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A Cluster‐Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins
by
Ojha, Chandra Shekhar Prasad
, Singh, Japjeet
, Singh, Vishal
in
Basins
/ bias correction and improvement in precipitation
/ Data assimilation
/ Data collection
/ Datasets
/ Earth
/ Ensemble precipitation
/ Gauges
/ gridded precipitation data sets
/ Himalayan region
/ Himalayan River Basin
/ Hydrologic data
/ India
/ landscapes
/ Nepal
/ Neural networks
/ Parameters
/ partitional clustering
/ Pattern analysis
/ Performance evaluation
/ Precipitation
/ Precipitation data
/ precipitation data development
/ Precipitation gauges
/ Precipitation patterns
/ Precipitation variations
/ Quantiles
/ Rain
/ Rainfall
/ Rainfall anomalies
/ Regional development
/ Regions
/ River basins
/ River networks
/ rivers
/ Root-mean-square errors
/ Seasonal variations
/ Seasonality
/ Time series
/ time series analysis
/ Upper Ganga Basin
/ water
/ Water resources
2025
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A Cluster‐Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins
by
Ojha, Chandra Shekhar Prasad
, Singh, Japjeet
, Singh, Vishal
in
Basins
/ bias correction and improvement in precipitation
/ Data assimilation
/ Data collection
/ Datasets
/ Earth
/ Ensemble precipitation
/ Gauges
/ gridded precipitation data sets
/ Himalayan region
/ Himalayan River Basin
/ Hydrologic data
/ India
/ landscapes
/ Nepal
/ Neural networks
/ Parameters
/ partitional clustering
/ Pattern analysis
/ Performance evaluation
/ Precipitation
/ Precipitation data
/ precipitation data development
/ Precipitation gauges
/ Precipitation patterns
/ Precipitation variations
/ Quantiles
/ Rain
/ Rainfall
/ Rainfall anomalies
/ Regional development
/ Regions
/ River basins
/ River networks
/ rivers
/ Root-mean-square errors
/ Seasonal variations
/ Seasonality
/ Time series
/ time series analysis
/ Upper Ganga Basin
/ water
/ Water resources
2025
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A Cluster‐Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins
by
Ojha, Chandra Shekhar Prasad
, Singh, Japjeet
, Singh, Vishal
in
Basins
/ bias correction and improvement in precipitation
/ Data assimilation
/ Data collection
/ Datasets
/ Earth
/ Ensemble precipitation
/ Gauges
/ gridded precipitation data sets
/ Himalayan region
/ Himalayan River Basin
/ Hydrologic data
/ India
/ landscapes
/ Nepal
/ Neural networks
/ Parameters
/ partitional clustering
/ Pattern analysis
/ Performance evaluation
/ Precipitation
/ Precipitation data
/ precipitation data development
/ Precipitation gauges
/ Precipitation patterns
/ Precipitation variations
/ Quantiles
/ Rain
/ Rainfall
/ Rainfall anomalies
/ Regional development
/ Regions
/ River basins
/ River networks
/ rivers
/ Root-mean-square errors
/ Seasonal variations
/ Seasonality
/ Time series
/ time series analysis
/ Upper Ganga Basin
/ water
/ Water resources
2025
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A Cluster‐Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins
Journal Article
A Cluster‐Based Data Assimilation Approach to Generate New Daily Gridded Time Series Precipitation Data in the Himalayan River Basins
2025
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Overview
Recent studies show variations in precipitation‐gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global‐scale effectiveness, but applying them to regional studies, particularly in small to medium‐sized sub‐basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based‐Minimum Error approach to assimilate different open‐source gridded precipitation data sets for forming an accurate precipitation product over small to medium‐sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km2. The study utilizes nine open‐source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station‐wise, grid‐wise, and elevation‐wise analyses using statistical parameters, quantile‐quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash‐Sutcliffe Efficiency (NSE), Coefficient of determination (R2), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and R2 values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub‐basins, especially in Himalayan regions with limited station data. Key Points A cluster‐based data assimilation approach to develop accurate gridded precipitation data in the Himalayan basins Consideration of topographic and climatic parameters to identify homogenous rainfall clusters to incorporate precipitation change Multi‐level evaluation of the newly developed gridded precipitation w.r.t. observed and open sources global gridded precipitation data sets
Publisher
John Wiley & Sons, Inc,Wiley
Subject
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