Soil CO₂ dynamics in a tree island soil of the Pantanal: the role of soil water potential.

The Pantanal is a biodiversity hotspot comprised of a mosaic of landforms that differ in vegetative assemblages and flooding dynamics. Tree islands provide refuge for terrestrial fauna during the flooding period and are particularly important to the regional ecosystem structure. Little soil CO₂ research has been conducted in this region. We evaluated soil CO₂ dynamics in relation to primary controlling environmental parameters (soil temperature and soil water). Soil respiration was computed using the gradient method using in situ infrared gas analyzers to directly measure CO₂ concentration within the soil profile. Due to the cost of the sensors and associated equipment, this study was unreplicated. Rather, we focus on the temporal relationships between soil CO₂ efflux and related environmental parameters. Soil CO₂ efflux during the study averaged 3.53 µmol CO₂ m⁻² s⁻¹, and was equivalent to an annual soil respiration of 1220 g C m⁻² y⁻¹. This efflux value, integrated over a year, is comparable to soil C stocks for 0-20 cm. Soil water potential was the measured parameter most strongly associated with soil CO₂ concentrations, with high CO₂ values observed only once soil water potential at the 10 cm depth approached zero. This relationship was exhibited across a spectrum of timescales and was found to be significant at a daily timescale across all seasons using conditional nonparametric spectral Granger causality analysis. Hydrology plays a significant role in controlling CO₂ efflux from the tree island soil, with soil CO₂ dynamics differing by wetting mechanism. During the wet-up period, direct precipitation infiltrates soil from above and results in pulses of CO₂ efflux from soil. The annual flood arrives later, and saturates soil from below. While CO₂ concentrations in soil grew very high under both wetting mechanisms, the change in soil CO₂ efflux was only significant when soils were wet from above

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management

Site location map showing the SESC Pantanal research site within the Northern Pantanal.

<p>The inset map shows the location of the Brazilian portion of the Pantanal.</p

Episodic behavior of soil respiration in response to soil water potential and soil moisture content at 10 and 30 cm depths.

<p>The arrows in the lower panels indicate the temporal direction of hysteresis loops.</p

Precipitation (A), soil respiration (B), soil CO₂ concentrations at 10 cm and 30 cm depths (C), soil water potential (D) and soil water content (E) during the December 2008 -December 2009 study period.

<p>Vertical boxes indicate the seasonal periods identified by factor analysis.</p

Granger-causality spectra between soil water potential (pF) and soil temperature (T_s) on soil CO₂ efflux (F₀).

<p>Results are presented for each temporal cluster identified in the factor analysis, with 90-day time series used for each cluster.</p