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Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk. [ABSTRACT FROM AUTHOR]

Agricultural productivity is strongly dependent on local climate conditions determined by meteorological parameters thus assessing the potential impact of the climate change and variability on regional agricultural systems has become crucial. To ensure food security, it is required to find under performing regions to investments and assess yields change in high-performing regions in coming decades under climate change and variability. In this study, we investigate the response of maize yield potential (Yp) on climate change scenario using Agricultural Production Systems sIMulator (APSIM) crop model over the Southwestern U.S. (SWUS) region. APSIM's modules are essentially point-based models representing the system at a single point in space. We develop automated modeling framework (ApsimRegions, 2013), which allows the APSIM to be run over a large domain with about a thousand points over the study area. Using 21-year period (1991-2011) of North American Regional Reanalysis (NARR) data, we perform sensitivity test of the maize Yp to assess the relative contribution of climate variables, by adding standard deviation of the climatological values. The results show that maximum and minimum temperature greatly contribute to the variation of maize yields over the SWUS on the interannual time scale, depending on geographical locations with varied local climates. In order to access data of present and future climate, we have completed high-resolution regional climate simulation by dynamically downscaling general circulation model results (GFDL-ESM2 M) using regional climate models (WRF and OLAM). In this study, 20 years of integration period is selected in both historical period (1981-2000) and future period (2031-2050). The potential maize yields in the future period under the RCP8.5 greenhouse gas concentrations pathways show that the yields are significantly changed comparing to the historical period. In the generally rising temperature regime, the projected Yp shows strong geospatial variations according to the regional climate characteristics. [ABSTRACT FROM AUTHOR]

Since sowing date of maize is sensitive to climate variability and changes, it is critical to examine how sowing dates affect maize yields in various temperature regimes in the southwestern US. In doing so, the ApsimRegions model, regionally extended version of the APSIM model, has been run over the southwestern US region at 32 km resolutions over the 21-year period from 1991 to 2011. The meteorological forcing dataset was North American Regional Reanalysis (NARR) and irrigation was assumed to be applied at a 95% soil water-holding capacity. The interannually-fixed and -variant sowing date runs (fixed and optimal run, respectively) showed 50∼300% increases of the yield potential over selected regions by optimizing sowing date. For the optimal run, it was found that earlier sowing dates are favorable for higher yields primarily by increasing the length of growing season in cold mountainous regions. In these regions, warmer than normal conditions in the sowing period tend to advance the sowing date, resulting in yield increases. Over the low-elevation warm regions (e.g., the southern parts of the Central Valley and Arizona), yields are less correlated with sowing dates and the length of growing season, perhaps because growing season temperatures are high enough for fast growth. Instead, in those warm regions, maize yields are sensitive to temperature variations during the sowing and late growing season due to the adverse effects of extremely high temperature events on maize development. These results indicate that appropriate adaptations for sowing date can enhance yield significantly, but the positive effect of advanced sowing on the maize yields in warmer spring is constrained in the cool environmental regions rather than the warm environmental regions in the southwestern US. [ABSTRACT FROM AUTHOR]