Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β, σ 2 ), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Trends in extreme daily temperature and rainfall have been analysed from 1961 to 2002 for the western Thailand (Mae Ping and Mae Klong river basins). Daily precipitation, maximum and minimum temperature data for 15 stations were analysed to calculate 21 extreme indices. The magnitude of trends was estimated using the linear regression method while its statistical significance was evaluated using the p-value at 5% significance level and Kendall-tau test. The result of the analysis depicts significant increase in the annual number of warm days and warm nights, with corresponding significant decreases in the annual number of cool days and cold nights. The warm spell duration indicator presents statistical rising trends. The trends for temperature indices are more consistent in the region compared to precipitation indices. There is insignificant decrease in annual total precipitation for nearly all stations. The maximum number of consecutive dry days (rain less than 1 mm) is increasing. The number of days with rainfall more than 10 and 20 mm has declined over both basins except at Kanchanaburi station. Analysis also reveals that there is less spatial coherent in other extreme indicators, namely, maximum 1-day and 5-days rainfall amount and simple daily intensity index.

Cancer incidence and/or mortality among individuals varies with diet, socio-culture, ethnicity, race, gender, and age. Similarly, environmental temperature modulates many biological functions. To study the effect of environment temperature on cancer incidence, the US population was selected. Because, county-wise cancer incidence rate data of various anatomical site-specific cancers and different races/ethnicities for both males and females are available. Moreover, the differences amongst the aforementioned factors among individuals are much less, as compared to the world population. Statistical analysis showed a negative correlation between the average annual temperature and cancer incidence rate at all anatomical sites and individually for 13 types (out of 16 types) of anatomical site-specific cancer incidence rates (e.g. uterine, bladder, thyroid, breast, esophagus, ovary, melanoma, non-Hodgkin lymphoma, leukemia, brain, pancreas, etc.) for females. Further analysis found a similar inverse trend in all races/ethnicities of the female population but not in all male races/ethnicities or anatomical site-specific cancers. Moreover, the majority of the counties having the top-most cancer incidence rate in females are located above the latitude 36.5°N. These findings indicate that living in a cold county in the United States might have a higher risk of cancer irrespective of cancer type (except cervical and liver) and races/ethnicities for females but not in all such cases for the male population.

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