京都大学 / 新制・課程博士 / 博士(工学) / 甲第25250号 / 工博第5209号 / 新制||工||1994(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 教授 角 哲也, 教授 森 信人 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

This thesis describes the use of a distrometer and two IBM-PC's to collect data in a large propagation experiment. The uses and methods of collecting drop size distribution are discussed as are the uses of IBM-PC's for both data collection and control. Methods of requiring the PC's to operate in real time are also included. / M.S.

Intensity-duration-frequency (IDF) curves are widely used for peak discharge estimation in designing hydraulic structures. The traditional Gumbel probability method entails selecting annual maximum rainfall depths (intensities) conditioned on a fixed time window width (which in general will not coincide with the rainfall event duration) from a continuous record to perform a frequency analysis in terms of the marginal distribution. The digitized database contains annual maximum intensities for selected discrete durations. This method presents problems when intensities are required for arbitrary durations which are not part of the selected durations. Accurate interpolated and especially extrapolated intensity values are hard to obtain. The present study offers two methods both involving a joint probability approach to overcome the deficiencies inherent in the traditional method of IDF analysis. The first joint probability approach employs Box-Cox and modulus transformations to transform original data to near bivariate normality. The second method does not require such a transformation. Instead, it uses the closed-form bivariate Burr III cumulative distribution to fit the data. Another advantage of the joint probability approach is that it allows one to gauge the rarity of certain extreme events, such as probable maximum precipitation, in terms of the joint occurrence of its extremely high intensity and a sufficiently long duration (e.g. 24 hours). The joint probability approach is applied to three data sets. The resulting conditional probability intensity estimates are quite close to those obtained by traditional Gumbel IDF analysis. In addition, reliable interpolated and extrapolated intensities are available because the approach essentially fits a flexible surface to the discrete data with the capability of providing a complete probabilistic structure. / Master of Science

Rainfall interception is one mechanism by which tree canopies can reduce surface runoff in urban areas. The objectives of this research were to: 1) quantify rainfall interception by urban tree canopies, and 2) determine the influence of vegetation and microenvironmental factors on rainfall interception rates. In the city of Denton, Texas, 30 mature post oak (Quercus stellata) and blackjack oak (Quercus marilandica) trees were selected for study. Trees were assigned to one of three categories: clusters of trees on greenspace (CG), isolated trees on greenspace (IG), and isolated trees surrounded by pavement (IP). Throughfall (the volume of water that travels through the canopy and reaches the soil surface) collectors were placed beneath these trees and rainfall collectors were placed in nearby open areas. Throughfall and rainfall were collected daily from 19 March to 4 July. Interception was calculated as the difference between throughfall and gross rainfall. Over the study period, there were 27 days with measurable rainfall; daily rainfall ranged from 1-51 mm. Over the sampling period, rainfall interception for individual trees ranged from -10% to 49%, indicating high spatial variability in interception. Percent interception was highest for the CG treatment (22.7 ± 3.8 SE), intermediate for IG (27.4 ± 2.3 SE), and lowest for IP (9.1 ± 4.9 SE). Factors like wind exposure, wind-driven rain and overall tree health may help explain this variability. This research will contribute to our knowledge of hydrological fluxes in urban areas and the role of urban green infrastructure in stormwater runoff mitigation.

