Precipitation throughfall (TF) plays an important role in the water balance of tropical forests. This study used 164 gauges to quantify precipitation and TF variability in a tropical pre‐montane transitional cloud forest on the Caribbean slope of the Cordillera Tilarán, Costa Rica, to identify the ecological and meteorological drivers of this variability. Daily TF measurements were taken from 28 June to 17 July 2012 and 12 June to 16 July 2013, for a total of 39 precipitation events. The total mean TF was 87.9 percent and TF at individual gauges ranged from 22.7 percent to 245.7 percent. Leaf area index (LAI) was calculated above each gauge using hemispheric photography for a mean study‐site LAI of 7.7. There was no statistically significant relationship between LAI and TF. However, the amount of TF was positively correlated with precipitation intensity, while the variability of TF was negatively correlated with precipitation intensity. Our calculations indicate that at least 61 gauges are required to obtain mean TF estimates with less than 5 percent error. This study demonstrates that TF is highly spatially heterogeneous due to multiple compounding effects.

Burning, grazing, and baling (hay harvesting) are common management practices for tallgrass pasture. To develop and adopt sustainable management practices, it is essential to better understand and quantify the impacts of management practices on plant phenology and carbon fluxes. In this study, we combined multiple data sources, including in-situ PhenoCam digital images, eddy covariance data, and satellite data (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS)) to examine the impacts of burning, baling, and grazing on canopy dynamics, plant phenology, and carbon fluxes in a tallgrass pasture in El Reno, Oklahoma in 2014. Landsat images were used to assess the baling area and the trajectory of vegetation recovery. MODIS vegetation indices (VIs) were used in the Vegetation Photosynthesis Model (VPM) to estimate gross primary production (GPP VPM) at a MODIS pixel for the flux tower (baled) site. For comparison between baled and unbaled conditions, we used MODIS VIs for a neighbor MODIS pixel (unbaled) and ran VPM. Daily PhenoCam images and green chromatic coordinate (GCC) tracked canopy dynamics and plant phenology well. The grassland greened up immediately after burning in April. GCC values showed two peaks with the similar magnitude because of quick recovery of grassland after baling. Satellite-derived VIs and GPP VPM showed that the pasture recovered in one month after baling. The GPP VPM matched well (R 2 = 0.89) with the eddy covariance-derived GPP (GPP EC). Grazing in the late growing season did not influence plant phenology (VIs and GCC) and carbon uptake (GPP) as plants were in the late growing stage. Neither did it affect GPP differently in those two conditions because of even grazing intensity. The reduction in GPP after baling was compensated by higher GPP after large rain events in late July and early September, causing little seasonal differences in GPP (-0.002 g C m-2 day-1) between the baled and unbaled conditions. Interactions of different management 3 practices with climate make it complicated to understand the impacts of different management practices on carbon dynamics and plant phenology. Thus, it is necessary to further investigate the responses of tallgrass pastures to different management practices under different climate regimes at multiple temporal and spatial scales.

Ice storms, defined by the US National Weather Service as freezing rain accumulations over 0.635 cm (0.25 inch), are often costly and destructive. Formation processes include the classic 'melting' process and supercooled warm rain process. Freezing rain is most commonly found ahead of a warm front or occlusion, where warm air is lifted over a cold shallow layer near the surface. Other synoptic patterns conducive to freezing rain include arctic fronts, isentropic lift over an arctic air mass, and cold air damming. Causes of spatial and temporal variations in freezing rain include, but are not limited to, terrain and proximity to water. Areas with the most occurrences of freezing rain in the United States include the Pacific Northwest, Upper Midwest, and Northeast/Appalachian regions. Empirical forecasting methods and numerical weather prediction are currently used to predict freezing rain. Successful forecasting of ice storm events requires evaluation of the thermodynamic profile of the atmosphere. Local effects such as proximity to water and topography must be taken into account, and non-linear processes such as latent heating and cooling must not be ignored. Ice accumulation can cause tree damage, which, in addition to breakage of electrical cables, can lead to power outages. Deposition of ice also impacts road, rail, and air travel, with associated economic costs due to lost hours as workers are unable to travel. Ice storms also provide a significant risk to human health and life, with falling debris and slippery surfaces being primary threats.

No abstract

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 = 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 class, moderate drought) to 77 % (0 and D 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.