Evaluation of daily crop reference evapotranspiration and sensitivity analysis of FAO Penman-Monteith equation using ERA5-Land reanalysis database in Sicily, Italy

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 295, P. 108732 - 108732

Published: Feb. 26, 2024

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference (ETo) represents atmospheric water demand each type, development stage, and management practices. Penman-Monteith equation version suggested by Food Agriculture Organization (FAO56-PM), used methods to estimate ETo. In several regions world, meteorological observations are not always available. recent reanalysis database ERA5-Land, released 2019, can be useful overcome this limit. provides, with a spatial grid 0.1° latitude longitude, hourly climate data such as air temperature, dew point solar radiation, wind speed all at 2.0 m above soil surface, except 10 m, apply FAO56-PM equation. objective research assess quality ERA5-Land variables daily ETo Sicily, Italy. effect weather station's elevation associated statistical indicators was also evaluated verify how morphology affects measurements. Finally, sensitivity analysis carried out identify which have influence on estimation. For period 2006–2015, comparison between global speed, relative humidity, measured from 39 ground stations then, through values were estimated using both databases. Root Mean Square Error (RMSE) Bias (MBE) confirm possibility considering suitable solution showed that good estimation depends mainly accuracy humidity temperature data.

Language: Английский

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Machine Learning Approaches for Predicting Reference Evapotranspiration: A Comparative Study Using Ground and Gridded Climate Data in Fes Region

World Water Policy, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

ABSTRACT Climate data are essential for agricultural planning and water resource management; however, their availability is limited in numerous regions of Africa. Gridded climate present a potential solution, yet, accuracy estimating reference evapotranspiration (ET o ) remains uncertain. This study aims to evaluate the performance gridded comparison ground‐based observations predicting ET Fes region Morocco. Two machine learning (ML) models, random forest (RF) long short‐term memory (LSTM), were trained tested on 10 years from both (AgERA5) ground (in situ) observation sources assess predictive capabilities. The results demonstrated that RF outperformed LSTM under fewer input parameter configurations, achieving R 2 > 0.70, while exhibited superior across all configurations 0.95. However, AgERA5 consistently underestimated compared observations. underestimation highlights need bias correction improve reliability. Addressing these limitations would allow datasets support better irrigation scheduling, enhance use efficiency, reduce crop stress with access localized data. demonstrates combining ML bridge gaps, emphasizing importance improving dataset practical applications management.

Language: Английский

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A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102271 - 102271

Published: Feb. 27, 2025

Language: Английский

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Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression

Geoscientific model development, Journal Year: 2023, Volume and Issue: 16(14), P. 4083 - 4112

Published: July 20, 2023

Abstract. The prediction of river water temperature is key importance in the field environmental science. Water datasets for low-order rivers are often short supply, leaving modelers with challenge extracting as much information possible from existing datasets. Therefore, identifying a suitable modeling solution large scarcity forcing great importance. In this study, five models, forced meteorological obtained fifth-generation atmospheric reanalysis, ERA5-Land, used to predict 83 (with 98 % missing data): three machine learning algorithms (random forest, artificial neural network and support vector regression), hybrid Air2stream model all available parameterizations multiple regression. hyperparameters were optimized tree-structured Parzen estimator, an oversampling–undersampling technique was generate synthetic training general terms, results study demonstrate vital hyperparameter optimization suggest that, practical perspective, when number predictor variables observed values limited, application models considered crucial. Basically, tested proved be best at least one station. root mean square error (RMSE) Nash–Sutcliffe efficiency (NSE) ensemble 2.75±1.00 0.56±0.48 ∘C, respectively. that performed overall random forest (annual – RMSE: 3.18±1.06 ∘C; NSE: 0.52±0.23). With technique, RMSE reduced 0.00 21.89 (μ=8.57 %; σ=8.21 %) NSE increased 1.1 217.0 (μ=40 σ=63 %). These proposed has potential significantly improve methods, well providing scope its larger other types dependent variables. also revealed existence logarithmic correlation among between predicted watershed time concentration. increases by average 0.1 ∘C 1 h increase concentration (watershed area: μ=106 km2; σ=153).

Language: Английский

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Large-sample hydrology – a few camels or a whole caravan?

