Impact of dust storm on the atmospheric boundary layer: a case study from western India

Natural Hazards - The present study focuses on investigating the impacts of a sudden dust storm on the atmospheric boundary layer (ABL) over Ahmedabad (23.02°N, 72.57°E), an urban site...     

Evaluation of extreme dust storm over the northwest Indo-Gangetic plain using WRF-Chem model

Natural Hazards - This study uses a high-resolution Weather Research and Forecasting model coupled with the chemistry module (WRF-Chem) to analyze the dust storm that occurred during 12?17...     

Street Versus Rooftop Level Concentrations of Fine Particles in a Cambridge Street Canyon

Dispersion of particles, as evidenced by changes in their number distributions (PNDs) and concentrations (PNCs), in urban street canyons, is still not well understood. This study compares measurements by a fast-response particle spectrometer (DMS500) of the PNDs and the PNCs (5–1000 nm, sampled at 1 Hz) at street and rooftop levels in a Cambridge UK street canyon, and examines mixing, physical and chemical conversion processes, and the competing influences of traffic volume and rooftop wind speed on the PNDs and the PNCs in various size ranges. PNCs at street level were ≈6.5 times higher than at rooftop. Street-level PNCs followed the traffic volume and decreased with increasing wind speed, showing a larger influence of wind speed on 30–300 nm particles than on 5–30 nm particles. Conversely, rooftop PNCs in the 5–30 nm size range increased with wind speed, whereas those for particles between 30 and 300 nm did not vary with wind speed.     

Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: a case application of the new HEC-RAS 5

Surat city of India, situated 100 km downstream of Ukai Dam and 19.4 km upstream from the mouth of River Tapi, has experienced the largest flood in 2006. The peak discharge of about 25,770 m³ s^?1 released from the Ukai Dam was responsible for a disaster. To assess the flood and find inundation in low-lying areas, simulation work is carried out under the 1D/2D couple hydrodynamic modeling. Two hundred ninety-nine cross sections, two hydraulic structures and five major bridges across the river are considered for 1D modeling, whereas a topographic map at 0.5 m contour interval was used to produce a 5 m grid, and SRTM (30 and 90 m) grid has been considered for Surat and the Lower Tapi Basin. The tidal level at the river mouth and the release from the Ukai Dam during 2006 flood are considered as the downstream and upstream boundaries, respectively. The model is simulated under the unsteady flow condition and validated for the year 2006. The simulated result shows that 9th August was the worst day in terms of flooding for Surat city and a maximum 75–77% area was under inundation. Out of seven zones, the west zone had the deepest flood and inundated under 4–5 m. Furthermore, inundation is simulated under the bank protection work (i.e., levees, retaining wall) constructed after the 2006 flood. The simulated results show that the major zones are safe against the inundation under 14,430 m³ s^?1 water releases from Ukai Dam except for the west zone. The study shows the 2D capability of new HEC-RAS 5 for flood inundation mapping and management studies.     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

Fuzzy logic based melting layer recognition from 3 GHz dual polarization radar: appraisal with NWP model and radio sounding observations

The advent of polarimetry makes it possible to categorize hydrometeor inferences more accurately by providing detailed information of the scattering properties. In light of this, the authors have developed a fuzzy logic based system for the recognition of melting layer in the atmosphere. The fuzzy system is based on characterizing melting layer scatterers from non-melting scatterers using five crisp inputs, namely, horizontal reflectivity (Z _H), differential reflectivity (Z _DR), co-polar correlation coefficient (ρ _HV), linear depolarization ratio (LDR) and height of radar measurements (H). For the implementation of melting layer recognition, the study employs the dual polarized signatures from the 3 GHz Chilbolton Advanced Meteorological Radar (CAMRA). Furthermore, a simple but effective averaging procedure for melting level estimation from a volume RHI scan is proposed. The proposed scheme has been evaluated with Weather Research and Forecasting (WRF) model simulated and radio soundings retrieved melting level height over a total of 84 RHI scan-based bright band cases. The results confirm that the estimated melting level heights from the proposed method are in good agreement with the WRF model and radio sounding observations. The 3 GHz radar melting level height estimates correspond with the R ² and RMSE values of 0.92 and 0.24 km, respectively, when compared to the radio soundings, and 0.93 and 0.21 km, respectively, when compared to the WRF model results. Moreover, the related R ² and RMSE values are reported as 0.93 and 0.22 km respectively between the WRF and radio soundings retrievals. This implies that the downscaled WRF modelled melting level height may also be used for operational or research needs.     

Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective

Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting structures by prioritization of mini-watersheds through morphometric analysis. In this study, the morphometric analysis and prioritization of ten mini-watersheds of Malesari watershed, situated in Bhavnagar district of Saurashtra region of Gujarat state, India, are studied. For prioritization of mini-watersheds, morphometric analysis is utilized by using the linear parameters such as bifurcation ratio, drainage density, stream frequency, texture ratio, and length of overland flow and shape parameters such as form factor, shape factor, elongation ratio, compactness constant, and circularity ratio. The different prioritization ranks are assigned after evaluation of the compound factor. Digital elevation model from Shuttle Radar Topography Mission, digitized contour, and other thematic layers like drainage order, drainage density, and geology are created and analyzed over ArcGIS 9.1 platform. Combining all thematic layers with soil and slope map, the best feasibility of positioning check dams in mini-watershed has been proposed, after validating the sites through the field surveys.     

Modelling of land use land cover change using earth observation data-sets of Tons River Basin,Madhya Pradesh,India

Abstract

An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed to classify the satellite images of year 1985, 1995, 2005 and 2015 to meet the magnitude of LULC change. Results showing the spatial pattern of the sub-basin is largely influenced by the biophysical and socio-economic drivers leading to growth of agricultural lands and built-up area in the basin. Simulated plausible future LULC changes for 2025 which is based on a CA MARKOV that integrates Markovian transition probabilities computed from satellite-derived LULC maps and a CA contiguity spatial filter (5 × 5). Further, the fragmentation analysis was performed to check the fragmentation scenario in the year 2025. The result for year 2025 with reasonably good accuracy will be useful to the planners, policy- and decision-makers.     

Above Ground Forest Phytomass Assessment in Southern Gujarat

Spectral modeling of above ground biomass (AGB) with field data collected in 48 field sites representing moist deciduous forest in Surat district is reported. Models were generated using LISS-III and MODIS data. The plot-wise field data was aggregated to MODIS pixel (250 m) using area weightages of forest/vegetation. The study reports that above ground phytomass varied from 6.13 t/ha to 389.166 t/ha while AGB phytomass estimated using area-weights for sites of 250×250 m, ranged from 5.534 t/ha to 134.082 t/ha. The contribution of bamboo in AGB has been found very high. The analysis indicated that the highest correlation between AGB phytomass and red band (R) of MODIS satellite data of October was (R²=0.7823) and R²=0.6998 with both NDVI of October data as well as NDVI_max. High correlation (R²=0.402) with IR band of February month was also found. The phytomass range obtained by using MODIS data varies from 0.147 t/ha to 182.16 t/ha. The mean biomass is 40.50 t/ha. Total biomass is 31.44 Mt. The mean Carbon density is 19.44 tC/ha in forest areas. The study is validation of region-wise spectral modeling approach that will be adopted for mapping vegetation carbon pool of the India under National Carbon Project of ISRO-Geosphere Biosphere Programme.     

Impact of ECMWF,NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian Region

The global model analysis has significant impact on the mesoscale model forecast as global model provides initial condition (IC) and lateral boundary conditions (LBC) for the mesoscale model. With this objective, four operational global model analyses prepared from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS), NCEP Global Forecasting System (GFS), and National Centre for Medium Range Weather Forecasting (NCMRWF) are used daily to generate IC and LBC of the mesoscale model during 13th December 2012 to 13th January 2013. The Weather Research and Forecasting (WRF) model version 3.4, broadly used for short-range weather forecast, is adopted in this study as mesoscale model. After initial comparison of global model analyses with Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles, daily WRF model forecasts initialized from global model analyses are compared with in situ observations and AIRS profiles. Results demonstrated that forecasts initialized from the ECMWF analysis are closer to AIRS-retrieved profiles and in situ observations compared to other global model analyses. No major differences are occurred in the WRF model forecasts when initialized from the NCEP GDAS and GFS analyses, whereas these two analyses have different spatial resolutions and observations used for assimilation. Maximum RMSD is seen in the NCMRWF analysis-based experiments when compared with AIRS-retrieved profiles. The rainfall prediction is also improved when WRF model is initialized from the ECMWF analysis compared to the NCEP and NCMRWF analyses.