Civil Engineering

 

ORCID IDs

Foad Foolad

First Advisor

Trenton E. Franz

Second Advisor

Ayse Kilic

Date of this Version

6-2018

Citation

Foolad, Foad. Integration of Remote Sensing and Proximal Sensing For Improvement of Field Scale Water Management. Doctoral Dissertation. University of Nebraska-Lincoln, 2018.

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy Major: Civil Engineering (Water Resources) Under the Supervision of Professor Trenton E. Franz and Professor Ayse Kilic Lincoln, Nebraska June, 2018.

Copyright (c) 2018 Foad Foolad

Abstract

Water is one of the most precious natural resources, and sustainable water resources development ‎‎is a significant challenge facing water managers over the coming decades. Accurate estimation of ‎‎the different components of the hydrologic cycle is key for water managers and planners in order ‎‎to achieve sustainable water resources development. The primary goal of this dissertation was to ‎investigate techniques to combine datasets acquired by remote and proximal sensing and in-situ ‎sensors for the improvement of monitoring near surface water fluxes. This dissertation is ‎separated into three site-specific case studies. First study, investigated the feasibility of using ‎inverse vadose zone modeling for field actual evapotranspiration (ETa) estimation. Results show ‎reasonable estimates of ETa, both daily and annually, from soil water content (SWC) sensors and ‎Cosmic-Ray Neutron Probes (CRNPs). Second study, combined remote and proximal sensing ‎methods to explore the spatial correlation between hydrological state variables and ET flux. ‎Comparison of the datasets reveal that SWC and ETa were linearly correlated but the correlation ‎between depth to the water table and ETa was weak. A simple multivariate linear regression ‎model was used to estimate ETa. The estimated ETa values were then compared to the time ETa ‎integration spline method. The comparison indicates similar seasonal ETa between the two ‎methods in 2015 ‎‎(wet) but a 20% reduction in 2016 (dry). The study highlights the challenge of ‎connecting hydrologic state variables with hydrologic flux estimates. Third study, evaluated the ‎functionality of automatically calibrated Earth Engine Evapotranspiration Flux (EEFlux) to the ‎existing mapping evapotranspiration at high resolution with internalized calibration (METRIC) ‎images in different locations. The comparison results showed that EEFlux is able to calculate ‎Reference evapotranspiration Fraction (ETrF) and ETa in agricultural areas comparable ‎‎(RMSE=0.13) to the ones from trained expert METRIC users. However, the EEFlux algorithm ‎needs to be improved to calculate ETrF and ETa in non-agricultural areas (RMSE=0.21). Given ‎the paucity of in-situ data across much of the globe the field of remote sensing offers an ‎alternative but requires users to be cautious and realistic about associated errors and uncertainty ‎on using such information to help construct a hydrologic budget.‎

Advisor: Trenton E. Franz and Ayse Kilic

Share

COinS