Reconstruction of daily rainfall data using the concepts of networks: Accounting for spatial connections in neighborhood selection

Tiwari, Shubham; Jha, Sanjeev; Sivakumar, Bellie

doi:10.1016/j.jhydrol.2019.124185

Cited by 32 publications

(13 citation statements)

References 24 publications

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“…The other method is formed by investigating network characteristics via network‐based metrics such as centrality or an analogue of the clustering coefficient. Both methods are rooted in spatial similarity measurements, and a recent example of their applications can be seen in Tiwari et al (). In RFFA, it is widely accepted that delineating homogeneous neighbors for each target basin can improve the accuracy in flood quantile estimation; therefore, the network‐identified neighbors for each node is preferred in this study.…”

Section: Methodsmentioning

confidence: 99%

A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis

Han

Ouarda

Rahman

et al. 2020

Water Resources Research

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Regional flood frequency analysis forms the basis for ascertaining design thresholds for extreme flow events for the purpose of resource management and design of hydraulic structures, especially at ungauged or partially gauged basins. A crucial step in this analysis is transferring available information from gauged sites to ungauged sites, which is achieved through delineation of homogeneous regions encompassing multiple catchment locations, followed by the formulation of a flood estimation model. While this process has been accomplished through a range of statistical homogenization alternatives, the present study offers a new approach rooted in the theory of complex networks, offering considerable advantages over what is traditionally followed. Data from 202 sites in Australia representing catchments of varying geographic, climatic, and vegetation attributes are used to assess the alternative proposed. The results are examined via (1) direct comparison of the location and number of homogeneous neighbors from network theory with results using canonical correlation analysis (CCA) and (2) assessing the accuracy of estimated flood quantiles by applying a common model that estimates flood quantiles using information from the two alternate groups of homogeneous sites (from network theory and CCA). Results show that network theory offers merit in delineating homogenous regions, with resulting design flood estimates showing improvements across different return periods compared to the CCA alternative used.

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Section: Methodsmentioning

confidence: 99%

A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis

Han

Ouarda

Rahman

et al. 2020

Water Resources Research

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“…We apply spatial interpolation (using Inverse Distance Weighing (IDW) method) of rainfall data and estimate the value of rainfall at the centroid of each subbasin, prior to reading input data in SWAT. We use the IDW method in its standard form (with number of neighbours = 5 and power parameter = 2; as discusses in Tiwari et al 66 ) to estimate the rainfall at the centroids of 14 subbasins.…”

Section: Methodsmentioning

confidence: 99%

“…Root Mean Square Error (RMSE). The RMSE 66 is the expression of the data around the line of best fit. The RMSE does not simply increase with the variance of the errors but increases with the variance of the frequency distribution of error magnitudes.…”

Section: Methodsmentioning

confidence: 99%

“…They also considered different correlation thresholds to identify the existence of links between stations. Tiwari et al 66 used complex network theory to reconstruct daily rainfall data and subsequently proposed two variants of Inverse Distance Weighing (IDW) interpolation. Despite the recent extensive use of network theory to study multiple hydrological processes, the suitability of the concept of complex networks to examine the node importance and subsequent gauge prioritization is limited.…”

Section: Quantification Of Node Importance In Rain Gauge Network: Infmentioning

confidence: 99%

“…The daily accumulated (0000 UTC) rainfall is calculated from the 3-hourly rainfall data. The rainfall data at the rain gauge stations are extracted using the Inverse Distance Weighting (IDW) interpolation of the TRMM gridded data (with number of neighbours = 5 and power parameter = 2; as discusses in Tiwari et al 66 ).…”

Section: Study Area and Datasetmentioning

confidence: 99%

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Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density

Tiwari

Jha

Singh

2020

Sci Rep

Self Cite

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Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation.

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New gridded rainfall dataset over the Korean peninsula: Gap infilling, reconstruction, and validation

Noh

Ahn

2021

Intl Journal of Climatology

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This study describes a new high‐resolution false(0.25°×0.25° latitude/longitude) gridded daily rainfall dataset (K‐Hidra version 2020) developed from rainfall records of 389 gauge stations irregularly distributed across the Korean peninsula. The observational datasets are often composed of incomplete time series covering different temporal periods with numerous missing values, informing that infilling process is essential. Three supportive but separated evaluation frameworks, which have been paid less attention to, are explicitly addressed to identify the proper infilling model in the study area. After a gridded rainfall dataset is created with infilled dataset, it is validated by comparing the gridded products obtained by observations. Furthermore, K‐Hidra is compared with other gridded rainfall estimates including Climate Prediction Center (CPC), Global Precipitation Climatology Project (GPCP), Asian Precipitation Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), and Tropical Rainfall Measuring Missing (TRMM) Multi‐satellite Precipitation Analysis (TMPA). Our results suggest that the elastic net model algorithm is the most effective in dealing with missing values in the study area. Results also demonstrate that the infilling process is vital to properly produce the reconstructed series although its effects substantially vary based on the size of the available data as well as the spatial and temporal distribution of the observed precipitation. Furthermore, K‐Hidra reveals that the Korean peninsula have experienced significant changes in precipitation‐related characteristics such as annual daily maximum precipitation and total annual dry days, likely leading to frequent hydrologic extremes. Lastly, K‐Hidra is compared with other estimates, CPC has a small bias and high correlations with the new dataset while precipitation variabilities are acceptably represented by all other gridded estimates. From the results demonstrated in this study, when officially released, K‐Hidra is expected to be useful for climate impact and rainfall variability analyses over the Korean peninsula.

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Reconstruction of daily rainfall data using the concepts of networks: Accounting for spatial connections in neighborhood selection

Cited by 32 publications

References 24 publications

A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis

A Network Approach for Delineating Homogeneous Regions in Regional Flood Frequency Analysis

Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density

New gridded rainfall dataset over the Korean peninsula: Gap infilling, reconstruction, and validation

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