This paper presents a methodology to downscale monthly precipitation to river basin scale in Indian context for special report of emission scenarios (SRES) using Support Vector Machine (SVM). In the methodology presented, probable predictor variables are extracted from (1) the National Center for Environmental Prediction (NCEP) reanalysis data set for the period 1971-2000 and (2) the simulations from the third generation Canadian general circulation model (CGCM3) for SRES emission scenarios A1B, A2, B1 and COMMIT for the period 1971-2100. These variables include both the thermodynamic and dynamic parameters and those which have a physically meaningful relationship with the precipitation. The NCEP variables which are realistically simulated by CGCM3 are chosen as potential predictors for seasonal stratification. The seasonal stratification involves identification of (1) the past wet and dry seasons through classification of the NCEP data on potential predictors into two clusters by the use of K-means clustering algorithm and (2) the future wet and dry seasons through classification of the CGCM3 data on potential predictors into two clusters by the use of nearest neighbour rule. Subsequently, a separate downscaling model is developed for each season to capture the relationship between the predictor variables and the predictand. For downscaling precipitation, the predictand is chosen as monthly Thiessen weighted precipitation for the river basin, whereas potential predictors are chosen as the NCEP variables which are correlated to the precipitation and are also realistically simulated by CGCM3. Implementation of the methodology presented is demonstrated by application to Malaprabha reservoir catchment in India which is considered to be a climatically sensitive region. The CGCM3 simulations are run through the calibrated and validated SVM downscaling model to obtain future projections of predictand for each of the four emission scenarios considered. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. The projected increase in precipitation is high for A2 scenario, whereas it is least for COMMIT scenario.

[1] A variety of methods are available to estimate values of meteorological variables at future times and at spatial scales that are appropriate for local climate change impact assessment. One commonly used method is Change Factor Methodology (CFM), sometimes referred to as delta change factor methodology. Although more sophisticated methods exist, CFM is still widely applicable and used in impact analysis studies. While there are a number of different ways by which change factors (CFs) can be calculated and used to estimate future climate scenarios, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. In this study several categories of CFM (additive versus multiplicative and single versus multiple) for a number of climate variables are compared and contrasted. The study employs several theoretical case studies, as well as a real example from Cannonsville watershed, which supplies water to New York City, USA. Results show that in cases when the frequency distribution of Global Climate Model (GCM) baseline climate is close to the frequency distribution of observed climate, or when the frequency distribution of GCM future climate is close to the frequency distribution of GCM baseline climate, additive and multiplicative single CFMs provide comparable results. Two options to guide the choice of CFM are suggested. The first option is a detailed methodological analysis for choosing the most appropriate CFM. The second option is a default method for use under circumstances in which a detailed methodological analysis is too cumbersome.

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ABSTRACT:In this paper, downscaling models are developed using a support vector machine (SVM) for obtaining projections of monthly mean maximum and minimum temperatures (T max and T min ) to river-basin scale. The effectiveness of the model is demonstrated through application to downscale the predictands for the catchment of the Malaprabha reservoir in India, which is considered to be a climatically sensitive region. The probable predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1978-2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 1978-2100. The predictor variables are classified into three groups, namely A, B and C. Large-scale atmospheric variables such as air temperature, zonal and meridional wind velocities at 925 mb which are often used for downscaling temperature are considered as predictors in Group A. Surface flux variables such as latent heat (LH), sensible heat, shortwave radiation and longwave radiation fluxes, which control temperature of the Earth's surface are tried as plausible predictors in Group B. Group C comprises of all the predictor variables in both the Groups A and B. The scatter plots and cross-correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to study the predictor-predictand relationships. The impact of trend in predictor variables on downscaled temperature was studied. The predictor, air temperature at 925 mb showed an increasing trend, while the rest of the predictors showed no trend. The performance of the SVM models that are developed, one for each combination of predictor group, predictand, calibration period and location-based stratification (land, land and ocean) of climate variables, was evaluated. In general, the models which use predictor variables pertaining to land surface improved the performance of SVM models for downscaling T max and T min .

Abstract:Snow is an important component of the water resources of New York State and the watersheds and reservoirs of New York City (NYC) water supply. In many of the NYC water supply watersheds the hydrologic regimes of high-elevation headwaters are linked to streamflow and channel processes in low-elevation stream reaches that serve as inputs to water supply reservoirs. To better simulate this linkage there is a need to understand spatial variations in snowpack and snowmelt. Snowmelt hydrology is an important component of the Soil and Water Assessment Tool (SWAT) model in watersheds where spring runoff is strongly affected by melting snow. This study compares model simulated snowpack and snowmelt at different elevation bands with snow survey data available for the Cannonsville reservoir watershed. Simulations examine the effects of parameterising the SWAT snowmelt sub-model using 1, 3, and 5 elevation bands by comparison with measured snow and streamflow. Comparison between measured and simulated snowpack produced correlation coefficients ranging from 0Ð35 to 0Ð85. Simulations of both daily and seasonal streamflow, improved when using 3 elevation bands with r 2 of 0Ð73 and E NS of 0Ð72. Streamflow simulations showed slightly lower model performance when basin elevation was assumed to be equal to snow survey site elevation, due to the snow survey sites being somewhat biased toward lower elevations. The effect of climate change was also evaluated and showed that under higher air temperatures in future climate change scenarios, SWAT indicated more precipitation falling as rain, increased and earlier snowmelt, and a reduced snowpack leading to a change in the pattern of streamflow, particularly during winter and early spring.

Understanding how climate change will affect plant phenology (shifts in the timing of plant activity) is central to many ecological and biogeochemical studies. This aspect of plant ecology often has been overlooked, but addressing the consequences of climate change for adaptive/mitigative management is now high on the list of priorities for funding agencies. This study is innovative because it uses growing degree days (GDD), which has existed since the 1730s, as an ecosystem indicator to study changing diurnal temperatures; their effects on different plant growth stages in the last century; and as a basis for development of future adaptive management strategies. Our results show the most recent time period (1980-2009) had the earliest emergence and the least variability among stations in the day at which the crop stage occurred for most stages except emergence and physiological maturity. 100 year linear trends in the stations indicated all seven crop stages except tassel initiation occurred earlier by one day per decade during the study period. The number of stations with significant decreases varied from 11 to 17 stations out of 23 stations in Kansas. Tassel initiation stage occurred later by one day per decade during the study period. The most recent time period (1980-2009) had the highest variability among stations and 30 year time periods. The variability in trends are higher in western Kansas when compared to eastern Kansas. This knowledge has transformative potential to improve our understanding of the occurrence and duration of the different plant growth stages, add local precision to earlier findings for changes in overall GDD that encompassed larger areas, and help explain the differences in trends from some earlier studies. These shifts in the phenology of agricultural plants as a result of climate change have implications for required increases in food production to feed the growing population.

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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-Climatic Change (2013) 120:169-181