Climate change and crop yields in the Indian Cardamom Hills, 1978–2007 CE

In this study we analyzed climate and crop yields data from Indian cardamom hills for the period 1978–2007 to investigate whether there were significant changes in weather elements, and if such changes have had significant impact on the production of spices and plantation crops. Spatial and temporal variations in air temperatures (maximum and minimum), rainfall and relative humidity are evident across stations. The mean air temperature increased significantly during the last 30?years; the greatest increase and the largest significant upward trend was observed in the daily temperature. The highest increase in minimum temperature was registered for June (0.37°C/18?years) at the Myladumpara station. December and January showed greater warming across the stations. Rainfall during the main monsoon months (June–September) showed a downward trend. Relative humidity showed increasing and decreasing trends, respectively, at the cardamom and tea growing tracts. The warming trend coupled with frequent wet and dry spells during the summer is likely to have a favorable effect on insect pests and disease causing organisms thereby pesticide consumption can go up both during excess rainfall and drought years. The incidence of many minor pest insects and disease pathogens has increased in the recent years of our study along with warming. Significant and slight increases in the yield of small cardamom (Elettaria cardamomum M.) and coffee (Coffea arabica), respectively, were noticed in the recent years.; however the improvement of yield in tea (Thea sinensis) and black pepper (Piper nigrum L.) has not been seen in our analysis.     

Investigating the impact of climate change on New York City’s primary water supply

Future climate scenarios projected by three different General Circulation Models and a delta-change methodology are used as input to the Generalized Watershed Loading Functions – Variable Source Area (GWLF-VSA) watershed model to simulate future inflows to reservoirs that are part of the New York City water supply system (NYCWSS). These inflows are in turn used as part of the NYC OASIS model designed to simulate operations for the NYCWSS. In this study future demands and operation rules are assumed stationary and future climate variability is based on historical data to which change factors were applied in order to develop the future scenarios. Our results for the West of Hudson portion of the NYCWSS suggest that future climate change will impact regional hydrology on a seasonal basis. The combined effect of projected increases in winter air temperatures, increased winter rain, and earlier snowmelt results in more runoff occurring during winter and slightly less runoff in early spring, increased spring and summer evapotranspiration, and reduction in number of days the system is under drought conditions. At subsystem level reservoir storages, water releases and spills appear to be higher and less variable during the winter months and are slightly reduced during summer. Under the projected future climate and assumptions in this study the NYC reservoir system continues to show high resilience, high annual reliability and relatively low vulnerability.     

Assessing impact of climate change on season length in Karnataka for IPCC SRES scenarios

Changes in seasons and season length are an indicator, as well as an effect, of climate change. Seasonal change profoundly affects the balance of life in ecosystems and impacts essential human activities such as agriculture and irrigation. This study investigates the uncertainty of season length in Karnataka state, India, due to the choice of scenarios, season type and number of seasons. Based on the type of season, the monthly sequences of variables (predictors) were selected from datasets of NCEP and Canadian General Circulation Model (CGCM3). Seasonal stratifications were carried out on the selected predictors using K-means clustering technique. The results of cluster analysis revealed increase in average, wet season length in A2, A1B and B1 scenarios towards the end of 21st century. The increase in season length was higher for A2 scenario whereas it was the least for B1 scenario. COMMIT scenario did not show any change in season length. However, no change in average warm and cold season length was observed across the four scenarios considered. The number of seasons was increased from 2 to 5. The results of the analysis revealed that no distinct cluster could be obtained when the number of seasons was increased beyond three.     

Long-term spatial and temporal trends in frost indices in Kansas, USA

Frost indices such as number of frost days (nFDs), number of frost-free days (nFFDs), last spring freeze (LSF), first fall freeze (FFF), and growing-season length (GSL) were calculated using daily minimum air temperature (T_min) from 23 centennial weather stations across Kansas during four time periods (through 1919, 1920–1949, 1950–1979, and 1980–2009). A frost day is defined as a day with T_min?<?0 °C. The long- and short-term trends in frost indices were analyzed at monthly, seasonal, and annual timescales. Probability of occurrence of the indices was analyzed at 5 %, 25 %, 50 %, 75 %, and 95 %. Results indicated a general increase in T_min from 1900 through 2009 causing a decrease in nFDs. LSF and FFF occurred earlier and later than normal in the year, respectively, thereby resulting in an increase in GSL. In general, northwest Kansas recorded the greatest nFD and lowest T_min, whereas southeast Kansas had the lowest nFD and highest T_min; however, the magnitude of the trends in these indices varied with location, time period, and time scales. Based on the long-term records in most stations, LSF occurred earlier by 0.1–1.9 days/decade, FFF occurred later by 0.2–0.9 day/decade, and GSL was longer by 0.1–2.5 day/decade. At the 50 % probability level, Independence in the south-eastern part of Kansas had the earliest LSF (6 April), latest FFF (29 October) and longest GSL (207 days). Oberlin (north-western Kansas) recorded the shortest GSL (156 days) and earliest FFF (7 October) had the latest LSF (2 May) at the 50 % probability level. A positive correlation was observed for combinations of indices (LSF and GSL) and elevation, whereas a negative correlation was found between FFF and elevation.     

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R _H in a river basin at monthly scale. Uncertainty in the future projections of R _H is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R _H are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R _H is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.