Reconstruction studies of seasonal rainfall utilizing stable isotope based proxy approach suffer from the limitations of time resolutions. Conventional methods and archives limit the achievable resolution to annual scales. However, high resolution reconstruction (seasonal to sub-weekly scale) can be achieved in proxy records where growth rates are high enough to leave spatial signatures in an organically or inorganically deposited layer such as growth bands. In this study, aragonitic skeleton of the gastropod Lissachatina fulica (Bowdich, Giant African Land Snails) is investigated with an aim to achieve sub-weekly scale reconstruction of the Indian monsoon rainfall. These terrestrial gastropods are native of Africa and highly invasive. Their evolution in the geological time period dates back to the Pliocene and is presently distributed across the tropical belt. They exhibit a high growth rate in the presence of water and high relative humidity in the environment. As a result, they are ideally suited for the task of palaeo seasonality reconstruction. The isotopic patterns recorded in their growth bands reveal composition of environmental water at seasonal time scales. In vitro studies were carried out on L. fulica to estimate their growth rates and growth responses to changes in the physical conditions within the culture chamber. The Indian monsoon rainfall exhibits characteristic dry spells that are generally sandwiched between periods of active phases of high rainfall during the South West monsoon season. These dry spells are typically characterized by rainfall with low intensity. Isotope fingerprinting of the rain water at daily time resolution, covering the years of 2007-10 exhibited distinct isotopic ratios for the dry and wet spells. Dry spells were clearly demarcated in the record with isotopically enriched signature. In addition, the study indentified the role of three distinct moisture sources on δ18O of rain water at Bangalore, India. The variability in the oxygen isotopic composition of the Indian monsoon rainfall is predominantly controlled by this source moisture variability at inter annual time scales, while temperature and amount of rainfall tend to dominate the variability in the precipitation isotopes at seasonal and weekly scales. Simultaneous isotopic analyses of both rainwater and shell carbonates growth bands were undertaken to understand their relationship to aid in high resolution reconstruction. Carbonate found in the growth bands of the gastropods, which is precipitated under equilibrium condition from rainwater, preserves the signature of rainfall. This provides an opportunity to reconstruct rainfall parameters (i.e. amount and moisture sources) knowing the variability in shell carbonates. Stable isotopic ratios measured across the growth bands of live shell specimens collected from the southern and eastern Indian regions (Bangalore and Kolkata, respectively) were compared with the rainfall isotope ratios at these two locations; signature of dry spells were clearly identified from the study of isotopic composition in the growth bands of the gastropod specimens. The approach was also extended to older samples from historical archives from eastern Indian region (Kolkata, East India). Individual specimens belonging to the same species of gastropod, which were collected during the monsoon season of the year 1918 were used for reconstructing the seasonal pattern in monsoon rainfall over the region. The record of variation in the isotopic composition seen in the shell was compared with the rainfall data from Indian Metrological Division observatory at Kolkata station. The year 1918 was characterized as a major drought year and the signature of dry period was seen preserved in the specimen. The work under taken in this thesis will widen the scope of seasonality reconstruction using terrestrial shell fossils from palaeo records, which have been rarely investigated in paleoclimate studies from the perspective of understanding the seasonal precipitation variability.

Reconstruction studies of seasonal rainfall utilizing stable isotope based proxy approach suffer from the limitations of time resolutions. Conventional methods and archives limit the achievable resolution to annual scales. However, high resolution reconstruction (seasonal to sub-weekly scale) can be achieved in proxy records where growth rates are high enough to leave spatial signatures in an organically or inorganically deposited layer such as growth bands. In this study, aragonitic skeleton of the gastropod Lissachatina fulica (Bowdich, Giant African Land Snails) is investigated with an aim to achieve sub-weekly scale reconstruction of the Indian monsoon rainfall. These terrestrial gastropods are native of Africa and highly invasive. Their evolution in the geological time period dates back to the Pliocene and is presently distributed across the tropical belt. They exhibit a high growth rate in the presence of water and high relative humidity in the environment. As a result, they are ideally suited for the task of palaeo seasonality reconstruction. The isotopic patterns recorded in their growth bands reveal composition of environmental water at seasonal time scales. In vitro studies were carried out on L. fulica to estimate