Hurricanes cause severe impacts on the ecosystem, which substantially affects the carbon cycle at the local or regional scale. During the hurricanes, the loss of many vegetation/trees in the forest and agricultural lands causes more carbon to be released into the atmosphere. Studying the effects of hurricanes on the terrestrial carbon cycle, which includes gross primary product (GPP), net ecosystem exchange (NEE), heterotrophic respiration (Rh), and their interactions with land-use change, flood, and others are critical to understand the effect on the terrestrial ecosystem. The main objective of this research was to evaluate the impact of three hurricanes (Harvey, Irma, and Maria in 2017) on the carbon cycle and study the interactions among the flood events, land uses, and terrestrial carbon cycling in the state of Texas, Florida, Puerto Rico using satellite measurements. This study analyzed the GPP, NEE, and Rh distributions in the coastal climate zones in Texas, Florida, and Puerto Rico during hurricane season using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were evaluated using CO2 flux data measured at EC flux site on the Prairie View A&M University Research Farm, Texas. Results showed Florida (Irma) had higher carbon emissions and lower GPP during the hurricane compared to Texas (Harvey), and Puerto Rico (Maria). For example, hurricanes Harvey (08/26/2017), Irma (09/10/2017), and Maria (09/20/2017) caused 2.6, 4.1, and 3.03 gC/m2, of carbon emissions when the recorded daily precipitations were 162, 135, and 241 mm, respectively. However, mostly carbon uptakes or low (<1 gC/m2) carbon emissions were observed on the same day in 2016 and 2018. The analysis showed that the amount of precipitation is not the only driving factor causing increased carbon emission; the characteristics of the drainage area also affect the carbon cycle and emission. Overall, the results showed that hurricanes increase carbon emissions. This study helps to understand the impact of hurricanes on the carbon cycle through analyses of spatial and temporal variations of carbon emission and uptake during the hurricane season.

In this research, we provided a framework to merge well-known satellite- and reanalysis-based global ET products (GLDAS, GLEAM, MOD16, and MERRA) using the triple collocation (TC) along with least-square based merging scheme without the utilization of high-quality ground measurement over East Asia. Firstly, the error characteristics of each ET product were statistically estimated using TC metrics with four different combinations. Results revealed that GLEAM showed the least error variance and highest product-truth correlation coefficient for most land cover types, followed by GLDAS, MERRA, and MOD16. TC-based error characteristics at different land cover types were reflected to parameterize weighting factors for individual ET products, and in turn, utilized in producing the merged ET estimates. Evaluation of merged ET estimates was conducted at 11 flux tower sites located within the study area. When relatively low-quality ET products (MERRA and MOD16) were used as input for TC metric, the accuracy of the merged ET estimates was better than those of the two individual ET products at all three land cover types. Furthermore, when two relatively high-quality ET products (GLEAM and GLDAS) were used as input for TC, the accuracy of merged ET estimates were greater than that of GLEAM and showed the highest statistical performance among the ET products over the three land cover types. Merged ET estimates from scenarios containing GLEAM and GLDAS showed MAE (RMSE) ranging from 0.275 to 0.692 mm/8 day (0.399 to 0.873 mm/8 day) and correlation coefficient ranging from 0.864 to 0.933 in compared to in-situ measurements. These statistics showed substantial improvement when compared to the original ET products (MAE: 0.327 to 1.172 mm/8 day, RMSE: 0.464 to 1.455 mm/8 day, and correlation coefficient: 0.636 to 0.925) over the three land cover types. These results confirmed that a TC-based merging framework could enhance the accuracy of terrestrial ET.