by Valentijn Pauwels, Monash University

On July 22, a meeting was held at Monash University in Melbourne. The goal was to coordinate a number of new projects related to soil moisture data assimilation.  A number of action items were defined, related to defining common study sites, data sets and models. 

The minutes are provided below.

 

Soil moisture data assimilation workshop

Monash University, Clayton Campus, July 22, 2014, 10 AM – 4 PM.

Attending: Imtiaz Dharssi, Daehyok Shin, Amgad Elmahdi, Luigi Renzullo, Narendra Tuteja, Carl Daamen, Adam Smith, Ian Grant, Mohsim Hafeez, Lucy Marshall, Sahani Pathiraja, Stefania Grimaldi, Yuan Li, Jeff Walker, Val Pauwels

Agenda Items

1.       Meeting objectives.  Jeff Walker clarifies that in the last ARC DP round, and in the bushfire CRC call, a number of data assimilation projects have been approved.  This has raised the idea to collaborate on a number of issues, and leverage off each other, rather than duplicate or triplicate efforts.

2.       Quick round of introductions.  The participants clarify their domain of expertise and their interest in the workshop:

–          Imtiaz Dharssi: Land surface modelling and data assimilation.

–          Daehyok Shin: Hydrologic predictions, seasonal and long term predictions. Has strong interest in an operational soil moisture product.

–          Amgad Elmahdi: Retrospective water resources assessment, radar research, integrated modelling.

–          Luigi Renzullo: Remote sensing, also data assimilation, focus on soil moisture.

–          Narendra Tuteja: Hydrologic predictions, developing seasonal forecast, long term climate change, also short term streamflow (7 days). Has an interest in the technology that these projects will lead to, as well as in catchment wetness.

–          Carl Daamen: Water resources assessment, catchment water balance at time scales past year to month. Has an interest in soil moisture products.

–          Adam Smith: Floods, land surface modelling, soil moisture, remote sensing. Is leading a project on the development of the AWRA modelling system and a daily updated soil moisture product (using AWRA-L output).

–          Ian Grant: Satellite applications, developing systems to convert satellite data into geophysical products, facilitating supply of data streams from external sources, real time operations to supply data streams to internal Bureau and some external users. Is interested in the needed data streams.

–          Mohsin Hafeez: Hydrological modelling. Wants to learn about synergies in the projects.

–          Lucy Marshall: Data assimilation and model uncertainty.

–          Jeff Walker: Data assimilation, soil moisture remote sensing.

–          Stefania Grimaldi: Hydraulic data assimilation.

–          Yuan Li: Hydrologic data assimilation (soil moisture and streamflow).

–          Val Pauwels: Hydrologic model optimization and data assimilation.

3.       The projects.

–          Val Pauwels:

o   Future Fellowship. Is focusing on removing bias from land surface models and remote sensing data through data assimilation. The test site is the Murray Darling basin, the model Jules. Will look at brightness temperature from SMOS (maybe SMAP) and using the Kalman Filter.

o   Bushfire CRC project.  The objective is to improve coupled hydrologic/hydraulic models through data assimilation. The hydrologic part will be improved through assimilation of streamflow observations and soil moisture data (Yuan Li working on this), the hydraulic part through water levels and flood extents (with Stefania Grimaldi working on this).  One test site has been identified (the Clarence river), another test site needs to be defined.  The GR4HUM model (GR4H with an extra layer for satellite observations) will be used as hydrologic model, a decision needs to be taken for the hydraulic model.

–          Imtiaz Dharssi: New BNHCRC project on improving land dryness measures and forecasts. Most end users are from the fire community.  The fire danger index uses a drought index developed in the 60s that is currently calculated using simplistic assumptions.  Will also develop an improved drought index through models, in the first place Jules and Cable.  Will assimilate ASCAT, SMOS, SMAP, and (possibly) GRACE data.  Is also interested in assimilating land surface temperatures and vegetation information (NDVI) in order to develop a better soil moisture product.  Clarifies that the objective is to deliver an operational ready product, based on daily updating, at the national scale.

–          Albert van Dijk: An ARC DP on forecasting droughts months ahead will start soon.  Multiple satellite data will be assimilated, including GRACE and radiometer data and remotely sensed vegetation observations.  A multiscale and multisource data assimilation algorithm needs to be developed for the AWRA model. A biophysical forecasting (crop growth) model will also be used. Scale is continental to global at 10km resolution.

–          Mohsin Hafeez: Works with the AWRA model to provide inputs to National Water Accounts and Australian Water Resources Assessments. Areas of work around soil moisture include: validating modelled (AWRA-L) soil moisture retrospectively with probes (OzNet, SASMAS and COSMOS) and satellite data (AMSR-E and ASCAT), in future using remotely sensed soil moisture for AWRA-L calibration and continuing Luigi’s work from WIRADA on data assimilation with the AWRA-L model (EnKF with AMSR-E and ASCAT), and delivering a daily updated soil moisture product using AWRA-L output.

–          Narendra Tuteja: Explains a number of projects.

o   Short term forecasting (to 7 days) at subdaily timesteps, which is the only one in Australia that works at time scales finer than daily. Works on 11 catchments in which 16 locations are forecasted. Produces deterministic forecasts using ACCES-G at 40 km res, and the SWIFT system (using the GR4-H).  Performs a dual pass streamflow bias correction. The 11 catchments should by December this year increase to 50.

o   Ensemble forecasting. Based on Alan Seeds work, combining radar data, and different spatial resolutions. Performs bias correction, and will use the same technology for flood forecasting. FEWS will be used for this latter purpose.

o   Seasonal forecasting. Forecasts are made three months ahead, only for streamflow at 74 locations. Applies the uncertainty work from Kuczera, including model averaging.

o   Long term forecasting. Good quality streamflow data identified for 220 hydrological reference stations. These sites are now part of the global runoff data center.

–          Luigi Renzullo: Explains the project to establish a satellite soil moisture products collection on NCI. Collaborates with the agricultural sector, which wants root zone soil moisture at resolutions to 10-100 m. Provides an overview of the soil moisture products that will be placed on NCI. Clarifies that the soil moisture products will be maintained regularly.

–          Lucy Marshall: Is involved in an ARC DP with Ashish Sharma and Yi Liu. Robert Parinussa will be hired as Research Fellow. The project is very similar to the BNH CRC project of Pauwels and Walker, and focusses on coupled hydrologic/hydraulic modelling and data assimilation.  Is currently defining a number of test catchments. The objective is to perform fundamental research on data assimilation and remote sensing. The project is also a collaboration with CSIRO. Currently synthetic data are used.

4.       Further steps to be taken.  After a discussion the following action points are agreed:

–          Monash to get access to NCI.

–          Hydrologic/hydraulic DA projects: BoM to provide list of 220+ (forecasting) catchments, if possible provide drainage area. Monash and UNSW to check if they align with their (possible) test sites.

–          Monash to provide BoM list of data-rich catchments.

–          Luigi Renzullo to check if meteorological forcings (AWAP and ANU) can be shared.

–          Monash to get SMOS data on NCI.

–          BoM set up a common Jules and AWRA dataset on NCI (5 km).

–          Monash to send around some review papers on RS and DA.

–          To all: if interested in sharing RS SM data, contact Luigi.

–          UNSW and Monash to agree on one common test catchment.