Long-term and high-resolution global time series of brightness temperature from copula-based fusion of SMAP enhanced and SMOS data

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of 25km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

Statistical and stochastic post-processing of regional climate model data: copula-based downscaling, disaggregation and multivariate bias correction

In order to delineate management or climate change adaptation strategies for natural or technical water bodies, impact studies are necessary. To this end, impact models are set up for a given region which requires time series of meteorological data as driving data. Regional climate models (RCMs) are capable of simulating gridded data sets of several meteorological variables. The advantages over observed data are that the time series are complete and that meteorological information is also provided for ungauged locations. Furthermore, climate change impact studies can be conducted by driving the simulations with different forcing variables for future periods. While the performance of RCMs generally increases with a higher spatio-temporal resolution, the computational and storage demand increases non-linearly which can impede such highly resolved simulations in practice. Furthermore, systematic biases of the univariate distributions and multivariate dependence structures are a common problem of RCM simulations on all spatio-temporal scales. Depending on the case study, meteorological data must fulfill different criteria. For instance, the spatio-temporal resolution of precipitation time series should be as fine as 1 km and 5 minutes in order to be used for urban hydrological impact models. To bridge the gap between the demands of impact modelers and available meteorological RCM data, different computationally efficient statistical and stochastic post-processing techniques have been developed to correct the bias and to increase the spatio-temporal resolution. The main meteorological variable treated in this thesis is precipitation due to its importance for hydrological impact studies. The models presented in this thesis belong to the classes of bias correction, downscaling and temporal disaggregation techniques. The focus of the developed methods lies on multivariate copulas. Copulas constitute a promising modeling approach for highly-skewed and mixed discrete-continuous variables like precipitation since the marginal distribution is treated separately from the dependence structure. This feature makes them useful for the modeling of different meteorological variables as well. While copulas have been utilized in the past to model precipitation and other meteorological variables that are relevant in hydrology, applications to RCM simulations are not very common. The first method is a geostatistical estimation technique for distribution parameters of daily precipitation for ungauged locations, so that a bias correction with Quantile Mapping can be performed. The second method is a spatial downscaling of coarse scale RCM precipitation fields to a finer resolved domain. The model is based on the Gaussian Copula and generates ensembles of daily precipitation fields that resemble the precipitation fields of fine scale RCM simulations. The third method disaggregates hourly precipitation time series simulated by an RCM to a resolution of 5 minutes. The Gaussian Copula was utilized to condition the simulation on both spatial and temporal precipitation amounts to respect the spatio-temporal dependence structure. The fourth method is an approach to simulate a meteorological variable conditional on other variables at the same location and time step. The method was developed to improve the inter-variable dependence structure of univariately bias corrected RCM simulations in an hourly resolution

Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging

Soil moisture (SM) plays an important role in the land surface energy balance and water cycle. Microwave remote sensing has been applied widely to estimate SM. However, the application of such data is generally restricted because of their coarse spatial resolution. Downscaling methods have been applied to predict fine-resolution SM from original data with coarse spatial resolution. Commonly, SM is highly spatially variable and, consequently, such local spatial heterogeneity should be considered in a downscaling process. Here, a hybrid geostatistical approach, which integrates geographically weighted regression and area-to-area kriging, is proposed for downscaling microwave SM products. The proposed geographically weighted area-to-area regression kriging (GWATARK) method combines fine-spatial-resolution optical remote sensing data and coarse-spatial-resolution passive microwave remote sensing data, because the combination of both information sources has great potential for mapping fine-spatial-resolution near-surface SM. The GWATARK method was evaluated by producing downscaled SM at 1-km resolution from the 25-km-resolution daily AMSR-2 SM product. Comparison of the downscaled predictions from the GWATARK method and two benchmark methods on three sets of covariates with in situ observations showed that the GWATARK method is more accurate than the two benchmarks. On average, the root-mean-square error value decreased by 20%. The use of additional covariates further increased the accuracy of the downscaled predictions, particularly when using topography-corrected land surface temperature and vegetation-temperature condition index covariates

Evaluation and disaggregation of climate model outputs for european drought prediction

Landwirtschaftliche Dürren führen zu hohen sozialen und wirtschaftlichen Schäden. Die Auswirkungen dieser Extremereignisse können mit der Hilfe von einem saisonalen Vorhersagesystem, welches Dürren mehrere Monate im Voraus prognostiziert, abgeschwächt werden. Das Vorhersagesystem, welches in dieser Arbeit entwickelt wird, basiert auf meteorologischen Prognosen des nordamerikanischen Multi-Modell Ensembles (NMME), die verwendet werden um das mesoskalige Hydrologische Modell (mHM) anzutreiben. Ein neu entwickeltes stochastisches Verfahren wird benutzt um die monatlichen Niederschlagsvorhersagen des NMME Datensatzes in tägliche zu disaggregieren. Dieses Verfahren erhält die räumliche Kovarianz von Niederschlag durch das neu vorgestellte "Anchor"-Sampling auf beliebig großen Gittern. Die erhaltenen Prognosen werden mit denen des Ensemble Streamflow Prediction (ESP) Ansatzes verglichen. Der Bewertungszeitraum reicht von 1983 bis 2009. Das Simulationsgebiet umfaßt große Teile Kontinentaleuropas. Die auf dem NMME basierenden Prognoses zeigen bei einer sechsmonatigen Vorhersagezeit eine 69% höhere Vorhersagegüte auf als die des ESP Ansatzes. Dabei gibt es eine substantielle räumliche und zeitliche Variabilität von bis zu 40%. Ein Standardansatz in der Dürrevorhersage ist es entweder die Prognosen der einzelnen Modelle oder die des gesamten Ensemblemittels auszuwerten. Das gesamte Ensemblemittel zeigte eine höhere Güte als jedes Einzelmodell. Die Vorhersagegüte eines Subensembles (eine Subgruppe von Modellen), welches vom neu entwickeltem Rückwärtseliminiatiosalgorithmus effizient gefunden wurde, ist jedoch nur 1% geringer. Die Anzahl der Modellläufe dieses Subensembles beträgt jedoch nur 60% der des gesamten Ensembles. Die in dieser Arbeit vorgestellten Methoden zur Identifizierung von Subensembles und zur zeitlichen Disaggregierung von Niederschlag sind nicht auf die hier betrachtete Anwendung beschränkt und können auch auf andere Anwendungen übertragen werden

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Copula-based stochastic modelling of evapotranspiration time series conditioned on rainfall as design tool in water resources management

In the last few decades, the frequency and intensity of water-related disasters, also called climate-related disasters, e.g. floods, storms, heat waves and droughts, has gone up considerably at both global and regional scales, causing significant damage to many societies and ecosystems. Understanding the behavior and frequency of these disasters is extremely important, not only for reducing their damages but also for the management of water resources. These disasters can often be characterized by multiple dependent variables and therefore require a flexible multivariate approach for studying such phenomena. In this study, we focus on copulas, which are multivariate functions that describe the dependence structure between stochastic variables, independently of their marginal behaviors. The study aimed at different potential applications of copulas in hydrology, such as a multivariate frequency analysis and a copula-based approach for assessing a rainfall model. And further, a stochastic copula-based evapotranspiration generator was developed. As an application, the potential impacts of climate change on river discharge was investigated partly based the latter generator