Since the winter of 2013–2014, California has experienced its most severe drought in recorded history, causing statewide water stress, severe economic loss and an extraordinary increase in wildfires. Identifying the effects of global warming on regional water cycle extremes, such as the ongoing drought in California, remains a challenge. Here we analyse large-ensemble and multi-model simulations that project the future of water cycle extremes in California as well as to understand those associations that pertain to changing climate oscillations under global warming. Both intense drought and excessive flooding are projected to increase by at least 50% towards the end of the twenty-first century; this projected increase in water cycle extremes is associated with a strengthened relation to El Niño and the Southern Oscillation (ENSO)—in particular, extreme El Niño and La Niña events that modulate California's climate not only through its warm and cold phases but also its precursor patterns.

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual to reference ET (fRET) based on remotely sensed land surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface energy balance model, a multi-scale ET remote sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET timeseries retrievals from multiple satellite platforms to generate estimates at both the high spatial (30m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.59 mm d-1 in ET at the daily timestep and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multiannual timescales.

1 2 The recent paper by Morillas et al. [Morillas, L. et al. Using radiometric surface temperature for 3 surface energy flux estimation in Mediterranean drylands from a two-source perspective, Remote 4Sens. Environ. 136, 234-246, 2013] evaluates the two-source model (TSM) of Norman et al. 5(1995) with revisions by Kustas and Norman (1999) over a semiarid tussock grassland site in 6 southeastern Spain. The TSM -in its current incarnation, the two-source energy balance model 7 (TSEB) -was applied to this landscape using ground-based infrared radiometer sensors to 8 estimate both the composite surface radiometric temperature and component soil and canopy 9 temperatures. Morillas et al. (2013) found the TSEB model substantially underestimated the 10 sensible H (and overestimated the latent heat LE) fluxes. Using the same data set from Morillas 11 et al. (2013), we were able to confirm their results. We also found energy transport and 12 exchange behavior derived from primarily the observations themselves to differ significantly 13 from a number of prior studies using land surface temperature for estimating heat fluxes with 14 one-source modeling approaches in semi-arid landscapes. However, revisions to key vegetation 15 inputs to TSEB and the soil resistance formulation resulted in a significant reduction in the bias 16 and root mean square error (RMSE) between model output of H and LE and the measurements 17 compared to the prior results from Morillas et al (2013). These included more representative 18 ground-based vegetation greenness and local leaf area index values as well as modifications to 19 the coefficients of the soil resistance formulation to account for the very rough (rocky) soil 20 surface conditions with a clumped canopy. This indicates that both limitations in remote 21 estimates of biophysical indicators of the canopy at the site and the lack of adjustment in soil 22 resistance formulation to account for site specific characteristics, contributed to the earlier 23 findings of Morillas et al. (2013). This suggests further studies need to be conducted to reduce 24 the uncertainties in the vegetation and land surface temperature input data in order to more 25 accurately assess the effects of the transport exchange processes of this Mediterranean landscape 26 on TSEB formulations. 27 28 29 Reporting errors in the modeled latent heat flux (LE) of approximately 90% mostly due to 30 a significant underestimate of the sensible heat flux (H) (70 Wm -2 ), the recent study by Morillas 31 et al. (2013) suggests that the two-source energy balance (TSEB) model, which has been 32 successfully applied to a wide variety of landscapes and climates (Kustas and Anderson, 2009), 33 could not produce reliable estimates of LE in a semiarid Mediterranean tussock grassland site in 34 southeast Spain (Balsa Blanca). The Balsa Blanca site is representative of arid regions which 35 cover ~25 % of the Earth's land surface (Fensholt et al., 2012) and are characterized by having 36 low LE fluxes resulting in H being the domi...

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.

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