We estimate net groundwater storage change in the Central Valley from April 2002 to September 2016 as the difference between inflows and outflows, precipitation, evapotranspiration, and changes in soil moisture and surface water storage. We also estimate total water storage change attributable to groundwater change using Gravity Recovery and Climate Experiment (GRACE) satellite data, which should be equivalent to our water balance estimates. Over two drought periods within our 14‐1/2 years study period (January 2007 to December 2009 and October 2012 to September 2016), we estimate from our water balance that a total of 16.5 km3 and 40.0 km3 of groundwater was lost, respectively. Our water balance‐based estimate of the overall groundwater loss over the 14‐1/2 years is −20.7 km3, which includes substantial recovery during nondrought periods The estimated rate of groundwater loss is greater during the recent drought (10.0 ± 0.2 versus 5.5 ± 0.3 km3/yr) than in the 2007–2009 drought, due to lower net inflows, a transition from row crops to trees, and higher crop water use, notwithstanding a reduction in irrigated area. The GRACE estimates of groundwater loss (−5.0 km3/yr for both water balance and GRACE during 2007–2009, and −11.2 km3/yr for GRACE versus −10 km3/yr for water balance during 2012–2016) are quite consistent for the two methods. However, over the entire study period, the GRACE‐based groundwater loss estimate is almost triple that from the water balance, mostly because GRACE does not indicate the between‐drought groundwater recovery that is inferred from our water balance.

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The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)-normalized by photosynthetically-active radiation (PAR)-to diagnose the ratio of transpiration to potential evaporation ('transpiration efficiency', τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5 • ) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth's Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations.However, perhaps unsurprisingly, current LSMs still constrain transpiration based on empirical relationships between stomatal conductance and environmental variables such as soil moisture, temperature and vapor pressure deficit [18][19][20]. These formulations are supported by limited observational evidence at global scales, and their assumptions affect the modelled heat and drought response of ecosystem transpiration, which remains uncertain in current models [7,21,22]. The effect of water stress on transpiration is typically introduced in LSMs through a dimensionless empirical factor based on an assumed relationship between soil moisture and stomatal conductance, net photosynthesis, mesophyll conductance and/or carboxylation rate [21,[23][24][25]. Pure hydrologic and remote-sensing models-which have no explicit photosynthesis and coupled stomatal dependence-use similar empirical water-stress functions to constrain the potential transpiration estimates from Penman-Monteith or Priestley and Taylor approaches [26][27][28][29][30]. Two-source energy balance models utilize remotely sensed surface temperatures to constrain transpiration, but often still rely on Penman-Monteith, Priestley and Taylor, or aerodynamic formulations [31], or solve for latent heat as the residual term in the energy balance [32,33].Given (a) the importance of transpiration for global hydrology and climate, (b) the dependency of transpiration on phenology and physiological respons...

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