Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate S Hobeichi, G Abramowitz, J Evans, A Ukkola Hydrology and Earth System Sciences 22 (2), 1317-1336, 2018 | 56 | 2018 |
Linear Optimal Runoff Aggregate (LORA): A global gridded synthesis runoff product S Hobeichi, G Abramowitz, J Evans, HE Beck Hydrology and Earth System Sciences 23 (2), 851-870, 2019 | 37 | 2019 |
Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts M Mu, MG De Kauwe, AM Ukkola, AJ Pitman, W Guo, S Hobeichi, ... Earth System Dynamics Discussions 2021, 1-29, 2021 | 28 | 2021 |
Remote sensing of Qatar nearshore habitats with perspectives for coastal management C Warren, J Dupont, M Abdel-Moati, S Hobeichi, D Palandro, S Purkis Marine pollution bulletin 105 (2), 641-653, 2016 | 28 | 2016 |
Conserving land–atmosphere synthesis suite (CLASS) S Hobeichi, G Abramowitz, J Evans Journal of Climate 33 (5), 1821-1844, 2020 | 26 | 2020 |
Robust historical evapotranspiration trends across climate regimes S Hobeichi, G Abramowitz, JP Evans Hydrology and Earth System Sciences 25 (7), 3855-3874, 2021 | 21 | 2021 |
Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network J Beringer, CE Moore, J Cleverly, DI Campbell, H Cleugh, MG De Kauwe, ... Global Change Biology 28 (11), 3489-3514, 2022 | 17 | 2022 |
Evaluating precipitation datasets using surface water and energy budget closure S Hobeichi, G Abramowitz, S Contractor, J Evans Journal of Hydrometeorology 21 (5), 989-1009, 2020 | 15 | 2020 |
Toward a robust, impact‐based, predictive drought metric S Hobeichi, G Abramowitz, JP Evans, A Ukkola Water Resources Research 58 (2), e2021WR031829, 2022 | 14 | 2022 |
Using machine learning to cut the cost of dynamical downscaling S Hobeichi, N Nishant, Y Shao, G Abramowitz, A Pitman, S Sherwood, ... Earth's Future 11 (3), e2022EF003291, 2023 | 13 | 2023 |
New Forest aboveground biomass maps of China integrating multiple datasets Z Chang, S Hobeichi, YP Wang, X Tang, G Abramowitz, Y Chen, N Cao, ... Remote Sensing 13 (15), 2892, 2021 | 12 | 2021 |
Reconciling historical changes in the hydrological cycle over land S Hobeichi, G Abramowitz, AM Ukkola, M De Kauwe, A Pitman, JP Evans, ... npj Climate and Atmospheric Science 5 (1), 17, 2022 | 10 | 2022 |
Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019 Z Chang, L Fan, JP Wigneron, YP Wang, P Ciais, J Chave, R Fensholt, ... Journal of Remote Sensing 3, 0005, 2023 | 8 | 2023 |
Derived optimal linear combination evapotranspiration—DOLCE v2. 1 S Hobeichi, G Abramowitz, JP Evans Research Data Australia. doi: doi 10, 2020 | 7 | 2020 |
Australia’s Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change A Devanand, GM Falster, ZE Gillett, S Hobeichi, CM Holgate, C Jin, M Mu, ... Science Advances 10 (10), eadj3460, 2024 | 6 | 2024 |
Reconciling historical changes in the hydrological cycle over land. npj Climate and Atmospheric Science, 5, 17 S Hobeichi, G Abramowitz, AM Ukkola, M De Kauwe, A Pitman, JP Evans, ... | 6 | 2022 |
Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation N Nishant, S Hobeichi, S Sherwood, G Abramowitz, Y Shao, C Bishop, ... Environmental Research Letters 18 (9), 094006, 2023 | 5 | 2023 |
What is the probability that a drought will break in Australia? A Devanand, JP Evans, G Abramowitz, S Hobeichi, AJ Pitman Weather and Climate Extremes 41, 100598, 2023 | 4 | 2023 |
Toward the development of a remote sensing and field data framework to aid management decisions in the State of Qatar coastal environment C Warren, J DuPont, M Abdel-Moati, S Hobeichi, D Palandro, S Purkis Qscience Proceedings 2015 (5), 13, 2015 | 4 | 2015 |
An LSTM-based downscaling framework for Australian precipitation projections M Bittner, S Hobeichi, M Zawish, S Diatta, R Ozioko, S Xu, A Jantsch NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning, 2023 | 3 | 2023 |