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(17), P. 4219 - 4237

Published: Sept. 12, 2024

Abstract. Large-sample datasets containing hydrometeorological time series and catchment attributes for hundreds of catchments in a country, many them known as “CAMELS” (Catchment Attributes MEteorology Studies), have revolutionized hydrological modelling enabled comparative analyses. The Caravan dataset is compilation several (CAMELS other) large-sample with uniform attribute names data structures. This simplifies hydrology across regions, continents, or the globe. However, use instead original CAMELS other may affect model results conclusions derived thereof. For dataset, meteorological forcing are based on ERA5-Land reanalysis data. Here, we describe differences between precipitation, temperature, potential evapotranspiration (Epot) 1252 CAMELS-US, CAMELS-BR, CAMELS-GB these dataset. Epot unrealistically high catchments, but there are, unsurprisingly, also considerable precipitation We show that from impairs calibration vast majority catchments; i.e. drop performance when using compared to datasets. mainly due Therefore, suggest extending included wherever possible so users can choose which they want at least indicating clearly come quality loss recommended. Moreover, not (and attributes, such aridity index) recommend should be replaced (or on) alternative estimates.

Language: Английский

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Shifted Global Vegetation Phenology in Response to Climate Changes and Its Feedback on Vegetation Carbon Uptake

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(9), P. 2288 - 2288

Published: April 26, 2023

Green vegetation plays a vital role in energy flows and matter cycles terrestrial ecosystems, phenology may not only be influenced by, but also impose active feedback on, climate changes. The phenological events of such as the start season (SOS), end (EOS), length (LOS) can respond to changes affect gross primary productivity (GPP). Here, we coupled satellite remote sensing imagery with FLUXNET observations systematically map shift SOS, EOS, LOS global vegetated area, explored their response fluctuations on GPP during last two decades. results indicated that 11.5% area showed significantly advanced trend 5.2% presented delayed EOS past decades, resulting prolonged 12.6% area. factors, including seasonal temperature precipitation, attributed shifts phenology, high spatial temporal difference. was positively correlated 20.2% total highlighting longer is likely promote productivity. from shifted serve an adaptation mechanism for ecosystems mitigate warming through improved carbon uptake atmosphere.

Language: Английский

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Assessing AgERA5 and MERRA-2 Global Climate Datasets for Small-Scale Agricultural Applications

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 263 - 263

Published: Feb. 24, 2025

AgERA5 (ECMWF) is a relatively new climate dataset specifically designed for agricultural applications. MERRA-2 (NASA) also used in applications; however, it was not this purpose. Despite the proven value of these datasets assessing global patterns, their effectiveness small-scale contexts remains unclear. This research aims to fill gap by suitability and performance precision irrigation management, which crucial regions with limited ground data availability. The wine-making region Nemea, Greece, its complex challenging terrain as characteristic case study. are assessed key weather variables planning, using detailed local meteorological station reference. results reveal that both products have serious limitations small scale scheduling applications contrast what reported previous studies other regions. uneven different due lack sufficient observation reanalysis calibration indicated. Comparing two datasets, outperforms MERRA-2, especially precipitation reference evapotranspiration. shows comparable potential occasionally matches or exceeds AgERA5’s performance. study findings underscore importance evaluating metanalysis application area before use agriculture, particularly topography.

Language: Английский

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Quantifying spatiotemporal and elevational precipitation gauge network uncertainty in the Canadian Rockies

Hydrology and earth system sciences, Journal Year: 2025, Volume and Issue: 29(4), P. 983 - 1000

Published: Feb. 25, 2025

Abstract. Uncertainty in estimating precipitation mountain headwaters can be transmitted to estimates of river discharge far downstream. Quantifying and reducing this uncertainty is needed better constrain the hydrological predictions rivers with headwaters. Spatial estimation fields accomplished through interpolation snowfall rainfall observations. These are often sparse mountains, so gauge density greatly affects uncertainty. Elevational lapse rates also influence as they vary widely between events, observations rarely made at multiple proximal elevations. Therefore, spatial, temporal, elevational domains need considered quantify network This study aims spatiotemporal spatial interpolated from gauged networks snowfall-dominated, triple continental divide Canadian Rockies Mackenzie, Nelson, Columbia, Fraser, Mississippi British Columbia Alberta Canada Montana USA. A 30-year (1991–2020) daily database was created region utilized generate kriging rates. The results indicate that coverage improved after drought 2001–2002, but it still insufficient decrease domain-scale uncertainty, because most gauges were deployed valley bottoms. Deploying above 2000 m identified having greatest cost benefits for decreasing region. High-elevation deployments associated university research other programs 2005 had a widespread impact on reduction recent period remains Nelson headwaters, whilst lowest findings show both components quantified order estimate use design Understanding then these uncertainties additional crucial more reliable prediction discharge.

Language: Английский

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A novel approach for estimating evapotranspiration by considering topographic effects in radiation over mountainous terrain

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 366, P. 110468 - 110468

Published: March 5, 2025

Language: Английский

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Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102343 - 102343

Published: March 30, 2025

Language: Английский

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