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, Год журнала: 2024, Номер 295, С. 108732 - 108732

Опубликована: Фев. 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.

Язык: Английский

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

Hydrology and earth system sciences, Год журнала: 2024, Номер 28(17), С. 4219 - 4237

Опубликована: Сен. 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.

Язык: 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, Год журнала: 2023, Номер 16(14), С. 4083 - 4112

Опубликована: Июль 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).

Язык: 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, Год журнала: 2025, Номер unknown

Опубликована: Янв. 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.

Язык: 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, Год журнала: 2025, Номер 58, С. 102271 - 102271

Опубликована: Фев. 27, 2025

Язык: Английский

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

Remote Sensing, Год журнала: 2023, Номер 15(9), С. 2288 - 2288

Опубликована: Апрель 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.

Язык: Английский

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Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Environmental Research Letters, Год журнала: 2023, Номер 18(9), С. 094007 - 094007

Опубликована: Июль 27, 2023

Abstract Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the system. Modeling potential, such as wind energy, typically involves use of speed time series in process. One most widely utilized datasets this regard is ERA5, which provides global meteorological information. Despite its broad coverage, coarse spatial resolution ERA5 data presents challenges examining local-scale effects on systems, battery storage for small-scale farms or community systems. In study, we introduce a robust statistical downscaling approach that utilizes machine learning improve from around 31 km × 1 km. To ensure optimal results, preprocessing step performed classify regions three classes based quality estimates. Subsequently, regression method applied each class downscale by considering relationship between data, observations weather stations, topographic metrics. Our results indicate significantly improves performance complex terrain. effectiveness robustness our approach, also perform thorough evaluations comparing with reference dataset COSMO-REA6 validating independent datasets.

Язык: Английский

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A Probabilistic Analysis of Drought Areal Extent Using SPEI-Based Severity-Area-Frequency Curves and Reanalysis Data

Water, Год журнала: 2023, Номер 15(17), С. 3141 - 3141

Опубликована: Сен. 1, 2023

Assessing and monitoring the spatial extent of drought is key importance to forecasting future evolution conditions taking timely preventive mitigation measures. A commonly used approach in regional analysis involves spatially interpolating meteorological variables (e.g., rainfall depth during specific time intervals, deviation from long-term average rainfall) or indices Standardized Precipitation Index, Evapotranspiration Index) computed at locations. While plotting a descriptor against corresponding percentage affected areas helps visualize historical drought, this falls short providing probabilistic characterization severity conditions. That can be overcome by identifying Severity-Area-Frequency (SAF) curves over region, which establishes link between features with chosen probability recurrence (or return period) proportion area experiencing those inferential analyses estimate these curves, analytical approaches offer better understanding main statistical that drive droughts. In research, technique introduced mathematically describe aiming probabilistically understand correlation severity, measured through SPEI index, region. This enables determination area’s where values fall below threshold, thus calculating likelihood observing SAF exceed observed one. The methodology tested using data ERA5-Land reanalysis project, specifically studying occurrences on Sicily Island, Italy, 1950 present. Overall, findings highlight improvements incorporating interdependence assessed variable, offering significant enhancement compared traditional for curve derivation. Moreover, they validate suitability analysis.

Язык: Английский

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Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data

Agricultural Water Management, Год журнала: 2023, Номер 289, С. 108556 - 108556

Опубликована: Окт. 23, 2023

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates weather prediction (NWP) models that show improved forecast skill. This circumstance leads to recurrent delivery revised reanalysis databases provide estimates for several decades back time by combining latest NWP with observations. Since climate studies agriculture water management applications require availability accurate reliable data, assessing performance products contributes informed choices potential proxy ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is regional product released Centre Medium-Range Weather Forecasts (ECMWF), August 2022. forced global ERA5 reanalysis, it provides resolution 5.5 km pan-European territory from 1984. For first literature, this study explores at 38 stations located Sicily, an Italian region Mediterranean climate, during irrigation seasons 2003–2022. objective lies evaluating respect air temperature, actual vapor pressure, wind speed solar radiation are input variables reference evapotranspiration, ETO, which a key variable quantifying volumes needed resources studies. accuracy ETO depends on those through equation provided Food Agriculture Organization United Nations (FAO), i.e., FAO Penman-Monteith equation. Here, also evaluated using inputs results performances excellent, especially determines present high reliability mean PBIAS NRMSE equal 5.6% 13%, respectively, over region. Those outcomes lead conclusion represents valid alternative measurements their spatial interpolation resource

Язык: Английский

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Water cycle changes in Czechia: a multi-source water budget perspective

Hydrology and earth system sciences, Год журнала: 2024, Номер 28(1), С. 1 - 19

Опубликована: Янв. 2, 2024

Abstract. The water cycle in Czechia has been observed to be changing recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns such changes remain poorly understood due heterogeneous network ground observations. This study relied on multiple state-of-the-art reanalyses hydrological modeling. Herein, we propose novel method for benchmarking hydroclimatic data fusion based budget closure. We ranked closure 96 different combinations precipitation, evapotranspiration, runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, TerraClimate. Then, used best-ranked describe over last 60 years. determined that is undergoing acceleration, evinced by increased atmospheric fluxes. increase annual total not as pronounced nor consistent resulting an overall decrease runoff. Furthermore, non-parametric bootstrapping revealed only are statistically significant at scale. At higher frequencies, identified heterogeneity when assessing seasonal Interestingly, most temporal occur during spring, while pattern change median values stems from summer cycle, which seasons within months changes.

Язык: Английский

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