Water Cycle

Thermal remote sensing datasets are increasingly providing more opportunities to produce global land surface energy and water fluxes estimates. Several international scientific plan, e.g. iLEAPS, ESA WACMOS, EU WATCH, NASA NEWS, and LandFlux_EVAL initiative of GEWEX, are looking for the possibility and suitability to generate surface turbulent heat flux maps over the global area by remote sensing technique. Here we report two main progress in application of thermal remote sensing data to global land surface energy balance retrievals under umbrella of these scientific plans.

Estimating the sensible heat flux (H) over vegetation from thermal infrared temperature requires an estimate of the excess resistance kb_1, the difference in turbulent transfer efficiency between momentum and scalars. kb_1 has been the subject of considerable interest in micrometeorology, land surface model, and surface turbulent fluxes simulations, but there still does not exist a uniform kb_1 method for use in remote sensing retrieval of land surface flux. This study is motivated by the application of one dimensional turbulent diffusion methods describing land-atmosphere interaction by using remote sensing global land surface variables. A new version of surface energy balance system (SEBS) model (Su, 2002) was developed in this work. The model uncertainties in the estimated turbulent heat flux for 7 different land covers (including needle forest, broadleaf forest, shrub, savanna, grassland, cropland, and sparsely vegetated land) has been discovered due to turbulent parametrization method. Sensible heat is significantly underestimated at forest sites due to its high kb_1 estimate for the canopy part. Using sensitivity analyses, we have identified the most critical parameters for kb_1 calculation. The critical parameters were optimized using flux stations located in different continents. With the new optimized parameter values, the wind speed profile extinction coefficient within the canopy can be applied to different canopy structures. The new parameters was found to be more robust for land-air exchange flux calculation over bare soil, short canopy, and tall canopies.

Remote sensing (RS) land surface temperature (LST) application to land surface flux retrievals has been limited to clear-sky, which makes RS LST based land surface energy or water flux are not either spatial or temporal continuous. Using MODIS Aqua and Terra polar satellite thermal sensors, we have developed an method for producing an accurate monthly mean LST for global land. Against ground truth measurements from 110 flux towers, the monthly mean LST has a mean bias lower than 1 K. Combing with other global meteorological dataset, the monthly LST was used to force SEBS model and a global land surface fluxes from 2000 until present at 5 km resolution has been produced. Some evaluations to the global land surface energy and water flux will also be presented. Additionally, a new way of using MODIS LST in global warming, drought, and numerical model is also innovated by this study. Numerical model based reanalysis data (ERA-interim) was also used to check the spatial distribution of the derived monthly mean LST. The highest LST errors in ERA-interim were positioned in Arctic and Antarctica snow surface by using MODIS observed monthly LST.

The rapid warming at northern latitude regions in recent decades has resulted in a lengthening of the growing season, greater photosynthetic activity and enhanced carbon sequestration by the ecosystem. These changes are likely to intensify summer droughts, tree mortality and wildfires. A potential major climate change feedback is the release of carbon-bearing compounds from soil thawing.

These changes make it important to have information on the land surface (particularly, soil moisture and temperature) at high northern latitude regions. In particular, the availability of soil moisture measurements from several satellite platforms provides an opportunity to address issues associated with the effects of climate change, e.g., assessing multi-decadal links between increasing temperatures, snow cover, soil moisture variability and vegetation dynamics. The relatively poor information on water cycle parameters for biomes at northern high latitudes make it important that efforts are expended on improving the representation of the water cycle at these latitudes.

In a collaboration between NILU and Met Norway, the soil moisture observations over Norway from the ESA satellite SMOS (Soil Moisture and Ocean Salinity) are being evaluated using in situ ground based soil moisture measurements, with specific reference to drought and flood episodes. Data assimilation of the quality-controlled SMOS soil moisture observations into a land surface model and a numerical weather prediction model will be used to assess the added value from satellite observations of soil moisture for improving the representation of the water cycle at high northern latitudes.

This presentation provides first results from this work. In particular, we discuss the evaluation of SMOS soil moisture data (and from other satellites) against ground-based in situ data over Norway; the performance of the SMOS soil moisture data for selected drought and flood conditions over Norway; and the first results from data assimilation experiments with land surface models and numerical weather prediction models.

We provide evidence of the value of satellite soil measurements over Norway, including their fidelity, and their impact at improving the representation of the hydrological cycle over northern high latitudes. We indicate benefits from these results for multi-decadal soil moisture datasets such as that from the ESA CCI soil moisture.

