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Accurate estimation of latent heat flux (LE) is critical in characterizing semiarid ecosystems. Many LE algorithms have been developed during the past few decades. However, the algorithms have not been directly compared, particularly over global semiarid ecosystems. In this paper, we evaluated the performance of five LE models over semiarid ecosystems such as grassland, shrub, and savanna using the Fluxnet dataset of 68 eddy covariance (EC) sites during the period 2000-2009. We also used a modern-era retrospective analysis for research and applications (MERRA) dataset, the Normalized Difference Vegetation Index (NDVI) and Fractional Photosynthetically Active Radiation (FPAR) from the moderate resolution imaging spectroradiometer (MODIS) products; the leaf area index (LAI) from the global land surface satellite (GLASS) products; and the digital elevation model (DEM) from shuttle radar topography mission (SRTM30) dataset to generate LE at region scale during the period [2003][2004][2005][2006]. The models were the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing based Penman-Monteith LE algorithm (RRS), the Priestley-Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL), the modified satellite-based Priestley-Taylor LE algorithm (MS-PT), and the semi-empirical Penman LE algorithm (UMD). Direct comparison with ground measured LE showed the PT-JPL and MS-PT algorithms had relative high performance over semiarid ecosystems with the coefficient of determination (R 2 ) ranging from 0.6 to 0.8 and root mean squared error (RMSE) of approximately 20 W/m 2 . Empirical parameters in the structure algorithms of MOD16 and RRS, and calibrated coefficients of the UMD algorithm may be the cause of the reduced performance of these LE algorithms with R 2 ranging from 0.5 to 0.7 and RMSE ranging from 20 to 35 W/m 2 for MOD16, RRS and UMD. Sensitivity analysis showed that radiation and vegetation terms were the dominating variables affecting LE Fluxes in global semiarid ecosystem.

Inter-basin water transfer projects might cause complex hydro-chemical and biological variation in the receiving aquatic ecosystems. Whether machine learning models can be used to predict changes in phytoplankton community composition caused by water transfer projects have rarely been studied. In the present study, we used machine learning models to predict the total algal cell densities and changes in phytoplankton community composition in Miyun reservoir caused by the middle route of the South-to-North Water Transfer Project (SNWTP). The model performances of four machine learning models, including regression trees (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) were evaluated and the best model was selected for further prediction. The results showed that the predictive accuracies (Pearson's correlation coefficient) of the models were RF (0.974), ANN (0.951), SVM (0.860), and RT (0.817) in the training step and RF (0.806), ANN (0.734), SVM (0.730), and RT (0.692) in the testing step. Therefore, the RF model was the best method for estimating total algal cell densities. Furthermore, the predicted accuracies of the RF model for dominant phytoplankton phyla (Cyanophyta, Chlorophyta, and Bacillariophyta) in Miyun reservoir ranged from 0.824 to 0.869 in the testing step. The predicted proportions with water transfer of the different phytoplankton phyla ranged from -8.88% to 9.93%, and the predicted dominant phyla with water transfer in each season remained unchanged compared to the phytoplankton succession without water transfer. The results of the present study provide a useful tool for predicting the changes in phytoplankton community caused by water transfer. The method is transferrable to other locations via establishment of models with relevant data to a particular area. Our findings help better understanding the possible changes in aquatic ecosystems influenced by inter-basin water transfer.

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Understanding future changes in global terrestrial near-surface wind speed (NSWS) in specific global warming level (GWL) is crucial for climate change adaption. Previous studies have projected the NSWS changes; however, the changes of NSWS with different GWLs have yet to be studied. In this paper, we employ the Max Planck Institute Earth System Model large ensembles to evaluate the contributions of different GWLs to the NSWS changes. The results show that the NSWS decreases over the Northern Hemisphere (NH) mid-to-high latitudes and increases over the Southern Hemisphere (SH) as the GWL increases by 1.5 °C–4.0 °C relative to the preindustrial period, and that these characteristics are more significant with the stronger GWL. The probability density of the NSWS shifts toward weak winds over NH and strong winds over SH between the current climate and the 4.0 °C GWL. Compared to 1.5 °C GWL, the NSWS decreases −0.066 m s−1 over NH and increases +0.065 m s−1 over SH with 4.0 °C GWL, especially for East Asia and South America, the decrease and increase are most significant, which reach −0.21 and +0.093 m s−1, respectively. Changes in the temperature gradient induced by global warming could be the primary factor causing the interhemispheric asymmetry of future NSWS changes. Intensified global warming induces the reduction in Hadley, Ferrell, and Polar cells over NH and the strengthening of the Hadley cell over SH could be another determinant of asymmetry changes in NSWS between two hemispheres.

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