The separate components of evapotranspiration (ET) elucidate the pathways and time scales over which water is returned to the atmosphere, but ecosystem‐scale measurements of transpiration (T) and ...evaporation (E) remain elusive. We propose a novel determination of E and T using multiyear eddy covariance estimates of ET and gross ecosystem photosynthesis (GEP). The method is applicable at water‐limited sites over time periods during which a linear regression between GEP (abscissa) and ET (ordinate) yields a positive ET axis intercept, an estimate of E. At four summer‐rainfall semiarid sites, T/ET increases to a peak coincident with maximum GEP and remains elevated as the growing season progresses, consistent with previous, direct measurements. The seasonal course of T/ET is related to increasing leaf area index and declining frequency of rainy days—an index of the wet surface conditions that promote E—suggesting both surface and climatic controls on ET partitioning.
Key Points
A new method is developed to partition ET using multiyear carbon and water flux measurements
The method is applied to semiarid sites and shows that E peaks at the start of rainy season and declines as the growing season progresses
Magnitudes and trends in E and T/ET are consistent with field observations and known effects from surface and climate controls
Quantifying how much and when precipitation (P) becomes runoff (R), evapotranspiration (ET), and drainage from the root zone (D) is key to understanding how climate and land use impact hydrology of ...the critical zone. We quantify water balance dynamics of a semiarid savanna with a summer/winter rainfall pattern with 13 years of water fluxes and soil moisture. We find multiyear P is partitioned 96% to ET and 7% to R, while D (−3%) is negligible when considering measurement uncertainty. While weather regulates ET over diurnal time scales, soil water inputs control seasonal to annual ET amounts. Seasonal water availability, estimated by soil moisture inputs, is more closely tracked by ET rather than time‐averaged soil moisture or P. Surprisingly, we find significant, episodic carryover of soil moisture from the summer to spring growing season. Abundant late‐summer P can supply ET in the subsequent spring, even after multimonth dry periods. However, over an annual cycle beginning in early summer, nearly all soil moisture is used by ET. Likewise, D beyond the monitored root zone, assisted by downward hydraulic distribution in plant roots, occurs within a season, but this is counteracted by subsequent ET extraction of deep moisture over the year. Thus, negligible long‐term D occurs, though there is considerable uncertainty in estimation of this small flux as the residual of much larger ones. These comprehensive, long‐term measurements support expectations about the overriding importance of ET in the dryland critical zone water balance and reveal an unexpected degree of interseasonal water storage.
Plain Language Summary
One of the most enduring and important questions for hydrology is how water input in the form of precipitation is partitioned among evapotranspiration, runoff, groundwater recharge, and storage of moisture in the soil. We quantified how precipitation was partitioned at a semiarid savanna site in Arizona, USA, with 13 years of data. We found that almost all of the precipitation goes into evapotranspiration with only a small of runoff and negligible recharge. Contrary to expectations, we saw significant, episodic carryover of soil moisture from the summer/fall growing season to the subsequent springtime when the plants awake from winter dormancy and extract the stored moisture. These comprehensive, long‐term measurements support expectations about the overriding importance of ET in semiarid watersheds' water balance and reveal a surprising degree of interseasonal water storage.
Key Points
Precipitation, runoff, evapotranspiration, soil moisture, and root zone drainage are quantified over 13 years at a semiarid savanna site
Evapotranspiration dominates water loss from the root zone, runoff is small, and deep drainage is minuscule
Carryover of soil moisture from summer rainfall to the following spring challenges assumptions about dryland soil water residence times
Nitrous oxide (N2O) emissions from soil contribute to global warming and are in turn substantially affected by climate change. However, climate change impacts on N2O production across terrestrial ...ecosystems remain poorly understood. Here, we synthesized 46 published studies of N2O fluxes and relevant soil functional genes (SFGs, that is, archaeal amoA, bacterial amoA, nosZ, narG, nirK and nirS) to assess their responses to increased temperature, increased or decreased precipitation amounts, and prolonged drought (no change in total precipitation but increase in precipitation intervals) in terrestrial ecosystem (i.e. grasslands, forests, shrublands, tundra and croplands). Across the data set, temperature increased N2O emissions by 33%. However, the effects were highly variable across biomes, with strongest temperature responses in shrublands, variable responses in forests and negative responses in tundra. The warming methods employed also influenced the effects of temperature on N2O emissions (most effectively induced by open‐top chambers). Whole‐day or whole‐year warming treatment significantly enhanced N2O emissions, but daytime, nighttime or short‐season warming did not have significant effects. Regardless of biome, treatment method and season, increased precipitation promoted N2O emission by an average of 55%, while decreased precipitation suppressed N2O emission by 31%, predominantly driven by changes in soil moisture. The effect size of precipitation changes on nirS and nosZ showed a U‐shape relationship with soil moisture; further insight into biotic mechanisms underlying N2O emission response to climate change remain limited by data availability, underlying a need for studies that report SFG. Our findings indicate that climate change substantially affects N2O emission and highlights the urgent need to incorporate this strong feedback into most climate models for convincing projection of future climate change.
