Abstract The dependence of inter‐annual ecosystem evapotranspiration (ET) partitioning on vegetation dynamics is crucial for understanding the long term coupling relationship between the water and carbon cycles. The objective of this study was to determine the spatial variation of ET partitioning ( T /ET) and the inter‐annual dependencies of T /ET on vegetation gross primary productivity (GPP). The investigation was conducted at the global scale based on seven global datasets and at the point scale at 37 flux sites. The spatial variations of T /ET were divided into three phases, that is, the rapidly increasing phase (P1), slowly increasing phase (P2), and decreasing phase (P3), which were located in arid and cold regions, temperate regions, and tropical rainforest regions, respectively. The three‐phase spatial variations were primarily driven by the different spatial variations of the two water use pathways (i.e., transpiration, T and evaporation, E ) in ecosystems with different productivity levels. In P1 and P2 ecosystems, the inter‐annual dependencies of T /ET on GPP were mostly positive, and in P3 ecosystems, it was mostly negative. This revealed a significantly decreased dependence as GPP increased, which was attributed to the different dependencies of T and E on GPP. Based on the effects of GPP on ET under climate variations (represented by precipitation, P ), ET had the smallest inter‐annual variations in most of global vegetated grid cells due to the joint regulation of T and E by GPP and P . This study highlights the significant role of vegetation productivity in regulating inter‐annual ET partitioning, and improves understandings on the coupled water‐carbon cycles.
Abstract Extensive networks of metastable ions link the major peaks in the electron impact mass spectra of two crown ethers containing 2,6‐pyrido units. High‐resolution mass measurements and the metastable peaks allow the elucidation of the fragmentation pathways. The spectra are influenced more by the presence of aromatic substituents than by the 2,6‐pyrido units.
This study successfully synthesized two distinct covalent organic frameworks (COFs): the rigid DAPO-TFPT-COF and the flexible DAPO-TFPC-COF. Both COFs boast high crystallinity, large specific surface area, and uniform pores. A...
Abstract The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.
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