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Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

This paper presents a composite approach for drought characterization and monitoring using in situ and remote sensing-based drought indicators. The study was carried out on one of the most drought-prone areas of India, i.e., the Marathwada region of Maharashtra. Meteorological, hydrological, and agricultural drought indices, namely standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI), respectively, were integrated to develop the composite drought index (CDI) using principal component analysis (PCA). SPI and SDI were computed using in situ precipitation and streamflow data, respectively, for 35 years (1980–2014), whereas VCI was computed using MODIS satellite data (500-m resolution) for 15 years (2000–2014) at 1-, 3-, and 5-month time scales. The time scales of drought indices were evaluated using historical drought years and foodgrain production of the region. The drought areas observed by SPI, SDI, and VCI at different time scales were correlated with foodgrain production during the kharif crop growing season for 15 years (2000–2014). The maximum correlation of foodgrain production was observed with 3-month SPI (r = − 0.72), 5-month SDI (r = − 0.40), and 5-month VCI (r = − 0.81) for meteorological, hydrological, and agricultural drought, respectively. Three-month SPI, 5-month SDI, and 5-month VCI were selected from each drought category to develop CDI. These drought indices were combined using weights derived through the PCA technique. The maximum weight was obtained for 3-month SPI (45.4%) followed by 5-month VCI (42.8%) and 5-month SDI (11.8%). The developed CDI products showed a strong relationship (r = − 0.85) with foodgrain production. The drought years observed by CDI were also closest to drought years declared by the State Government. The time series trend of the drought-affected area observed by CDI, 3-month SPI, and 5-month VCI resembled the drought patterns very closely, especially during the drought years. The spatio-temporal analysis of individual drought indices and CDI with foodgrain production deviation showed that CDI was better for capturing drought conditions than individual indicators. The study suggested that an individual or single indicator is not sufficient to capture the actual drought severity and its magnitudes; therefore, using a composite approach could be a good choice for effective drought assessment and monitoring in the region. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET), one of the key components of the hydrological system, plays a major role in the water availability assessment in a region. ET is a complex process, and calculating ET from reference evapotranspiration (ET0) is one of the popular methods, where pan evaporation (EPan) value is converted to ET0 by multiplying the EPan value with the pan coefficient (Kp) value. The Kp value depends on the climatic factors; hence, the determination of Kp varies for different regions. This paper investigates the spatial and temporal variation of the Kp values for the northeastern region (NER) of India having diverse topography and climatic conditions. Observed EPan data and ET0 calculated for ten selected stations of the eco-sensitive region NER of India were used for calculating the Kp values in monthly, seasonal, and annual timescales. Spatial variation of the Kp was identified by interpolation method, and temporal trends in the Kp values were identified by using the parametric method (Linear regression test) and non-parametric (Mann-Kendall test (MK)) after the pre-whitening of the time series. The average monthly Kp values in the NER of India varied from 0.788 in March at Umiam to 2.462 in July at Gangtok, whereas the annual Kp varied from 0.978 at Imphal to 1.822 at Gangtok. The seasonal Kp was found to be high during monsoon (1.401) and low during post-monsoon (1.017). Trend analysis shows that Kp was found to be increasing in annual, pre-monsoon, and monsoon seasons, whereas decreasing trends in Kp were found during winter and post-monsoon in the NER. The spatial distribution of Kp depicted that the values were found high in the northwest region and low in the remaining parts of the NER of India. The spatial distribution of trends and their magnitudes were found to be decreasing in the northwest and some parts of the northeast and southwest, whereas increasing trends were found in the central and southern parts of the NER. Results of Pettit's test for change point analysis observed that all but four stations of the NER witnessed the change point after 1990. Assessment of the Kp could be helpful for the estimation of actual evapotranspiration in the region, which further could be useful for water management in agriculture and other water sectors. [ABSTRACT FROM AUTHOR]

