Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.3390/w9120946 |
A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature | |
Arismendi, Ivan1; Dunham, Jason B.2; Heck, Michael P.2; Schultz, Luke D.2,3; Hockman-Wert, David2 | |
通讯作者 | Arismendi, Ivan |
来源期刊 | WATER
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ISSN | 2073-4441 |
出版年 | 2017 |
卷号 | 9期号:12 |
英文摘要 | Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015-2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature. |
英文关键词 | flow permanence stream drying Hidden Markov Models stream networks |
类型 | Article |
语种 | 英语 |
国家 | USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000419225500040 |
WOS关键词 | HIDDEN MARKOV-MODELS ; HEADWATER STREAMS ; DURATION ; LOGGERS ; DROUGHT |
WOS类目 | Water Resources |
WOS研究方向 | Water Resources |
来源机构 | United States Geological Survey |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/202850 |
作者单位 | 1.Oregon State Univ, Dept Fisheries & Wildlife, 104 Nash Hall, Corvallis, OR 97331 USA; 2.US Geol Survey, Forest & Rangeland Ecosyst Sci Ctr, 3200 SW Jefferson Way, Corvallis, OR 97331 USA; 3.Wyoming Game & Fish Dept, POB 850, Pinedale, WY 82941 USA |
推荐引用方式 GB/T 7714 | Arismendi, Ivan,Dunham, Jason B.,Heck, Michael P.,et al. A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature[J]. United States Geological Survey,2017,9(12). |
APA | Arismendi, Ivan,Dunham, Jason B.,Heck, Michael P.,Schultz, Luke D.,&Hockman-Wert, David.(2017).A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature.WATER,9(12). |
MLA | Arismendi, Ivan,et al."A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature".WATER 9.12(2017). |
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