This study investigates the potential of satellite observations to analyze the water cycle budget from global to regional scales. Under a changing climate, the global hydrological cycle is expected to accelerate and intensify. This can have significant societal impacts, for instance on population water availability or on modifications of the drought and flood patterns. Changes have already been observed locally on precipitation or evapotranspiration but no consensus has been reached at the global scale and for other parameters (e.g. runoff). One of the main reasons is that consistent satellite description of the present and past hydrological cycle is still not available with the needed accuracy, despite significant efforts at international level, within the Global Energy and Water Experiment (GEWEX) for instance. In particular, the water budget cannot be closed when using only satellite datasets.

In this study, we applied the integration methodology developed in Aires (2014) by using real satellite observations over some regional basins. The methodology allows combining satellite-retrieved datasets, weighting them using their uncertainty, and it ensures the water budget closure. It provides basin-scale estimates of the 4 water budget components (precipitation P, evapotranspiration E, water storage change dS, and runoff R). A comparison with in situ observations of P and E demonstrated that the integrated dataset improved the estimation of both components (Munier et al. 2015). Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently the integrated datasets are model-independent and can be used for model calibration or validation.

The integrated dataset is then used as a reference to establish a simple and coherent calibration procedure for each satellite dataset. A Closure Correction Model (CCM) allows standardizing the various datasets for each component, and at the same time, it decreases the budget residual (P-E-dS-R). Ten large basins for which runoff observations are available have been considered. Results showed that the budget residual decreased when using the corrected datasets (Munier & Aires 2016). The CCM was initially designed at the basin-level. In order to obtain a global calibration procedure, able to correct the satellite estimates at the pixel level to obtain a description of the water cycle with a global coverage, a global methodology needed to be put in place. In order to account for the variety of hydro-climatic conditions, 4 classes were defined based on net precipitation (P-E) and NDVI mean values. The CCM was calibrated for each dataset and each class, and then applied on each pixel and at the global scale. This new global, integrated satellite dataset resulted in a reduced budget residual average.

An ESA project ESA/WACMOS-Med has just started to capitalize on these methodological developments to study the water cycle in the Mediterranean region. In addition to the terrestrial water cycle, the ocean and the atmosphere compartments will be added to the integration process and several case studies will benefit from the new integrated satellite dataset for river discharge, ocean circulation, and drought forecasting.

S. Munier, F. Aires,  A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water cycle budget, Remote Sens. Lett., submitted, 2016.

Munier, S., F. Aires, S. Schlaffer, C. Prigent, F. Papa, P. Maisongrande, and M. Pan, Combining datasets of satellite retrieved products. Part II: Evaluation on the Mississippi Basin and closure correction model, J. Geophys. Res., 10/2014, DOI: 10.1002/2014JD021953, 2015.

Aires, F. Combining datasets of satellite retrieved products. Part I: Methodology and water budget closure, J. of Hydrometeor., 10.1175/JHM-D-13-0148.1, 2014.

The use of spaceborne microwave synthetic aperture radars (SARs) is becoming a well-established tool in several Earth remote sensing disciplines, such as flood monitoring, earthquakes analysis, ground target classification and many others. Satellite SARs ensure high spatial resolution imaging (on the order of meters) in almost all-weather conditions and suitable coverage (especially new generation of SAR based missions). Indeed, for frequencies above C-band, the impact of precipitating clouds may significantly impair the signal backscattered from the ground (e.g. Ferrazzoli and Schiavon 1997) even if the probability is quite low (Danklmayer et al. 2009). Nonetheless, the impact of precipitating clouds on both amplitude and phase-compressed SAR signals cannot be neglected, as recently reassessed by using X-band SARs (X-SAR) currently in orbit, such as COSMO-SkyMed (CSK) and TerraSAR-X (TSX) (e.g. Marzano et al. 2010, Baldini et al. 2014). On the other side, this sensitivity paves the way to the use of SARs as an instrument for observing and quantifying atmospheric precipitations.

This work aims at assessing the impacts of the atmospheric precipitations on X-SAR images and proposing innovative and enhanced X-SARs products, such as precipitation maps and cloud masks, exploiting the high spatial resolution, revisit time and coverage of last generation X-SARs. X-SARs give an unprecedented opportunity to final users (e.g. hydrologists) to inject observations of rain fields at catchment scale and at a spatial resolution as never before, into their models for flood forecasting. Nevertheless, research is still at an early stage and several issues have to be addressed.

