Using remotely sensed Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data, an investigation on extreme rainfall events (EREs) during the monsoon season has been conducted over the Northwest Himalaya (NWH) for the period of 1998–2013. The satellite precipitation data have been validated with gridded rain gauge data prepared by India Meteorological Department (IMD) using standard statistical measures. A strong positive correlation of 0.88 is found between both, supporting the use of 3B42 V7 for the study of rainfall over the region. The EREs have been identified using three indices corresponding to 98th, 99th, and 99.99th percentiles of the rainfall distribution over the region. The 98th and 99th percentile thresholds are suggested to be considered as extreme and very extreme events respectively whereas 99.99th percentile may correspond to the cloudburst events. The parametric t‐test results indicate a significant increasing trend of the frequency of EREs whereas non‐parametric Mann‐Kendall test results yield no significant trend of EREs over the study region. As the sample size is small, therefore the significance of these results may not be ascertained. The elevation exhibits strong inverse relation with frequency and intensity of EREs over the NWH. A strong negative correlation of ∼0.8 and a poor negative correlation of ∼0.48 are obtained between the elevation and frequency of extremes exceeding 98th and 99th percentiles, and frequency of the cloudburst events, respectively. Whereas, elevation shows strong negative correlations of −0.85, −0.84, and −0.81 with rainfall intensities associated with 98th, 99th, and 99.99th percentiles, respectively. The plains and foothills of the NWH region experience the highest frequency of EREs. However, the peaks of the highest frequency of events are also observed at different elevation ranges at state‐level analysis. This study is a contribution to the on‐going research of extreme events over the mountainous terrain including disaster management study.
Future trends in debris flow activity are constructed based on bias-corrected climate change projections using two meteorological proxies: daily precipitation and Convective Available Potential Energy (CAPE) combined with specific humidity for two Alpine areas. Along with a comparison between proxies, future number of days with debris flows are analyzed with respect to different regional and global climate models, Representative Concentration Pathways (RCPs), and area for quantile mapping. Two different base periods are also analyzed, as debris flows were observed on only 6 (17) days between 1950 and 1979, yet on 18 (49) days between 1980 and 2009 for Fella River, NE Italy (Barcelonnette, SE French Alps). For both areas, future climate projections vary between no change up to an increase of 6.0 % per decade in days with debris flow occurrences towards the end of 21st century. In Barcelonnette, the base period and proxy have a bigger impact on the future number of debris flow days than the climate model or RCP used. In Fella River, the base period, RCP, and proxy used define the future range. Therefore the selection of proxy, base period and downscaling technique should be carefully considered for future climate change impact studies concerning debris flow activity and associated fast-moving landslides.
Stochastic weather generators simulate synthetic weather data while maintaining statistical properties of the observations. A new semi-parametric algorithm for multi-site precipitation has been published recently by Breinl et al. (2013), who used a univariate Markov process to simulate precipitation occurrence at multiple sites for two small rain gauge networks. Precipitation amounts were simulated in a two-step process by first resampling observations and then sampling and reshuffling of parametric precipitation amounts. In the present study, the precipitation model by Breinl et al. (2013, J. Hydrol. 498: 23-35) is implemented in a weather generation framework for daily precipitation and temperature. It is extended to a considerably larger gauge station network of 19 stations and further improved to reduce the duplication of historical records in the simulation. Autoregressive-moving-average models (ARMA) are used to simulate mean daily temperature at three sites. Power transformations reduce the bias of simulated temperature extremes. Precipitation amounts are simulated by means of hybrid distributions consisting of a Weibull distribution for low precipitation amounts and a generalized Pareto distribution (GPD) for moderate and extreme precipitation amounts. The proposed weather generator is particularly suitable for assessing hydrometeorological hazards such as flooding as it reproduces the spatial variability of precipitation very well and can generate unobserved extremes.
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