Land use mapping of kandi belt of jammu region

Digital image processing on IRS-1C-LISS-III data acquired on October 13, 1998 has been carried out to map the land use classes in part of the Kandi belt, the submontane tract lying in the Outer Himalaya of Jammu region of Jammu and Kashmir. Supervised classification has been combined with rule-based classification to delineate various land use classes. The various categories of land use in the area recognized are forest, agriculture, riverbed, urban, fallow, wasteland and water. Forest is dominant along the upper boundary of the Kandi belt (along Siwalik) and on ridges, whereas, agriculture land is mainly along the lower boundary (along Sirowal) of the study area.     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Characterizing Indian Ocean manganese nodule-bearing seafloor using multi-beam angular backscatter

Application of quantitative angular backscatter modelling to manganese nodule-bearing areas of the Central Indian Ocean Basin (CIOB) has been initiated at NIO during the year 1998. Studies were aimed to establish the suitability of seafloor backscattering in delineating seafloor parameters characteristic of nodule-rich sediments. In this paper, processed Hydrosweep multi-beam backscatter data from 45 spot locations in the CIOB (where nodule samples are available) were analysed to estimate seafloor and sediment volume roughness parameters. The application of a composite roughness model to a nodule-bearing region (6,600 km²) of the CIOB, to determine seafloor interface roughness parameters from a multi-beam backscatter dataset, shows only four power law sets. The results attest 80% of the nodule-bearing seafloor to be smooth in terms of interface roughness parameters at micro-topographic level. The sediment volume roughness parameters are dominant only in 29% of the smooth interface roughness sites. This indicates that 51% of the seafloor area possesses negligible (interface and volume) roughness. A critical analysis using pseudo-side-scan records from 12 selected locations in the study area affirms the combined importance of the seafloor interface and sediment volume roughness parameters for precise determination of manganese nodule abundance.     

Flood frequency under the influence of trends in the Pearl River basin,China: changing patterns,causes and implications

Using a nonstationary flood frequency model, this study investigates the impact of trends on the estimation of flood frequencies and flood magnification factors. Analysis of annual peak streamflow data from 28 hydrological stations across the Pearl River basin, China, shows that: (1) northeast parts of the West and the North River basins are dominated by increasing annual peak streamflow, whereas decreasing trends of annual peak streamflow are prevailing in other regions of the Pearl River basin; (2) trends significantly impact the estimation of flood frequencies. The changing frequency of the same flood magnitude is related to the changing magnitude or significance/insignificance of trends, larger increasing frequency can be detected for stations with significant increasing trends of annual peak streamflow and vice versa, and smaller increasing magnitude for stations with not significant increasing annual peak streamflow, pointing to the critical impact of trends on estimation of flood frequencies; (3) larger‐than‐1 flood magnification factors are observed mainly in the northeast parts of the West River basin and in the North River basin, implying magnifying flood processes in these regions and a higher flood risk in comparison with design flood‐control standards; and (4) changes in hydrological extremes result from the integrated influence of human activities and climate change. Generally, magnifying flood regimes in the northeast Pearl River basin and in the North River basin are mainly the result of intensifying precipitation regime; smaller‐than‐1 flood magnification factors along the mainstream of the West River basin and also in the East River basin are the result of hydrological regulations of water reservoirs. Copyright © 2014 John Wiley & Sons, Ltd.     

At‐a‐station hydraulic geometry relations, 2: calibration and testing

Using a large number of data sets obtained from various sources, the geometric relations derived in Part 1 are calibrated and verified using the split sampling approach. The calibration of parameters shows that the change in stream power is not shared equally among hydraulic variables and that the unevenness depends on the boundary conditions to be satisfied by the channel under consideration. The agreement between the observed values of the hydraulic variables and those predicted by the derived relations is close for the verification data set and lends credence to the hypotheses employed in this study. Copyright © 2007 John Wiley & Sons, Ltd.     

