Ayumi Kotani

Response of gross ecosystem productivity, light use efficiency, and water use efficiency of Mongolian steppe to seasonal variations in soil moisture

8 [1] The examination of vegetation productivity and use of light and water resources is 9 important for understanding the carbon and water cycles in semiarid and arid 10 environments. We made continuous measurements of carbon dioxide and water vapor 11 fluxes over an arid steppe ecosystem in Mongolia by using the eddy covariance (EC) 12 technique. These measurements allow an examination of EC-estimated gross ecosystem 13 productivity (GEP), light use efficiency (LUE), and water use efficiency (WUE) of the 14 steppe. Daily variations of GEP, LUE, and WUE were associated with daily variations of 15 incident photosynthetically active radiation (PAR), ambient temperature (Ta), and vapor 16 pressure deficit (VPD). The magnitudes of these variations were also dependent on canopy 17 development. On the daily basis, GEP linearly correlated with evapotranspiration rate and 18 PAR. LUE correlated positively with leaf area index, Ta, and soil moisture availability but 19 negatively with the surface reflectivity for short-wave solar radiation. Throughout the 20 growing season, both GEP and LUE responded strongly to precipitation-fed soil moisture 21 in the top 20 cm of the soil. An examination of the responses of LUE and WUE to PAR 22 under different soil moisture conditions shows that when soil water availability exceeded 23 VPD, the steppe was most efficient in light use, whereas it was less efficient in water use. 24 The multivariate analysis of variance also suggests that soil moisture availability, 25 especially water status in the upper 20-cm soil layer with dense distribution of grass roots, 26 is the most significant factor that governs GEP, WUE, and LUE. This study provides a 27 preliminary assessment of the use of available water and light by the Mongolian arid 28 steppe ecosystems under seasonally varying soil moisture conditions. A better 29 understanding of these functional responses is required to predict how climate change may 30 affect arid steppe ecosystems.

Persistence and memory timescales in root-zone soil moisture dynamics

The memory timescale that characterizes root-zone soil moisture remains the dominant measure in seasonal forecasts of land-climate interactions. This memory is a quasi-deterministic timescale associated with the losses (e.g. evapotranspiration) from the soil column and is often interpreted as persistence in soil moisture states. Persistence, however, represents a distribution of time periods where soil moisture resides above or below some prescribed threshold, and is therefore inherently probabilistic. Using multiple soil moisture datasets collected at high resolution (sub-hourly) across different biomes and climates, this paper explores the differences, underlying dynamics, and relative importance of memory and persistence timescales in root-zone soil moisture. A first-order Markov process, commonly used to interpret soil moisture fluctuations derived from climate simulations, is also used as a reference model. Persistence durations of soil moisture below the plant water-stress level (chosen as the threshold), and the temporal spectrum of up- and down-crossings of this threshold, are compared to the memory timescale and spectrum of the full time series, respectively. The results indicate that despite the differences between meteorological drivers, the spectrum of threshold-crossings is similar across sites, and follows a unique relation with that of the full soil moisture series. The distribution of persistence times exhibits an approximate stretched exponential type and reflects a likelihood of exceeding the memory at all sites. However, the rainfall counterpart of these distributions shows that persistence of dry atmospheric periods is less likely at sites with long soil moisture memory. The cluster exponent, a measure of the density of threshold crossings in a time frame, reveals that the clustering tendency in rainfall events (on-off switches) does not translate directly to clustering in soil moisture. This is particularly the case in climates where rainfall and evapotranspiration are out of phase, resulting in less ordered (more independent) persistence in soil moisture than in rainfall.

New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8days are reproduced (e.g., r2=0.73 and 0.42 for 8day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models. (Less)

Radiative and precipitation controls on root zone soil moisture spectra

Temporal variability in root zone soil moisture content (w) exhibits a Lorentzian spectrum with memory dictated by a damping term when forced with white-noise precipitation. In the context of regional dimming, radiation and precipitation variability are needed to reproduce w trends prompting interest in how the w memory is altered by radiative forcing. A hierarchy of models that sequentially introduce the spectrum of precipitation, net radiation, and the effect of w on evaporative and drainage losses was used to analyze the spectrum of w at subtropical and temperate forested sites. Reproducing the w spectra at long time scales necessitated simultaneous precipitation and net radiation measurements depending on site conditions. The w memory inferred from observed w spectra was 25–38 days, larger than that determined from maximum wet evapotranspiration and field capacity. The w memory can be reasonably inferred from the Lorentzian spectrum when precipitation and evapotranspiration are in phase.