Work Description

Contents of DeepBlue repository associated with Hille et al. (submitted to Geophysical Research Letters August 13 2021):

The data here consist of 2,561 individual storm events from 30-minute precipitation time series that occurred over central Nepal during the summer monsoon season (June-September) between 2010-2018. The precipitation datasets used to derive this storm dataset are (1) NASA’s Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG)-V06 30-minute, 0.1x0.1 km global dataset (Huffman et al., 2019) and (2), local daily rain gauge data recorded by the Nepal Department of Hydrology and Meteorology (DHM) and the International Centre for Integrated MOuntain Development (ICIMOD) (Shea et al., 2015). We scale the summed IMERG 30-minute grid cell precipitation at each gauge to match daily rainfall records such that total daily GPM rainfall is equal to total gauge daily rainfall. The IMERG record is supplemented with the Tropical Rainfall Measuring Mission (TRMM) 3B42V6 data collected prior to the 2014 inception of the IMERG catalog. Individual storms are separated from the 30-minute point location time series using a minimum dry period between storm arrivals (Driscoll, 1989; Dunkerley, 2008; Gaál et al., 2014; Schleiss & Smith, 2016; Schleiss, 2017). We define extreme rainfall events (EREs) over the lognormal 90th percentile of all averaged storm intensities at each rain gauge location. The resulting storm dataset is a summary table of all EREs that occurred during the monsoon seasons between 2010-2018 with five notable characteristics: storm duration (hrs), average intensity (mm/hr), peak 30-minute intensity (mm/hr), prior rainfall to the storm peak (mm) and total storm depth (mm).

We utilize both satellite and ground-based precipitation resources to optimize the spatial and temporal resolution of precipitation records in this region. Satellite precipitation products underestimate rainfall over central Nepal (Nepal et al., 2021; Sharma, Chen, et al., 2020; Sharma, Khadka, et al., 2020), but provide high temporal resolution. Rain gauge datasets are prone to innacuracies through wind under-catch and maintenance errors (Pollock et al., 2018; Rodda & Dixon, 2012), and only provide daily scale precipitation records. More details on the pros and cons of each dataset can be found in the manuscript associated with this repository (Hille et al.). Links to the data used in this analysis are provided below under the RESOURCES header. IMERG data is publicly available by request through NASA. Rain gauge records from ICIMOD are also publicly available for download. The DHM rain gauge data is proprietary and available for research use by request only (a link to the request documentation is provided below).

The bulk analysis of this work consists of a multivariate statistical approach to quantifying variability in EREs over the central Nepal Himalaya. We use a K-means agglomerative cluster analysis to partition storms into dominant spatial clusters. Individual EREs are assigned to one of 10 clusters based on the five prescribed storm characteristics. We then assign each gauge station to a cluster based on the cluster in which a majority of the EREs at that station were grouped. This results in a spatial distribution of cluster values based on the dominant storm characteristics observed at each gauge station. We pair the clustering results with a principal components analysis (PCA) based on the correlation matrix of the ERE dataset to asses the interdependent relationships between ERE characteristics in their respective spatial clusters.

Finally, we compare the clustering and PCA results to a catalog of monsoon-driven landslides between 2010-2014 over the central Nepalese Himalaya to relate ERE characteristics to spatial patterns in landsliding. We limit the landslide catalog to 2010-2014 prior to the 2015 Mw7.8 Gorkha earthquake to avoid including a legacy earthquake effect on monsoon-driven landsliding. More details on this methodology are presented in the Geophysical Research Letters journal article associated with this dataset.

RESOURCES:
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IMERG data overview: https://gpm.nasa.gov/data/imerg

IMERG data availability via Giovanni user interface: https://giovanni.gsfc.nasa.gov/giovanni/

IMERG data availability via STORM portal (bulk data download): https://storm.pps.eosdis.nasa.gov/storm/data/Service.jsp?serviceName=Order

DHM data request: http://dhm.gov.np/requestfordata/

ICIMOD Kyanjin station data availability: http://rds.icimod.org/Home/DataDetail?metadataId=22464&searchlist=True

SOFTWARE:
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ArcGIS Pro version 2.6.2
RStudio version 1.4.1717
R version 4.1.0

LIST OF FILES IN DATA REPOSITORY:
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File: 'EREs_2010_2018.csv'
Description: Table of 2,561 extreme rainfall events (EREs) that occurred between 2010-2018 monsoon seasons, with attached storm characteristics (e.g., duration, intensity, peak intensity, prior rainfall and depth) and date of occurrence.

