Methods to estimate evapotranspiration in humid and subtropical climate conditions

Vishwakarma, Dinesh Kumar; Pandey, Kusum; Kaur, Arshdeep; Kushwaha, Nand Lal; Kumar, Rohitashw; Ali, Rawshan; Elbeltagi, Ahmed; Kuriqi, Alban

doi:10.1016/j.agwat.2021.107378

Cited by 82 publications

(38 citation statements)

References 45 publications

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“…The SRM computer program involves the assessment of model accuracy through the graphical representation of the estimated hydrograph and observed runoff. In addition to the plots, the SRM uses two performance indices, the percent volume difference (Dv) and the coefficient of determination (R 2 ) [24,32,[51][52][53]. The coefficient of determination is estimated as follows:…”

Section: Model Accuracymentioning

confidence: 99%

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

et al. 2022

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Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The Dv and R2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2 °C, and (c) a temperature rise of +2 °C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increased by 53% in summer (April–September). In contrast, the projected increased discharge for scenarios (a) and (c) was 37% and 67%, respectively. The SRM efficiently forecasts future water supplies due to snowmelt runoff in high elevation, data-scarce mountain environments.

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Section: Model Accuracymentioning

confidence: 99%

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

et al. 2022

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“…Various studies have been conducted in various regions of the globe to ascertain the optimised ETo model (Farzanpour et al, 2019;Jing et al, 2019;Khodayvandie et al, 2020;Salam et al, 2020;Goh et al, 2021;Sharafi & Ghaleni, 2021;Trajkovic & Gocic, 2021;Vishwakarma et al, 2022). Tables 3 and 4 In Contrast, the SEE of the modified Hargreavesmethod varied from (0.57) mm per day at Kakul to 22.61 mm per day at Saidu Sharif.…”

Section: Overall Discussionmentioning

confidence: 99%

An effective evapotranspiration estimation scheme based on statistical indicators for sustainable environments in humid and semi-arid area of Khyber Pakhtunkhwa, Pakistan

et al. 2021

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Reference evapotranspiration (ETo) is critical for irrigation design and water management in rainfed and irrigated agriculture. The Penman-Monteith (FAO-56(PM)) equation was demonstrated to be the most reliable and adaptive to a wide range of humid to semi-arid climates. However, it requires several environmental parameters (e.g., wind speed, solar radiation), rarely available in developing countries. Therefore, numerous temperature-based formulas have been designed to address this issue for various environments. Their calibration and validation against the local climate frequently lead to increases in performance. We revised the Hargreaves exponent (EH) and substituted a value of (0.16) for the original value (0.5). The modified Hargreaves formula enhances the ETo predictions with a mean absolute error ranging from (0.791) mm per day for Balakot to (2.36) mm per day in Risalpur, averaging (3.797) mm per day, as compared to the Hargreaves-Samani (16.827) mm per day. In general, all the selected models showed high accuracy. However, the modified Hargreaves equation appeared to be the most promising results. It ranked first in (50%) of the whole area based on the standard error of estimate for estimating ETo in Khyber Pakhtunkhwa. Additional research must be conducted to determine the study's relevance to other regions.

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“…Models evaluation and comparison of actual and forecasted data of water temperature were achieved based on several performances metrics, namely, ( i ) Pearson correlation coefficient (PCC), ( ii ) the mean absolute error (MAE), ( iii ) the root mean square error (RMSE), ( iv ) the relative absolute error (RAE), ( v ) the coefficient of determination R 2 , and ( vi ) the root-relative square error (RRSE), calculated as follows (Shukla et al 2021 ; Vishwakarma et al 2022 ): …”

Section: Statistical Performance Assessmentmentioning

confidence: 99%

“…The values of RMSE range from 0 to ∞, PCC −1 to 1, R 2 0 to 1, MAE 0 to ∞, and RAE and RRSE 0 to 1. Good forecasting accuracy corresponds to a value of PCC and R 2 nearly equal to 1, while for the other metrics, their values should be close to zero (Yaseen et al 2016 , 2018 ; Ayele et al 2017 ; Shukla et al 2021 ; Demir 2022 ; Pham et al 2022 ; Vishwakarma et al 2022 ). Each one of these measures’ descriptive performances is as follows: The lower the value of MAE and RMSE and near to zero MBE, the better the model performance (Vishwakarma et al 2022 ).…”

Section: Statistical Performance Assessmentmentioning

confidence: 99%

“…Good forecasting accuracy corresponds to a value of PCC and R 2 nearly equal to 1, while for the other metrics, their values should be close to zero (Yaseen et al 2016 , 2018 ; Ayele et al 2017 ; Shukla et al 2021 ; Demir 2022 ; Pham et al 2022 ; Vishwakarma et al 2022 ). Each one of these measures’ descriptive performances is as follows: The lower the value of MAE and RMSE and near to zero MBE, the better the model performance (Vishwakarma et al 2022 ). For R 2 : Very good (0.7 < R 2 ≤ 1); good (0.6 < R 2 ≤ 0.7); satisfactory (0.5 < R 2 ≤ 0.6); and unsatisfactory ( R 2 ≤ 0.5) (Ayele et al 2017 ).…”

Section: Statistical Performance Assessmentmentioning

confidence: 99%

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Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Vishwakarma

Ali

Bhat

et al. 2022

Environ Sci Pollut Res

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Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior ( R 2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

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Methods to estimate evapotranspiration in humid and subtropical climate conditions

Cited by 82 publications

References 45 publications

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

An effective evapotranspiration estimation scheme based on statistical indicators for sustainable environments in humid and semi-arid area of Khyber Pakhtunkhwa, Pakistan

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

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