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To evaluate isotopic tracers at natural abundances by providing basic isotope data of the hydrological investigations and assessing the impacts of different factors on the water cycle, a total of 197 water samples were collected from the Laohugou Glacial catchment in the Shule River basin northwestern China during the 2013 ablation seasons and analyzed their H- and O-isotope composition. The results showed that the isotopic composition of precipitation in the Qilianshan Station in the Laohugou Glacial catchment was remarkable variability. Correspondingly, a higher slope of δ18O-δD diagram, with an average of 8.74, is obtained based on the precipitation samples collected on the Glacier No.12, mainly attributed to the lower temperature on the glacier surface. Because of percolation and elution, the isotopic composition at the bottom of the firn is nearly steady. The δ18O /altitude gradients for precipitation and melt water were -0.37‰/100 m and -0.34‰/100 m, respectively. Exposed to the air and influenced by strong ablation and evaporation, the isotopic values and the δ18O vs δD diagram of the glacial surface ice show no altitudinal effect, indicating that glacier ice has the similar origins with the firn. The variation of isotopic composition in the melt water,varying from -10.7‰ to -16.9‰ (δ18O) and from -61.1‰ to -122.1‰ (δD) indicates the recharging of snowmelt and glacial ice melt water produced at different altitudes. With a mean value of -13.3‰ for δ18O and -89.7‰ for δD, the isotopic composition of the stream water is much closer to the melt water, indicating that stream water is mainly recharged by the ablation water. Our results of the stable isotopic compositions in natural water in the Laohugou Glacial catchment indicate the fractionations and the smoothing fluctuations of the stable isotopes during evaporation, infiltration and mixture.

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...

Based on the experiments conducted in an irrigated intercropping field in Zhangye Oasis in the middle reaches of Heihe River basin in 2004, the characteristics of radiation budget are analyzed. Furthermore, energy balance is cal- culated by using Bowen-Ratio Energy Balance (BREB) method. The results show that the ratio of the absorbed radiation to the incoming short radiation in intercropping crop canopy-soil system is increasing with growing stages, from 0.81 in the initial growing stage (IGS) to 0.86 in the late growing stage (LGS). The net radiation, which is smaller in IGS, in- creases rapidly in the first period of the middle growing stage (MGS) and reaches the maximum value in the second period of MGS. It then somewhat decreases in LGS. The ratio of net radiation to total radiation has a similar trend with the net radiation. In the whole growing stages, latent heat flux, which takes up 70% or so of the net radiation, is the dominant item in energy balance. Sensible heat flux shares 20% of the net radiation and soil heat flux has a percentage of 10%. The characteristics of heat balance vary distinctly in different growing stages. In IGS, the ratios of latent heat flux, sensible heat flux and soil heat flux to net radiation are 44.5%, 23.8% and 31.7% respectively. In MGS, with the in- creasing of latent heat flux and the decreasing of sensible heat flux and soil heat flux, the ratios turn into 84.4%, 6.3% and 9.3%. In LGS, the soil heat flux maintains 0W/m 2 or so, and latent heat flux and sensible heat flux take up 61.4% and 38.6% respectively. The energy balance also shows an obvious daily variation characteristic.

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.