Hydrologic regionalization using wavelet-based multiscale entropy method

Agarwal, Ankit; Rathinasamy, Maheswaran; Sehgal, Vinit; Khosa, Rakesh; Sivakumar, Bellie; Bernhofer, C.

doi:10.1016/j.jhydrol.2016.03.023

Cited by 97 publications

(41 citation statements)

References 53 publications

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“…Any peak falling outside the COI has presumably been reduced in magnitude due to zero padding necessary to deal with the finite length of the time series. To test the statistical significance of WPS, a background Fourier spectrum is chosen (Addison, 2005;Agarwal et al, 2016a, b).…”

Section: Resultsmentioning

confidence: 99%

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Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Agarwal

Marwan

Rathinasamy

et al. 2017

Nonlin. Processes Geophys.

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Abstract. The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.

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Section: Resultsmentioning

confidence: 99%

“…In order to emphasize the features present in the data, we use the global wavelet spectrum (Fig. 9f, g, and h) which is defined as the time average of the WPS (Agarwal et al, 2016a, b;Mallat, 1989).…”

Section: Resultsmentioning

confidence: 99%

Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Agarwal

Marwan

Rathinasamy

et al. 2017

Nonlin. Processes Geophys.

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“…Extensive literature is available on wavelet-based models for a diverse set of problems in hydrological modeling like monsoonal flood forecasting (Sehgal et al, 2014c), drought forecasting (Kim and Valdés, 2003), streamflow (Adamowski, 2008;Coulibaly and Burn, 2004;Maheswaran and Khosa, 2012b;Nanda et al, 2016), precipitation (Kim, 2004;Lu, 2002;Partal and Kişi, 2007), climatic downscaling (Sehgal et al, 2016), hydrologic regionalization (Agarwal et al, 2016a;Agarwal et al, 2016b) and understanding the coherence between different hydro-climatic variables at multiple time-frequency resolutions like soil moisture (Lakshmi et al, 2004;Tang and Piechota, 2009), El Nino and Southern Oscillation (Torrence and Webster, 1998;Torrence and Webster, 1999) etc.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations

Sehgal

Sridhar

Tyagi

2017

Journal of Hydrology

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This study proposes a multi-wavelet Bayesian ensemble of two Land Surface Models (LSMs) using in-situ observations for accurate estimation of soil moisture for Contiguous United States (CONUS). In the absence of a continuous, accurate in-situ soil moisture dataset at high spatial resolution, an ensemble of Noah and Mosaic LSMs is derived by performing a Bayesian Model Averaging (BMA) of several wavelet-based multi-resolution regression models (WR) of the simulated soil moisture from the LSMs and in-situ volumetric soil moisture dataset obtained from the U.S. Climate Reference Network (USCRN) field stations. This provides a proxy to the in-situ soil moisture dataset at 1/8 th degree spatial resolution called Hybrid Soil Moisture (HSM) for three soil layers (1-10 cm, 10-40 cm and 40-100 cm) for the CONUS. The derived HSM is used further to study the layer-wise response of soil moisture to drought, highlighting the necessity of the ensemble approach and soil profile perspective for drought analysis. A correlation analysis between HSM, the long-term (PDSI, PHDI, SPI-9, SPI-12 and SPI-24) and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices is carried out for the nine climate regions of the U.S. indicating a higher sensitivity of soil moisture to drought conditions for the Southern U.S. Furthermore, a layerwise soil moisture percentile approach is proposed and applied for drought reconstruction in CONUS with a focus on the Southern U.S. for the year 2011.

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“…The wavelet-based multiscale entropy ca pture the information of the embedded evolution processes, as it is an effective measure of th For a large-scale basin, the homogeneity issue is a common problem when we perform the modelling of terrestrial hydrological processes and the relevant dynamic predictions. The catchment regionalization is an important step [4], such as when estimating hydrological parameters of the ungagged basin [5], selecting optimal models for local hydrological variable predictions, and studying the possible runoff response features [6]. Meanwhile, the regionalization of hydrological variables should be more appropriate across different scales, as its evolution incorporates the results of many surrounding factors [5,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The catchment regionalization is an important step [4], such as when estimating hydrological parameters of the ungagged basin [5], selecting optimal models for local hydrological variable predictions, and studying the possible runoff response features [6]. Meanwhile, the regionalization of hydrological variables should be more appropriate across different scales, as its evolution incorporates the results of many surrounding factors [5,7,8]. Among them, the divergences of water resource variables related to time-space scales could be quantified by the approaches presented in [9].…”

Section: Introductionmentioning

confidence: 99%

Regionalization of Daily Soil Moisture Dynamics Using Wavelet-Based Multiscale Entropy and Principal Component Analysis

Sun

Niu

2019

Entropy

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Hydrological regionalization is a useful step in hydrological modeling and prediction. The regionalization is not always straightforward, however, due to the lack of long-term hydrological data and the complex multi-scale variability features embedded in the data. This study examines the multiscale soil moisture variability for the simulated data on a grid cell base obtained from a large-scale hydrological model, and clusters the grid-cell based soil moisture data using wavelet-based multiscale entropy and principal component analysis, over the Xijiang River basin in South China, for the period of 2002–2010. The effective regionalization, for 169 grid cells with the special resolution of 0.5° × 0.5°, produced homogeneous groups based on the pattern of wavelet-based entropy information. Four distinct modes explain 80.14% of the total embedded variability of the transformed wavelet power across different timescales. Moreover, the possible implications of the regionalization results for local hydrological applications, such as parameter estimation for an ungagged catchment and designing a uniform prediction strategy for a sub-area in a large-scale basin, are discussed.

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Hydrologic regionalization using wavelet-based multiscale entropy method

Cited by 97 publications

References 53 publications

Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations

Regionalization of Daily Soil Moisture Dynamics Using Wavelet-Based Multiscale Entropy and Principal Component Analysis

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