The developed algorithm allows precipitating areas to be distinguished by flooded and permanent water surfaces or wet snow absorption, all of them looking “dark” (low backscattering) in SAR images. The identification of such areas is critical and necessary both for floods analysis and precipitation retrieval, because misinterpretations could cause severe estimation errors in both fields. This pre-processing algorithm is mainly based on image segmentation techniques and fuzzy logic (e.g. Pulvirenti et al. 2014 and Mori et al. 2012). Ancillary data, such as a local incidence angle map and a land cover map are also used. The second step of the procedure consists of a precipitation retrieval algorithm, developed in previous works by Marzano et al. (2010, 2011), and up to now applied only to pixels where rain is known to be present.

This methodology has been applied to 14 CSK and TSX study cases, acquired within the European FP7 project EartH2Observe over Italy and United States. These areas have been selected for the possibility of observing both hurricane-like intense events and continental mid-latitude ones. Moreover, they offer the opportunity to verify and validate the proposed methodology using weather radars or rain gauge networks. Results obtained until now show fairly good performances of the precipitation maps, both in terms of cell localization and quantification. A couple of examples are reported in the included file.

The above investigations, both theoretical and experimental, suggest exploring the possibility offered by instruments operating at higher frequencies. In order to investigate the potential of Ka SARs, we have further developed the 2D numerical simulator of SAR images in presence of rain, described in (Marzano et al., 2012), to extend it to Ka-band and polarimetric observations (Mori et al. 2015). This has been performed in the frame of an ESA funded project [Contract ESTEC N. 4000109477/13/nl/lvh] supporting the feasibility analysis of a spaceborne Ka-SAR system. Indeed, Ka-band shows a very high sensitivity to atmospheric hydrometeors and this offers interesting possibilities towards multifrequency atmospheric precipitations retrieval. In this work simulated Ka-SAR numerical scenarios will be also discussed.

The consequences of climate change represent a major threat to sustainable development and growth in Southern Africa. Understanding the impact on the geo- and biosphere is therefore of great importance in this particular region. In this context the Kalahari salt pans (also known as playas or sabkhas) and their peripheral saline and alkaline habitats are an ecosystem of major interest. They are very sensitive to environmental conditions, and as thus can be analyzed hydrological, mineralogical and ecological responses to climatic variations. Furthermore, alkaline soils and sediments are a potentially significant storage of global carbon. Geochemical studies showed that total carbon stock of salt pans is approximately an order of magnitude greater compared to the neighboring Kalahari Sands. Although their organic carbon content is negligible salt pan sediments typically contain high concentrations of inorganic carbon in the form of calcium and magnesium carbonate and bicarbonate. These minerals can directly originate from parent material (lithogenic), or from dissolution and in-situ precipitation carbonates (pedogenic). Thus far the surface distribution of these minerals have been only assessed mono-temporally and on a coarse regional scale, but the dynamic of the salt pans, especially the formation of evaporites, is still uncertain and poorly understood. One of the key drivers in the formation and resolution of evaporites is the hydrological regime. Local moisture conditions attributed to differences in groundwater flow as well as seasonal flooding have to be considered. Multitemporal remote sensing techniques allow us to derive this recent dynamic of these salt pans and improve the understanding of major physical processes in these dryland environments. Furthermore spaceborne hyperspectral data can give insight to the current surface mineralogy on a physical basis and provide the intra pan distribution of evaporites.

In this study we focus on the analyses of salt and clay Namibia Kalahari pans based on satellite multispectral time-series and on hyperspectral imagery. A change detection analysis is applied using the Iterative-reweighted Multivariate Alteration Detection (iMAD) method to identify and investigate surface changes based on the Landsat archive imagery covering the period 1984-2015. For the complete Landsat time series, a total of 130 bi-temporal change maps have been derived and classified into major change regions.  As a parameter of major interest the moisture regime of the pan is investigated using the Normalized Difference Wetness Index (NDWI). The wetness information is related to precipitation variability derived from satellite daily rainfall estimations from the Tropical Rainfall Measuring Mission (TRMM). By merging this information the actual susceptibility of the salt pans on rain events and groundwater flow can be identified. Furthermore valuable insights about the buildup or loss potential of evaporites can be gained when the information about hydrological regime is complemented by a mineralogical analysis. On the basis of thoroughly preprocessed EO-1 Hyperion hyperspectral Images acquired in 2013-2014 linked with geochemical field data, mineralogical maps of the pans were obtained using spectral matching techniques and the current distribution of characteristic surface evaporites was characterized. With this work we demonstrate that by fusing the potential of multitemporal and hyperspectral spaceborne data, accurate spatiotemporal patterns on surface processes can be resolved and related to physical processes in the Kalahari pans. In particular, different change areas could be identified within some parts of the pans that feature varying surface crust types (halite, gypsum) which assumingly form as a result of alternating hydrological conditions.