This paper presents a detailed spatiotemporal analysis of the rainfall variability, seasonality, and the extreme characteristics of Tehri catchment located in the lower Himalayan region in India. To this end, the daily rainfall data is extracted from 22 grids for 117 years (1901–2017) from the high-resolution (0.25° × 0.25°) gridded observation dataset. Monthly rainfall distribution is evaluated using precipitation concentration index (PCI) and seasonality index. The extreme rainfall indices, viz., maximum 1-day rainfall (Rx1Day), maximum 5-day rainfall (Rx5Day), number of rainy days (NxRainy), total precipitation in rainy days (PRCPTOT), number of heavy rainfall events (NxHeavy), maximum consecutive wet days (CWD), and simple daily intensity index (SDII) are computed for each year considering the thresholds suggested by India Meteorological Department (IMD). The Mann–Whitney-Pettitt test when applied to the annual rainfall time series revealed the year 1958 to be the statistically significant change point. The non-parametric modified Mann–Kendall and Sen's slope tests are employed to detect the trend in monthly, seasonal, annual rainfall time series, extreme precipitation indices, and seasonality indices for both the pre- and post-1958 periods. The annual rainfall over the grids mostly possessed higher negative trends during 1959–2017 than those during 1901–1958, mainly due to the decreasing trends in post-monsoon and winter seasons. Compared to 1901–1958, NxRainy, CWD, and PRCPTOT exhibited a remarkable decreasing trend whereas NxHeavy, Rx1Day, Rx5Day, and SDII exhibited higher positive trends during 1959–2017, indicating intensification of precipitation. The precipitation over the catchment has been more concentrated in the latter epochs of monsoon season and annual rainfall and it is also evident from the increasing trends of the seasonality indices. There is no such study dealing comprehensively with identification of extreme characteristics, seasonality/concentration characteristics, and various categorical trends of precipitation in a Himalayan region reported in literature. This study will be useful in understanding the decreasing trend of precipitation volume coupled with increasing intensity and concentration and it is quite critical for a Himalayan catchment. [ABSTRACT FROM AUTHOR]

This paper delineates the process of activation of soot or black carbon particles into cloud droplets over Chennai, India (an Asian megacity). Archaic wood-fired cooking stove emissions from crowded shantytown settlements dispense copious amounts of carbonaceous particles into the city's atmosphere during morning and evening cooking periods. Traffic emissions also add considerably to the city's particulate matter loads. At other times, when these sources are reduced (e.g. during non-cooking periods), a pall of residual pollution hovers above the city. This study assesses this problem definitively through process modelling studies undertaken during a typical pre-monsoonal month (October) when the levels of pollution are very high. The novelty of this paper includes a clear quantification of the percentage of activated nuclei resulting from these sources using a sophisticated cloud chemical parcel model. The results show that 85% of the soot and black carbon particles are activated into very small low-lying cloud droplets within Chennai's boundary layer. We further show for the first time that the presence of these particles results in a slowing down of the auto-conversion process (cloud water to rain droplets). This study could serve as a pointer for other Asian Cities with a sizeable shantytown population in India and elsewhere. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) is a phenomenon which may have adverse effects on our environment and is stimulated as a result of urbanization or land cover changes. Thermal remote sensing has been found beneficial to study the effect of urbanization on UHI intensity. This paper analyses the variation in land surface temperature (LST) with land cover changes in Varanasi city of India from 1989 to 2018 using Landsat satellite images. A new index named Urban Heat Intensity Ratio Index (UHIRI) was proposed to quantify the urban heat intensity from 1989 to 2018 which was found to increase from 0.36 in year 1989 to 0.87 in year 2018. Further, contribution of each land cover towards UHI was determined using land cover contribution index (LCCI). The negative value of LCCI for water and vegetation indicates its negative contribution towards UHI, whereas positive value of LCCI for bare soil and built-ups depicted its positive contribution towards UHI. The LCCI value for urban land cover shows significant increase in 29 years, i.e. 0.49, 1.43, 3.40 and 4.37 for years 1989, 1997, 2008 and 2018, respectively. The changes in normalized LST from years 1989 to 2018 for the conversion of bare land to built-ups and vegetation to built-ups were found to be as −0.11 and 0.42, respectively. This led to conclusion that the replacement of vegetation with urban land cover has severe impact on increasing UHI intensity. [ABSTRACT FROM AUTHOR]

