Modeling the distribution of headwater streams using topoclimatic indices, remote sensing and machine learning.

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Joshua L. Erickson , Zachary A. Holden , James A. Efta
{"title":"Modeling the distribution of headwater streams using topoclimatic indices, remote sensing and machine learning.","authors":"Joshua L. Erickson ,&nbsp;Zachary A. Holden ,&nbsp;James A. Efta","doi":"10.1016/j.hydroa.2023.100165","DOIUrl":null,"url":null,"abstract":"<div><p>Headwater streams (HWS) are ecologically important components of montane ecosystems. However, they are difficult to map and may not be accurately represented in existing spatial datasets. We used topographically resolved climatic water balance data and satellite indices retrieved from Google Earth Engine to model the occurrence (presence or absence) of HWS across Northwest Montana. A multi-scale feature selection (MSFS) procedure and boosted regression tree models/machine learning algorithms were used to identify variables associated with HWS occurrence. In final model evaluation, models that included climatic water balance deficit were more accurate (83.5% ranging from 82.9% to 83.7%) than using only terrain indices (81.1% ranging from 80.7% to 81.4%) and improved upon estimates of stream extent represented by the National Hydrography Dataset Plus High Resolution (NHDPlus HR) (82.7% ranging from 82.5% to 83.1%). Including topoclimate captured the varying effect of upslope accumulated area across a strong moisture gradient. Multi-scale cross-validation, coupled with a MSFS algorithm allowed us to find a parsimonious model that was not immediately evident using standard cross-validation procedures. More accurate spatial model predictions of HWS have potential for immediate application in land and water resource management, where significant field time can be spent identifying potential stream impacts prior to contracting and planning.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"21 ","pages":"Article 100165"},"PeriodicalIF":3.1000,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915523000196/pdfft?md5=f57e063afc97ddaf4df4a2eb4731152d&pid=1-s2.0-S2589915523000196-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589915523000196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Headwater streams (HWS) are ecologically important components of montane ecosystems. However, they are difficult to map and may not be accurately represented in existing spatial datasets. We used topographically resolved climatic water balance data and satellite indices retrieved from Google Earth Engine to model the occurrence (presence or absence) of HWS across Northwest Montana. A multi-scale feature selection (MSFS) procedure and boosted regression tree models/machine learning algorithms were used to identify variables associated with HWS occurrence. In final model evaluation, models that included climatic water balance deficit were more accurate (83.5% ranging from 82.9% to 83.7%) than using only terrain indices (81.1% ranging from 80.7% to 81.4%) and improved upon estimates of stream extent represented by the National Hydrography Dataset Plus High Resolution (NHDPlus HR) (82.7% ranging from 82.5% to 83.1%). Including topoclimate captured the varying effect of upslope accumulated area across a strong moisture gradient. Multi-scale cross-validation, coupled with a MSFS algorithm allowed us to find a parsimonious model that was not immediately evident using standard cross-validation procedures. More accurate spatial model predictions of HWS have potential for immediate application in land and water resource management, where significant field time can be spent identifying potential stream impacts prior to contracting and planning.

利用地形气候指数、遥感和机器学习对水源分布进行建模。
源流是山地生态系统的重要组成部分。然而,它们很难绘制,并且可能无法在现有的空间数据集中准确地表示。我们使用地形分辨率的气候水平衡数据和从谷歌地球引擎检索的卫星指数来模拟蒙大拿州西北部HWS的发生(存在或不存在)。使用多尺度特征选择(MSFS)程序和增强回归树模型/机器学习算法来识别与HWS发生相关的变量。在最终的模型评估中,包含气候水平衡赤字的模型比仅使用地形指数(80.7% ~ 81.4%,81.1%)的模型更准确(82.9% ~ 83.7%,83.5%),并且比国家水文数据集加高分辨率(NHDPlus HR)代表的河流范围估计(82.7%,82.5% ~ 83.1%)的模型更好。包括地形气候捕获的变化效应的上坡累积面积跨越一个强的湿度梯度。多尺度交叉验证,加上MSFS算法,使我们能够找到一个简约的模型,使用标准交叉验证程序不能立即明显。更准确的HWS空间模型预测有可能立即应用于土地和水资源管理,在承包和规划之前,可以花费大量的现场时间来识别潜在的溪流影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信