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Abstract Octalenobisterphenylene1(also known as terphenylene dimer) was synthesized from 3,3′,5,5′‐tetraaryl‐substituted biaryl bytert‐butyllithium‐mediated cyclization followed by oxidative coupling. This one‐pot two‐step protocol facilitated the successive formation of four four‐membered and two eight‐membered rings. Treatment of1with sodium metal, followed by crystallization from THF, yielded the remarkable diradical dianion [(1^•–)₂]²⁻, where the two molecular halves are connected by four σ bonds. The cyclodimerization is driven by the pronounced reactivity and strain of the central six‐membered ring within the [3]phenylene subunit. The structure and diradical nature of [(Na⁺)₂(1^•–)₂] were confirmed through X‐ray crystallography, DFT computations, and¹H NMR and ESR spectra. These investigations revealed that the two spins, one on each molecular half, exhibit minimal mutual interaction. 

Abstract Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply‐demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi‐arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices. 

Abstract Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate. 

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data. 

These datasets accompany a publication in Geophysical Research Letters by Martens et al. (2024), entitled: "GNSS Geodesy Quantifies Water-Storage Gains and Drought Improvements in California Spurred by Atmospheric Rivers." Please refer to the manuscript and supporting information for additional details.Dataset 1: Seasonal Changes in TWS based on the Mean and Median of the Solution SetWe estimate net gains in water storage during the fall and winter of each year (October to March) using the mean TWS solutions from all nine inversion products, subtracting the average storage for October from the average storage for March in the following year. One-sigma standard deviations are computed as the square root of the sum of the variances for October and for March. The variance in each month is computed based on the nine independent estimates of mean monthly storage (see “GNSS Analysis and Inversion” in the Supporting Information).The dataset includes net gains in water storage for both the Sierra Nevada and the SST watersheds (see header lines). For each watershed, results are provided in units of volume (km³) and in units of equivalent water height (mm). Furthermore, for each watershed, we also provide the total storage gains based on non-detrended and linearly detrended time series. In columns four and five, respectively, we provide estimates of snow water equivalent (SWE) from SNODAS (National Operational Hydrologic Remote Sensing Center, 2023) and water-storage changes in surface reservoirs from CDEC (California Data Exchange Center, 2023). In the final column, we provide estimates of net gains in subsurface storage (soil moisture plus groundwater), which are computed by subtracting SWE and reservoir storage from total storage.For each data block, the columns are: (1) time period (October of the starting year to March of the following year); (2) average gain in total water storage constrained by nine inversions of GNSS data; (3) one-sigma standard deviation in the average gain in total water storage; (4) gain in snow water equivalent, computed by subtracting the average snow storage in October from the average snow storage in March of the following year; (5) gain in reservoir storage (CDEC database; within the boundaries of each watershed), computed by subtracting the average reservoir storage in October from the average reservoir storage in March of the following year; and (6) average gain in subsurface water storage, estimated as the average gain in total water storage minus the average gain in snow storage minus the gain in reservoir storage.For the period from October 2022 to March 2023, we also compute mean gains in total water storage using daily estimates of TWS. Here, we subtract the average storage for the first week in October 2022 (1-7 October) from the average storage for the last week in March 2023 (26 March – 1 April). The one-sigma standard deviation is computed as the square root of the sum of the variances for the first week in October and the last week in March. The variance in each week is computed based on the nine independent estimates of daily storage over seven days (63 values per week). The storage gains for 2022-2023 computed using these methods are distinguished in the datafile by an asterisk (2022-2023*; final row in each data section).Dataset 1a provides estimates of storage changes based on the mean and standard deviation of the solution set. Dataset 1b provides estimates of storage changes based on the median and inter-quartile range of the solution set.Dataset 2: Estimated Changes in TWS in the Sierra NevadaChanges in TWS (units of volume: km³) in the Sierra Nevada watersheds. The first column represents the date (YYYY-MM-DD). For monthly solutions, the TWS solutions apply to the month leading up to that date. The remaining nine columns represent each of the nine solutions described in the text. “UM” represents the University of Montana, “SIO” represents the Scripps Institution of Oceanography, and “JPL” represents the Jet Propulsion Laboratory. “NGL” refers to the use of GNSS analysis products from the Nevada Geodetic Laboratory, “CWU” refers to Central Washington University, and “MEaSUREs” refers to the Making Earth System Data Records for Use in Research Environments program. The time series have not been detrended.We highlight that we have added changes in reservoir storage (see Dataset 8) back into the JPL solutions, since reservoir storage had been modeled and removed from the GNSS time series prior to inversion in the JPL workflow (see “Detailed Description of Methods” in the Supporting Information). Thus, the storage values presented here for JPL differ slightly from storage values pulled directly from Dataset 6 and integrated over the area of the Sierra Nevada watersheds.Dataset 3: Estimated Changes in TWS in the Sacramento-San Joaquin-Tulare BasinSame as Dataset 2, except that data apply to the Sacramento-San Joaquin-Tulare (SST) Basin.Dataset 4: Inversion Products (SIO)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Scripps Institution of Oceanography (SIO) using the methods described in the Supporting Information.Dataset 5: Inversion Products (UM)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the University of Montana (UM) using the methods described in the Supporting Information.Dataset 6: Inversion Products (JPL)Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Jet Propulsion Laboratory (JPL) using the methods described in the Supporting Information.Dataset 7: Lists of Excluded StationsStations are excluded from an inversion for TWS change based on a variety of criteria (detailed in the Supporting Information), including poroelastic behavior, high noise levels, and susceptibility to volcanic deformation. This dataset provides lists of excluded stations from each institution generating inversion products (SIO, UM, JPL).Dataset 8: Lists of Reservoirs and LakesLists of reservoirs and lakes from the California Data Exchange Center (CDEC) (California Data Exchange Center, 2023), which are shown in Figures 1 and 2 of the main manuscript. In the interest of figure clarity, Figure 1 depicts only those reservoirs that exhibited volume changes of at least 0.15 km³ during the first half of WY23.Dataset 8a includes all reservoirs and lakes in California that exhibited volume changes of at least 0.15 km³ between October 2022 and March 2023. The threshold of 0.15 km³ represents a natural break in the distribution of volume changes at all reservoirs and lakes in California over that period (169 reservoirs and lakes in total). Most of the 169 reservoirs and lakes exhibited volume changes near zero km³. Datasets 8b and 8c include subsets of reservoirs and lakes (from Dataset 8a) that fall within the boundaries of the Sierra Nevada and SST watersheds.Furthermore, in the JPL data-processing and inversion workflow (see “Detailed Description of Methods” in the Supporting Information), surface displacements induced by volume changes in select lakes and reservoirs are modeled and removed from GNSS time series prior to inversion. The water-storage changes in the lakes and reservoirs are then added back into the solutions for water storage, derived from the inversion of GNSS data. Dataset 8d includes the list of reservoirs used in the JPL workflow.Dataset 9: Interseismic Strain Accumulation along the Cascadia Subduction ZoneJPL and UM remove interseismic strain accumulation associated with locking of the Cascadia subduction zone using an updated version of the Li et al. model (Li et al., 2018); see Supporting Information Section 2d. The dataset lists the east, north, and up velocity corrections (in the 4^th, 5^th, and 6^th columns of the dataset, respectively) at each station; units are mm/year. The station ID, latitude, and longitude are listed in columns one, two, and three, respectively, of the dataset.Dataset 10: Days Impacted by Atmospheric RiversA list of days impacted by atmospheric rivers within (a) the HUC-2 boundary for California from 1 January 2008 until 1 April 2023 [Dataset 10a] and (b) the Sierra Nevada and SST watersheds from 1 October 2022 until 1 April 2023 [Dataset 10b]. File formats: [decimal year; integrated water-vapor transport (IVT) in kg m^-1 s^-1; AR category; and calendar date as a two-digit year followed by a three-character month followed by a two-digit day]. The AR category reflects the peak intensity anywhere within the watershed. We use the detection and classification methods of (Ralph et al., 2019; Rutz et al., 2014, 2019). See also Supporting Information Section 2i.Dataset 10c provides a list of days and times when ARs made landfall along the California coast between October 1980 and September 2023, based on the MERRA-2 reanalysis using the methods of (Rutz et al., 2014, 2019). Only coastal grid cells are included. File format: [year, month, day, hour, latitude, longitude, and IVT in kg m^-1 s^-1]. Values are sorted by time (year, month, day, hour) and then by latitude. See also Supporting Information Section 2g. 