Positive/enhancing impacts and negative/suppressing impacts between two variables are indicated by red and blue lines respectively. Nitrification and denitrification processes are indicated by purple and yellow lines respectively. Black dashed lines indicate equal variables.
Global‐scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such analyses are poorly constrained by ...measured CO2 exchange in drylands. Here we address this observation gap with eddy covariance data from 25 sites in the water‐limited Southwest region of North America with observed ranges in annual precipitation of 100–1000 mm, annual temperatures of 2–25°C, and records of 3–10 years (150 site‐years in total). Annual fluxes were integrated using site‐specific ecohydrologic years to group precipitation with resulting ecosystem exchanges. We found a wide range of carbon sink/source function, with mean annual net ecosystem production (NEP) varying from ‐350 to +330 gCm−2 across sites with diverse vegetation types, contrasting with the more constant sink typically measured in mesic ecosystems. In this region, only forest‐dominated sites were consistent carbon sinks. Interannual variability of NEP, gross ecosystem production (GEP), and ecosystem respiration (Reco) was larger than for mesic regions, and half the sites switched between functioning as C sinks/C sources in wet/dry years. The sites demonstrated coherent responses of GEP and NEP to anomalies in annual evapotranspiration (ET), used here as a proxy for annually available water after hydrologic losses. Notably, GEP and Reco were negatively related to temperature, both interannually within site and spatially across sites, in contrast to positive temperature effects commonly reported for mesic ecosystems. Models based on MODIS satellite observations matched the cross‐site spatial pattern in mean annual GEP but consistently underestimated mean annual ET by ~50%. Importantly, the MODIS‐based models captured only 20–30% of interannual variation magnitude. These results suggest the contribution of this dryland region to variability of regional to global CO2 exchange may be up to 3–5 times larger than current estimates.
Global‐scale studies suggest that drylands dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink, but direct measurements are lacking; 25 eddy covariance sites in the water‐limited southwest of North America showed wide‐ranging carbon sink/source function, contrasting with the persistent sink typically measured in mesic ecosystems. Interannual variability of CO2 exchange was larger than for mesic regions, and half the sites switched between functioning as C sinks/sources in wet/dry years. CO2 exchanges were negatively related to temperature, in contrast to positive effects commonly reported for mesic ecosystems. MODIS‐based models captured only 20–30% of interannual variation, suggesting this dryland region may contribute 3–5 times more variability to global carbon and water cycles than current estimates.
Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate ...understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts.
Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, ...dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.
•We highlight major historical milestones in dryland remote sensing.•Drylands have historically been important regions for remote sensing innovation.•Drylands, in turn, have emerged as key drivers of variability in land CO2 uptake.•Dryland remote sensing faces unique challenges not encountered in many ecosystems.•We recommend new assets and techniques as a path forward for dryland remote sensing.
Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, ...https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends).
Plain Language Summary
Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends.