their growth rates and growth responses to changes in the physical conditions within the culture chamber. The Indian monsoon rainfall exhibits characteristic dry spells that are generally sandwiched between periods of active phases of high rainfall during the South West monsoon season. These dry spells are typically characterized by rainfall with low intensity. Isotope fingerprinting of the rain water at daily time resolution, covering the years of 2007-10 exhibited distinct isotopic ratios for the dry and wet spells. Dry spells were clearly demarcated in the record with isotopically enriched signature. In addition, the study indentified the role of three distinct moisture sources on δ18O of rain water at Bangalore, India. The variability in the oxygen isotopic composition of the Indian monsoon rainfall is predominantly controlled by this source moisture variability at inter annual time scales, while temperature and amount of rainfall tend to dominate the variability in the precipitation isotopes at seasonal and weekly scales. Simultaneous isotopic analyses of both rainwater and shell carbonates growth bands were undertaken to understand their relationship to aid in high resolution reconstruction. Carbonate found in the growth bands of the gastropods, which is precipitated under equilibrium condition from rainwater, preserves the signature of rainfall. This provides an opportunity to reconstruct rainfall parameters (i.e. amount and moisture sources) knowing the variability in shell carbonates. Stable isotopic ratios measured across the growth bands of live shell specimens collected from the southern and eastern Indian regions (Bangalore and Kolkata, respectively) were compared with the rainfall isotope ratios at these two locations; signature of dry spells were clearly identified from the study of isotopic composition in the growth bands of the gastropod specimens. The approach was also extended to older samples from historical archives from eastern Indian region (Kolkata, East India). Individual specimens belonging to the same species of gastropod, which were collected during the monsoon season of the year 1918 were used for reconstructing the seasonal pattern in monsoon rainfall over the region. The record of variation in the isotopic composition seen in the shell was compared with the rainfall data from Indian Metrological Division observatory at Kolkata station. The year 1918 was characterized as a major drought year and the signature of dry period was seen preserved in the specimen. The work under taken in this thesis will widen the scope of seasonality reconstruction using terrestrial shell fossils from palaeo records, which have been rarely investigated in paleoclimate studies from the perspective of understanding the seasonal precipitation variability.

In the current study, we have presented a systematic analysis of the diurnal cycle of rainfall over the Indian region using satellite observations, and evaluated the ability of the Weather Research and Forecasting Model (WRF) to simulate some of the salient features of the observed diurnal characteristics of rainfall. Using high resolution simulations, we also investigate the underlying mechanisms of some of the observed diurnal signatures of rainfall. Using the Tropical Rain-fall Measuring Mission (TRMM) 3-hourly, 0.25 ×0.25 degree 3B42 rainfall product for nine years (1999-2007), we extract the finer spatial structure of the diurnal scale signature of Indian summer monsoon rainfall. Using harmonic analysis, we construct a signal corresponding to diurnal and sub-diurnal variability. Subsequently, the 3-hourly time-period or the octet of rain-fall peak for this filtered signal, referred to as the “peak octet,” is estimated with care taken to eliminate spurious peaks arising out of Gibbs oscillations. Our analysis suggests that over the Bay of Bengal, there are three distinct modes of the peak octet of diurnal rainfall corresponding to 1130, 1430 and 1730 IST, from north central to south Bay. This finding could be seen to be consistent with southward propagation of the diurnal rainfall pattern reported by earlier studies. Over the Arabian sea, there is a spatially coherent pattern in the mode of the peak octet (1430 IST), in a region where it rains for more than 30% of the time. In the equatorial Indian Ocean, while most of the western part shows a late night/early morning peak, the eastern part does not show a spatially coherent pattern in the mode of the peak octet, owing to the occurrence of a dual maxima (early morning and early/late afternoon). The Himalayan foothills were found to have a mode of peak octet corresponding to 0230 IST, whereas over the Burmese mountains and the Western Ghats (west coast of India) the rainfall peaks during late afternoon/early evening (1430-1730 IST). This implies that the phase of the diurnal cycle over inland orography (e.g., Himalayas) is significantly different from coastal orography (e.g., Western Ghats). We also find that over the Gangetic plains, the peak octet is around 1430 IST, a few hours earlier compared to the typical early evening maxima over land. The second part of our study involves evaluating the ability of the Weather Research and Fore-casting Model (WRF) to simulate the observed diurnal rainfall