Geology topics

Emma G. Reich

Publications and source records attributed to Emma G. Reich.

3 recordsLinked to original sources

USGS2025

Precipitation pulse dynamics are not ubiquitous: A global meta-analysis of plant and ecosystem carbon- and water-related pulse responses

Ecosystem responses to precipitation pulses (“pulse responses”) exert a large control over global carbon, water, and energy cycles. However, it is unclear how the timing and magnitude of pulse responses will vary across ecosystems as precipitation regimes shift under accelerating climate change. To address this issue, this study evaluates how plants and ecosystems respond to precipitation pulses and explores potential implications of altered precipitation regimes for the carbon and water cycles. In particular, we conducted a global meta-analysis to quantify the magnitude and timing of plant and ecosystem carbon-related ( A net , NPP, GPP, R eco , R bg ) and water-related (ET, T, Ψ, g s ) responses to 587 precipitation pulses. By analyzing pulse-response metrics published in the primary literature, we evaluated the characteristics of those pulse responses. We assessed whether precipitation pulses lead to a classic pulse response (i.e., a hump-shaped response as described by the pulse-reserve framework), a linear pulse response, a combination of classic and linear, or a lack of a pulse response. If a pulse response occurred, we explored the factors that drove its timing, magnitude, and speed. Our meta-analyses revealed that the classic, hump-shaped response is not ubiquitous, as it only accounted for 52% of the pulse responses. However, when a pulse response did occur, carbon-related responses to precipitation pulses were larger in magnitude (e.g., larger peak) than water-related pulse responses at relatively arid sites. However, at relatively mesic sites, this relationship reversed (i.e., water-related responses to precipitation pulses were larger than carbon-related responses). Additionally, larger precipitation pulse amounts increased water-related response magnitudes more than carbon-related response magnitudes across both arid and mesic sites. Therefore, under future precipitation intensification, carbon-related responses to precipitation pulses may become more decoupled from water-related pulse responses in wetter biomes but more coupled to water-related pulse responses in drier biomes.

Global Change Biology↗

USGS2025

Filling the gaps: A Bayesian mixture model for imputing missing soil water content data

Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water-limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well (R2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap-fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

New Mexico↗

USGS2025

Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico↗