Vulnerability and adaptation to climate variability and water stress in Uttarakhand State, India

This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions.     

Variation of ambient SO2 over Delhi

The temporal variation of ambient SO₂ and the chemical composition of particulate matters (PM_2.5 and PM₁₀) were studied at National Physical Laboratory (NPL), New Delhi (28°38′N, 77°10′E). Spatial variation of SO₂ at seven air quality monitoring stations over Delhi was also studied simultaneously. Wide range of ambient SO₂ was recorded during winter (2.55 to 17.43 ppb) compare to other seasons. SO₂ mixing ratio was recorded significantly high at industrial sites during winter and summer; however, no significant spatial difference in SO₂ mixing ratio was recorded during monsoon. SO ₄ ^2? /(SO₂+SO ₄ ^2? ) ratio was recorded high (0.74) during winter and low (0.69) during summer. Monthly variation of PSCF was analyzed using HYSPLIT seven days backward trajectories and daily average SO₂ data. PSCF analysis suggests that, during winter (December, January, February) ambient SO₂ at the study site might have contributed from long distance sources, located towards west and southwest directions; during monsoon (July, August, September) marine contribution was noticed; whereas, during summer (April, May and June) it was from regional sources (located within few 100 km of study site). During winter there was significant contribution from the long distance sources located in western Asia, northwestern Pakistan, Rajasthan and Punjab provinces of India. Coal used in thermal power plants at Panipat (in the northwestern side) and Faridabad (in the southeastern side), local industries, soil erosion and biomass burning may be major contributing factors for SO₂ during summer. The study establishes that the transport sector may not be the major source of ambient SO₂ in Delhi.     

Seasonal characteristics and sources of carbonaceous components and elements of PM10 (2010–2019) in Delhi,India

In this study we present the seasonal chemical characteristics and potential sources of PM₁₀ at an urban location of Delhi, India during 2010?2019. The concentrations of carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC) and water insoluble organic carbon (WIOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Pb, Cr, F, Cl, Br, P, S, K, As, Na, Mg, Ca, B, Ni, Mo, V, Sr, Zr and Rb) in PM₁₀ were estimated to explore their possible sources. The annual average concentration (2010–2019) of PM₁₀ was computed as 227?±?97 µg m^?3 with a range of 34?734 µg m^?3. The total carbonaceous aerosols in PM₁₀ was accounted for 22.5% of PM₁₀ mass concentration, whereas elements contribution to PM₁₀ was estimated to be 17% of PM₁₀. The statistical analysis of OC vs. EC and OC vs. WSOC of PM₁₀ reveals their common sources (biomass burning and/or fossil fuel combustion) during all the seasons. Enrichment factors (EFs) of the elements and the relationship of Al with other crustal metals (Fe, Ca, Mg and Ti) of PM₁₀ indicates the abundance of mineral dust over Delhi. Principal component analysis (PCA) extracted the five major sources [industrial emission (IE), biomass burning?+?fossil fuel combustion (BB?+?FFC), soil dust, vehicular emissions (VE) and sodium and magnesium salts (SMS)] of PM₁₀ in Delhi, India. Back trajectory and cluster analysis of airmass parcel indicate that the pollutants approaching to Delhi are mainly from Pakistan, IGP region, Arabian Sea and Bay of Bengal.

     

Trend analysis of rainfall time series for Sindh river basin in India

The main goal of this paper is to estimate a set of optimal seasonal, daily, and hourly values of atmospheric turbidity and surface radiative parameters Ångström’s turbidity coefficient (β), Ångström’s wavelength exponent (α), aerosol single scattering albedo (ω_o), forward scatterance (F_c) and average surface albedo (ρ_g), using the Brute Force multidimensional minimization method to minimize the difference between measured and simulated solar irradiance components, expressed as cost functions. In order to simulate the components of short-wave solar irradiance (direct, diffuse and global) for clear sky conditions, incidents on a horizontal surface in the Metropolitan Area of Rio de Janeiro (MARJ), Brazil (22° 51′ 27″ S, 43° 13′ 58″ W), we use two parameterized broadband solar irradiance models, called CPCR2 and Iqbal C, based on synoptic information. The meteorological variables such as precipitable water (u_w) and ozone concentration (u_o) required by the broadband solar models were obtained from moderate-resolution imaging spectroradiometer (MODIS) sensor on Terra and Aqua NASA platforms. For the implementation and validation processes, we use global and diffuse solar irradiance data measured by the radiometric platform of LabMiM, located in the north area of the MARJ. The data were measured between the years 2010 and 2012 at 1-min intervals. The performance of solar irradiance models using optimal parameters was evaluated with several quantitative statistical indicators and a subset of measured solar irradiance data. Some daily results for Ångström’s wavelength exponent α were compared with Ångström’s parameter (440–870 nm) values obtained by aerosol robotic network (AERONET) for 11 days, showing an acceptable level of agreement. Results for Ångström’s turbidity coefficient β, associated with the amount of aerosols in the atmosphere, show a seasonal pattern according with increased precipitation during summer months (December–February) in the MARJ.     