characteristics. It also includes conducting high resolution simulations to explore the underlying physical mechanisms of the observed diurnal signatures of rainfall. The model (at 54km resolution) is integrated for the month of July 2006 since this period was particularly favourable for the study of diurnal cycle. We first evaluate the sensitivity of the model to the prescribed sea surface temperature (SST) by using two different SST datasets, namely Final Analyses (FNL) and Real-time Global (RTG). The overall performance of RTG SST was found to be better than FNL, and hence it was used for further model simulations. Next, we investigated the impact of different parameterisations (convective, microphysical, boundary layer, radiation and land surface) on the simulation of diurnal cycle of rainfall. Following this sensitivity study, we identified the suite of physical parameterisations in the model that “best” reproduces the observed diurnal characteristics of Indian monsoon rainfall. The “best” model configuration was used to conduct two nested simulations with one-way, three-level nesting (54-18-6km) over central India and Bay of Bengal. While the 54km and 18km simulations were conducted for July 2006, the 6km simulation was carried out for the period 18-24 July 2006. This period was chosen for our study since it is composed of an active period (19-21 July 2006), followed by a break period (22-24 July 2006). At 6km grid-spacing the model is able to realistically simulate the active and break phases in rainfall. During the chosen active phase, we find that the observed rainfall over central India tends to reach a maximum in the late night/early morning hours. This is in contrast to the observed climatological diurnal maxima of late evening hours. Interestingly, the 6km simulation for the active phase is able to reproduce this late night/early morning maxima. Upon further analysis, we find that this is because of the strong moisture convergence at the mid-troposphere during 2030-2330 IST, leading to the rainfall peak seen during 2330-0230 IST. Based on our analysis, we conclude that during both active and break phases of summer monsoon, mid-level moisture convergence seems to be one of the primary factors governing the phase of the diurnal cycle of rainfall. Over the Bay of Bengal, the 6km model simulation is in very good agreement with observations, particularly during the active phase. The southward propagation observed during 19-20 July 2006, which was not captured by the coarse resolution simulation (54km), is exceedingly well captured by the 6km simulation. The positive anomalies in specific humidity attain a maxima during 2030-0230 IST in the north and during 0830-1430 IST in the south. This confirms the role of moisture convergence in the southward propagation of rainfall. Equally importantly we find that while low level moisture convergence is dominant in the north Bay, it is the mid-level moisture convergence that is predominant in the south Bay.

The scarcity of data poses a challenging obstacle for the study of natural disasters, especially in developing countries where the social vulnerability plays as important a role as the physical vulnerability.  The work presented in this thesis is oriented towards the demonstration of the usefulness of press archives as a data source for the temporal and spatial analysis of landslides in Tegucigalpa, Honduras for the period between 1980 and 2005.  In the last four decades, Tegucigalpa has been characterized by a disorganized urban growth that has significantly contributed to the destabilization of the city’s slopes.  In the first part of the thesis, a description of the database compilation procedure is provided.  The limitations of using data derived from press archives have also been addressed to indicate how these affect the subsequent landslide analyses.  In the second part, the temporal richness offered by press archives has allowed the establishment of rainfall thresholds for landslide occurrence.  Through the use of the critical rainfall intensity method, the analysis of rainfall thresholds for 7, 15, 30 and 60 antecedent days shows that the number of yielded false alarms increases with the threshold duration.  A new method based on the rainfall frequency contour lines was proposed to improve the distinction between days with and without landslides.  This method also offers the possibility to identify the landslides that may only occur with a major contribution of anthropogenic disturbances as well as those landslides induced by high-magnitude rainfall events.  In the third part, the matrix method has been employed to construct two landslide susceptibility maps: one based on the multi-temporal press-based landslide inventory and a second one based on the landslide inventory derived from an aerial photograph interpretation carried out in 2014.  Despite the low spatial accuracy provided by the press archives in locating the landslides, both maps exhibit 69% of consistency in the susceptibility classes and a good agreement in the areas with the highest propensity to landslides.  Finally, the integration of these studies with major actions required to improve the process of landslide data collection is proposed to prepare Tegucigalpa for future landslides.

