Regression Modeling of Baseflow and Baseflow Index for Michigan USA

Yinqin Zhang,Laurent Ahiablame,Junmin Liu,Bernard Engel

doi:10.3390/w5041797

Yinqin Zhang, Laurent Ahiablame + Show 2 more

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https://doi.org/10.3390/w5041797

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Abstract

Baseflow plays an important role in maintaining streamflow. Seventeen gauged watersheds and their characteristics were used to develop regression models for annual baseflow and baseflow index (BFI) estimation in Michigan. Baseflow was estimated from daily streamflow records using the two-parameter recursive digital filter method for baseflow separation of the Web-based Hydrograph Analysis Tool (WHAT) program. Three equations (two for annual baseflow and one for BFI estimation) were developed and validated. Results indicated that observed average annual baseflow ranged from 162 to 345 mm, and BFI varied from 0.45 to 0.80 during 1967–2011. The average BFI value during the study period was 0.71, suggesting that about 70% of long-term streamflow in the studied watersheds were likely supported by baseflow. The regression models estimated baseﬂow and BFI with relative errors (RE) varying from −29% to 48% and from −14% to 19%, respectively. In absence of reliable information to determine groundwater discharge in streams and rivers, these equations can be used to estimate BFI and annual baseﬂow in Michigan.

Highlights

Baseflow is a very important component of streamflow generated from groundwater inflow or discharge
Ford River is located in the southwestern Upper Peninsula (UP) and is adjacent to Wisconsin, so the climate of this watershed may be influenced by continental climate rather than lake-effect climate, causing relatively little precipitation and large seasonal variability in the watershed
Twelve out of 17 watersheds were used for model development and the remaining five watersheds were used for model validation (1967–2011)

Summary

Introduction

Baseflow is a very important component of streamflow generated from groundwater inflow or discharge. Baseflow is generally derived from available streamflow records using hydrograph separation techniques such as graphical methods [1], recession-curve methods [2], analytical methods [3,4], mass-balance methods [5,6], and digital baseflow filter methods [7,8]. Many of these techniques have been automated with computer programming (e.g., PART [9], HYSEP [10], BFI [11], UKIH [12], BFLOW [13], and WHAT [14]) to assist in baseflow separation. Santhi et al [23] utilized regression analysis to relate relief, percentage of sand and effective rainfall to baseflow index (BFI) and baseflow volume for the conterminous United

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