追求
项目
量化生态学中的不确定性:来自小流域研究的例子
约翰·坎贝尔.露丝·D. 马克·柳井. 格林，嘉莉·罗斯·莱文，玛丽·贝丝·亚当斯， 道格拉斯一. 唐纳德·伯恩斯. 布索，马克·E. 香农·L·哈蒙. 吉恩·拉多. 把, 威廉H. 乔丹·麦克道尔. Stephen D . Parman. 詹姆斯. 斯坦利和 詹姆斯米. 沃斯. 为《网赌平台》做准备.
描述
Measurement from ecosystems are often reported without taking uncertainty into account. This omisison stems in part from the fact that each ecosystem in unique, making it 难以复制采样单元. 没有复制，它仍然很重要 to know the uncertainty in the measurements that go into describing ecosystem pools 或者通量，这是小流域研究中经常遇到的挑战. 一个 of the initial 追求 working groups was tasked with developing methods for quantifying 小流域应用中的不确定性. 约翰·坎贝尔领导了这项工作， 它使用了由LTER, USGS和USFS管理的流域的例子.
网站:
HJ Andrews Experimental Forest and LTER (Blue River, OR); Biscuit Brook (Frost Valley, NY); Coweeta水文实验室 and LTER (Otto, NC); Fernow Experimental Forest (Parsons, WV); Hubbard Brook Experimental Forest and LTER (West 桑顿,NH); Luquillo Experimental Forest and LTER (Luquillo, Puerto Rico); Marcell Experimental Forest (Grand Rapids, MN) ; Niwot Ridge LTER (Roosevelt National Forest, CO); Sleepers River 研究流域(丹维尔，佛蒙特州).
资金来源
The NSF LTER Network Office funded a Working Group to quantify uncertainty in hydrologic 2011年投入产出预算.
Uncertainty of precipitation inputs including 模式的不确定性和自然变率
LaDeau,年代., J.L. 坎贝尔,米.B. 绿色,R.D. 柳井正. 为大气环境做准备
摘要
在建设中.
资助:
National Science Foundation EArly-concept Grant for Exploratory 研究 (EAGER) Award # 1216092，以及第二个LTER综合工作组拨款.
Uncertainty of streamwater outputs in five contrasting headwater catchments including 模式的不确定性和自然变率
Brent Aulenbach, Doug Burns, Jamie Shanley, Ruth 柳井正, Kikang Bae, Adam Wild, Yang
杨，易“托尼”董
为…做准备 ?
描述
在估算河流溶质通量时，有许多不确定的来源. 通量 is the product of discharge and concentration (summed over time), each of which has 测量本身的不确定度. 放电几乎可以连续测量， but concentrations are usually determined from discrete samples, which increases uncertainty dependent on sampling frequency and how concentrations are assigned for the periods 样本之间. 样本之间的间隙可以通过线性插值或 by 模型 that that use the relations between concentration and continuously measured 或已知变量，如流量、季节、温度和时间. 对于这个项目， developed in cooperation with 追求 (Quantifying Uncertainty in Ecosystem Studies), we evaluated uncertainty for three flux estimation methods and three different sampling 频率(每月，每周，每周加上事件). 调查的成分 溶解NO 3 ， Si, SO 4 , and dissolved organic carbon (DOC), solutes whose concentration dynamics exhibit 强烈对比的行为. 该评价为期10年，于 five small, forested watersheds in Georgia, New Hampshire, New York, Puerto Rico, 和佛蒙特州. 建立了每一种溶质的浓度回归模型 五个流域的三个采样频率中的一个. 然后计算通量 using (1) a linear interpolation approach, (2) a regression-model method, and (3) the composite method – which combines the regression-model method for estimating concentrations and the linear interpolation method for correcting model residuals to the observed 样品浓度. 我们认为通量的最佳估计是使用 在最高采样频率下的复合方法. 我们还评估了其重要性 of sampling frequency and estimation method on flux estimate uncertainty; flux uncertainty was dependent on the variability characteristics of each solute and varied for different 报告期(e).g. 10年，研究周期vs. 每年对. 每月). 的实用性 of the two regression model based flux estimation approaches was dependent upon the 回归模型可以解释的浓度变异量. 我们的研究结果 can guide the development of optimal sampling strategies by weighing sampling frequency with improvements in uncertainty in stream 通量估计 for solutes with particular 变异性特征. 适当的通量估计方法是依赖的 on a combination of sampling frequency and the strength of concentration regression 模型.