Estimates of probable maximum precipitation (PMP) and corresponding probable maximum flood (PMF) are necessary for planning, design, and risk assessment of flood control structures whose failure could have catastrophic consequences. For PMP estimation, multifractal approach (MA) is deemed to be better than conventional approaches, which are based either on statistical concepts or physical aspects. The MA yields physically meaningful PMP estimates by attempting to capture scale-invariant multiplicative cascade mechanism inherent in rainfall. This paper attempts to gain insights into the performance of MA by comparing PMP estimates obtained using the approach with those resulting from the use of two widely used empirical approaches (storm maximization approach (SMA) and Hershfield method (HM)) on two flood-prone river basins (Mahanadi and Godavari) in India. The results indicate that rainfall data of the two river basins exhibit multifractal properties, and the use of MA has an advantage over HM and SMA in estimating PMP corresponding to longer durations (>3 days). PMP estimates obtained using HM are generally lower (higher) than those obtained using SMA for 1-day (higher) duration. PMP maps are prepared for the two Indian river basins corresponding to 1-day to 5-day durations. Further, PMP estimates obtained based on the PMP maps are provided for 18 catchments in the Mahanadi basin and 53 catchments in the Godavari river basin. [ABSTRACT FROM AUTHOR]

Coordinated Regional Climate Downscaling Experiment (CORDEX) has developed high-resolution, dynamically downscaled data across the world. This paper investigates the spatio-temporal correspondence between CORDEX data for South Asia (CORDEX-SA) and observed precipitation data to assess its reliability. The entire Indian mainland is considered as study region. Both monthly and seasonal analyses are undertaken along with the extreme magnitudes during historical (1971–2005) and future (2006–2100) periods. The outputs of two regional climate models (RCMs), viz. regional climate model, version-4.4 (RegCM4) and Rossby Centre regional Atmospheric model, version-4 (RCA4), participating in CORDEX-SA program, are considered for medium (RCP4.5) and high (RCP8.5) emission scenarios. The CORDEX-SA is found to well represents in central India, but significant bias in the mean precipitation (± 200 mm) is noticed at northern, north-eastern, southern and coastal regions. The inferior performance of both the RCMs is noticed over high rainfall regions. Specifically, more than 70% area is found to have mean bias more than ± 100 mm. Performance of RCA4 is better in North and Himalayan regions, where 67% of area is found to be within the aforementioned threshold, which is 23.64% in case of RegCM4. Performance of RegCM4 is marginally better in the peninsular India as compared with RCA4. In case of extreme magnitudes, the insignificant correspondance between CORDEX-SA data and observed precipitation is noticed over the many parts of India. Results of this study are important to check the applicability of CORDEX data in various impact assessment studies. [ABSTRACT FROM AUTHOR]

Forecasting of Indian summer monsoon rainfall (ISMR) is a complex problem for the hydrologists and meteorologists. The time series and data-driven methods have been used as complementary tools for forecasting ISMR against the complex physically based dynamical models due to scarcity of data and the simplicity of former approaches. The use of hybrid decomposition data–driven models is the recent improvement among the different approaches for rainfall forecasting, but these approaches differ significantly in the framework adopted. This paper presents an adaptive hybrid modelling framework so called Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN) model for forecasting ISMR, which performs the forecasts adaptively as and when new information is added. The performance of the popular EEMD-ANN hybrid hindcast and forecast experiments in the prediction of All-India SMR and southwest monsoon rainfall of the state of Kerala is compared with the proposed method. The AEEMD-ANN method achieved a predictive skill of 0.78 and 0.91, respectively for rainfall predictions for Kerala and All-India. AEEMD-ANN method performed reasonably well in capturing the hydrologic extremes when compared with EEMD-ANN forecast method, with better accuracy in capturing the drought years. The proposed method is found to be successful in capturing the extreme low SWM rainfall of year 2002 for All-India and the extreme high rainfall of Kerala 2018 with an error percentage of 1.09% and 0.52%, respectively. [ABSTRACT FROM AUTHOR]

