Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration

Khanal, Rajendra; Dhungel, Sulochan; Brewer, Simon; Barber, Michael E.

doi:10.3390/atmos12121565

Cited by 5 publications

(4 citation statements)

References 62 publications

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“…This is probably because RF averages values at each node's end when building decision trees, causing extremely high values to be averaged with low values. This was observed in a similar study by Khanal et al [82].…”

Section: Rf Model Performance Evaluationsupporting

confidence: 85%

“…The random forest (RF) regression model [80,81] was used to model the association between climate extremes and crop ET. Studies show that when compared to linear and generalized additive models, the random forest machine learning (ML) model performed better in estimating linear and non-linear relationships involving climate change parameters with minimal errors [41,82]. RFs are regression-type ML techniques that are devised for creating a prediction ensemble utilizing numerous decision trees that are randomly trained on a subset of the input data [80].…”

Section: Development Of Machine Learning Model and Performance Evalua...mentioning

confidence: 99%

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Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Igwe

Sharda

Hefley

2023

Land

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Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.

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Section: Rf Model Performance Evaluationsupporting

confidence: 85%

Section: Development Of Machine Learning Model and Performance Evalua...mentioning

confidence: 99%

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Igwe

Sharda

Hefley

2023

Land

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“…We applied widely used evapoTranspiration mapping at high Resolution with Internalized Calibration (METRIC) model to quantify ET [36][37][38]. METRIC is an image processing tool for mapping ET as a residual of the energy balance at the Earth's surface.…”

Section: Metric Modelmentioning

confidence: 99%

Integration of Unmanned Aerial Vehicle Imagery with Landsat Imagery for Better Watershed Scale ET Prediction

Khanal

Barber

2023

Hydrology

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Evapotranspiration (ET) is a critical component of the water cycle, and an accurate prediction of ET is essential for water resource management, irrigation scheduling, and agricultural productivity. Traditionally, ET has been estimated using satellite-based remote sensing, which provides synoptic coverage but can be limited in spatial resolution and accuracy. Unmanned aerial vehicles (UAVs) offer improved ET prediction by providing high-resolution imagery of the Earth’s surface but are limited to a small area. Therefore, UAV and satellite images provide complementary data, but the integration of these two data for ET prediction has received limited attention. This paper presents a method that integrates UAV and satellite imagery for improved ET prediction and applies it to five crops (corn, rye grass, wheat, and alfalfa) from agricultural fields in the Walla Walla of eastern Washington State. We collected UAV and satellite data for five crops and used the combination of remote sensing models and statistical techniques to estimate ET. We show that UAV-based ET can be integrated with the Landsat-based ET with the application of integration factors. Our result shows that the Root Mean Square Error (RMSE) of daily ET for corn (Zea mays), rye grass (Lolium perenne), wheat (Triticum aestivum), peas (Pisum sativum), and alfalfa (Medicago sativa) can be improved by the application of the integration factor to the Landsat based ET in the range of (35.75–65.52%). We also explore the variability and effect of partial cloud on UAV-based ET estimation. Our findings have implications for the use of UAVs in water resource management and highlight the importance of considering multiple sources of data in ET prediction.

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“…Because of overcrowding, many cities already face the problem of unemployment, water scarcity, sanitation, health hazards, degradation of environmental quality, and development of slums (Flörke et al, 2018;Khanal et al, 2021a;Ooi and Phua, 2007;Vardoulakis et al, 2016). In addition, climate change is likely to aggravate certain urban health problems, the severity of extreme weather events, water scarcity, and disturbing urban ecology (Heal et al, 2013;Khanal et al, 2021b;Vardoulakis et al, 2014). To cope with this challenge of high urbanization many cities around the world are already expanding their boundaries into their periphery (Ahani and Dadashpoor, 2021;Dadashpoor and Malekzadeh, 2021).…”

Section: Introductionmentioning

confidence: 99%

An approach of geological mapping and geotechnical investigation: Case study from the Lalitpur Metropolitan City, Nepal

Rizal,

Paudyal

2023

Journal of Nepal Geological Society

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Urbanization is rapidly growing throughout the world, especially in the least developed and developing countries necessitating proper land use planning. However, the lack of sufficient study has created the possibility of unprecedented geohazards. In this study, a detailed geological and engineering geological mapping along with the geotechnical analysis of soils and sediments from the Bhainsepati–Pharsidol area of Lalitpur District has been carried out. Detailed geological maps and geological cross-sections were prepared on the scale of 1:25,000 following the most adopted stratigraphy of central Nepal for both hard rocks and Quaternary deposits. Two mappable geological units like the Tistung Formation and the Chandragiri Limestone of the Phulchauki Group are found within the hard rock succession at the study area’s hill sections. Similarly, the Quaternary succession is mapped into the Lukundol Formation and the Chapagaon Terrace deposits of Kathmandu Valley sediments from older to younger succession respectively. There is a distinct erosional unconformity between the rocks of the Phulchauki Group and the Quaternary Valley sediments. A kinematic analysis of discontinuities developed in the rocks was carried out in the rock slopes found in the area. Both field and laboratory analyses of soil samples were carried out for the representative samples. The laboratory tests include moisture content, specific gravity, particle size distribution, soil consistency, compaction, and consolidation properties. Finally, an engineering geological map was prepared on the same scale as geological mapping showing the details of the distribution of geo-materials. This study is expected to be valuable to make proper land-use decisions for town planning and developmental activities in the rapidly growing city of Nepal.

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Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration

Cited by 5 publications

References 62 publications

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Integration of Unmanned Aerial Vehicle Imagery with Landsat Imagery for Better Watershed Scale ET Prediction

An approach of geological mapping and geotechnical investigation: Case study from the Lalitpur Metropolitan City, Nepal

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