Enhanced gross primary production and evapotranspiration in juniper-encroached grasslands.

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change in the future. This land cover change is expected to alter the carbon and water cycles, but it remains uncertain how and to what extent the carbon and water cycles may change with WPE into grasslands under current climate. In this study, we examined the difference of vegetation indices (VIs), evapotranspiration (ET), gross primary production (GPP), and solar-induced chlorophyll fluorescence (SIF) during 2000-2010 between grasslands and juniper-encroached grasslands. We also quantitatively assessed the changes of GPP and ET for grasslands with different proportions of juniper encroachment (JWPE). Our results suggested that JWPE increased the GPP, ET, greenness-related VIs, and SIF of grasslands. Mean annual GPP and ET were, respectively, ~55% and ~45% higher when grasslands were completely converted into juniper forests under contemporary climate during 2000-2010. The enhancement of annual GPP and ET for grasslands with JWPE varied over years ranging from about +20% GPP (~+30% for ET) in the wettest year (2007) to about twice as much GPP (~+55% for ET) in the severe drought year (2006) relative to grasslands without encroachment. Additionally, the differences in GPP and ET showed significant seasonal dynamics. During the peak growing season (May-August), GPP and ET for grasslands with JWPE were ~30% and ~40% higher on average. This analysis provided insights into how and to what degree carbon and water cycles were impacted by JWPE, which is vital to understanding how JWPE and ecological succession will affect the regional and global carbon and water budgets in the future.

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS.

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

Improving a Biogeochemical Model to Simulate Microbial-Mediated Carbon Dynamics in Agricultural Ecosystems.

Soil microbes drive decomposition of soil organic matter (SOM) and regulate soil carbon (C) dynamics. Process-based models have been developed to quantify changes in soil organic carbon (SOC) and carbon dioxide (CO2) fluxes in agricultural ecosystems. However, microbial processes related to SOM decomposition have not been, or are inadequately, represented in these models, limiting predictions of SOC responses to changes in microbial activities. In this study, we developed a microbial-mediated decomposition model based on a widely used biogeochemical model, DeNitrification-DeComposition (DNDC), to simulate C dynamics in agricultural ecosystems. The model simulates organic matter decomposition, soil respiration, and SOC formation by simulating microbial and enzyme dynamics and their controls on decomposition, and considering impacts of climate, soil, crop, and farming management practices (FMPs) on C dynamics. When evaluated against field observations of net ecosystem CO2 exchange (NEE) and SOC change in two winter wheat systems, the model successfully captured both NEE and SOC changes under different FMPs. Inclusion of microbial processes improved the model's performance in simulating peak CO2 fluxes induced by residue return, primarily by capturing priming effects of residue inputs. We also investigated impacts of microbial physiology, SOM, and FMPs on soil C dynamics. Our results demonstrated that residue or manure input drove microbial activity and predominantly regulated the CO2 fluxes, and manure amendment largely regulated long-term SOC change. The microbial physiology had considerable impacts on the microbial activities and soil C dynamics, emphasizing the necessity of considering microbial physiology and activities when assessing soil C dynamics in agricultural ecosystems.

Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma.

Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO2) and water vapor (H2O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO2 and H2O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO2 exchange (NEE) reached seasonal peaks of -9.4 and -8.8 g C m-2 in 2015 and -6.2 and -7.5 g C m-2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ±â€¯13 and 33 ±â€¯9 g C m-2 year-1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (-128 ±â€¯69 and -119 ±â€¯53 g C m-2 year-1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day-1 in 2015, and 7.2 and 8.2 mm day-1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets.

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.