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File: 'Stations_Loc_Info.csv'
Description: Location and topographical information of rain gauge stations where the 30-minute point location time series are recorded. This table includes the station name and DHM index number, latitude, longitude, distance to the Himalayan range front (km), elevation, slope and "type" (the dominant cluster to which the station was assigned at the end of this methodology).

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File: 'Nepal_Monsoon_Cluster_Analysis.R'
Description: This file is an RStudio script that runs a K-means cluster and PCA analyses on the ERE dataset. Results from this analysis are described and interpreted in Hille et al.

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File: 'Monsoon_Landslides_2010_2014.zip'
Description: An ArcGIS-compatible shapefile of all mapped landslides that occurred during the summer monsoon seasons between 2010-2014. Hand-mapped from aerial imagery post- and pre-event. Details on the mapping protocol are included in the Supporting Information document to Hille et al.

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For questions please contact:
Madeline Hille
madhille@umich.edu

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References:

Driscoll, E. D. , P. G. E. , S. E. W. , & S. P. E. (1989). Analysis of Storm Event Characteristics for Selected Rainfall Gages Through Out the United States.

Dunkerley, D. (2008, December 30). Identifying individual rain events from pluviograph records: A review with analysis of data from an Australian dryland site. Hydrological Processes. https://doi.org/10.1002/hyp.7122

Gaál, L., Molnar, P., & Szolgay, J. (2014). Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland. Hydrology and Earth System Sciences, 18(5), 1561–1573. https://doi.org/10.5194/hess-18-1561-2014

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., & Tan, J. (2019). Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation.

Nepal, B., Shrestha, D., Sharma, S., Singh Shrestha, M., Aryal, D., & Shrestha, N. (2021). Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal. https://doi.org/10.3390/atmos

Pollock, M. D., O’Donnell, G., Quinn, P., Dutton, M., Black, A., Wilkinson, M. E., et al. (2018). Quantifying and Mitigating Wind-Induced Undercatch in Rainfall Measurements. Water Resources Research, 54(6), 3863–3875. https://doi.org/10.1029/2017WR022421

Rodda, J. C., & Dixon, H. (2012). Rainfall measurement revisited. Weather, 67(5), 128–131. https://doi.org/10.1002/wea.875

Schleiss, M., & Smith, J. A. (2016). Two simple metrics for quantifying rainfall intermittency: The burstiness and memory of interamount times. Journal of Hydrometeorology, 17(1), 421–436. https://doi.org/10.1175/JHM-D-15-0078.1

Schleiss, M. (2017). Scaling and distributional properties of precipitation interamount times. Journal of Hydrometeorology, 18(4), 1167–1184. https://doi.org/10.1175/JHM-D-16-0221.1

Sharma, S., Chen, Y., Zhou, X., Yang, K., Li, X., Niu, X., et al. (2020). Evaluation of GPM-Era satellite precipitation products on the southern slopes of the central Himalayas against rain gauge data. Remote Sensing, 12(11). https://doi.org/10.3390/rs12111836

Sharma, S., Khadka, N., Hamal, K., Shrestha, D., Talchabhadel, R., & Chen, Y. (2020). How Accurately Can Satellite Products (TMPA and IMERG) Detect Precipitation Patterns, Extremities, and Drought Across the Nepalese Himalaya? Earth and Space Science, 7(8). https://doi.org/10.1029/2020EA001315

Shea, J. M., Wagnon, P., Immerzeel, W. W., Biron, R., Brun, F., & Pellicciotti, F. (2015, April 3). A comparative high-altitude meteorological analysis from three catchments in the Nepalese Himalaya. https://doi.org/10.1080/07900627.2015.1020417