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Abstract: Over the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure-volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions-which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.

In this study, prominent dust source areas are identified along with their plume extent using high temporal frequency satellite observations. Hourly dust plume observations of the Dust Belt from geostationary‐orbit satellites are analyzed for the 2017‐12–2022‐11 period. To identify dust source areas and their extents, we back‐track plumes to their source, assessing source areas in terms of emission frequency, contribution, and plume extent patterns. This method advances over traditional source allocation techniques that rely on polar‐orbiting satellites based on a few daily passes and meteorological wind fields for backtracking. Our findings indicate that Boreal summer is the most intense season for most sources, except in the Southern Sahara, which experiences winterly winds. Our analysis also reveals significant contributions from regions within the Sahara that experience expansive but infrequent dust storms, highlighting the importance of considering both frequency and magnitude in understanding dust emissions. Plain Language Summary: This study focuses on the role of mineral dust, a significant climate constituent originating mainly from arid regions. Identifying where dust comes from and where it goes is essential for understanding how it affects the climate. To do so, we employ high temporal frequency satellite data to backtrack dust plumes to their sources, revealing key dust‐emitting regions and their seasonal variations. We also quantify each source area's frequency and magnitude of emissions and the spatial distribution of emanating dust plumes. Our findings show that summer is the peak season for most areas, except for the strongest dust sources, located in Southern Sahara, which peak in winter driven by seasonal winds. Additionally, some Saharan source areas exhibit severe storms but have been under‐emphasized because they were measured by the frequency of their storms. Key Points: High‐temporal‐resolution images from geostationary‐orbit satellites enable precise back‐tracking of dust source areasDust source areas are analyzed in terms of their emission frequency, contribution to the regional atmosphere, and corresponding plume extentSummer is the peak dust emission season for most sources in West Asia and Sahara and ephemeral lake beds are active during the dry season [ABSTRACT FROM AUTHOR]

To bridge the knowledge gap between (a) our (instantaneous‐to‐seasonal‐scale) process understanding of plants and water and (b) our projections of long‐term coupled feedbacks between the terrestrial water and carbon cycles, we must uncover what the dominant dynamics are linking fluxes of water and carbon. This study uses the simplest empirical dynamical systems models—two‐dimensional linear models—and observation‐based data from satellites, eddy covariance towers, weather stations, and machine‐learning‐derived products to determine the dominant sub‐annual timescales coupling carbon uptake and (normalized) evaporation fluxes. We find two dominant modes across the Contiguous United States: (1) a negative correlation timescale on the order of a few days during which landscapes dry after precipitation and plants increase their carbon uptake through photosynthetic upregulation. (2) A slow, seasonal‐scale positive covariation through which landscape drying leads to decreased growth and carbon uptake. The slow (positively correlated) process dominates the joint distribution of local water and carbon variables, leading to similar behaviors across space, biomes, and climate regions. We propose that vegetation cover/leaf area variables link this behavior across space, leading to strong emergent spatial patterns of water/carbon coupling in the mean. The spatial pattern of local temporal dynamics—positively sloped tangent lines to a convex long‐term mean‐state curve—is surprisingly strong, and can serve as a benchmark for coupled Earth System Models. We show that many such models do not represent this emergent mean‐state pattern, and hypothesize that this may be due to lack of water‐carbon feedbacks at daily scales. [ABSTRACT FROM AUTHOR]

Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December-February (DJF)] of the "Pacific Coast California" (PCCA) region and the summer [June-August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979-2005 and validated with 2006-14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.

Terrestrial carbon uptake and water availability have coupled feedbacks; specifically water uptake for plant growth and soil drying via transpiration. While we might expect this coupling over time at arid sites, climatic water availability also widely covaries geographically with biomass variables that control photosynthetic rates. Using eddy covariance data globally, we find convex, positively‐covarying relations between carbon uptake and a turbulent flux metric controlled by land surface moisture (r = 0.73 monthly across sites) at the site level. We estimate a general, empirical relationship based on site‐wise water‐carbon dynamics. Most sites, and the general relationship, show strong power‐law dependence, implicating the role of sub‐seasonal land‐cover dynamics. We also find that long‐term mean carbon/water states follow a similar convex relationship to the site‐specific temporal dynamics. We discuss opportunities and caveats for space‐for‐time frameworks of carbon/water feedback processes globally. Plain Language Summary: The amount of carbon dioxide that plants take from the air depends on how plants respond to water and water stress. At the same time, plants also control the loss of water from the landscape through transpiration. While we think of individual stressed plants in dry conditions as being very sensitive to the time‐variability in water available to them, we find here that the average carbon uptake by plants across climate zones is similarly related to climatic water availability. Water availability and carbon uptake rates seem not to depend strongly on location, so that most locations behave quite similarly, across many climates. This can be explained by similar structural changes in the vegetation canopy in response to water stress, whether it be over time at a site or across regions with different mean climate conditions. Key Points: Landscape carbon uptake displays strong relationships with landscape evaporative fractionThese relations are non‐linear both at single sites and across bioclimates at eddy covariance tower sitesChanges in structural canopy‐scale variables (e.g., leaf‐area) may dominate the coupling of water and carbon cycles similarly in both space and time [ABSTRACT FROM AUTHOR]

At the end of its first year of operation, we compare soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission to simulations from a land surface model with meteorological forcing downscaled from observations/reanalysis and in situ observations from sparse monitoring networks within continental United States (CONUS). The radar failure limits the duration of comparisons for the active and combined products (~3 months). Nevertheless, the passive product compares very well against in situ observations over CONUS. On average, SMAP compares to the in situ data even better than the land surface model and provides significant added value on top of the model and thus good potential for data assimilation. At large scale, SMAP is in good agreement with the model in most of CONUS with less-than-expected degradation over mountainous areas. Lower correlation between SMAP and the model is seen in the forested east CONUS and significantly lower over the Canadian boreal forests.

An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979-2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. © 2014 American Meteorological Society.