A series of rhenium(I) diimine complexes cis,trans-[Re(dmb)(CO)(2)(PR(1)R(2)R(3))(PR(4)R(5)R(6))](+) (dmb=4,4'-dimethyl-2,2'-bipyridine, R(n)=phenyl or alkyl), each of which bears two phosphine ligands with various numbers of phenyl groups, has been synthesized by using the photochemical ligand-substitution reaction. Detailed studies of the structural features, not only in the crystal but also in solution, indicate that the number of phenyl groups is a crucial factor in controlling the rotational conformation of the phosphine ligands, which in turn determines the extent of the π-π interaction between the aromatic diimine ligand and the phenyl group(s). The π-π interaction strongly affected both electrochemical and photophysical properties: 1) the oxidation power of the Re complex became stronger, 2) the lifetime of the excited state became longer, and 3) the Stokes shift between the (1) MLCT absorption band and emission from the corresponding (3) MLCT excited state became smaller. In particular, the diphenyl and triphenyl phosphine had much greater influence on the properties than the monophenyl phosphine ligand. Dual emission was observed from the different rotational conformers of the complexes with an intermediate number of phenyl groups in the phosphine ligands.

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.