Development of pan coefficients for estimating evapotranspiration from riparian woody vegetation

The long‐term ‘Millennium Drought’ has put significant pressure on water resources across Australia. In southeastern Australia and in particular the Murray‐Darling Basin, removal of exotic, high‐water‐use Salix trees may provide a means to return water to the environment. This paper describes a simple model to estimate evapotranspiration of two introduced Salix species under non‐water‐limited conditions across seven biogeoclimatic zones in Australia. In this study, Salix evapotranspiration was calculated using the Penman–Monteith model. Field measurements of leaf area index and stomatal conductance for Salix babylonica and Salix fragilis were used to parameterize the models. Each model was validated using extensive field estimates of evapotranspiration from a semi‐arid (S. babylonica, r² = 0.88) and cool temperate (S. fragilis, r² = 0.99) region. Modelled mean annual evapotranspiration showed strong agreement with field measurements, being within 32 and 2 mm year^?1 for S. babylonica and S. fragilis, respectively. Monthly pan coefficients (the ratio of mean evapotranspiration to mean pan evaporation) were developed from 30 years of meteorological data, for 30 key reference sites across Australia for both species using the validated Penman–Monteith models. Open‐water evaporation was estimated from field measurements and was used to develop a simple linear regression model for open‐water evaporation across the 30 reference sites. Differences between modelled evapotranspiration and open‐water evaporation at each site provide an indication of the amount of water that might be returned to the environment from removal of in‐stream Salix species. The monthly pan coefficient method reported has application across riparian environments worldwide where measured evapotranspiration is available for model validation. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimating groundwater recharge in lowland watersheds

Little attention has been given to the role of groundwater in the hydrological cycle of lowland watersheds. Our objective in this study was to estimate total recharge to groundwater by analysing water table response to storm events and the rate at which water was transferred into the shallow aquifer. This was conducted at three sites in a rural watershed in the lower Atlantic coastal plain near Charleston, South Carolina, USA. A novel version of the water table fluctuation method was used to estimate total recharge to the shallow aquifer by comparing hourly data of water table position following storm events and measuring water table recession behavior, rather than subjective graphical analysis methods. Also, shallow aquifer recharge rates (vertical fluxes) were estimated using Darcy's Law by comparing static water levels in a water table well and in a shallow piezometer during dry periods. The total annual recharge estimated ranged from 107 ± 39 mm·yr^–1 (5–10% of annual precipitation) at a poorly drained topographic low area to 1140 ± 230 mm·yr^–1 (62–94% of annual precipitation) for a moderately well‐drained upland site. The average aquifer recharge rate was 114 ± 60 mm·yr^–1, which is similar to previous estimations of base flow for the ephemeral third‐order streams in this watershed. The difference in the two methods may have been caused by processes not accounted for in the Darcy flux method, soil moisture deficits, and average evapotranspiration demand, which is about 1100 mm·yr^–1 for this region. Although other factors also can affect partitioning of recharge, an integrated approach to inspecting easily gathered groundwater data can provide information on an often neglected aspect of water budget estimation. We also discuss the effects of land use change on recharge reduction, given a typical development scenario for the region. Copyright © 2011 John Wiley & Sons, Ltd.     

Tropical cyclone prediction over Bay of Bengal: a comparison of the performance of NCEP operational HWRF, NCAR ARW, and MM5 models

Much progress has been made in the area of tropical cyclone prediction using high-resolution mesoscale models based on community models developed at National Centers for Environmental Predication (NCEP) and National Center for Atmospheric Research (NCAR). While most of these model research and development activities are focused on predicting hurricanes in the Atlantic and Eastern Pacific domains, there has been much interest in using these models for tropical cyclone prediction in the North Indian Ocean region, particularly for Bay of Bengal storms that are known historically causing severe damage to life and property. In this study, the advanced operational hurricane modeling system developed at NCEP, known as the Hurricane Weather Research and Forecast (HWRF) model, is used to simulate two recent Bay of Bengal tropical cyclones??Nargis of November 2007 and Sidr of April 2008. The advanced NCEP operational vortex initialization procedure is adapted for simulating these Bay of Bengal tropical cyclones. Two additional regional models, the NCAR Advanced Research WRF and NCAR/Penn State University Mesoscale Model version 5 (MM5) are also used in simulating these storms. Results from these experiments highlight the superior performance of HWRF model over other models in predicting the Bay of Bengal cyclones. These results also suggest the need for a sophisticated vortex initialization procedure in conjunction with a model designed exclusively for tropical cyclone prediction for operational considerations.     

Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields

Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m⁻² and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R² = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m³ ha⁻¹). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.