of salinity problems. Much more work is needed in the areas of field verification of models and increasing their utility for management and policy-making decisions.
Assessment of Long-Term Salinity Changes in an Irrigated Stream-Aquifer System
Changes in salinity in groundwater and surface water in the Arkansas River valley of southeastern Colorado are primarily related to irrigation practices. A solute transport model was applied to an 11-mile reach of the valley to compute salinity changes in response to spatially and temporally varying stresses. The model was calibrated in 1973 using detailed field measurements made during 1971 and 1972. In 1973 the calibrated model was used to predict that a gradual long-term increase in groundwater salinity of about 2–3% per year would occur if the observed irrigation practices continued. The study area was resampled during the winter of 1982 to help evaluate if any long-term changes in salinity are actually occurring. Nonparametric and parametric statistical tests were used to help assess the significance of observed changes in groundwater salinity. These tests indicate that a statistically significant increase in salinity occurred between the winters of 1971 and 1972 (the model calibration period). However, a comparison of the winter 1972 and winter 1982 data indicates that no significant net change in salinity has occurred during this 10-year period. An analysis of the few available historical data (1895, 1923, 1959–1961, and 1964) supports the hypothesis that groundwater salinity in this irrigated area has reached a long-term dynamic equilibrium in response to irrigation practices. The model predictions of long-term salinity increases were invalid probably because the calibration period occurred during a short-term annual trend of increasing salinity in the river (and hence in leaky irrigation canals and in applied irrigation water), which was not representative of the long-term trend.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.