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Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

Providing safe drinking water for the entire world's population is essential for ensuring sustainable development. The presence of harmful compounds in aquifers, majorly toxic elements, is a serious environmental concern around the globe. This research aimed to quantify for the initial period the amounts of toxic elements in freshwater in the Dehradun Industrial Region of Uttrakhand, India, as well as the associated health risks. The PTEs (potentially toxic elements) Fe, Cd, Mn, Cu, Cr and Pb, Zn, Ni is measured by AAS and compared to BIS and WHO requirements for drinking safety. The order of mean trace element values in all groundwater samples were determined as Fe > Zn > Cu > Ni > Co > Cd > Pb. HPI was discovered to be higher than high class during the research period (HPI > 30), but under the severe contamination criterion of 100. Iron's MI and PI values were consistently over the threshold limit during the research period, and certain toxic elements were discovered exceptionally near the threshold limit, indicating a severe future influence on groundwater quality. According to PCA (principal component analysis), CM (correlation matrix), and potential health hazard, maximum levels of toxic elements in groundwater in the Dehradun region are attributed to land use patterns, anthropogenic activity, industrial activity, fertilizer and pesticide leaching, and residential waste into the aquifer system. The findings of this study could aid local planners and policymakers in preventing health risks from contaminated aquifers through the deployment of suitable monitoring and mitigation measures. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) estimates are often unavailable at fine spatial resolutions due to the lack of dense climatic networks. The present study was taken up to develop a methodology for obtaining a gridded Penman-Monteith (PM) ET0 dataset for Karnataka State, India, through spatial interpolation of estimates obtained at climate stations. Three interpolation techniques, Inverse Distance Weighted (IDW), Kriging, and Point-Based Sentinel Hospitals Area Distance (P-BSHADE), were employed to derive PM ET0 estimates. Considering the historical climate records of 67 stations located in Karnataka State for the period of 2006–2016, the interpolation methodologies were evaluated using a cross-validation procedure. Results showed that the performance of IDW was better with significant error reduction at validation sites. Subsequently, the IDW algorithm was used to derive daily PM ET0 values at 0.25° × 0.25° grids over the Karnataka State, India. Finally, the comparative analysis of developed PM ET0 gridded data product against three global ET0 products indicated the developed gridded ET0 to be most accurate across different agroclimatic zones. Web links of the developed product have been created in an effort to share the data on ET0, which is a critical input in a variety of studies in earth sciences. [ABSTRACT FROM AUTHOR]

In the recent era of globalization and climatic uncertainties, farmers are continuously adopting new strategies and crops to ensure substantial production and income. However, these adaptations always need to be based on systematic decisions; otherwise, the entire environment may degrade. The present study is one of such attempts to understand where to adopt an emerging crop (Maize) in lower Gangetic West Bengal. The decision on suitable sites was taken based on the analysis of fourteen important indicators in a GIS integrated fuzzy-MCE environment aided with different geostatistical interpolation models. The output shows that 18 and 30% of the total agricultural area of the study region is highly suitable and suitable for Maize cultivation following the FAO-based suitability classification arena. Spatially, it can be said that the farmers from a few blocks of the study area, namely Chanchal-I, II, Harishchandrapur I, II, Lalgola, Bamangola, Ratua-I, II, Suti, Burwan, etc., can ensure a sustainable profit by adopting Maize cultivation. [ABSTRACT FROM AUTHOR]

A statistically sufficient number of rainfall estimates is necessary to model precipitation-induced landslide hazards accurately. This is particularly important where existing gauges are scanty and widespread, such as in Kerala State of southern India, an area characterized by heavy monsoon rains and thus inherently prone to massive landslides. To identify potential new gauge sites, we evaluated general slope stability, landslide density, and land cover. We optimized locations for installing new rain gauges through a sequential process based on interpolation errors; this was validated by comparing the resulting statistics with a random selection. Based on these procedures, we installed eight new rain gauges. We also used a satellitegauge algorithm (conditional merging) to assess the effect of rain gauge network expansion for precipitation measurements. The Pearson correlation coefficient indicated statistically significant measurement improvement after the new rain gauge installation. We applied the leave-one-out cross-validation (LOOCV) test to improve rain gauge modeling. We divided the study area into rain gauge influence sections and calculated the root mean square error (RMSE) for each. We found that the optimized expanded rain gauge network locally produced a 20–25 percent reduction of RMSE compared with the original gauge distribution. [ABSTRACT FROM AUTHOR]

