Rainfall In Mountainous Region Research Articles

Combining rainfall-induced shallow landslides and subsequent debris flows for hazard chain prediction

Landslides, debris flows, and other destructive natural hazards induced by heavy rainfall in mountainous regions are sometimes not independent but combined to form a disaster chain. Based on the integral link between the triggering of the landslide and the subsequent debris flow, we propose an approach that combines the Transient Rainfall Infiltration and Grid-Based Regional Slope Stability (TRIGRS) model and the Rapid Mass Movements Simulation (RAMMS) model to achieve hourly hazard prediction. The results indicate that the TRIGRS model performed well in predicting the spatial distribution of the shallow landslides, with a success rate of 81.86%. Thus, it is reasonable to use it as the initial input for debris flow simulations. The relationship between the landslide area and the accumulated rainfall obtained using the TRIGRS model is a power-law relationship, which provides a reference for regions that lack rainfall data to predict the material source of a debris flow. The coupled model was found to have a good accuracy of 76.77% in simulating the debris flow. This was close to the debris flow simulation based on the interpreted landslides, and it still produced reasonable results and a more practical value. Furthermore, the proposed coupled model can dynamically predict disasters by the hour based on actual rainfall events. Therefore, the results of this study help provide a more complete hazard prediction picture for rainfall-induced landslide-debris flow hazards in mountainous regions.

Comparative study of performance of real-time satellite-derived rainfall in Swat Catchment

Most of the conventional models require rainfall data for realistic modeling results, and where ground data is scarce, remotely sensed data plays a vital role. For monitoring, hydrological models require near real-time observations to allow for effective planning and forecasting. However, monitoring rainfall in mountainous region is difficult because of inaccessibility and sparse gauge density. However, the accurateness of these satellite estimates over different spatial and temporal scales is unknown. The study intended at carrying out a comparative analysis of satellite rainfall estimates as a substitution for ground-based rainfall observations in the Swat Catchment. Limited availability of temporally continuous available data records in Pakistan has been a problem and has effected the reliability of modeling results. As well as, data is not freely available and cost is the biggest hindrance to its usage. So, remotely sensed data plays a vital role both in terms of timely availability and its free of charge. For this region, only two remotely sensed gridded data products are freely available, i.e., NOAA RFE CA and TRMM RT. Respective two products have been analyzed by various verification statistics. RFE CA proves better probability of detection, false alarm ratio, threat score, and equitable threat score than TRMM RT. The outcome of this comparative study concludes that for hydrological modeling purposes, RFE CA data is the best choice in this region. The annual bias for RFE CA and TRMM RT is 14% (over-estimation) and 18% (under-estimation) over the years having coefficient of determination with the ground-based data of 0.87 and 0.76, respectively, on annual basis. The result shows the suitability of RFE CA for effective monthly rainfall-runoff modeling in Swat Catchment, Khyber Pakhtunkhawa, Pakistan.

Clustering Data and Incorporating Topographical Variables for Improving Spatial Interpolation of Rainfall in Mountainous Region

This study was an attempt to quantify the improvement in the accuracy of rainfall interpolation in the mountainous terrain by clustering of rainfall data at the data preparation stage and incorporating topographical variables at interpolation stage. The univariate kriging techniques, ordinary kriging (OK), simple kriging and universal kriging (UK) were compared with multivariate kriging method of ordinary cokriging (OCK). The elevation, slope and terrain ruggedness index (TRI) computed from digital elevation model were incorporated as explanatory variable in OCK. These algorithms were applied to the normal annual and seasonal rainfall data points located in Central Himalayas of Uttarakhand region. The study area was divided into two different zones of lowland and upland based on the elevation variability. This zonation was then used as a basis for clustering the rainfall data. The performance of the interpolation techniques was assessed for subdivided regions and compared with the results obtained for complete region. The evaluation was based on absolute error metrics of root mean square error (RMSE) in combination with RMSE-observations standard deviation ratio. The absolute percentage error (APE) statistics was calculated for every observation points and then the percentage of data for with APE ≤ 30% (APE30) was analyzed. It was observed that performance efficiency of the interpolation methods improves by 5-20% if the rainfall data is clustered based on homogeneity of terrain elevation as against considering the complete set of data. In complex terrain, the inclusion of topographical variables improves the cokriging based rainfall prediction if it is correlated with rainfall.

DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas, India

In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km2. A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield’s percent better. It was observed that the stratification of rainfall data resulted in 5–20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods.

Linkages between Orographic Forcing and the Scaling Properties of Convective Rainfall in Mountainous Regions

Abstract Heavy rainfall over mountainous terrain often results in catastrophic flooding and presents a great challenge for forecasters. Statistical downscaling methods provide a way to bridge across the scale gap between rainfall forecasts from numerical weather prediction models and the high-resolution needs of hydrologic models for flash flood prediction. In this study, multiscale statistical analysis was used to analyze several heavy convective rainfall events that produced catastrophic flooding in the Appalachian Mountains and Front Range of the Rocky Mountains with the motivation of developing predictive relationships for a priori parameter estimation needed in downscaling applications. The multiscale behavior of the rainfall was analyzed over time and linked to underlying topographic elevation and predominate orographic forcing. It was found that in storms located on the leeward side (i.e., the side of the mountain facing away from the upper-level winds), one of the parameters increased with increasing topographic elevation. An opposite trend was found for this parameter in storms located on the windward side (i.e., the side of the mountain facing toward the direction of the upper-level winds). These trends held across different geographical regions and both upslope and downslope directions of storm motion. The interaction between orographic and meteorological forcings was found to be important in developing predictive relationships for the multiscale statistical parameters.

