Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions : the Murray-Darling basin in Australia as a test case

The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray-Darling basin in Australia. The hydrometeorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010-2011). Next, SMOS brightness temperature observations are sequentially assimi-lated into the coupled SUPERFLEX-CMEM model (20102015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM). This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average

Woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1

L-band brightness temperature assimilation into a distributed hydrological model : application to the Community Land Model over Australia

An increase in global population and climate change will lead to added stresses on water resources. This requires hydrological monitoring systems which can provide critical information on the hydrological state to decision-makers. One key hydrological variable is soil moisture, which codetermines several hydrological processes such as infiltration, runoff production and evaporation. Moreover, soil moisture plays an important role in the formation of droughts and floods. Land surface models are able to provide soil moisture information across large spatial scales but can lack accuracy. Remote sensing offers great potential in providing independent information. Through data assimilation techniques, the model can be steered towards better predictions by assimilating remotely sensed soil moisture information. In this thesis, observations from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions are assimilated into the Community Land Model (CLM) over Australia. Both SMOS and SMAP carry radiometers and are the first dedicated missions to observe soil moisture from space. They operate within the L-band frequency, which is considered optimal for soil moisture. CLM is a state-of-the-art land surface model that provides all necessary information to integrate these observations. The focus on Australia allows the use of high-quality observations that are not contaminated by Radio Frequency Interference, as is the case in many other parts of the world. The thesis explores in detail (i) the impacts of assimilating long time series on soil moisture probability levels, (ii) the use of SMOS and SMAP within a common assimilation scheme, and (iii) the effects of an improved land scape representation within CLM on the assimilation performance. Briefly explored is also the potential of downscaling the observations by using model information at higher resolution. The assimilation improves the soil moisture simulations and the findings support the integration of L-band observations into operational monitoring systems

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E-t) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (S-t), i.e., the reduction of E-t from its theoretical maximum. Then, we embed the new S-t formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the S-t formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate S-t and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable. Global evaporation is a key climatic process that remains highly uncertain. Here, the authors shed light on this process with a novel hybrid model that integrates a deep learning representation of ecosystem stress within a physics-based framework

SMOS and SMAP brightness temperature assimilation over the Murrumbidgee Basin

With the launch of the Soil Moisture and Ocean Salinity (SMOS) mission in 2009 and the Soil Moisture Active-Passive (SMAP) mission in 2015, a wealth of L-band brightness temperature (Tb) observations has become available. In this letter, SMOS and SMAP Tbs are assimilated separately into the Community Land Model over the Murrumbidgee basin in south-east Australia from April 2015 to August 2017. To overcome the seasonal Tb observation-minus-forecast biases, Tb anomalies from the seasonal climatology are assimilated. The use of climatologies derived from either SMOS or SMAP observations using either 2 years or 7 years of data yields nearly identical results, highlighting the limited sensitivity to the climatology computation and their interchangeability. The temporal correlation between soil moisture data assimilation results and in situ observations is slightly improved for top-layer soil moisture (+0.04) and for root-zone soil moisture (+0.05). The soil moisture anomaly correlation improves moderately for the top-layer soil moisture (+0.15), with a smaller positive impact on the root zone (+0.05)

Ten years of GLEAM : a review of scientific advances and applications

During the past decades, consistent efforts have been undertaken to model the Earth's hydrological cycle. Multiple mathematical models have been designed to understand, predict, and manage water resources, particularly under the context of climate change. A variable that has traditionally received limited attention by the hydrological community—but that is crucial to understand the links to climate—is terrestrial evaporation. The Global Land Evaporation Amsterdam Model (GLEAM) was developed ten years ago with the goal to derive terrestrial evaporation from satellite imagery. Since then, GLEAM has been used in a variety of applications, including trend analysis, drought and heatwave studies, hydrological model calibration and validation, water budget assessment, and studies of changes in vegetation. To streamline the development of the model and improve its ability and accuracy in capturing the spatiotemporal patterns of evaporation, while tailoring the development to the needs of stakeholders, it is important to review previous studies and highlight the potential strengths and weaknesses of the model. Therefore, in this study, we provide a literature review of the GLEAM model applications and its accuracy. The results of this metanalysis indicate that GLEAM is preferentially used in climate studies, potentially due to its coarse (25 km) spatial resolution being a limiting factor for its use in water management and, particularly, agricultural applications. Validations to date suggest that, while GLEAM provides a relatively accurate evaporation dataset, its performance over short canopies requires further improvement. Two major sources of uncertainty in the GLEAM algorithm have been identified: (1) the modelling of evaporative stress in response to water limitation, (2) the need to consider below canopy evaporation estimates for a more realistic attribution of evaporation to its different sources. These potential drawbacks of the model could be alleviated by combining the current algorithm with a machine learning-based approach for a next generation of the model. Likewise, ongoing activities of running the model at high (100 m–1 km) resolutions open possibilities to utilise the data for water and agricultural management applications

Sentinel-1 backscatter assimilation using support vector regression or the water cloud model at European soil moisture sites

Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM (Delta R = +0.037) or SVR (Delta R = +0.025)