Satellite Remote Sensing in Hydrological Data Assimilation

Water resources are one of the most crucial essences of life on Earth. Due to its importance, studying water through hydrology has long been of attention. Any major changes in the availability, distribution, or interaction of water stored in the land can significantly affect the people locally as well as globally (Gain et al. 2012; Gosling and Arnell 2016). These changes can be imposed by various factors such as long-term climate change (e.g., Brekke et al. 2009; Cisneros et al. 2014), extreme climatic events (e.g., Beller-Simms et al. 2014; Nagy et al. 2018), and anthropogenic impacts (e.g., Sondergaard et al. 2007; Liyanage and Yamada 2017).

Satellite remote sensing with a wide range of platforms and on-board sensors has changed our view of Earth and its hydrology remarkably. They offer various type of observations on large scales and now covering more than decades of measurements.

Ever-increasing computational power in computers during the last few decades has revolutionized our perception of hydrology through models. These models are established to (partially) reflect the natural or human-induced hydrological processes (Dingman 2002), and further to predict system behaviours.

Various datasets are currently available to improve hydrologic models, and specifically land surface models thanks to advanced satellite developments for monitoring Earth’s hydrology. Some of these have already been utilised for data assimilation objectives while there are more to be done in hydrologic modelling.

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, we assess the performance of established sequential data assimilation techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement six versions of the classical ensemble Kalman filters (EnKF), as well as Particle filters.

Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modeling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations.

Assimilating Gravity Recovery And Climate Experiment (GRACE) data into land hydrological models provides a valuable opportunity to improve the models’ forecasts and increases our knowledge of terrestrial water storages (TWS). The assimilation, however, may harm the consistency between hydrological water fluxes, namely precipitation, evaporation, discharge, and water storage changes. To address this issue, we propose a weak constrained ensemble Kalman filter (WCEnKF) that maintains estimated water budgets in balance with other water fluxes. Therefore, in this study, GRACE terrestrial water storages data are assimilated into the World-Wide Water Resources Assessment (W3RA) hydrological model over the Earth’s land areas covering 2002–2012. Multi-mission remotely sensed precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM) and evaporation products from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as ground-based water discharge measurements are applied to close the water balance equation. The proposed WCEnKF contains two update steps; first, it incorporates observations from GRACE to improve model simulations of water storages, and second, uses the additional observations of precipitation, evaporation, and water discharge to establish the water budget closure. These steps are designed to account for error information associated with the included observation sets during the assimilation process. In order to evaluate the assimilation results, in addition to monitoring the water budget closure errors, in-situ groundwater measurements over the Mississippi River Basin in the US and the Murray-Darling Basin in Australia are used. Our results indicate approximately 24% improvement in the WCEnKF groundwater estimates over both basins compared to the use of (constraint-free) EnKF. WCEnKF also further reduces imbalance errors by approximately 82.53% (on average) and at the same time increases the correlations between the assimilation solutions and the water fluxes.

The standard ensemble data assimilation schemes often violate the dynamical balances of hydrological models, on their estimates, in particular, the fundamental water balance equation, which relates water storage and water flux changes. The present study aims at extending the recently introduced Weak Constrained Ensemble Kalman Filter (WCEnKF) to a more general framework, namely unsupervised WCEnKF (UWCEnKF), in which the covariance of the water balance model is no longer known, thus requiring its estimation along with the model state variables. This extension is introduced because WCEnKF was found to be strongly sensitive to the (manual) choice of this covariance.

Data assimilation, which relies on explicit knowledge of dynamical models, is a well-known approach that addresses models’ limitations due to various reasons, such as errors in input and forcing datasets. This approach, however, requires intensive computational efforts, especially for high dimensional systems such as distributed hydrological models. Alternatively, data-driven methods offer comparable solutions when the physics underlying the models are unknown. For the first time in a hydrological context, a non-parametric framework is implemented here to improve model estimates using available observations. This method uses Takens delay-coordinate method to reconstruct the dynamics of the system within a Kalman filtering framework, called the Kalman-Takens filter. A synthetic experiment is undertaken to fully investigate the capability of the proposed method by comparing its performance with that of a standard assimilation framework based on an adaptive unscented Kalman filter (AUKF). Furthermore, using terrestrial water storage (TWS) estimates obtained from the Gravity Recovery And Climate Experiment (GRACE) mission, both filters are applied to a real case scenario to update different water storages over Australia. In-situ groundwater and soil moisture measurements within Australia are used to further evaluate the results.

With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale.

Groundwater depletion, due to both unsustainable water use and a decrease in precipitation, has been reported in many parts of Iran. In order to analyze these changes during the recent decade, in this study, we assimilate Terrestrial Water Storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) into the World-Wide Water Resources Assessment (W3RA) model. This assimilation improves model derived water storage simulations by introducing missing trends and correcting the amplitude and phase of seasonal water storage variations. The Ensemble Square-Root Filter (EnSRF) technique is applied, which showed stable performance in propagating errors during the assimilation period (2002–2012). Our focus is on sub-surface water storage changes including groundwater and soil moisture variations within six major drainage divisions covering the whole Iran including its eastern part (East), Caspian Sea, Centre, Sarakhs, Persian Gulf and Oman Sea, and Lake Urmia.

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country’s water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003–2013, and their interactions with rainfall in Bangladesh.

Constant monitoring of total water storage (TWS; surface, groundwater, and soil moisture) is essential for water management and policy decisions, especially due to the impacts of climate change and anthropogenic factors. Moreover, for most countries in Africa, Asia, and South America that depend on soil moisture and groundwater for agricultural productivity, monitoring of climate change and anthropogenic impacts on TWS becomes crucial. Hydrological models are widely being used to monitor water storage changes in various regions around the world.