The temporal distribution of rainfall , viz. the distribution of rainfall intensity during a storm, is an important factor affecting the timing and magnitude of peak flow from a catchment and hence the flood-generating potential of rainfall events. It is also one of the primary inputs into hydrological models used for hydraulic design purposes. The use of short duration rainfall data inherently accounts for the temporal distribution of rainfall, however, there is a relative paucity of short duration data when compared to the more abundantly available daily data. One method of overcoming this is to disaggregate courser-scale data to a finer resolution, e.g. daily to hourly. A daily to hourly rainfall disaggregation model developed by Boughton (2000b) in Australia has been modified and applied in South Africa. The primary part of the model is the . distribution of R, which is the fraction of the daily total that occurs in the hour of maximum rainfall. A random number is used to sample from the distribution of R at the site of interest. The sample value of R determines the other 23 values, which then undergo a clustering procedure. This clustered sequence is then arranged into 1 of 24 possible temporal arrangements, depending when the hour the maximum rainfall occurs. The structure of the model allows for the production of 480 different temporal distributions with variation between uniform and non-uniform rainfall. The model was then regionalised to allow for application at sites where daily rainfall data, but no short duration data, were available. The model was evaluated at 15 different locations in differing climatic regions in South Africa. At each location, observed hourly rainfall data were aggregated to yield 24-hour values and these were then disaggregated using the methodology. Results show that the model is able to retain the daily total and most of the characteristics of the hourly rainfall at the site, for when both at-site and regional information are used. The model, however, is less capable of simulating statistics related to the sequencing of hourly rainfalls, e.g. autocorrelations. The model also tends to over-estimate design rainfalls, particularly for the shorter durations . / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.

In the present work, methodology of statistical analysis of change evolved by Kundzewicz and Robson (204) is revised to obtain a robust methodology named as “Comprehensive Aproach” which addresses research gaps of earlier method, as also those found by literature review. Main aspects of the revised method are: 1) importance of graphical representations as first step, in which, if line spectrum has constant spectral density function then time series is random and no need of further trend detection, 2) importance of computation of statistical parameters of data for deciding type of step change test to be used and for cross checking results of exploratory data analysis (EDA), 3) application of EDA, statistical parameters and checking assumption(s) about the data by statistical test(s) is suggested and also results of these steps can be used to cross check results of each other, 4) suggested basis for selection of step change test(s) i.e. evaluation of two aspects of step change viz. detection and location of step change, 5) suggested basis for selection of trend detection tests i.e. evaluation of all four aspects of trend viz. magnitude, statistical significance, beginning and end of trend and nature of trend, 6) evaluation of regional significance is suggested as essential wherever applicable. The revised method i.e. “Comprehensive Approach” is applied for the trend detection of rainfall of seven homogenous rainfall regions and al India at annual, monthly and seasonal temporal scales for three time periods 1901-203, 1948-203 and 1970-203. Between 100 N to 300 N, there was marked increase in precipitation from 190 to 1950s, but decrease after about 1970 (Trenberth et al., 207). Thus starting years of three time periods are selected as 1901, 1948 and 1970. To have similarity of end year, in analysis periods given in chapters 1, 2 and chapters 3, 4; their end years are kept close to each other i.e. end year of analysis periods is 203 in chapters 1, 2 and end year of analysis periods is 204 in chapters 3, 4. Thus 203 are considered as common end year of three time periods. Burn and Elnur (202) sugested that least number of years required for ensuring statistical validity of results of trend detection are 25 years. So in the third time period (1970-203), the duration is 34 years which is more than 25 years. Three time periods are having data of 103 years (1901-203), 56 years (1948-203) and 34 years (1970- 203) so effect of different time durations on trend detection analysis results is studied. Also temporal scales used in trend detection analysis are annual, monthly and seasonal (4 seasons) thus presence of trend is assessed in these main temporal scales. Results of the analysis showed that, statistically significant trends are found in: 1) winter rainfall time series of peninsular India (PENIN) region for the time period 1901-203, 2) pre-monsoon rainfall time series of north west India (NWIND) and central north east India (CNEIN) regions for the time period 1948-203, 3) monsoon