Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula

The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDición de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (¿. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version

A Hybrid Approach Combining Conceptual Hydrological Models, Support Vector Machines and Remote Sensing Data for Streamflow Simulation

Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission

Validation practices for satellite based earth observation data across communities

Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state-of-the-art methods of satellite validation and documents their similarities and differences. First the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given

GEWEX water vapor assessment (G-VAP): final report

Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global.El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe

Land Surface Data Assimilation of Satellite Derived Surface Soil Moisture : Towards an Integrated Representation of the Arctic Hydrological Cycle

The ability to accurately determine soil water content (soil moisture) over large areas of the Earth’s surface has potential implications in meteorology, hydrology, water and natural hazards management. The advent of space-based microwave sensors, found to be sensitive to surface soil moisture, has allowed for long-term studies of soil moisture dynamics at the global scale. There are, however, areas where remote sensing of soil moisture is prone to errors because, e.g., complex topography, surface water, dense vegetation, frozen soil or snow cover affect the retrieval. This is particularly the case for the northern high latitudes, which is a region subject to more rapid warming than the global mean and also is identified as an important region for studying 21st century climate change. Land surface models can help to close these observation gaps and provide high spatiotemporal coverage of the variables of interest. Models are only approximations of the real world and they can experience errors in, for example, their initialization and/or parameterization. In the past 20 years the research field of land surface data assimilation has undergone rapid developments, and it has provided a potential solution to the aforementioned problems. Land surface data assimilation offers a compromise between model and observations, and by minimization of their total errors it creates an analysis state which is superior to the model and observation alone. This thesis focuses on the implementation of a land surface data assimilation system, its applications and how to improve the separate elements that goes into such a framework. My ultimate goal is to improve the representation of soil moisture over northern high latitudes using land surface data assimilation. In my three papers, I first show how soil moisture data assimilation can correct random errors in the precipitation fields used to drive the land surface model. A result which indicates that a land surface model, driven by uncorrected precipitation, can have the same skill as a land surface model driven by bias-corrected precipitation. I show that passive microwave remote sensing can be utilized to monitor drought over regions of the world where this was thought to be impractical. I do this by creating a novel drought index based on passive microwave observations, and I validate the new index by comparing it with output from a land surface data assimilation system. Finally, I address knowledge gaps in the modelling of microwave emissions over northern high latitudes. In particular, I study the impact of neglecting multiplescattering terms from vegetation in the radiative transfer models of microwave emission. My three papers show that: (i) land surface data assimilation can improve surface soil moisture estimates at regional scales, (ii) passive microwave observations carries more information about the land surface over northern high latitudes than explored in the retrieval processing chain and (iii) including multiple-scattering terms in microwave radiative transfer models has the potential to increase the sensitivity for surface soil moisture below dense vegetation, and decrease biases between modelled and observed brightness temperature. In sum, my three papers lay the foundation for a land data assimilation system applicable to monitor the hydrological cycle over northern high latitudes