Results 141 to 150 of about 21,602 (236)

Evaluating machine learning models for estimating evapotranspiration in Colombia's Cauca River Valley. [PDF]

Sci Rep
Rueda Cadavid JF, Cañón Castillo DM, Mendoza Castiblanco CJ, Rodríguez Vásquez AF, Cortés Bello CA.   +4 more
europepmc   +1 more source

Assessing water requirements and suitability for apple growth at county scale in China: a phenological modeling approach during key growth stages. [PDF]

Front Plant Sci
Ru X, He T, Yan G, He Y, Zhu Z, Yu Q, He J.   +6 more
europepmc   +1 more source

The qualitative and quantitative relationship between the spatiotemporal variations of potential evapotranspiration and meteorological variables in the Hexi corridor, Northwest China. [PDF]

Sci Rep
Ma Y, Niu Z, Wang X, Sun D.
europepmc   +1 more source

Latitudinal divergence in runoff responses to global forestation due to forest-atmosphere feedbacks. [PDF]

Nat Commun
Kan F, Lian X, Xu H, Tang S, Cui J, Huntingford C, Sun M, Li X, Piao S.   +8 more
europepmc   +1 more source

Warming accelerates global drought severity. [PDF]

Nature
Gebrechorkos SH, Sheffield J, Vicente-Serrano SM, Funk C, Miralles DG, Peng J, Dyer E, Talib J, Beck HE, Singer MB, Dadson SJ.   +10 more
europepmc   +1 more source

PDSI_CMIP6: an ensemble CMIP6-projected self-calibrated Palmer drought severity index dataset. [PDF]

Sci Data
Xiong J, Yang Y.
europepmc   +1 more source

High-resolution spatially interpolated FAO Penman-Monteith crop reference evapotranspiration maps of Sicily Island (Italy) and Jucar River system (Spain) using AgERA5 and ERA5-Land reanalysis datasets.

J Hydrol Reg Stud
Garcia-Prats A, Carricondo-Antón JM, Ippolito M, De Caro D, Jiménez-Bello MA, Manzano-Juárez J, Pulido-Velazquez M.   +6 more
europepmc   +1 more source

Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. [PDF]

PLoS One
Kim Y, Garcia M, Andrew Black T, Johnson MS.   +3 more
europepmc   +1 more source

Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates. [PDF]

Sci Rep
Acharki S, Raza A, Vishwakarma DK, Amharref M, Bernoussi AS, Singh SK, Al-Ansari N, Dewidar AZ, Al-Othman AA, Mattar MA.   +9 more
europepmc   +1 more source

Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction. [PDF]

Sci Rep
Lv X, Li Y, Zhangzhong L, Tong C, Wei Y, Li G, Yang Y.   +6 more
europepmc   +1 more source