A Determination Method of Rainfall Type Based on Rainfall-Induced Slope Instability

This work presents a determination method of rainfall types based on rainfall-induced slope instability to eliminate the current dilemma of the inconsistent classification of rainfall types. Firstly, 5,808 scenarios of slope instability are simulated with 11 kinds of soil properties under 528 designed intensity-duration (I−D) conditions. Then through analysis of the I−D conditions when slope failure occurred, rainfall is classified into two types: short-duration − high-intensity (SH) type, and long-duration − low-intensity (LL) type. According to the analysis results, it indicates that rainfall types affect the initiation of slope failure, i.e., different I−D conditions will affect the slope failure initiation under LL type rainfall, while the slope failure initiation will not be affected by the change of I−D conditions under SH type rainfall. In addition, the results show that the classification of rainfall types does not depend on the soil shear strength parameters (cohesion and internal friction angle), although the change of soil shear strength parameters will cause the shift of threshold curve of slope failure in the I−D conditions two-dimensional (2D) plane. The findings in this study benefit to understanding the effect of rainfall type on the mechanism of slope failure initiation, which will promote the development of an early warning system of slope failure in the future by considering the identification of rainfall types.

Equivalence of Rainfall Type and Maximum Discharge to the Drainage Channel Capacity

This research will identify the equivalence of the serial rainfall to the design flood. The equivalence of serial rainfall data is very necessary to solve the flooding problem. A case study is in the University of Brawijaya-Malang-East Java Province-Indonesia. The methodology consists of analyzing the design flood using the Nakayasu Synthetic Unit Hydrograph and then analyzing the equivalence of cumulative serial rainfall to the design flood. The equivalence of rainfall in this research discusses two items: the rainfall equivalence to the hydraulic and hydrologic conditions. Based on the hydraulic condition, the capacity of the drainage channel can store the rainfall for 85.77 mm for the return period of 2 years; 105.86 mm for the return period of 5 years; and 119.26 mm for the return period of 10 years. However, based on the hydrologic condition, for the design flood with the return period of 2 years is 382.25 m3/s, and it has the equivalence close to the discharged recording of AWLR Gadang that is 386.76 m3/s which is due to the rainfall for 11 mm (the category of heavy rain); for the return period of 5 years, the design flood is 471.07 m3/s, and it is equivalent with the flood discharge of 463.73 m3/s that is caused by the rainfall of 12.1 mm (the category of heavy rain); for the return period of 10 years, the design flood is 589.99 m3/s, and it is caused by the rainfall of 13.4 mm (the category of heavy rain). Based on the hydraulic and hydrologic condition, the capacity of the drainage channel that is installed in the campus region of Brawijaya University, it can be concluded that for the return period of 2 years, the drainage channel is only able to reduce 41% of flood volume; for the return period of 5 years, the drainage channel is only able to reduce 26% of flood volume; however, for the return period of 10 years, the drainage channel is only able to reduce 23% of flood volume.

Clustering of Rainfall Types Using Micro Rain Radar and Laser Disdrometer Observations in the Tropical Andes

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.

Attenuation Correction of X-Band Radar Reflectivity Using Adjacent Multiple Microwave Links

Rain attenuation can hinder the implementation of quantitative precipitation estimations using X-band weather radar. Numerous studies have been conducted on correcting the attenuation of radar reflectivity by utilizing a dual-polarimetric radar and an arbitrary-oriented microwave link; however, there is a need to optimize the required number of microwave links and their locations. In this study, we tested four attenuation correction methods and proposed a novel algorithm based on the sole use of adjacent multiple microwave links. The attenuation of the X-band radar reflectivity was corrected by performing forward iterations at each link, and the correction coefficients were statistically analyzed to reduce the instability problem. The algorithms of each method were evaluated by studying the cases of convective and stratiform rainfall, and then validated by comparing the corrected reflectivity of the X-band radar with the qualitatively controlled reflectivity of the S-band radar. The new method was as efficient as the conventional method based on the specific differential phase of dual-polarimetric radar. Furthermore, the correction coefficient was more effectively optimized and stabilized using seven microwave links rather than a single link, and no further independent reference data were required. In addition, the attenuation correction also accounted for spatiotemporal differentiation depending on the rainfall type, and could recover the physical structure of the rainfall. The method developed herein can facilitate estimations of quantitative rainfall in developing countries where dual-polarization weather radars are not common. The exploitation of microwave link data is a promising method for rainfall remote sensing.

