Estimation of Net Rice Production through Improved CASA Model by Addition of Soil Suitability Constant (ħα)

Raza, Syed Muhammad Hassan; Mahmood, Syed Amer

doi:10.3390/su10061788

Cited by 23 publications

(20 citation statements)

References 90 publications

(151 reference statements)

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“…CASA is a light use efficiency model that directly measures net primary production based on the principle that under sufficient water and field fertility, vegetation production is related to only absorbed photosynthetically active radiation. It calculates NPP as the product of fraction of photosynthetically active radiation absorbed by the canopy (FPAR), incident photosynthetically active radiation (PAR), and light use efficiency (LUE) represented in Equation 1 (Masek & Collatz 2006;Garbulsky et al 2008;Goerner et al 2011;Namayanga 2002;Raza & Mahmood 2018). Net primary production provides an accurate regular measure of biomass which subsequently is important for defining seasonal and annual terrestrial carbon fluxes (Bajracharya 2008;Bonino 2006;Lu et.al.…”

Section: Methodsmentioning

confidence: 99%

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

2020

JEES

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Forests are important for regulation of the global carbon balance. Increase in forest biomass enhances atmospheric carbon sequestration while decrease in forest biomass contributes to carbon dioxide emissions. World over, forest biomass has been declining due to forest loss and degradation. The South West Mau has experienced significant forest loss since 1964. The decline is posited to have significant impacts on carbon sequestration, carbon storage, carbon dioxide emissions and status of atmospheric carbon dioxide. This study assessed interannual trend and variability as well as change point detection in carbon sequestration in South West Mau Forest, Kenya between 1985 and 2015. Above ground biomass carbon sequestration was quantified based on the Carnegie-Ames-Stanford Approach (CASA) and carbon fraction for tropical climate domain. Carbon sequestration dynamics were characterized by increase-decrease cycles of approximately 3 years and low interannual variability (CV= 9.13). It emerged that South West Mau Forest was a net carbon emitter with a carbon sequestration balance of-588.40 Kg/ha between 1985 and 2015.

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Section: Methodsmentioning

confidence: 99%

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

2020

JEES

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“…The computed ET o were then used to construct the training and testing data for the ML models. Using the FAO56 PME [7], ET o [mm/h] is computed by [86,87]. (R s /R so ) is the relative shortwave radiation, representing the cloud cover, defined as…”

Section: Fao56 Penman-monteith Equation (Fao56 Pme)mentioning

confidence: 99%

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

Başağaoğlu¹,

Chakraborty

Winterle³

2021

Water

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Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.

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“…where σ is the Stefan-Boltzmann constant (4.903 × 10 −9 MJ / ( (Zhang et al, 2008;Raza and Mahmood, 2018). (R s /R so ) is the relative shortwave radiation, representing the cloud cover, defined as…”

Section: Penman-monteith Equation (Pme)mentioning

confidence: 99%

A Hybridized NGBoost-XGBoost Framework for Robust Evaporation and Evapotranspiration Prediction

Başağaoğlu¹,

Chakraborty

Winterle³

2020

Preprint

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Abstract. We analyze the relationship between potential evapotranspiration (ETo), actual evapotranspiration (ETa), and surface water evaporation (Esw) in the semi-arid south-central Texas, using hourly climate data, daily lake evaporation measurements, and daily actual evapotranspiration measurements from an eddy covariance (EC) tower. The deterministic analysis reveals that ETo set the upper bound for ETa, but the lower bound for Esw in the study area. Unprecedentedly, we demonstrate that a newly developed probabilistic machine learning (ML) model, using a hybridized NGBoost-XGBoost framework, can accurately predict the daily ETo, Esw, & ETa from local climate data. The probabilistic approach exhibits great potential in overcoming data uncertainties, in which 99 % of the ETo, 90 % of the Esw, and 91 % of the ETa test data at three watersheds were within the model's 95 % prediction interval. The probabilistic ML model results suggest that the proposed framework can serve as a robust and computationally more efficient tool than the hourly Penman-Monteith equation to predict the ETo while avoiding computationally-involved net solar radiation calculations. Additionally, the performance analysis of the probabilistic ML model indicates that it can be successfully implemented in practice to overcome the uncertainties associated with pan evaporation & pan coefficients in Esw estimates, and to offset the high capital & operational costs of EC towers used for Ea measurements. Finally, we demonstrate, for the first time, a coalition game theory approach to identify the order of importance, dependencies & interactions of climatic variables on the ML-based ETo, Esw, and ETa predictions. New knowledge gained through the game theory approach is beneficial to strategically locate weather stations for enhanced evapo(transpi)ration predictions, and plan out sustainability and resilience efforts, as part of water management and habitat conservation plans.

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Estimation of Net Rice Production through Improved CASA Model by Addition of Soil Suitability Constant (ħα)

Cited by 23 publications

References 90 publications

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

Carbon Sequestration by the Above Ground Biomass Pool in the South West Mau Forest of Kenya, 1985 - 2015

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

A Hybridized NGBoost-XGBoost Framework for Robust Evaporation and Evapotranspiration Prediction

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