Satellite inspired hydrology in an uncertain future: a H SAF and HEPEX workshop

Filipe Aires (LERMA/ESTELLUS)

A new satellite dataset merging and uncertainty characterisation constrained by the terrestrial water budget

Abstract to follow

Clement Albergel (CNRM - Université de Toulouse, Météo-France, CNRS)

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

This study investigates the capability of LDAS-Monde global offline LDAS to monitor and forecast the impact of extreme events on Land Surface Variables (LSVs). LDAS-Monde is driven by ERA-5 atmospheric forcing from ECMWF and is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the ISBA land surface model.

A global 2010-2018, 0.25°x0.25º spatial resolution, reanalysis of the LSVs is first evaluated thanks to global estimates of SSM, LAI, evapotranspiration, Gross Primary Production, Sun Induced Fluorescence and several in situ measurements of soil moisture, river discharge, and flux measurements.  This 9-yr reanalysis is used to provide a climatology of the LSVs. Significant anomalies are used to decide on where to focus for a more detailed monitoring and forecasting activity. 19 regions across the globe were investigated for 2018. Two of them, presenting large negative anomalies of SSM and LAI were further analysed: Western-Europe and the Murray-Darling river basin in southeastern Australia. LDAS-Monde was operated forced by ECMWF IFS high-resolution atmospheric analysis leading to a 0.1°x0.1° reanalysis. It complements the coarse-resolution LDAS-Monde operated using ERA5. The IFS forecast capacity initialised by LDAS-Monde analysis is also presented.

Carlo De Michele (Politecnico di Milano)

About the use of snow satellite products in hydrological modelling and balancing model complexity and data requirements

Satellite snow products represent a precious resource in hydrology for calibrating, validating, and improving the performances of models, through data assimilation.

Here, I will provide some examples of application of snow satellite products to hydrological problems and investigate the issue of balancing model complexity and input data requirements in snow hydrology.

Christian Kummerow (Colorado State University)

Projected Advances in the Remote Sensing of Precipitation

The talk will review the current status of satellite and surface networks capable of providing precipitation with high space/time resolution with a view towards the future.  While ground based radar networks continue to see systematic although measured progress, there will be a rapid increase in satellite capabilities both in terms of microwave sensors made possible by new Cube- and MicroSat technology, as well increased sampling frequency from the next generation of geostationary VIS/IR sensors. This will stress our ability to create general products that satisfy broad user categories with varying requirements for resolution, quality and stability.  Instead it will likely require the production of more focused products such as hydrology.  A second emerging trend is for satellite algorithms, through maturation and the recent increased use of machine learning, to fully exploit the available information content. This will likely lead to greater emphasis being placed on error estimation, while existing challenges, posed by such conditions as drizzle and orographic precipitation is requiring greater model input leading to more synergistic frameworks.

Hans Lievens (KU Leuven)

Snow depth observations from Sentinel-1 over the Northern Hemisphere mountain ranges

The snow depth in the world’s mountains ranks among the most uncertain variables in hydrology. Estimates from the interpolation of local measurements are unrealistic where they are sparse, atmospheric models poorly estimate snowfall, and current snow remote sensing observations have inherent limitations. Yet, accurate snow depth estimates are critically needed to provide information on the associated water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Here, we demonstrate the unprecedented ability of the Sentinel-1 mission to map ~weekly snow depth in the Northern Hemisphere mountains at 1-km² resolution. An evaluation with measurements from ~4,000 sites and reanalysis data demonstrates that the Sentinel-1 observations capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. The latter is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. The Sentinel-1 observations offer new opportunities for data assimilation, to improve the initialization of numerical weather, seasonal and climate predictions. The long-term continuity of the ESA and Copernicus Sentinel-1 constellation is a strong asset, offering the frequent and continual observations that are required for monitoring the cryosphere.

Kari Luojus (Finnish Meteorological Institute)

EO-based retrieval of snow cover, overview of selected snow products and their quality assessment

Monitoring terrestrial snow cover on continental and global scales is complicated by large differences on regional and local conditions as well as large gaps in surface observing networks. Therefore satellite-based observations provide the best spatially and temporally extensive means for snow cover monitoring on hemispherical scale.

