Modeling Surface Water Dynamics and Key Drivers across Australia’s Murray-Darling Basin from Space

Background and Data

A new Framework for Modeling Inundation Dynamics from Space (2)

Results

C. Here, we used an unprecedented Landsat time series of surface water maps(1) (30x30m , >25.000 images), to develop statistical inundation models and quantify the role of driver variables across the Murray-Darling Basin ( ̴1mio. km2), which is Austrlia’s bread basket and subject to competing demands over limited water resources

We modeled surface water extent separately for hydrologically distinct floodplain, floodplain-lake and non-floodplain areas within eco-hydrological zones and 10 x 10 km grid cells

Conclusions

Modeling the large 2010 La Nina Floods in the Paroo River (3)

Satellite-observed Surface Water Extent [%] Modeled Surface Water Extent [%]

Satellite-observed Surface Water Extent [%]

Rainfall [mm/16days]

Soil Moisture [m3/m3]

Evapotranspiration [mm/16days]

River Flow [ML/day]

River Flow [m3/sec]

Surface water extent (SWEt) was modeled continuously as a function of previous extent (SWE(t-1)), laged discharge (Q), rainfall (P), evapotranspiration (ET) and soil moisture (SM) using a generalized additive regression model (GAM), where s= smooth function

Satellite-based time series of surface water extent and other hydro-climatic variables are rapidly becoming more available and our models represent cost-effective and transferable tools for using these time series to improve the management of limited surface water resources and to better quantify large scale flooding processes

2012

1986

16 days

We quantified flood travel times between river gauges and grid cells (lag(Q)) which led to accurate flood models for 18,521 individual modeling areas (avgerage r2 = 0.7)

We used the statistical models to predict missing time steps (yellow bars) when the satellite’s view was obstructed by cloud cover during flooding with high validated accuracy (r2 = 0.9)

Methods

Time series of model variables (2)

1) Tulbure, M.G. et al., 2016. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region. Remote Sensing of Environment, 178, pp.142–157.

2) Heimhuber, V., Tulbure, M.G. & Broich, M., 2016. Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data. , pp.2227–2250.

3) Heimhuber, V., Tulbure, M.G. & Broich, M., Under Review. Modeling multi-decadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data. Water Resources Research

Valentin Heimhuber, Mirela Tulbure,

Mark Broich valentin.heimhuber@unsw.edu.au

Rainfall (BoM gridded)

Soil Moisture (Satellites)

Evapotranspiration (AWRA-L)

(Australian Geofrabric)

𝑆𝑊𝐸𝑡 = β1𝑆𝑊𝐸(𝑡−1) + 𝑠(𝑙𝑎𝑔 𝑄 ) + β3𝑃 + β4𝐸𝑇 + β4𝑆𝑀

The role of hydro-climatic drivers in surface water dynamics differed across the basin and between different types of water bodies (floodplains shown), with a hotspot in the north-west

Basin wide model performance and variable importance (3)

RMSE improvement through (P, ET, SM)

North South Division

A. Periodically inundated surface water areas such as floodplains are hotspots of biodiversity but have suffered alarming declines globally in recent history

B. Despite their importance, their long-term surface water dynamics and hydro-climatic drivers remain poorly quantified on continental scales

Paroo Zone

Paroo flow lag times [days] (2)

Paroo model r2 per grid cell (2)