About 23% of the surface area and 44% of the volume of all the lakes are occupied by saline lakes in the world. Importantly, agricultural diversion, illegal encroachment, pollution, and invasive species could cause these lakes to dry up completely or partially by 2025. Illegal saltpan encroachment is causing Sambhar, India's largest saline lake, to shrink by 4.23% every decade. This study aims to characterize the soil parameters where halophytes are growing. A literature survey was conducted for halophytes and soil characteristics. The study area was divided into four zones for stratified random sampling. Soil sampling was conducted in February 2021. The soil indicators for halophyte selected were pH, electrical conductivity, moisture, salinity, organic carbon, and organic matter. The obtained results were interpolated in the geospatial platform for soil characteristic mapping. It is found that no research is conducted on halophytes of the lake. Studies on soil are also inconsistent and only six common parameters could be identified. Results show that the pH ranged 9.37–7.66, electrical conductivity was 16.1–0.38, moisture 23.37%–1.2%, organic carbon 3.29%–0.15%, organic matter 5.6%–0.2%, and salinity 8.86%–0.72%. Though these results show improved condition as compared to last few years, in long term, the lake is desiccating. During the UN Decade of Ecosystem Restoration (2021–2030), if these causes are not addressed, the ecosystem may completely dry up. [ABSTRACT FROM AUTHOR]

This study characterises the quality of groundwater for the Ludhiana district of Punjab, India by analysing water samples collected from 152 locations spread across 3767 km2. The samples were analysed for 18 parameters consisting of pH, EC, TDS, TA, TH, major anions and cations. The parameter values have been used to calculate the drinking water quality index of the study area which suggests that 2.6, 57.9, 32.9, 4 and 2.6% of the samples fall under the excellent, good, poor, very poor and unsuitable categories, respectively. The sequence of abundance for ions (in meq/l) as revealed from the laboratory tests is Na+ (37.1%) > Ca2+ (30.8%) > Mg2+ (29.1%) > K+ (2.8%) for cations and HCO3− (80%) > Cl¯ (8.9%) > CO32− (6.5%) > SO42− (3.9%) > NO3−, F−, PO43− (< 1%) for anions. The spatial variability of these parameters has been depicted through the use of interpolation maps. Evaluation of different ionic ratios indicates that carbonate weathering and silicate weathering are both significantly affecting the groundwater chemistry with a slight dominance of carbonate weathering. Also, the ion exchange process is taking place in the area as confirmed by CAI index values. In terms of saturation index, the groundwater is undersaturated with respect to halite, fluorite and sylvite, whereas it is supersaturated for calcite, dolomite and aragonite minerals. The principal components in PCA explained 75.4% of the total variance with 29.1 and 28.3% contributions from PC1 and PC2. Both of these components indicate towards the geogenic and anthropogenic influence on groundwater mineralization of the area. The analysis suggests that groundwater for the study area is suitable for drinking in most of the region expect in a few places. Such a study could be used to understand the current status of groundwater quality in the area, the results of which can be used to prevent further contamination and sustain the resource for the future. [ABSTRACT FROM AUTHOR]

Dental and skeletal fluorosis caused by consuming high-fluoride groundwater has been reported over several decades globally. Prediction maps to estimate the fluoride contaminated area rely on interpolation methods. This study presents a comparison of the accuracy of nine spatial interpolation methods in predicting the fluoride in groundwater. Leave-one-out cross-validation (LOOCV), hold-out validation and validation with an independent dataset were used to assess the precision of the interpolation methods. This is the first study on fluoride with a large dataset (N = 13,585) applied at the regional level in India. Our findings showed that the inverse distance weighted (IDW) algorithm outperformed other methods in terms of less discrepancy between measured and predicted fluoride. IDW and local polynomial interpolation (LPI) were the only methods to predict contaminated areas (fluoride > 1.5 mg/L). However, the area estimated by the typical assessment of the percentage of unsuitable samples was much higher (6.1%) compared to that estimated by IDW (0.2%) and LPI (0.2%). LOOCV provided viable results than the other two validation methods. Interpolation methods are accompanied with uncertainty which are regulated by the sample size, sample density, sample distribution, minimum and maximum measured concentrations, smoothing and border effects. Drawing a comparison among variegated interpolation methods capturing a wide range of prediction uncertainty is suggested rather than relying on one method exclusively. The high-fluoride areas identified in this study can be used by the Government in planning remediation actions. [ABSTRACT FROM AUTHOR]

