Measuring water availability with limited ground data: assessing the feasibility of an entirely remote-sensing-based hydrologic budget of the Rufiji Basin, Tanzania, using TRMM, GRACE, MODIS, SRB, and AIRS

Abstract

This study explores the feasibility of an entirely satellite remote sensing (RS)‐based hydrologic budget model for a ground data‐constrained basin, the Rufiji basin in Tanzania, from the balance of runoff (Q), precipitation (P), storage change (ΔS), and evapotranspiration (ET). P was determined from the Tropical Rainfall Measuring Mission, ΔS from the Gravity Recovery and Climate Experiment, and ET from the Moderate Resolution Imaging Spectroradiometer, the surface radiation budget, and the Atmosphere Infrared Radiation Sounder. Q was estimated as a residual of the water balance and tested against measured Q for a sub‐basin of the Rufiji (the Usangu basin) where ground measurements were available (R2 = 0.58, slope = 1.9, root mean square error = 29 mm/month, bias = 14%). We also tested a geographical information system (GIS)‐driven (ArcCN‐runoff) runoff model (R2 = 0.64, slope = 0.43, root mean square error = 39 mm/month). We conducted an error propagation analysis from each of the model's hydrologic components (P, ET, and ΔS). We find that the RS‐based model amplitude is most sensitive to ET and slightly less so to P, whereas the model's seasonal trends are most sensitive to ∆S. Although RS–GIS‐driven models are becoming increasingly used, our results indicate that long‐term water resource assessment policy and management may be more appropriate than ‘instantaneous’ or short‐term water resource assessment. However, our analyses help develop a series of tools and techniques to progress our understanding of RS–GIS in water resource management of data‐constrained basins at the level of a water resource manager. Copyright © 2012 John Wiley & Sons, Ltd.

On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets*

“…The model can be driven entirely with remote sensing and does not include or require calibration, spinup, or initialization. 1 For one class of products, those based on AIRS meteorological data, we substitute daily mean air temperature for daily maximum air temperature (see Armanios and Fisher 2014). We calculated terrestrial ET globally for ice-free land, as defined by Friedl et al (2010).…”

Assessing hydro-ecological vulnerability using microwave radiometric measurements from WindSat

“…In an effort to explore the relations between the water dynamics and ecosystem processes, several studies (Anyamba, Tucker, & Mahoney, 2002;Armanios & Fisher, 2014;Chen, De Jeu, Liu, Van der Werf, & Dolman, 2014;Davenport & Nicholson, 1993;Deshmukh, 1984;Eklundh, 1998;Nicholson, Davenport, & Malo, 1990;Richard & Poccard, 1998;Shinoda, 1995) have investigated the response of vegetation to P/SM patterns, by examining the spatiotemporal patterns and inter-relations of P/SM and the Normalized Difference Vegetation Index (NDVI) in EA and other sensitive regions of the globe. While spectral vegetation indices, such as NDVI, have been extensively used to analyze spatiotemporal variations in vegetation distribution, their use to quantify vegetation status or behavior is plagued by several shortcomings, such as its high sensitivity to atmospheric influences (cloud, aerosols), constraints due to seasonal decreases in solar illumination, its limitation in monitoring only the top of the canopy (Liu, de Jeu, McCabe, Evans, & van Dijk, 2011), its proneness to saturation in dense canopies (Liu, van Dijk, McCabe, Evans, & Jeu, 2012), and its weak and indirect link with water content (Pettorelli, 2013).…”

Evaluation of remotely sensed data for estimating recharge to an outcrop zone of the Guarani Aquifer System (South America)

“…However, in most developing countries, hydrological ground measurements (Btruth^) data are scarce (Swenson and Wahr 2009), and rarely more than one method has been used to estimate recharge. In this context, remote sensing (RS) arises as a potential water-resource management tool to provide information on water-budget components (Oliveira et al 2014;Armanios and Fisher 2014).…”