Rainfall-type landslide prediction based on landslide mechanism

<p>In general the determination of landslide rainfall threshold is mainly based on the empirical statistics of historical landslide disaster and rainfall. However, which often results in unsound prediction accuracy of regional rainfall-type landslide due to neglecting the difference effect of rainfall on diverse types of landslide disaster. In order to obtain accurate critical threshold of rainfall inducing landslides, based on the influence of rainfall on landslide mechanism and hydrological, in this paper a precise geological model is established and the soil water, ground water level and slope position shift of the landslides are monitored in real-time. By coupling the simulation results with the relationship between rainfall process and slope deformation, the regulation of slope failure induced by rainfall is discussed. The results indicate that a cumulative rainfall of 150 mm can make the landslide fully saturated, and generate the overall landslide instability along the soil-rock interface. Moreover, when the cumulative rainfall reaches 90 millimeter and lasts for more than 3 days, the displacement of bedding rock landslide exceeds 10 cm. This may because of the deterioration of the mechanical properties and the increase of the pore water pressure caused by the rainfall infiltration. The prediction criteria for landslide instability established from mechanism analysis can provide a theoretical basis for accurate prediction of rain-sensitive landslides.</p>

A novel classification rainfall type using a clustering approach in the tropical Andes.

<p>Information on the vertical profile of rainfall is important to improve our knowledge about microphysical processes that govern the formation of the hydrometeors. In addition, the vertical profile helps improving the quantitative precipitation estimation from scanning weather radars and may be useful to improve the parameterization of cloud microphysical processes in numerical models. Usually, rainfall types (e.g, stratiform and convective) are defined by using some rainfall characteristics of its vertical profile such as intensity and velocity. Furthermore, certain thresholds for these variables need to be defined to separate the rainfall classes. However, studies about the vertical profile of rainfall showed that the vertical variability of rainfall highly depends on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Besides, the identification of thresholds can become too subjective and, thus, influence the identification of rainfall types. In regions of complex topography such as the Tropical Andes, rainfall vertical profile studies are very scarce and they show that rainfall classification has similar drawbacks such as the identification of thresholds. Thus, this study aims to develop a new methodology for rainfall events classification by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics (e.g., duration, intensity, drop size distribution) of each rainfall type. The study was carried out using data retrieved from a K-band Doppler Micro Rain Radar (MRR) that records rainfall characteristics such as rainfall intensity, drop velocity, reflectivity profile, drop size distribution (DSD), and liquid water content (LWC). The MRR was located in the tropical Andes, at 2600 m a.s.l., in the city of Cuenca, Ecuador.  Three years of data were available for the study with a temporal resolution of 1 minute.  First, the rainfall events were identified by using three criteria: minimum inter-event, minimum total accumulation, and minimum duration. Then, by using the k-means approach, several iterations with different number of clusters each were evaluated and consequently, three representative rainfall classes were found. These classes showed certain transitions (e.g., for rainfall intensity, velocity and drop size distribution) that separated the rainfall classes. The distributions of these rainfall event characteristics were compared with those found in the literature. This novel classification provided new insights about the variability of the rainfall in this tropical mountain setting and how its characteristics revealed distinctive patterns of the rainfall processes. Finally, since the rain types were identified by a data-driven method, it ensured an objective separation of the rainfall events. Thus, the application of this method in other sites will allow contrasting previous findings regarding the suitability of the tailor-used thresholds for rainfall classification.</p>

Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.