Information content of soil hydrology in a west Amazon watershed as informed by GRACE

Elias C Massoud,John R Worden,John T Reager,A Anthony Bloom,Paul A Levine,Marcos Longo

doi:10.5194/hess-26-1407-2022

Abstract

Abstract. The seasonal-to-decadal terrestrial water balance on river basin scales depends on several well-characterized but uncertain soil physical processes, including soil moisture, plant available water, rooting depth, and recharge to lower soil layers. Reducing uncertainties in these quantities using observations is a key step toward improving the data fidelity and skill of land surface models. In this study, we quantitatively characterize the capability of Gravity Recovery and Climate Experiment (NASA-GRACE) measurements – a key constraint on total water storage (TWS) – to inform and constrain these processes. We use a reduced-complexity physically based model capable of simulating the hydrologic cycle, and we apply Bayesian inference on the model parameters using a Markov chain Monte Carlo algorithm, to minimize mismatches between model-simulated and GRACE-observed TWS anomalies. Based on the prior and posterior model parameter distributions, we further quantify information gain with regard to terrestrial water states, associated fluxes, and time-invariant process parameters. We show that the data-constrained terrestrial water storage model can capture basic physics of the hydrologic cycle for a watershed in the western Amazon during the period January 2003 through December 2012, with an r2 of 0.98 and root mean square error of 30.99 mm between observed and simulated TWS. Furthermore, we show a reduction of uncertainty in many of the parameters and state variables, ranging from a 2 % reduction in uncertainty for the porosity parameter to an 85 % reduction for the rooting depth parameter. The annual and interannual variability of the system are also simulated accurately, with the model simulations capturing the impacts of the 2005–2006 and 2010–2011 South American droughts. The results shown here suggest the potential of using gravimetric observations of TWS to identify and constrain key parameters in soil hydrologic models.

Highlights

The terrestrial water balance depends on many physical processes, including soil moisture, plant available water (PAW), rooting depth, recharge to lower soil layers, among others, and these processes depend on each other in a dynamic way (Margulis et al, 2006; Massoud et al, 2019a, 2020a)
We demonstrate the ability of the decadal Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) record to inform and reduce uncertainties of terrestrial hydrologic processes regulating the seasonal and inter-annual variability of TWS in the western Amazon, the Gavião watershed, for the period January 2003 through December 2012
The observed data in this case study is the GRACE satellite observations, and our goal is to find the optimal set of model parameters, θ, that produces a model simulation, X(θ ), which maximizes the fit, or the likelihood, relative to the observations

Summary

Introduction

The terrestrial water balance depends on many physical processes, including soil moisture, plant available water (PAW), rooting depth, recharge to lower soil layers, among others, and these processes depend on each other in a dynamic way (Margulis et al, 2006; Massoud et al, 2019a, 2020a). Some variables, such as precipitation, surface runoff, or soil moisture, can be directly observed in the field or by airborne measurements (Walker et al, 2004; Swenson et al, 2006; Durand et al, 2009; Liu et al, 2019), but other processes, such as evapotranspiration (ET) or groundwater storage changes, are more difficult to detect and observe (Tapley et al, 2004; Pascolini-Campbell et al, 2020). Massoud et al.: Information content of soil hydrology in a west Amazon watershed simulations with observations (Girotto et al, 2016; Khaki et al, 2017, 2018; Quetin et al, 2020; Sawada, 2020), limiting the need for process representation in the model and increasing the efficiency in the inference of unknown physical processes, such as hydrologic variables that cannot be directly measured

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Conclusion