Forests absorb atmospheric carbon and hence play a vital role in carbon sequestration and climate regulation. Recent research emphasizes developing technology and methods to understand the carbon sequestration potential in various forest ecosystems. Forest stand height estimation is one of the crucial parameters in allometry that estimates forest biomass. An attempt is made in this study to map forest stand height in tropical and sub-tropical forests in India using recently launched spaceborne LiDAR platforms Ice Cloud and Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). A geostatistical kriging approach is used to interpolate the forest stand height, and the generated stand height surface is validated using ground truth samples. The results showed that GEDI data performed better with an RMSE of 3.99 m and 2.62 m in tropical forests than the ICESat-2 data, which showed an RMSE of 5.71 m and 5.08 m, respectively. A similar pattern was observed in sub-tropical forests where GEDI modelled stand height outperformed ICESat-2 modelled stand height. This analysis demonstrates the potential of existing spaceborne LiDAR platforms in interpolating forest stand height at different forest types. Also, the research emphasizes the necessity of a high density of LiDAR footprints spread in both across- and along-track directions for accurate interpolation of forest stand height. [ABSTRACT FROM AUTHOR]

The spatial distribution of soil pH, electrical conductivity, available nitrogen, phosphorus and potassium concentrations at Anaimalai block of Coimbatore district, India was interpolated by geostatistical analysis to create digital maps of soil NPK that renders numerous information for agro based policy making. 1627 soil samples in 30,700 acres were gathered by composite soil sampling method and rendered interpolation analysis to predict the soil parameters at unsampled locations. Based on geostatistical, deterministic and interpolation with barriers methods 13 interpolation models were generated and compared to validate the precision. Interpolation reveals the deficiency of soil available nitrogen and phosphorus whereas available potassium was in sufficient range. Radial basis function, inverse distance weighting, probability kriging, empirical Bayesian kriging and local polynomial interpolations were validated by linear correlation method that reveals the precisions of models with R2 values of 0.74, 0.82, 0.98, 0.89 and 0.79 (soil pH, EC, nitrogen, phosphorus and potassium respectively). [ABSTRACT FROM AUTHOR]

In this study, a quantitative assessment of spatial extent of arid and semi-arid climatic zones of India was performed for the period from 1988 to 2018 using potential evapo-transpiration (PET) calculated by Modified Penman Method, estimated from global climate data sets. Climatic water balances computed for 625 stations across the country are used for classifying to bio-climate types based on moisture index and areas falling under arid climatic zones in India are delineated using ArcGIS 10.5. It was noticed a considerable changes in the country's arid and semi-arid climatic zones between the two periods; 1992 and 2018. Overall, there has been a net percent change in hyper arid, typic arid and semi arid (dry) areas is 5.62, 1.62 and 7.17 percent, respectively. Dryness and wetness are increasing in different parts of the country. There is also change in rainfall, PET and moisture index over a period of time which is vital to determine aridity pattern of any region. Thus, results are of great significance for studying the assessment of temporal and spatial dry climatic water balance of India, which can help immensely in the management of water resources and sustainability of crop production under changing climatic conditions. [ABSTRACT FROM AUTHOR]