rainfall time series of west central India (WCIND) region for the time period 1948-203, 4) August month rainfall time series of north east India (NEIND) region for the time period 1901-203, 5) June month rainfall time series of NEIND region for the time period 1948-203, 6) Also regionally significant trends are detected in pre- monsoon rainfall time series of five homogeneous regions for the time period 1948-203. Regionally significant trends are detected in pre-monsoon rainfall time series of five homogeneous regions for the time period 1948-203. But effect of cross correlation between rainfall time series of stations of subdivisions and between the sub-divisions in a region is not accounted in the field/regional significance evaluation and Hegel et al. (207) suggested that reactions to external forcing in trends of regional precipitation trends exhibit weak signal to noise ratios and likely to exhibit strong variations in space because of dependency of precipitation on geographic parameters like pornography and atmospheric circulation. Thus attribution of precipitation is more difficult. Also Saikranthi et al. (2013) suggested that homogeneity of rainfall zones may change in future. So, attribution of trends detected in pre-monsoon rainfall time series of five homogeneous regions was not possible. The results of statistically significant trends are confirmed by smoothing curves, innovative trend analysis plots and Sen.’s slope estimates. Contributions by present trend detection study on rainfall of homogenous regions by using “Comprehensive Approach” method are: 1) modification of guidelines of statistical analysis of change to evolve a robust method termed as “Comprehensive Approach”, 2) systematic trend detection analysis is performed pertaining to the rainfall of core monsoon India (CORIN) region and homogeneous India (HOMIN) region, which was not done earlier, 3) systematic trend detection analysis is performed on the rainfall of al India and seven homogenous regions concurrently for aforesaid temporal scales and time periods (except regional significance evaluation only for five homogeneous regions), which was not done earlier, 4) Man Kendal test with block bootstrapping approach (MKBBS) test (not effected by serial correlation) is used for trend detection of serially correlated data and Man Kendal (MK) test is used for trend detection of serially uncorrelated data. Sen.’s slope is used for evaluation of trend magnitude, 5) evaluation of field/regional significance of trends in rainfall over five homogenous regions is performed, which was not done earlier, 6) Location of beginning, end and progress of trend in rainfall of all India and seven homogenous regions concurrently is performed, which was not done earlier. As mentioned aforesaid, attribution of regionally significant trends detected in pre-monsoon rainfall time series of five homogeneous regions for the time period 1948-203 was not possible because of non-accounting of effects of cross correlation, attribution of rainfall is difficult and homogeneity of rainfall zones may change in future as discussed above in detail. So a thorough investigation about trends in rainfall, three temperatures (minimum, mean and maximum) and stream flow at regional (basin) scale was proposed to be ascertained. As Tapi basin is exposed to occurrence of heavy floods (Joshi and Shah, 2014) and it is climatically sensitive (Bhamare and Agone, 201; Gosain et al. 206; Deshpande et al., 2016), it is considered as study area. The trend detection analysis of gridded data (chapter 4) and regional time series (chapter 3) of rainfall and three temperatures data (1971-204) along with that for station data of stream flow (1979-204) of five gauging stations (chapter 4) is carried out using “Comprehensive Approach” for all temporal scales. Common available end year of data of rainfall, temperature and stream flow was 204 as data after 204 was not available for stream flow for all five gauging stations. Also data of rainfall (0.50 x 0.50) was available from year 1971, which was common starting year among data of rainfall and three temperatures. Also common starting year of stream flow data was 1979. Because of unavailability of rainfall data (0.50 x 0.50) before 1971, the three time periods used in chapters 1 and 2 are not used in chapters 3 and 4, thus only one time period is used for rainfall and three temperatures (1971-204) and stream flow (1979-204). The analysis has shown the presence of regionally significant rends in the gridded data of annual mean temperature (Tmean) and winter Tmean over Tapi basin apart from significant trends found in regional time series of annual Tmean and winter Tmean of Tapi basin. Monthly, winter and pre- monsoon stream flow volume time series have also shown regionally significant trends over five gauging stations of Tapi basin. Main contributions of the trend detection analysis of hydro- climatic variables of Tapi basin are: 1) grid wise, regional scale and station wise trend detection of three temperatures, rainfall and stream flow respectively is performed, which was not done earlier, 2) regional significance evaluation of gridded data (rainfall and three temperatures) and station data of stream flow (five stream flow gauging stations) is