Evapotranspiration Intensification Over Unchanged Temperate Vegetation in the Baltic Countries Is Being Driven by Climate Shifts

Montibeller, Bruno; Jaagus, Jaak; Mander, Ülo; Uuemaa, Evelyn

doi:10.3389/ffgc.2021.663327

Cited by 3 publications

(1 citation statement)

References 78 publications

(92 reference statements)

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“…ET of greenhouse crops is governed by many factors. Most approaches to developing an effective irrigation program depend on the creation of an accurate mathematical model of evapotranspiration [ 5 , 6 , 7 ]. Current models that predict ET for greenhouse crops consist mainly of empirically based equations using modern mathematical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model

Zhao

et al. 2022

Plants

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Crop evapotranspiration estimation is a key parameter for achieving functional irrigation systems. However, ET is difficult to directly measure, so an ideal solution was to develop a simulation model to obtain ET. There are many ways to calculate ET, most of which use models based on the Penman–Monteith equation, but they are often inaccurate when applied to greenhouse crop evapotranspiration. The use of machine learning models to predict ET has gradually increased, but research into their application for greenhouse crops is relatively rare. We used experimental data for three years (2019–2021) to model the effects on ET of eight meteorological factors (net solar radiation (Rn), mean temperature (Ta), minimum temperature (Tamin), maximum temperature (Tamax), relative humidity (RH), minimum relative humidity (RHmin), maximum relative humidity (RHmax), and wind speed (V)) using a greenhouse drip irrigated tomato crop ET prediction model (XGBR-ET) that was based on XGBoost regression (XGBR). The model was compared with seven other common regression models (linear regression (LR), support vector regression (SVR), K neighbors regression (KNR), random forest regression (RFR), AdaBoost regression (ABR), bagging regression (BR), and gradient boosting regression (GBR)). The results showed that Rn, Ta, and Tamax were positively correlated with ET, and that Tamin, RH, RHmin, RHmax, and V were negatively correlated with ET. Rn had the greatest correlation with ET (r = 0.89), and V had the least correlation with ET (r = 0.43). The eight models were ordered, in terms of prediction accuracy, XGBR-ET > GBR-ET > SVR-ET > ABR-ET > BR-ET > LR-ET > KNR-ET > RFR-ET. The statistical indicators mean square error (0.032), root mean square error (0.163), mean absolute error (0.132), mean absolute percentage error (4.47%), and coefficient of determination (0.981) of XGBR-ET showed that XGBR-ET modeled daily ET for greenhouse tomatoes well. The parameters of the XGBR-ET model were ablated to show that the order of importance of meteorological factors on XGBR-ET was Rn > RH > RHmin> Tamax> RHmax> Tamin> Ta> V. Selecting Rn, RH, RHmin, Tamax, and Tamin as model input variables using XGBR ensured the prediction accuracy of the model (mean square error 0.047). This study has value as a reference for the simplification of the calculation of evapotranspiration for drip irrigated greenhouse tomato crops using a novel application of machine learning as a basis for an effective irrigation program.

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Section: Introductionmentioning

confidence: 99%

Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model

Zhao

et al. 2022

Plants

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Increased carbon assimilation and efficient water usage may not compensate for carbon loss in European forests

Montibeller

Marshall

Mander

et al. 2022

Commun Earth Environ

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Phenological responses of vegetation to global warming impact ecosystem gross primary production and evapotranspiration. However, high resolution and large spatial scale observational evidence of such responses in undisturbed core forest areas is lacking. Here, we analyse MODIS satellite data to assess monthly trends in gross primary productivity and evapotranspiration across undisturbed core forest areas in Europe between 2000 and 2020. Both parameters increased during the early spring and late autumn in nearly half of the total undisturbed core forest area (3601.5 km2). Enhanced productivity drove increased water-use-efficiency (the ratio of gross primary productivity to evapotranspiration). However, productivity increases during spring and autumn were not sufficient to compensate for summertime decreases in 25% of core forest areas. Overall, 20% of total gross primary productivity across all European forest core areas was offset by forest areas that exhibited a net decrease in productivity.

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Water Balance Calculation for a Transboundary Aquifer System between Estonia and Latvia

Hunt,

Marandi,

Retike

2023

Water

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Groundwater management in transboundary aquifers is complex due to the hidden nature of groundwater and its intricate interactions with surface water. The overarching issue lies in the need for improved tools and strategies to manage transboundary aquifers effectively, given their critical importance to regional water resources and ecosystems. In addressing these challenges, this study focuses on a specific transboundary aquifer system between Estonia and Latvia, situated within the sedimentary rocks of North-East Europe. Here, we propose a conceptual model approach as a valuable tool for comprehending the hydrogeological characteristics of transboundary aquifers and supporting more efficient management strategies. Utilizing open-source software MODFLOW-6, our developed conceptual model integrates the aquifer system’s geological and hydrogeological framework and simulates groundwater flow under transient conditions. Our findings highlight a profound hydrological connection between groundwater and surface water, particularly within the first 200 m of the aquifer. Despite varying cross-border groundwater flows, collaboration remains vital for sustainable transboundary aquifer management, aligning with international agreements such as the Water Convention and the EU Water Framework Directive, benefiting similar systems.

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Evapotranspiration Intensification Over Unchanged Temperate Vegetation in the Baltic Countries Is Being Driven by Climate Shifts

Cited by 3 publications

References 78 publications

Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model

Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model

Increased carbon assimilation and efficient water usage may not compensate for carbon loss in European forests

Water Balance Calculation for a Transboundary Aquifer System between Estonia and Latvia

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