The temporal variation in maximum (Tmax), mean (Tmean) and minimum (Tmin) temperature for 63 years (1951–2013) were analysed over the Wainganga sub-basin, part of the Godawari basin using Mann–Kendall (MK) test and Sen's Slope Estimator (SSE). The shifting in temperature pattern was analysed using Pettitt's test, Standard Normal Homogeneity Test (SNHT) and Buishand's test. Results of the MK test and SSE for monthly temperatures show both upward and downward trends. Seasonal analysis shows the positive trend of all temperatures. It has been found that Tmax, Tmean and Tmin are rising by 0.5 ºC, 0.37ºC and 0.25ºC, respectively, during the study period. Spatial analysis using inverse distance weighted interpolation method (IDW) shows maximum variation in Tmin. The annual mean temperature in the southern parts of the basin shows a significant positive trend. [ABSTRACT FROM AUTHOR]

In India, especially in the state of Haryana in the northwestern parts, groundwater is the key source of water supply for domestic, industrial and agricultural uses. In recent years, the dependence on groundwater has increased drastically over the state due to speedy expansion of area under rice–wheat cultivation and therefore the states' aquifers have started experiencing over-exploitation. The aim of this study was to compute the magnitudes of groundwater level trends and abrupt change point detections spatially and temporally based on secondary data obtained from the Groundwater Cell, Department of Agriculture, Government of Haryana with respect to 118 community development blocks for pre- and post-monsoon seasons during 1990–2013. Three statistical models, namely Mann–Kendall test, Sen's slope estimator and simple linear regression, were applied to understand the trend and rate of change in groundwater level, whereas the abrupt change point detection analysis was carried out using Pettitt's test, standard normal homogeneity test, Buishand's range test and von Neumann ratio test. The results indicate that the average depth to groundwater level during pre-monsoon season in the state has ranged from 3.42 to 43.90 m below ground level (mbgl), whereas the average depth to groundwater level during post-monsoon season has ranged from 2.48 to 43.68 mbgl. The mean of groundwater level fluctuations during pre- and post-monsoon seasons was found to be − 6.0 and − 6.7 mbgl, respectively. The study highlights that the negative (falling) trends are much more pronounced (significant at 0.01% probability level) than positive (rising) ones, which corroborate the unsustainable groundwater development in the state. The abrupt change point detection analysis of groundwater levels indicates toward different change points, with maximum change points between the years 2000–2006 during pre- and post-monsoon seasons. This can be attributed to the advanced transplanting of rice crop by farmers much before the start of rainy season. In addition, both anthropogenic and climatic factors have led to groundwater depletion in the study area. These findings may be helpful for the planners and policy makers toward judicious utilization of groundwater resources in the state. [ABSTRACT FROM AUTHOR]

Background: Smart grid systems require high-quality Phasor Measurement Unit (PMU) data for proper operation, control, and decision-making. Missing PMU data may lead to improper actions or even blackouts. While the conventional cubic interpolation methods based on the solution of a set of linear equations to solve for the cubic spline coefficients have been applied by many researchers for interpolation of missing data, the computational complexity increases non-linearly with increasing data size., Methods: In this work, a modified recurrent equation-based cubic spline interpolation procedure for recovering missing PMU data is proposed. The recurrent equation-based method makes the computations of spline constants simpler. Using PMU data from the State Load Despatch Center (SLDC) in Madhya Pradesh, India, a comparison of the root mean square error (RMSE) values and time of calculation (ToC) is calculated for both methods., Results: The modified recurrent relation method could retrieve missing values 10 times faster when compared to the conventional cubic interpolation method based on the solution of a set of linear equations. The RMSE values have shown the proposed method is effective even for special cases of missing values (edges, continuous missing values)., Conclusions: The proposed method can retrieve any number of missing values at any location using observed data with a minimal number of calculations., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Thangaraj S et al.)