It is important to estimate a high-resolution spatiotemporal distribution of temperature rise under climate change for effective adaptation and mitigation planning. This paper studies the threshold crossing time (TCT) for eight Indian warming targets (IWTs) between 1.5 and 5 °C based on regional climate models (RCMs) participating in The Coordinated Regional Downscaling Experiment (CORDEX)—South Asia project. To evaluate the performance of CORDEX experiments in simulating the near-surface mean temperature, the model performance index (MPI) is used which represents that the CanESM experiment performs best for simulating Tmean over India. All India average Tmean projected by the multi-model ensemble (MME) represents that 1.5 °C TCT is about 2033 ± 3 and 2028 ± 3 in RCP4.5 and RCP8.5 scenarios, respectively. During the twenty-first century, Indian subcontinent can experience five IWTs (1.5–3.5 °C) under RCP4.5 and all eight under scenario RCP8.5. The fastest warming region in India is the western Himalayan region (North India) and north-west region (Indian Thar Desert). [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify relative roles of different land-atmospheric conditions, apart from sea surface temperature (SST), in determining early vs. late summer monsoon intensity over India in a high resolution general circulation model (GCM). We find that in its early phase (June-July; JJ), pre-monsoon land-atmospheric processes play major role to modulate the precipitation over Indian region. These effects of pre-monsoon conditions decrease substantially during its later phase (August-September; AS) for which the interannual variation is mainly governed by the low frequency northward propagating intraseasonal oscillations. This intraseasonal variability which is related to mean vertical wind shear has a significant role during the early phase of monsoon as well. Further, using multiple linear regression, we show that interannual variation of early and late monsoon rainfall over India is best explained when all these land-atmospheric parameters are taken together. Our study delineates the relative role of different processes affecting early versus later summer monsoon rainfall over India that can be used for determining its subseasonal predictability. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005). [ABSTRACT FROM AUTHOR]

In this paper, the changes in convective activity over heat-low region over northwest India during contrasting phases of effective strength index (ESI) tendency have been examined. During contrasting phases of ESI tendency, evolution of surface pressure and temperature field over India from winter to spring is exactly opposite, and hence, the heat-low over northwest India depicts temporal and spatial variations in magnitude and location. During positive ESI tendency, the evolution of surface cooling and high surface pressure from winter to spring suggests reduced convective activity over heat-low region, while during the negative phase of ESI tendency, anomalously warm surface temperatures and low surface pressure evolve from winter to spring suggesting enhanced convective activity over the heat low region. The temporal variability in the relationship between surface temperature/pressure over heat-low region and Indian summer monsoon rainfall (ISMR) is also examined in this paper. During positive ESI tendency, heat-low temperature anomaly in February is significantly associated with ISMR, whereas during negative ESI tendency, both temperature/pressure over heat-low region in May are significantly associated with ISMR. These parameters may help in long range forecasting of ISMR. [ABSTRACT FROM AUTHOR]

The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses. [ABSTRACT FROM AUTHOR]

B Correction to: Theoretical and Applied Climatology b https://doi.org/10.1007/s00704-022-04168-4 The affiliation of one of the authors (Pranaya Diwate) has been wrongly mentioned in the paper. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments. [ABSTRACT FROM AUTHOR]

