An Ensemble Hydrologic Modeling System for Runoff and Evapotranspiration Evaluation over an Agricultural Watershed

Himanshu, Sushil Kumar; Pandey, Ashish; Madolli, Mallappa J.; Palmate, Santosh S.; Kumar, Ashish; Patidar, Nitesh; Yadav, Basant

doi:10.1007/s12524-022-01634-4

Cited by 5 publications

(1 citation statement)

References 75 publications

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“…Kling, H et al proposed a new approach to conceptual rainfall-runoff model regionalization based on ensemble modelling and model averaging [9]. Himanshu et al used the ensemble model as a decision tool to overcome uncertainties in modelling hydrological processes [10].…”

Section: Introductionmentioning

confidence: 99%

A runoff forecast model with selective ensemble method

Tang,

Qin,

Liu

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Highly accurate runoff forecasting is essential for the efficient use of water resources. Considering the non-linearity and randomness of runoff sequences, a selective ensemble forecasting method combined with the PSO algorithm is proposed. Firstly, sub-learners are selected for homogeneous ensemble and the parameters of each sub-learner are rate-determined on the training set. Then, the PSO algorithm is used to assign weights based on the performance of the sub-learners on the validation set to obtain the selective ensemble model. Finally, the selective ensemble method was validated on the test set. Experiments were performed using runoff data from the WuLong station in the Yangtze River basin, and the results show that the selective ensemble method can provide more accurate forecast results than homogeneous ensemble with the same average weights of the learners.

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Section: Introductionmentioning

confidence: 99%

A runoff forecast model with selective ensemble method

Tang,

Qin,

Liu

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

View full text Add to dashboard Cite

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Characterization and assessment of hydrological droughts using GloFAS streamflow data for the Narmada River Basin, India

Swain

Mishra

Pandey

et al. 2023

Environ Sci Pollut Res

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A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

Swain,

Mishra,

Nandi

et al. 2024

Appl Water Sci

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The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon precipitation. The DM value is highest for Jaisalmer and lowest for Bhilwara district. The findings reveal DPI as an efficient tool for assessing drought years, particularly in arid climatic conditions. Moreover, as the DM value increases for a precipitation series, the DPI becomes more effective in capturing drought events.

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An Ensemble Hydrologic Modeling System for Runoff and Evapotranspiration Evaluation over an Agricultural Watershed

Cited by 5 publications

References 75 publications

A runoff forecast model with selective ensemble method

A runoff forecast model with selective ensemble method

Characterization and assessment of hydrological droughts using GloFAS streamflow data for the Narmada River Basin, India

A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

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