Regional base-flow index in arid landscapes using machine learning and instrumented records

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies Pub Date : 2025-09-18 DOI:10.1016/j.ejrh.2025.102778

Caelum Mroczek , Abraham E. Springer , Neha Gupta , Temuulen Sankey , Benjamin Lucas

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引用次数: 0

Abstract

Study region

This study focuses on Arizona, a dryland state in the southwestern United States with marked variability in climate, elevation, and hydrogeology. Arizona spans two major physiographic regions, the Colorado Plateau and the Basin and Range, each exhibiting distinct hydrologic behavior.

Study focus

We quantify long-term base-flow index (BFI) patterns and trends across Arizona and develop a predictive framework for ungauged basins. BFI was calculated at 205 USGS stream gauges using a recursive digital filter applied to multi-decadal streamflow records. Coincident trends in precipitation, temperature, and evapotranspiration were analyzed to assess climate–base-flow relationships. We trained an eXtreme Gradient Boosting (XGBoost) model on hydroclimatic and physiographic variables to estimate long-term BFI from 1991 to 2020 at the 8-digit Hydrologic Unit Code (HUC) scale.

New hydrological insights for the region

Groundwater discharge accounts for approximately 32 % of streamflow in Arizona, with substantial spatial variability linked to topography, land cover, and climate. High BFI values are found in forested headwaters with spring-fed and snowmelt-driven systems, while low values dominate the state’s arid lowlands. Declining BFI trends were most pronounced in monsoon-dominated, warm-dry, and low-slope basins. Precipitation was the strongest climate correlate of BFI trends, underscoring the importance of climate variability for dryland base flow. This integration of observational records and machine learning provides new insights into groundwater–surface water interactions and offers a transferable framework for water resource assessment in data-scarce dryland regions globally.

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基于机器学习和仪器记录的干旱景观区域基流指数

研究区域本研究的重点是亚利桑那州，这是美国西南部的一个旱地州，在气候、海拔和水文地质方面都有明显的变化。亚利桑那州横跨两个主要的地理区域，科罗拉多高原和盆地和山脉，每一个都表现出不同的水文行为。我们量化了整个亚利桑那州的长期基流指数（BFI）模式和趋势，并为未测量的盆地开发了预测框架。BFI是在205个美国地质勘探局的流量测量仪上计算的，使用递归数字滤波器应用于多年代际流量记录。对降水、温度和蒸散的一致趋势进行了分析，以评估气候基础流的关系。在8位水文单位码（HUC）尺度下，我们训练了一个基于水文气候和地理变量的极端梯度增强（XGBoost）模型来估计1991 - 2020年的长期BFI。地下水流量约占亚利桑那州河流流量的32% %，具有与地形、土地覆盖和气候相关的巨大空间变异性。高BFI值出现在有泉水和融雪驱动系统的森林源头，而低BFI值则出现在干旱的低地。BFI下降趋势在季风主导、暖干和低坡盆地最为明显。降水是BFI趋势的最强气候相关，强调了气候变率对旱地基流的重要性。这种观测记录和机器学习的整合为地下水-地表水相互作用提供了新的见解，并为全球数据稀缺的旱地地区的水资源评估提供了一个可转移的框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)

CiteScore

6.70

自引率

8.50%

发文量

284

审稿时长

60 days

期刊介绍： Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.