A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

Das, Sarit Kumar; Maity, Rajib

doi:10.1002/2014wr016043

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…However, multi-dimensionality can hinder the accurate interpretation of the effective information and thus dimensionality reduction is always helpful in the prediction process. We used the Supervised Principal Component Analysis (SPCA), which is one of the most effective tools for dimensionality reduction 33,34 . The SPCA utilizes the Hilbert-Schmidt Independence Criterion (HSIC) and develops the principal components based on an orthogonal transformation of the input matrix 35 .…”

Section: Resultsmentioning

confidence: 99%

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms

Pal

Maity

Ratnam

et al. 2020

Sci Rep

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The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used nonlinear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall's tau correlation coefficient between the monthly EMI index and the monthly anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the eMi is underestimated for the strong events. the analysis also indicates the SVR to perform better than the Rf method in forecasting the eMi. The El Niño (La Niña) Modoki (ENSO Modoki, hereafter EM) 1 is a newly acknowledged phenomenon characterized by warm (cool) central Pacific sea surface temperature (SST) flanked by cool (warm) eastern and western Pacific SSTs. The EM events affect the global climate at various time scales. The EM affects the equatorial or near equatorial countries by the modified Walker circulation with rising (sinking) motion in the central equatorial Pacific and sinking (rising) motion over the west and east Pacific during EM warm (cold) events and other parts of the globe are affected by the atmospheric teleconnections due to the distribution of heating associated with the equatorial SST anomalies during the EM events 2-8. Although impacts of EM events have been well established, the EM is apparently not so well predicted at long lead times by current operational climate forecast models 2,9-14. The Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) coupled seasonal forecast model showed a partial success in predicting differences between Modoki and canonical El Niños one season ahead with correlation coefficient more than 0.6 15,16. APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system shows the ability to predict the patterns of tropical Pacific SST anomaly (SSTA) of the Modoki events four months ahead with a high correlation coefficient i.e. 0.8 17. However, the predictability of anomalous SST patterns in the APCC MME is seasonally dependent. The IAP-DecPreS near-term climate prediction system, though could predict the EMI with a good skill (correlations coefficient of 0.62 and 0.53) at 4 and 7 months lead, has limited skill (correlation coefficient 0.43) at a lead time of 10 months and beyond 18. All the above models have difficulties in forecasting the amplitude of the EMI events though they forecast the phase of the EMI realistically. The limited skill in predicting the ENSO Modoki index (EMI) in terms of long lead times by the c...

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Section: Resultsmentioning

confidence: 99%

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms

Pal

Maity

Ratnam

et al. 2020

Sci Rep

Self Cite

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“…Two of the most popular statistics used to select the most appropriate (best fit) copula are Kolmogorov-Smirnov (T n ) and Cramér-von Mises (S n ). The T n statistics measure the absolute distance between the empirical copula C n , obtained directly from data and a parametric copula function C u (Genest et al 2009;Das and Maity 2015). The empirical copula function C n is defined by…”

Section: ) Best-fit Bivariate Copula Model For Nonzero Positive Pairs Of Sdv and Obsmentioning

confidence: 99%

“…Copulas are statistical tools used to model the dependence between two or more random variables in order to develop a joint distribution between them (Nelsen 2006). Multivariate studies involving copulas in hydrology include the analysis of droughts (e.g., Laux et al 2009;Madadgar and Moradkhani 2013;Zhang et al 2013;Borgomeo et al 2015), rainfall (Maity and Nagesh Kumar 2008), evaluation of the modeling of the spatial dependence of rainfall by regional climate models (Hobaek Haff et al 2015), downscaling of rainfall (van den Berg et al 2011;Ben Alaya et al 2014;Lorenz et al 2018), soil moisture prediction (Das and Maity 2015;Pal et al 2017), streamflow prediction in ungauged catchments (Samaniego et al 2010), floods (Sraj et al 2015), and catchment compatibility studies (Grimaldi et al 2016). Copula-based models are also used for the bias correction of RCM output (Laux et al 2011;Mao et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

Maity

Suman

Laux

et al. 2019

Journal of Hydrometeorology

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Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

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“…By contrast, the copula method directly simulates the multivariate distribution of multiple random variables, providing a theoretical framework for the multivariate frequency analysis of various variables (Sklar 1959). In the past decade, the copula method has widely used in the multivariate frequency analysis of drought characteristics (Jha et al 2019(Jha et al , 2020Lee et al 2013;Xu et al 2015;Wu et al 2021;Zhang et al 2013), precipitation, runoff and flood (Wee and Shitan 2013;Renard and Lang 2007), and other hydro-meteorological variables like soil moisture, temperature, and sea level (Das and Maity 2015;Rana et al 2017;Zhang et al 2007). With this in mind, this study will apply the copula method to present basin-scale analysis of the joint return periods of the paired FD characteristics.…”

Section: Introductionmentioning

confidence: 99%

SESR-based Identification of Flash Drought and Multivariate Drought Risk Probability Assessment in the Pearl River Basin, China

Chen¹,

Wu²,

Yeh³

et al. 2021

Preprint

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Flash drought (FD) is characterized by the rapid onset and development of drought conditions. It usually occurs during the growing seasons, causing more severe impacts on agriculture and society than the slowly-evolving droughts. Based on the Standard Evaporative Stress Ratio (SESR), this study presents an assessment of the spatio-temporal variability of the joint return periods of FD characteristics in the Pearl River basin (PRB), southern China. Three FD characteristics (i.e., duration D, intensity I, peak P) are extracted at each 0.25o×0.25o grid point over the PRB by the Runs theory. Four marginal distribution functions (Gamma, Exponential, Generalized Extreme Value and Lognormal) are used to fit FD characteristics, while three Archimedean Copula functions (Clayton, Frank and Gumbel) are used for generating the joint distributions of various paired FD characteristics. The results indicate that Lognormal is the best-fitted marginal distribution function of FD characteristics in most parts of PRB, while Frank and Clayton are the best-fitted Copula of the joint PDFs of three pairs of FD characteristics in most parts of PRB. During 1953–2013, the FD events are more frequent in eastern PRB (> 40 events) than western PRB (<10 events), and larger FD characteristics (D and I) are also found in eastern PRB than western PRB. The return period of each FD characteristic is smaller in eastern PRB than western PRB, leading to smaller joint return periods of three paired FD characteristics (D-I, D-P, P-I) in eastern PRB than western PRB. Overall, our results suggest that the risk of FD is gradually increased from the west to the east of the PRB.

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A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

Cited by 14 publications

References 44 publications

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms

Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

SESR-based Identification of Flash Drought and Multivariate Drought Risk Probability Assessment in the Pearl River Basin, China

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