Dams are the major source of water for irrigation in India. As a result, any variability in rainfall over the catchment area of dams leads to huge economic loss. It also triggers water-sharing conflicts. Hence, understating the variation in rainfall in association with dams becomes essential. For the present study, the upper Godavari basin, primarily representing the semi-arid region in India, is selected. To understand the rainfall variation, the Mann–Kendall trend test is employed after checking the homogeneity of the data. Using the inverse distance weighted technique, the monthly, seasonal and annual trends in rainfall are interpolated. These interpolated results are then superimposed over the location of the dams. The results depict that the rainfall decreases in June, while it increases in August for most of the stations. The highest increase in monsoon rainfall is experienced in Akole (11.7 mm/year), while Khuldabad has experienced the maximum decline (− 6.58 mm/year) in rainfall. When the spatial variability of annual rainfall is considered, it is mostly declining in the eastern part of the basin. This area of decreasing rainfall accounts for more than 35% of the total basin area. Around 34 dams with capacity of more than 0.5 MCM (million cubic meter) are situated here. As a result of declining rainfall, the water supply from these dams might get adversely affected. Hence, for the upper Godavari basin, based on the rainfall variability analysis, policies should focus on demand side management of water resources. [ABSTRACT FROM AUTHOR]

The good quality of groundwater is important for future planning and management. The present study has been undertaken to provide an overview on the status of groundwater quality through physico-chemical parameters namely pH, alkalinity, total hardness, total dissolved solids, chloride, fluoride, nitrate, phosphate and iron through laboratory analysis. Samples have been collected from seven wells located in Chennai coastal region of state Tamilnadu, India. The laboratory-based results shows that the mean value of pH is 7.29, alkalinity (308.57 mg/l), total hardness (285.71 mg/l), chloride (175.71 mg/l), iron (0.71 mg/l), nitrate (13.57 mg/l), phosphorus (2.71 mg/l) and total dissolved solids (924 mg/l), respectively. The geo-database water quality parameters were created in Geographical Information System. Further, the kriging a geo-statistical method of interpolation is applied to know the health of groundwater in non-sampled area. This interpolation method has been used to predict spatial distribution physico-chemical parameters in the form of contour. Local planners and policymakers may utilize these results for efficient management of groundwater resources in this area.parameters in the form of contour. Local planners and policy makers may utilize these results for efficient management of groundwater resources in this area. [ABSTRACT FROM AUTHOR]

ABSTRACT The knowledge of rainfall-elevation relationship forms an important input for modelling the orographic rainfall in mountainous areas. The conventional regression and geostatistical techniques assume the rainfall and elevation relationship to be constant; however, in complex terrain these exhibit non-stationarity in their relationship. This study proposes a novel spatial rainfall modelling framework, stratified geographically weighted regression-residual kriging (s- GWRK) that integrates the benefit of spatially clustered data as an input, local-scale model and incorporation of spatial correlation structure of residuals. The application of the framework is demonstrated in Indian Himalayas of Uttarakhand region with two spatial clusters of normal annual rainfall data, lowland and upland, based on natural clustering technique. The performance of the proposed model is compared with ordinary co-kriging ( OCK), ordinary least square regression ( OLS), geographically weighted regression ( GWR) and geographically weighted regression kriging ( GWRK) that incorporates elevation as predictor variable. Model evaluation shows that s- GWRK performed best with root mean square error ( RMSE) of 153.98 and index of agreement ( d) of 0.92. OLS performed least with RMSE of 461.2 and d value of 0.32. OCK using elevation as auxiliary variable performed better than OLS with RMSE of 454.7 and d value of 0.53. The predictive capability of s- GWRK was validated using the data collected from rain gauge network in the mountainous terrain of Ashburton in New Zealand and Bhutan. An improvement of 30% and 55% in RMSE was observed compared to OCK for Ashburton and Bhutan respectively. For Uttarakhand, Ashburton and Bhutan, the positive rainfall lapse rate derived from GWR model of rainfall-elevation relation, ranged from 0.01-1.3, 0.2-1.43 and 0.09-0.4 mm m−1. [ABSTRACT FROM AUTHOR]