网站:
Biscuit Brook (Frost Valley, NY), Hubbard Brook Experimental Forest and LTER (West 桑顿,NH), Luquillo Experimental Forest and LTER (Luquillo, Puerto Rico), Panola Mountain (Stockbridge, 睡眠河研究流域(丹维尔，佛蒙特州)
资金
The NSF LTER Network Office funded a Working Group to quantify uncertainty in hydrologic 2011年投入产出预算. 2011年秋季和2012年春季的务虚会 2012 and the Spring of 2013, four students at SUNY Environmental Science and Forestry 参加班级项目的努力.
Spatial variance of precipitation: optimizing interpolation efficiency and mitigating 不确定性
约书亚·A·罗伯特1杰弗里·R·泰勒1, 2，露丝D柳井3亚当·斯基布4、张学松5劳埃德·斯威夫特6. 1国家生态观测站网络(NEON)， Boulder, CO 80301; 2科罗拉多大学博尔德分校北极与高山研究所; 3博彩平台环境科学与林业学院，锡拉丘兹，纽约州; 4堪萨斯州立大学康扎草原生物站，曼哈顿，堪萨斯州;5西北太平洋国家实验室，华盛顿州里奇兰; 6Coweeta水文实验室，奥托，北卡罗来纳州.
描述
Fifty-six years of precipitation data from over one hundred gauges were analyzed to determine the spatial distribution that describes a majority of the annual precipitation variance within the Coweeta水文实验室 Watershed in North Carolina, USA. 常用的插值方法(如.g.克里格等.)来量化显著性 of individual gauges relative to the total variance of annual precipitation within 一个分水岭. 然后按其升序从分析中删除量规 importance and as a function of the interpolation methods to derive an optimal sampling 政权. 由此产生的制度i)解释了系统总方差的大部分; ii) identifies spatial correlations among gauges as functions of local climatic characteristics, and iii) optimizes sampling efficiency as a function of the spatial uncertainty of Coweeta流域的年降水量. 这一制度随后被应用于 具有相似或不同地形和气候特征的其他地区. 虽然 such features vary among watersheds, we argue that spatial 不确定性 associated with scarcely sampled networks can be evaluated using a densely sampled network, such 作为Coweeta.
网站
Coweeta水文实验室北卡罗来纳州奥托.
Quantifying uncertainty in gap filling of long-term hydrologic datasets for nutrient 预算:LTER网络的案例研究
克雷格·R. 看，杰里米·海沃德，露丝·D. 柳井，道格·摩尔，马克·B. 绿色
为tbd做准备.
描述
所有长期数据集都包含缺失或不可用的数据(缺口). 虽然其中很多 gaps are inevitable, when calculating solute inputs from precipitation or outputs 从流中，不可能简单地忽略缺失的值. 的不确定性 associated with gap-filling estimates is not commonly reported or propagated into 通量估计. 我们希望描述这些跨站点的差距的原因 both volume and solute chemistry in long-term precipitation and steamflow datasets. To quantify the uncertainty associated with different gap-filling methods, we are applying them to a series of "fake gaps," and comparing the estimates with measured 值.
网站
HJ Andrews Experimental Forest and LTER (Blue River, OR); Coweeta水文实验室 and LTER (Otto, NC); Hubbard Brook Experimental Forest and LTER (West 桑顿,NH); 塞维利亚国家野生动物保护区(新墨西哥州索科罗)
资金
数据集是在NSF LTER项目的几个周期下收集的.