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Glacier-melt-induced changes in runoff are of concern in northwestern China where glacier runoff is a major source for irrigation, industries and ecosystems. Samples were collected in different water mediums such as precipitation, glacial ice/snowcover, meltwater, groundwater and streamwater for the analysis of stable isotopes and solute contents during the 2009 runoff season in the Laohugou Glacial Catchment. The multi-compare results of δ18O values showed that significant difference existed in different water mediums. Source waters of streamflow were determined using data of isotopic and geochemical tracers and a three-component hydrograph separation model. The results indicated that meltwater dominated (69.9 ± 2.7%) streamflow at the catchment. Precipitation and groundwater contributed 17.3 ± 2.3% and 12.8 ± 2.4% of the total discharge, respectively. According to the monthly hydrograph, the contribution of snow and glacier meltwater varied from 57.4% (September) to 79.1% (May), and that of prec...

Funder: Cambridge Trust, Funder: National Research Foundation Singapore, Funder: Alexander von Humboldt-Stiftung, Funder: China Scholarship Council, In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.