ABSTRACT Spatial climate datasets currently available for Bhutan are limited by weather station data availability, spatial resolution or interpolation methodology. This article presents new datasets for monthly maximum temperature, minimum temperature, precipitation and vapour pressure climate normals interpolated for the 1986-2015 reference period using trivariate smoothing splines. The inclusion of standardized day time Moderate Resolution Imaging Spectroradiometer ( MODIS) land surface temperature ( LST) values as partial spline dependencies reduced cross validated root mean square error ( RMSE) for maximum temperature by up to 16.0% and was most effective between March and September. Using both a topographic index of relative elevation and standardized night time MODIS LST values as partial spline dependencies reduced monthly mean minimum temperature RMSE by up to 23.4%. Neither variable was effective for minimum temperature interpolation between June and September. High humidity, extensive cloud cover and heavy precipitation occur during these months, which are likely to suppress the formation of temperature inversions that typically form under clear, calm conditions. These new temperature and precipitation surfaces show distinct differences from the WorldClim and CRU CL 2.0 datasets, which do not use weather stations within Bhutan for calibration. New precipitation surfaces better describe the heavy rainfall experienced in the southern foothills while retaining the effect of orography throughout the central valleys and ranges. The development of vapour pressure surfaces also allow for the calculation of ecologically important variables such as vapour pressure deficit, and may also be useful for solar radiation modelling in the region. The different datasets presented in this article will facilitate ecological and agricultural research in Bhutan and provide high quality surfaces needed for future climate change scenarios. [ABSTRACT FROM AUTHOR]

In the past two decades, Tamilnadu received frightening flood frequently cause serious smash up to the environment and survive defeat. Kanchipuram district is situated next to Chennai city. It has worst affected area during 2015 flood due to increasing population, encroachment of the water bodies and disappearance of lakes, shrinking of marshy land. It started from November 8th 2015 to December 1st 2015 with two cyclonic low depressions which cause due to northeast monsoon accommodate with ELNINO phenomenon. This study has to bring out the damage assessment in the affected areas by house hold survey with prepared questionnaire pattern from literature review and this paper presently describes the flood assessment in the study area using GIS interpolation technique. From this assessment the result indicate, peak rainfall occur in TambramTaluk, Thirukalukundram and Chengalpattu Taluk respectively. Single day average rainfall on 02.12.2015 of the Kancheepuram District had reached 343.20mm and out of 10 rain gauge stations, 8 stations recorded more than 250mm in a day which casuse fully inundated in the low lying areas and the numbers of people has relocated from low lying area and people got worst experience, lot of thrash about due to power shut down, without transport. [ABSTRACT FROM AUTHOR]

Water quality analysis is prime important to understand the wealth of living organism and environmental condition in water bodies. In GIS, ordinary Kriging interpolation method has been applied for surface analysis of water quality parameters such as temperature, salinity, pH, chlorophyll-a (Chl-a) and suspend sediment (SS), to carried comparison analysis between monsoon and summer season in lagoon at Vedaranyam wetland. Kriging is an approach to mapping the water quality parameters for analyzing the dispersion patterns. The results of the spatial distribution analysis has been indicated in lagoon around salt pan and aquaculture area have high saline condition reach about 36 parts per thousand (ppt) and in monsoon seasons suspended sediment reach maximum due to river runoff in the study area. [ABSTRACT FROM AUTHOR]

Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day. [ABSTRACT FROM AUTHOR]

Qualitative and quantitative evolution of the Swarna estuary, during the past 38 years (1967-2005), has been analysed by integrating the results of field surveys, satellite images, hydrodynamic modelling and topographic data in GIS context. Significant changes in erosion/accretion patterns of the estuarine banks, inlet configuration and adjacent spits, and origin, morphology, orientation and areal extent of fifteen braided islands have been estimated. Hydrodynamics of the region is studied through modelling, and probable flow pattern that is responsible for morphological changes is discussed. The simulated flow patterns indicate that current speeds are of the order of 10-20 cm/s inside the estuary and of the order of 40 cm/s at the mouth. The Right bank (Rb) of the estuary is subject to net erosion and the Left bank (Lb) is subject to net accretion. On an average, 20-30% of the area of all braided islands would be flooded, if the water level rises by 0.7 to 1.0 m. Islands of this estuary are morphologically very dynamic (migrating towards north, northeast and southwest directions) and subject to net accretion, whereas only one island (i.e., the Padu) has lost its major portion of landmass. A conceptual inverse distance weighted (IDW) interpolation model has been generated and evaluated possible flooding on the estuarine shoals and braided islands, due to heavy rainfall and/or sea-level rise. The model reveals that the possibility of flooding will be higher for those islands which are located close to the Rb than those close to the Lb. [ABSTRACT FROM AUTHOR]

Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables. [ABSTRACT FROM AUTHOR]

Abstract: The Wind Energy Potential for India has been assessed, assuming as if the whole of the country (apart from the urban and the Himalayan areas) is covered with windfarms, by an innovative approach using GIS platform, wind speed measurements under government’s program and the NCEP/NCAR Reanalysis data. 1 [1] The NCEP/NCAR Reanalysis Project is a joint project between the National Centres for Environmental Prediction (NCEP, formerly "NMC") and the National Centre for Atmospheric Research (NCAR). The goal of this joint effort is to produce new atmospheric analysis using historical data and as well to produce analysis of the current atmospheric state (Climate Data Assimilation System, CDAS). It contains the information of climate variables with different time setups. For the analysis, data has been collected from different data sources like, COADS surface marine data, the comprehensive Ocean-Atmospheric Data set includes ships, fixed buoys, drifting buoys, pack-ice buoys, near- surface data from ocean station reports. Still some work is in progress to collect all the surface marine data for 1947-79. Aircraft data, Surface land synoptic data, special sensing microwave imager data, surface wind speeds, satellite cloud drift winds, and satellite sounder data. The methodology involves setting up a grid of 1 sqkm resolution over areas other than urban settlements and Himalayan regions, computation of wind speeds at boundary layer level through vertical extrapolation of known or measured mean annual wind speed and interpolation of the extrapolated wind speeds to arrive at a mean annual wind field at boundary layer level and then computation of wind speed at the hub-height of the wind turbine. Power output from a standard wind turbine is computed and only areas showing a Plant Load Factor (PLF) higher than 15% are considered in the potential assessment. The results of this exercise indicate the potential for windfarms in India to be significantly higher than what was assumed earlier. The analysis and its revalidation using data measured at varying heights in different parts of the country establishe this approach as useful and perhaps a powerful tool to undertake wind resource potential assessments. This analysis and the results are discussed in the backdrop of the general energy scenario in India and earlier assessments of wind potential in the country. [Copyright &y& Elsevier]

The impact of rainfall interpolation techniques and unit hydrograph estimation has been explored for four gauged locations in the Brahmani basin in east India. The use of ground-based and satellite-based data, coupled with testing two interpolation techniques (Thiessen polygon and inverse distance weighting), can yield improved rainfall estimates and fits to observed flows. Due to the presence of significant errors in the areal rainfall estimate it was found that identification of known errors in rainfall data can assist in focusing model calibration on catchment response, thereby reducing the uncertainty in model parameter values. Similarly, using several approaches to estimate the unit hydrograph can assist in reducing uncertainty. The resulting performance of the model for the gauged sites in the Brahmani basin gave Nash-Sutcliffe efficiency (NSE) values for the calibration period of 0.6-0.7. For this basin, the inverse distance weighting approach corrected for spatial variation in rainfall distribution generally gave the best fits to the observed streamflow. Sensitivity to errors in the rainfall surface limits the applicability for this approach in modelling the flows in ungauged basins, however. [ABSTRACT FROM AUTHOR]

Evaluation of interpolation technique was investigated for air quality parameters such as suspended particulate matter (SPM), sulphur dioxide (SO2) and nitrogen dioxide (NO2) in ambient air of Port Blair, India. The interpolation methods such as inverse distance weighted (IDW) and kriging were tested to develop air quality model. The interpolated and corresponding measured values were compared by three statistical test such as mean absolute error (MAE), root mean square error (RMSE) and index of agreement (d). The ANOVA showed significant site wise variation for SPM, SO2 and NO2 concentrations. The concentration of SPM, NO2 and SO2 ranged between 35-174, 5-24 and 4-11 µg/m³ respectively. The observed concentrations of air quality parameters were found below the permissible limit of Central Pollution Control Board of India. The IDW showed better similarity between measured and interpolated values of SPM, SO2 and NO2 than kriging method. MAE and RMSE were low for air quality parameters in IDW method. The'd' was observed higher in IDW method, which attributes better similarity between measured and interpolated values. Interpolation methods and statistical result have been discussed in detail for assessment and monitoring of air quality parameters. [ABSTRACT FROM AUTHOR]