Abstract Atmospheric rivers (ARs) deliver significant and essential precipitation to the western United States (US) with consequential interannual variability. The intensity and frequency of ARs strongly influence reservoir levels, mountain snowpack, and groundwater recharge, which are key drivers of water‐resource availability and natural hazards. Between October 2022 and April 2023, western states experienced exceptionally heavy precipitation from several families of powerful ARs. Using observations of surface‐loading deformation from Global Navigation Satellite Systems, we find that terrestrial water‐storage gains exceeded 100% of normal within vital California watersheds. Independent water‐storage solutions derived from different data‐analysis and inversion methods provide an important measure of precision. The sustained storage increases, which we show are closely associated with ARs at daily‐to‐weekly timescales, alleviated both meteorological and hydrological drought conditions in the region, with a lag in hydrological‐drought improvements. Quantifying water‐storage recovery associated with extreme precipitation after drought advances understanding of an increasingly variable hydrologic cycle. 

Accurate hydrologic modeling is vital to characterizing how the terrestrial water cycle responds to climate change. Pure deep learning (DL) models have been shown to outperform process-based ones while remaining difficult to interpret. More recently, differentiable physics-informed machine learning models with a physical backbone can systematically integrate physical equations and DL, predicting untrained variables and processes with high performance. However, it is unclear if such models are competitive for global-scale applications with a simple backbone. Therefore, we use – for the first time at this scale – differentiable hydrologic models (full name δHBV-globe1.0-hydroDL, shortened to δHBV here) to simulate the rainfall–runoff processes for 3753 basins around the world. Moreover, we compare the δHBV models to a purely data-driven long short-term memory (LSTM) model to examine their strengths and limitations. Both LSTM and the δHBV models provide competitive daily hydrologic simulation capabilities in global basins, with median Kling–Gupta efficiency values close to or higher than 0.7 (and 0.78 with LSTM for a subset of 1675 basins with long-term discharge records), significantly outperforming traditional models. Moreover, regionalized differentiable models demonstrated stronger spatial generalization ability (median KGE 0.64) than a traditional parameter regionalization approach (median KGE 0.46) and even LSTM for ungauged region tests across continents. Nevertheless, relative to LSTM, the differentiable model was hampered by structural deficiencies for cold or polar regions, highly arid regions, and basins with significant human impacts. This study also sets the benchmark for hydrologic estimates around the world and builds a foundation for improving global hydrologic simulations.