The objective of the ESA Snow CCI is to generate homogenized long-time series of daily global snow extent (SE) maps from optical and daily global snow water equivalent (SWE) products from passive microwave satellite data. 

The goal of the EUMETSAT H SAF “snow cluster” and the Copernicus Global Land Service “cryosphere theme”, is production of satellite based near real time maps of various snow cover parameters.

The uncertainty associated with current hemispherical datasets on both SE and SWE are significant. There are significant differences even between the well-established snow products which has been made obvious in the ESA SnowPEx project that has investigated and inter-compared the available SE and SWE products.

A brief overview of these ESA, EUMETSAT and Copernicus frameworks is presented. Overview will consider snow cover extent and snow water equivalent retrieval approaches and products, both for operational near-real time purposes and historical climate data records of snow.

Jude Musuuza (Swedish Meteorological and Hydrological Institute)

The impact of satellite data assimilation on hydrologic model performance

The very scales at which data may be required and the accessibility of sampling points quickly renders certain direct measurements impractical e.g., changes in land cover, sea surface temperature, snow in mountains, etc. Satellite-derived products are a valuable source of such data and overcomes the large spatial extent limitations. The data is sometimes at a reasonable temporal resolution as well, which addresses the sampling frequency challenges. The satellite data, at the large spatial scales can provide boundary conditions for constraining numerical models but can also be assimilated to improve their predictions. In this study we show the effect of assimilating the actual and potential evapotranspiration, the snow water equivalent and the fractional snow cover; and the different combinations of the four products into the hydrologic model HYPE. We assessed the dependency of the hydrologic model performance by assimilating different products on: i) whether the model is calibrated to the data to be assimilated or not, ii) observation error model assumptions, iii) gaussification of non-Gaussian model and observation fields iv) ensemble size, v) assumptions regarding error model for perturbing the model forcing data (ensemble generation).

Jan Seibert (Department of Geography, University of Zurich)

Snow processes in bucket-type hydrological models – does increased realism lead to better simulations?

Bucket-type hydrological models such as the HBV model are widely popular, because of their relatively low data and computational demands. This is the case of the snow routine in the HBV model. While this approach usually results in good simulations, improvements are possible. We explored and tested different alternatives to the design of the snow routine of HBV-light such as considering a gradual transition between snowfall and rainfall, or implementing a seasonally variable degree-day factor. Furthermore, we evaluate the value of different data sources for model calibration and testing as well as the importance of different model formulation in the snow routine for simulations for changed climate conditions. We quantify the usefulness of the snow-routine modifications for simulations in alpine and other snow-covered areas. We also discuss the balance between increasing the realism and the preservation of the inherent simplicity of the HBV model. Results indicate that few model modifications result in clear model performance improvements whereas many modifications, despite seemingly increasing the model realism, did not lead to improved model performances. Furthermore, we demonstrate how different formulations of the snow routine might lead to different simulations for changed climate conditions and potentially can create artefacts, which are often overlooked.

Albrecht Weerts (Deltares)

Improving hydrological prediction through data assimilation: results from the IMPREX and eWaterCycle II projects

Improving sharpness/reliability of hydrological forecasts is key to increase the value of a warning service. Hydrological data assimilation is one possible way to increase the accuracy (and possibly reliability). Various studies in assimilation of various ECVs (water level, discharge, soil moisture) show that accuracy can be indeed be improved. Here, we show results from the H2020 IMPREX project on assimilation of lake levels and discharge into a hydrological model of the Rhine using OpenDA (www.openda.org) and an open source hydrological modelling framework wflow (https://github.com/openstreams/wflow).  This tool is also used to assimilate available discharge measurements in the W3RA model connected with a kinematic wave subsurface routing model in NRT in an operational global flow forecasting system (GLOFFIS).

Within the ongoing eWatercylce II project in cooperation with the Dutch eScience Centre (https://www.esciencecenter.nl/project/ewatercycle-ii) we aim to enable joint assimilation of discharges and for instance soil moisture using local analysis, we hope to present first results from this work.