Carbon stock distribution in different tropical forest types in India is rarely studied although India is a country with mega-diversity. The present study estimates the biomass and carbon stock of major tropical forest types in India, and attempts to identify suitable interpolation techniques for mapping carbon stock. Empirically derived allometric equations and carbon conversion coefficients were used to estimate the aboveground biomass and carbon stock, respectively. The point estimates were interpolated to spatial surface using different interpolation techniques. Two main modelling approaches were implemented: deterministic modelling and stochastic modelling. Deterministic modelling was to interpolate point information using similarities between measured points (inverse distance weighted (IDW) interpolation), and fitting a smoothing curve along the measured points (polynomial interpolation). In stochastic modelling, ordinary kriging (OK) was employed using parameters derived from semivariograms. The results showed that the average carbon stock in the study area was 84 t/ha. The highest carbon stock was in evergreen forest and the lowest in thorny scrub forest. Validation of the model using the mean and RMS errors indicated that ordinary kriging performs better than IDW and polynomial interpolations. [ABSTRACT FROM AUTHOR]

Abstract: Soil (regolith) depth is a crucial input for modeling earth surface phenomena. However, most studies ignore its spatial variability. Techniques that map the spatial variability of soil depth are of three types: (1) physically-based; (2) empirico-statistical from environmental correlates; and (3) interpolation from point observations. In an anthropogenic landscape, soil depth does not depend primarily on natural processes, making it difficult to apply a physically-based approach. The present study compares empirico-statistical methods with geostatistical methods for predicting soil depth in such a landscape: Aruvikkal catchment (9.5 km2) in the Western Ghats of Kerala, India. Regression kriging applied on blocks of 20 m by 20 m using the environmental covariates elevation, slope, aspect, curvature, wetness index, land use and distance from streams, proved to be the best predictor of soil depth. This model explains 52% of the variability of soil depth in the catchment; with a prediction variance of 0.05 to 0.19. A Gaussian simulation was attempted for a more realistic visualization of the depth, as opposed to the smooth kriging prediction. The most important explanatory variable of soil depth in this landscape is land use, as expected from the strong human intervention. [Copyright &y& Elsevier]

The article presents a method which calculates the surface water budget to obtain spatial estimates of rainfall at the Indian west coast including the Mandovi River and the Sahvadris range. In the said hydrological method, the rainfall maps obtained by a multivariate interpolation scheme have been used to capture the sharp increase in rainfall on the windward and leeward slopes. According to the authors, the method provides a better estimate of rainfall across the area, particularly the Mandovi domain. It states that the subjective extraction of the ridge line can reduce the underestimation of rainfall in the mountainous terrain.

Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The Distance Weighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose. [ABSTRACT FROM AUTHOR]

The work incorporates the field data collection, assessment, and use of interpolation and extrapolation methods in modeling the existence of pollutant in groundwater, namely nitrate, and future prediction. To view the variation of concentration of pollutants in any form, 3D representation is required. However, not many techniques are available to achieve the objective accurately. Many modeling studies are done but very little is known about the variation of a group of chemical parameters at vertical scale in any of the climatic zone or habitable region. This kind of study is needed to help stakeholders for better planning. Earlier studies do not show the variation of chemical parameters (contaminants) at vertical scale in the climatic zone, and modellings are very objective specific. Present work presents 3D models using Inverse Distance Weighting technique in Matlab. The concentration pattern of nitrate is studied in 3D and presented in lucid manner at regional scale. The 3D block presentation demonstrates its affiliation and dispersion. The relationship from these models between parameter fixation and profundity shows the presence of distinct layers up to desired depth. The relationship plots are developed to extract the information how the groundwater quality is being transmitted beneath the surface. The projection is verified with the real field data, which will help in future resource management actions and minimize the pollution risks to mankind and the environment. The modeling helps in selecting the danger zones for ground water recharge and discharge for natural cause of elevated concentration of nitrate in groundwater. This study opens up the methodology for finding the variation of other contaminants against depth and that of total water quality.