Key Points
We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall test
Recomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall test
No perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data
Recent bark beetle epidemics have caused regional‐scale tree mortality in many snowmelt‐dominated headwater catchments of western North America. Initial expectations of increased streamflow have not ...been supported by observations, and the basin‐scale response of annual streamflow is largely unknown. Here we quantified annual streamflow responses during the decade following tree die‐off in eight infested catchments in the Colorado River headwaters and one nearby control catchment. We employed three alternative empirical methods: (i) double‐mass comparison between impacted and control catchments, (ii) runoff ratio comparison before and after die‐off, and (iii) time‐trend analysis using climate‐driven linear models. In contrast to streamflow increases predicted by historical paired catchment studies and recent modeling, we did not detect streamflow changes in most basins following die‐off, while one basin consistently showed decreased streamflow. The three analysis methods produced generally consistent results, with time‐trend analysis showing precipitation was the strongest predictor of streamflow variability (R2 = 74–96%). Time‐trend analysis revealed post‐die‐off streamflow decreased in three catchments by 11–29%, with no change in the other five catchments. Although counter to initial expectations, these results are consistent with increased transpiration by surviving vegetation and the growing body of literature documenting increased snow sublimation and evaporation from the subcanopy following die‐off in water‐limited, snow‐dominated forests. The observations presented here challenge the widespread expectation that streamflow will increase following beetle‐induced forest die‐off and highlight the need to better understand the processes driving hydrologic response to forest disturbance.
Key Points:
Streamflow did not increase as predicted
Three empirical methods produced consistent results
Weak, variable streamflow response is consistent with recent process literature
• Heavy rainfall events are expected to increase in frequency and severity in the future. However, their effects on natural ecosystems are largely unknown, in particular with different seasonal ...timing of the events and recurrence over multiple years.
• We conducted a 4 yr manipulative experiment to explore grassland response to heavy rainfall imposed in either the middle of, or late in, the growing season in Inner Mongolia, China. We measured hierarchical responses at individual, community and ecosystem levels.
• Surprisingly, above-ground biomass remained stable in the face of heavy rainfall, regardless of seasonal timing, whereas heavy rainfall late in the growing season had consistent negative impacts on below-ground and total biomass. However, such negative biomass effects were not significant for heavy rainfall in the middle of the growing season. By contrast, heavy rainfall in the middle of the growing season had greater positive effects on ecosystem CO₂ exchanges, mainly reflected in the latter 2 yr of the 4 yr experiment. This two-stage response of CO₂ fluxes was regulated by increased community-level leaf area and leaf-level photosynthesis and interannual variability of natural precipitation.
• Overall, our study demonstrates that ecosystem impacts of heavy rainfall events crucially depend on the seasonal timing and multiannual recurrence. Plant physiological and morphological adjustment appeared to improve the capacity of the ecosystem to respond positively to heavy rainfall.
In the semiarid interior western USA, where a majority of surface water supply comes from mountain forests, high‐resolution aerial lidar‐based surveys are commonly used to study snow. These surveys ...provide rich information about snow depth, but they are usually not accompanied with spatially explicit measurements of snow density, which leads to uncertainty in the estimation of snow water equivalent (SWE). In this study, we use a novel approach to distribute ~300 field measurements of snow density with artificial neural networks. We combine the resulting density maps with aerial lidar snow depth measurements, bias corrected with a very large and precisely geolocated array of field‐measured snow depths (~4,000 observations), to create and validate maps of snow depth, snow density, and SWE over two sites along Arizona's Mogollon Rim in February and March 2017. These maps show differences between midwinter and late‐winter snow conditions. In particular, compared to that of snow depth, the spatial variability of snow density is smaller for the later snow survey than the earlier snow survey. These gridded data also show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter and late‐winter snow surveys. Overall, the lidar artificial neural network SWE estimates can be as much as 30% different than if Snow Telemetry density were used with lidar snow depths to estimate SWE.
Plain Language Summary
In the western USA, a majority of surface water originates from mountain snowmelt. Knowing the quantity of water in the snowpack, called snow water equivalent (SWE), is critical for water supply forecasts and management of rivers and streams for water delivery and hydropower. In this study, we develop a new method to estimate SWE by combining aerial remote sensing maps of snow depth with snow density maps generated through machine learning of hundreds of field measurements of snow density. This study finds that on a given date, snow density can vary widely, highlighting the importance of considering its spatial variability when estimating SWE. These gridded data show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter versus late winter snow surveys. In addition, we show that using spatially variable maps of snow density can impact watershed‐scale SWE estimates by up to 30% as compared to using snow density measurements from commonly used snow monitoring stations. The method described in this study will be useful for generating SWE estimates for water supply monitoring, evaluating snow models, and understanding how changing mountain forests might impact SWE.
Key Points
It is important to consider the spatial variability of snow density to create spatially distributed maps of SWE from lidar data
Relative to that of snow depth, snow density variability is higher earlier in the snow season
The representativeness of snow depth, snow density, and SWE observations from SNOTEL stations varies at different points in the snow season
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