This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to analyze the trends and variability in extreme temperature indices and its impact on rice-wheat productivity over two districts of Bihar, India, which is part of the middle Indo-Gangetic Basin. Mann-Kendall non-parametric test was employed for detection of trend and Sen slope was determined to quantify the magnitude of such trends. We have analyzed 10 extreme temperature indices for monthly and seasonally. The influence of extreme temperature indices on rice-wheat productivity was determined using correlation analysis. As far as Patna is concerned, if the number of cool days during September ≥10, the rice productivity will increase due to the availability of sufficient duration to fill up the grain. However, higher warm days during all the months except June will affect the productivity. A significant negative correlation was noticed between maximum value of minimum temperature during September and rice productivity. Highly significant positive correlation was noticed between number of cool days during September with rice productivity while it was highly significant negative correlation in the case of number of warm days during the same month. As far as Samastipur is concerned, a negative correlation was noticed between wheat productivity and maximum value of maximum temperature (TXx) during February, but not statistically significant. The higher temperature may affect the kernel weight and thereby yield. It is seen that a critical value of TXx ≥29.2 °C will be harmful to wheat crop during February. A significant positive correlation of number of cool nights with wheat productivity also supports the above relationship. The critical values of extreme temperature indices during rice and wheat growing months provide an indicator to assess the vulnerability of rice-wheat productivity to temperature for Patna and Samastipur districts and there is a need to prepare an adaptive strategy and also develop thermo-insensitive rice-wheat high yielding varieties suitable for this region to sustain rice-wheat productivity under projected climate change situation. [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]

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures ( T and T) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948-2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001-2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T and T for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method. [ABSTRACT FROM AUTHOR]

There is a lack of observed data-based studies examining the role of enhanced soil moisture conditions (due to irrigation) on the prevailing precipitation. Therefore, in the present study, we have examined the impacts of the Green Revolution (GR) related expansion of irrigation and changes in dry season (the rabi (November to May) and the zaid (March to June)) precipitation in India. The results for some regions indicated decreasing and increasing trend in precipitation during the pre- and post-GR periods, respectively. For example, in eastern Madhya Pradesh, the pre- and post-GR precipitation trends for the zaid season were −0.45 and 2.40 mm year, respectively. On the other hand, some regions reported lower rate of decline in precipitation during the post-GR period. This paper suggests that both positive and lower declining trend during the post-GR period were linked to increased precipitation due to the introduction of irrigation. The study has found up to 69 mm (121%) increase in total amount of precipitation for growing seasons during the post-GR period. Moreover, a 175% increase in average precipitation was also recorded. All irrigated regions show a notable increase in precipitation during post-GR growing seasons. It was found that differences in growing season average precipitation between the pre- and post-GR periods were statistically significant for most of the regions. For further verification of results, the MM5 and Noah land surface model were applied. These applications show changes in precipitation and various precipitation controlling factors due to changes in soil moisture. [ABSTRACT FROM AUTHOR]

This paper provides an insight into the long-term trends of the four seasonal and annual precipitations in various climatological regions and sub-regions in India. The trends were useful to investigate whether Indian seasonal rainfall is changing in terms of magnitude or location-wise. Trends were assessed over the period of 1954-2003 using parametric ordinary least square fits and non-parametric Mann-Kendall technique. The trend significance was tested at the 95% confidence level. Apart from the trends for individual climatological regions in India and the average for the whole of India, trends were also specifically determined for the possible smaller geographical areas in order to understand how different the trends would be from the bigger spatial scales. The smaller geographical regions consist of the whole southwestern continental state of Kerala. It was shown that there are decreasing trends in the spring and monsoon rainfall and increasing trends in the autumn and winter rainfalls. These changes are not always homogeneous over various regions, even in the very short scales implying a careful regional analysis would be necessary for drawing conclusions regarding agro-ecological or other local projects requiring change in rainfall information. Furthermore, the differences between the trend magnitudes and directions from the two different methods are significantly small and fall well within the significance limit for all the cases investigated in Indian regions (except where noted). [ABSTRACT FROM AUTHOR]

Statistical characterizations of rainfall structure over two tropical stations in southern India are reported in this paper based on the 2-year rainfall data. The statistical characterizations has been based on cumulative distribution function, exceedance of threshold values, dependence of the intensity of rainfall on the event duration, seasonal variability, and worst months concept as well as diurnal variability. These results are needed to give the detailed insights to the system designers for the development of communication gadgets needed for better service, serve as a vital tool to estimate signal outages in a year over the region and for proper planning of radio communication in the region. Finally, the study shows that the recent International Telecommunications Union Recommendations (ITU-R) value underestimated rain rate for 0.01% exceedance for the two locations. [ABSTRACT FROM AUTHOR]