Kearney Michael R
School of BioSciences, The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Ecology. 2019 Nov;100(11):e02829. doi: 10.1002/ecy.2829. Epub 2019 Aug 14.
Microclimatic data are required for many problems in pure and applied ecology. This data includes aboveground convective and radiative conditions as well as soil temperature and moisture. In cold regions, the connection between above- and belowground microclimates via snow cover is also critically important. Here I describe a data set of hourly microclimates for the continental United States, simulated from the years 1979 to 2017 across a grid of 2,287 locations approximately 60 km apart. The data were generated with the NicheMapR microclimate model, driven by 0.04° gridded daily meteorological forcing data (air temperature, wind speed, humidity, solar radiation, air pressure, and rainfall). The aboveground microclimate variables include horizontal plane solar radiation, solar zenith angle, sky temperature (from which downwelling longwave radiation can be computed), air temperature, relative humidity, and wind speed at 1 and 200 cm height, and snow depth. The belowground variables include soil temperature, pore humidity, soil moisture, and soil water potential for 0, 2.5, 5, 10, 15, 20, 30, 50, 100, and 200 cm belowground. The computations are for four shade levels (0%, 50%, 75%, and 90%). The predictions are validated against detailed soil temperature, soil moisture, and snow observations and show enhanced performance over existing microclimatic data for the United States. The data set can be used for a wide variety of applications, including the computation of heat and water budgets of organisms, the potential for vegetation growth, and the computation of stress and growth indices. The use of daily forcing data also allows assessments of the consequences of extreme events including heat waves and drought. Example applications are provided for computing plant growth potential, lizard egg development and body temperature, and mammalian energy and water requirements. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.
微气候数据对于纯生态学和应用生态学中的许多问题而言都是必需的。这些数据包括地上对流和辐射条件以及土壤温度和湿度。在寒冷地区,通过积雪实现的地上和地下微气候之间的联系也至关重要。在此,我描述了一组美国大陆每小时微气候数据集,该数据集是通过NicheMapR微气候模型模拟得出的,模拟时间跨度为1979年至2017年,覆盖了2287个位置的网格,这些位置彼此相距约60公里。数据由0.04°网格化的每日气象强迫数据(气温、风速、湿度、太阳辐射、气压和降雨量)驱动生成。地上微气候变量包括水平面太阳辐射、太阳天顶角、天空温度(可据此计算下行长波辐射)、气温、相对湿度以及1厘米和200厘米高度处的风速,还有积雪深度。地下变量包括地下0、2.5、5、10、15、20、30、50、100和200厘米处的土壤温度、孔隙湿度、土壤湿度和土壤水势。计算针对四种遮荫水平(0%、50%、75%和90%)。这些预测通过详细的土壤温度、土壤湿度和积雪观测进行了验证,并且相较于美国现有的微气候数据,表现出了更高的准确性。该数据集可用于多种应用,包括生物体热量和水分平衡的计算、植被生长潜力的评估以及压力和生长指数的计算。使用每日强迫数据还能够评估包括热浪和干旱在内的极端事件的影响。文中提供了计算植物生长潜力、蜥蜴卵发育和体温以及哺乳动物能量和水分需求的示例应用。除了引用本数据论文外,使用该数据集不存在版权或专有限制。