A new approach to verify the model core with idealized experiments

Summary ?A newly developed ocean general circulation model has been tested and verified with some idealized experiments. Generally two types of idealized experiments have been done here. First types are called as “symmetric experiments” and second types are called as “transport experiments”. The first types of experiment help to correct the model core and any deficiency from boundary conditions. The second types of experiment are the type of validation experiment. In both the experiments there are no continents, so in the first type of experiments where symmetric forcings are provided one can expect that model should maintain the symmetric nature. In the second type of experiments one can expect that model should respond correctly to the wind forcings, if no wind curl is present in the wind forcing there will be no circulation in the extratropics and if there is no wind the equator there will be no circulation. The model reproduces the possible envisaged results of these experiments and gives the confidence for performing the realistic integration. Received February 20, 2002; accepted July 7, 2002 Published online: February 20, 2003     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Intercomparison of Oceansat-2 and ASCAT Winds with In Situ Buoy Observations and Short-Term Numerical Forecasts

Sea surface winds from the Oceansat-2 scatterometer (OSCAT) are important inputs to Numerical Weather Prediction (NWP) models. The Indian Space Research Organization (ISRO) recently updated the OSCAT retrieval algorithm in order to generate better products. An attempt has been made in this study to evaluate the updated OSCAT winds using buoy observations and the 6-hour short-term forecasts from the T574L64 model from the National Centre for Medium Range Weather Forecasting (NCMRWF) during the 2011 monsoon. The results of the OSCAT evaluation are also compared with those from the Advanced Scatterometer (ASCAT) on-board the Meteorological Operational Satellite-A (MetOp-A) which were evaluated in the same way. The root mean square differences (RMSDs) for wind speed and direction, are within 2?m?s^?1 and 20° for both scatterometers. The RMSDs for OSCAT are slightly higher than those for ASCAT, and this difference may be attributed in part to the difference in frequency and resolution of the scatterometer payloads. The bias and standard deviation for ASCAT winds are also lower than those for OSCAT winds with respect to the model short-range forecast, and this can be attributed to the regular assimilation of ASCAT winds in the model.     

On radiative forcing of sulphate aerosol produced from ion-promoted nucleation mechanisms in an atmospheric global model

A significant fraction of the total number of particles present in the atmosphere is formed by nucleation in the gas phase. Nucleation and the subsequent growth process influence both number concentration of particles and their size distribution besides chemical and optical properties of atmospheric aerosols. Sulphate aerosol nucleation mechanisms promoted by ions have been evaluated here in a tropospheric interactive chemistry-aerosol module for mass and number concentration in a global atmospheric model. The indirect radiative forcing of sulphate particles is assessed in this model; indirect radiative forcing is different for ion-induced (IIN) and ion-mediated (IMN) mechanisms. The indirect radiative forcing in 10-year simulation runs has been calculated as ?1.42?W/m² (IIN) and ?1.54?W/m² (IMN). The 5% emission of primary sulphate particles in simulations changes the indirect radiative forcing from ?1.42 to ?1.44?W/m² for IIN case, and from ?1.54 to ?1.55 W/m² for the IMN case. More precisely, owing to greater nucleation rates, IMN mechanisms produces greater cooling than the IIN mechanisms in the backdrop that both mechanisms produce almost identical distribution of CDNC in their pre-industrial runs. The inclusion of primary particles in simulations with IIN and IMN mechanisms increases both CDNC and the indirect radiative forcing.