performed, which was not done earlier, 3) all four aspects of trend of hydro-climatic variables are evaluated, which was not done earlier, 4) systematic trend detection study of gridded, regional and station data of hydro-climatic variables is performed in present study which was not done earlier. After detection of regionally significant trends, next step is finding the causal factors through attribution study. Once causal factors of climate change observed in given variable are found, then remedial measures can be carried out for minimizing the effect of these factors on climate change observed in given variable. There are three main methods of attribution found in literature viz. finger printing, optimal finger printing and artificial neural network (ANN) model. In finger printing method only the leading empirical orthogonal function (EOF) is used, so this method is conservative. In optimal finger printing, multivariate regression is used, which has certain assumptions which are difficult to be fulfilled in the case of climate studies as climate is essentially a non-linear dynamic system. ANN being non-linear in nature provides the required solution for the attribution problem related to climate. Attribution of regionally significant trends detected in monthly, winter and pre-monsoon stream flow volume time series of five gauging stations of Tape basin is not performed because five gauging stations were not representative of entire Tapi basin and two out of the five gauging stations have missing data greater than 15%. Number of significant monotonically increasing trends are more in winter gridded Tmean data as compared to annual gridded Tmean data. Thus attribution analysis of winter gridded Tmean data has given first priority followed by attribution of annual gridded Tmean data. ANN model is developed for the attribution of climate change observed in gridded data of winter Tmean and annual Tmean in three steps: 1) input variable selection (IVS) based on partial mutual information (PMI), 2) data splitting using k-means clustering method and Neyman allocation, 3) ANN model formulation by using best training algorithm among Levenberg-Marquardt (LM) algorithm, scaled conjugate gradient (SCG) algorithm and Broyden, Fletcher, Goldfarb, and Shano (BFGS) algorithm and optimum number of hidden neurons (varying from 1 to 3) corresponding to performance in terms of mean squared error (MSE) and to use these in final ANN model formulation with computation of performance evaluation measures (PEMs). Aforesaid third step is repeated for 50 iterations for each input forcing and given target output to minimize any random variation due to reinitialization of training algorithms. Also random variations due to initialization of ANN model are minimized by keeping initial weights and biases equal to zero. Final PEMs evaluated were the averages of 50 iterations as mentioned aforesaid. Target outputs used in two ANN attribution models are time series of regional winter Tmean and regional annual Tmean. Also in some cases of ANN model formulations, network parameters are kept less than number of data points in the training set for minimizing overriding. Inputs for ANN model were circulation indices and regional, global and national scale input variables. The inputs selected by PMI based input selection (PMIS) algorithm in the step of IVS of both ANN attribution models are seen to be subjected to natural and anthropogenic forcing, which undisputedly shows significant role of anthropogenic activities in observed climate change in aforesaid two gridded temperature variables. Also ranking of input forcing is performed in both the ANN attribution models according to their final PEM values. In the case of ANN attribution model for regional winter Tmean time series, dominant role of natural (‘nat’) input forcing is found behind the given climate change as compared to anthropogenic (‘anth’) input forcing. Among ‘anth’ inputs, effect of land cover (‘Landcover’) input forcing is found to be dominant as compared to green house gases (‘GHgases’) input forcing. Among ‘Landcover’ inputs, urban landcover input was found to be one of the important inputs. In the case of ANN attribution model for regional annual Tmean time series, dominant role of ‘anth’ input forcing is found behind the given climate change as compared to ‘nat’ input forcing. Among ‘anth’ inputs, there is dominant role of ‘Landcover’ input forcing as compared to ‘GHgases’ input forcing. Among ‘Landcover’ inputs, urban landcover input was found to be one of the important inputs. Contributions of attribution study are: 1) checking of input independence and significance by using PMI IVS method, which was not performed earlier, 2) division of data in such a way that al patterns of whole data are present in training, testing and validation subsets and the statistical properties of these subsets are similar to each other and to whole data, which was not performed earlier, 3) using LM, SCG and BFGS algorithms which are converging fatly as compared to Windrow-Hof algorithm and gradient descent algorithm. Also these three algorithms are les liable to be get stuck in local minima, 4) using land cover data as input forcing to ANN model used for attribution of climate change, which was not done earlier.