A New Parsimonious Methodology of Mapping the Spatial Variability of Annual Maximum Rainfall in Mountainous Environments

Abstract The estimation of rare frequency rainfall is an essential prerequisite for the design of engineering structures and to determine risk areas. Index-based methods are among the most applied for regional frequency analysis of hydrological variables such as discharge and rainfall and comprise two stages: the mapping of a scale or “index” factor and the derivation of rainfall growth curves. The underlying hypothesis of these methods is that cumulative distribution functions of a certain random variable can be assumed homogeneous on a given region, except for the index factor, which varies spatially in that region and is often represented by the expected value of the random variable itself at a given location. Methods either to single out homogeneous regions or to evaluate the index factor can be purely statistical and physically based. In this paper a robust and transferable physically based methodology is proposed to estimate the index factor for rainfall in mountainous regions referred to in the following text as “index rainfall.” Index rainfall is defined as the expected value of annual rainfall maxima recorded in a fixed time window: a time window of 1 h is used. Reliable estimates of the index rainfall are obtained at ungauged sites by applying a relationship, based on a multivariate linear regression obtained at gauged sites, of rainfall and selected synthetic descriptors for atmospheric climate and orography. An extended and general set of descriptors is chosen from parameters that are considered in the literature to affect rainfall intensity. The relevant relief descriptors, defining slope, elevation, orientation, etc., at a given location, are extracted from digital elevation models (DEMs). A 2D Fourier series analysis of the DEM is performed and a spectral analysis is carried out to single out the components with the highest morphological information content. The synthetic relief descriptors are evaluated along different cross sections of the 2D truncated Fourier series to single out the role of the prevailing convection direction of extreme rainfall-producing meteorological patterns. The optimal descriptor subset for the study area is then extracted to maximize transferability of the method. Application to the Italian and French Alps and the Apennines shows encouraging results. Descriptor subset extraction has been tested and validated on independent subsets of index rainfall estimates in the regions. Results demonstrate that the proposed method is robust, transferable, and reliable for the evaluation of the index rainfall in ungauged sites.

A NEW ESTIMATION METHOD OF PRECIPITABLE WATER VAPOR FOR ADVANCEMENT OF THE RAINFALL PREDICTION SYSTEM RUNNING AT THE ACTUAL SITE

It is very important for appropriate dam operation and disaster prevention to predict severe rainfall in mountainous region. For last several years, we have developed rainfall prediction system for the Kurobe-Valley, which is consist of steep mountains and valleys. The system has been already installed and used at the dam administration offices. The system had a weak point in terms of the rainfall prediction especially at the beginning of rainfall event. In this paper, we reveal that precipitable water vapor is related with rainfall event, and propose a new index which is applicable for the signal of the beginning of rainfall event for real-time estimation.

Meteorological and earth observation remote sensing data for mass movement preparedness

One of the major problems of the efficient synergetic use of remote sensing data for natural disaster mitigation is the fusion of various meteo- and geodata sets of significantly different spatial resolution. On the other hand, different morphological types of mass movements are based on alternate concepts i.e. of generation which, again has to be initially reflected in differing methodological approaches. The unifying idea and stronghold, however, of the presented approach is the precipitation parameters which trigger the debris flows. Albeit, frequently there are no relevant precipitation climatic data available. Despite significant drawbacks in the integration of Landsat and/or SPOT data sound hazard zonation maps can be generated. For a test area in the French Alps it has been shown that remote sensing data can be used to predict potential debris flow events. High temporal frequency remote sensing data from the Meteosat series of satellites allow the identification of cloud clusters most likely to result in intense rainfall which are, in turn, likely to initiate debris flow activity. Video evidence, field observations and an empirical debris flow model linked to an instantaneous rain gauge were used to ascertain the exact times of debris flow initiation. Due to the high spatial and temporal variability of rainfall in mountainous regions and the large areas vulnerable to debris flows compared to the coverages of these observations, there are, however, restrictions on the use of these data for large regions to provide early warning of debris flow events operationally. Additionally, the possible timeliness of warnings using such observations is restricted to the relatively short interval between the onset of the triggering phenomena and the hazard event. The remote sensing techniques developed in this study, allow warning of potential debris flow events to be derived before the triggering phenomenon occurs, by attempting to recognise the evolution if intense rain-bearing clouds. In this role the meteorological remote sensing data are not used to retrieve rainfall amounts but, instead, to derive rain cloud properties that produce debris flow triggering conditions.

STUDY ON THE DEPENDENCE PROPERTIES OF RAINFALL DISTRIBUTIONS ON TOPOGRAPHY BASED ON THE ALTITUDE DEPENDENCE LINE

For the purpose of making a stochastic model of rainfall distribution, two remarkable features of rainfall distributions were analyzed using data of a precipitation radar. One is “altitude dependence line”, which is an index of the dependence of rainfall on topography. The other is “time accumulation process”, which represents the distribution properties changing with accumulation of rainfall. As a result, the dependence properties of rainfall on topography was quantified based on the altitude dependence line. The results also indicated that the time accumulation process of rainfall in mountainous regions could be divided into three stages, and its boundary time scales and the properties of each stage were determined. Additionally, the main cause of topographic effect on rainfall distributions was also discovered through the analysis of types of rainfall events.