1	Recent advances in soil moisture measurement instrumentation and the potential for online estimation of catchment status for flood and climate forecasting: some experience from semi-arid catchments Garry Willgoose Earth and Biosphere Institute University of Leeds
2	Coworkers Walker, Rudiger, Grayson, Western: U. Melbourne Kalma, Hemikara, Hancock, Saco: U. Newcastle (Aust) Houser: NASA Hydrology Woods: NIWA, NZ Entekhabi: MIT
3	The Core Hydrology Question How will emerging microwave remote sensing techniques for soil moisture assist in estimating the hydrology of catchments ERS (early 90’s) AMSR (current) Hydros (planned) Can these techniques be integrated with new field instrumentation such as TDR?
4	SASMAS Objectives To ground validate AMSR-E measurements To test data assimilation of SM using AMSR-E or surrogate To test data assimilation of SM using discharge data (in heavily vegetated areas) To understand scaling properties of SM from Ha to 100km2 scale in semi-arid To better understanding C, P balance in semi-arid catchments To understand floodplain as a temp storage for sediment from hillslope to river.
5	Time Domain Reflectometry TDR Integrated depth measurement at a point Difficult to install near surface Poor in cracking soils
6	Microwave Remote Sensing Typical wavelengths see top few cms of soil water and canopy water, impacted by soil surface condition (roughness). Repeat rate at best Radiometer: twice/day @ low space resolution (10-30 km) Radar: ~once month @ high resolution (20-30m) NOT measuring state of interest: whole profile soil water at catchment scale=ET.
7	But we can model profile soil water state … “Frequent” measurements of surface soil moisture and model to simulate profile. Potentially with sufficient soil data can remote sense soil depth and water holding capacity.
8	Synthetic Simulations
9	Field Data Dotted simulations (surface moisture DA) best track the long-term data and the rise in May.
10	What about spatial patterns? Tarrawarra site (Grayson, Western, Willgoose, McMahon) Switch from arid (disorganised) to humid (organised). Is arid data disorganised or is it deterministically linked to spatially random soils properties? Single probe calibration.
11	SASMAS 01 Sampling 40 x 50km area North of Goulburn River within unforested region 4 teams over 3 days Sampled area about scale of AMSR pixel 225 soil moisture samples sites (4 gravimetric, 5 TDR), 194 veg samples
12	Soil Moisture Results (SASMAS’1 field campaign)
13	The Stanley micro-site 1km x 2km for look at hillslope organisation of soil moisture. Semi-arid => not topographic index … soils, veg? 7 permanent TDR sites, 1-3 levels in the soil Runoff gauging
14	Sample of a at-a-point time series Strong response to rainfall and good correlation between depths.
15	Stanley Deep Soil Moisture Good correlation over 2km Appears likely to be able to calibrate a single probe (i.e. difference between sites due to permanent effects) Soil moisture correlations are parallel => soil moisture process is vertical rather than a lateral topographic index type process
16	Stanley Surface Soil Moisture Correlation of surface soil moistures not as good Cross correlation with deeper soil moistures also not as good Is +/- 10% accuracy good enough? Implications for remote sensing Soil moisture correlations definitely parallel
17	Short distance (sample scale) correlation Significant correlation scale of 0.2-0.5m. None up to 10m. Apparently unrelated to vegetation patterns. Also unrelated to SM status. Soils? Implication: Hand held sampling is unrepeatable at the hillslope scale, though fixed sites indicate significant spatial correlation at this scale. More handheld sampling planned in March for the 10-1000m scale. If SM correlation can be used as surrogate for soil variability what drives the soil variability? Implications for hydrology?
18	A tentative Conclusion from field data There appears to be a nontrivial spatial correlation 1-3 km (from surface soil moisture maps). Still processing recent SASMAS field campaigns. This correlation appears to be consistent through time (from correlation between permanent stations) We can assimilate profile soil moisture from surface measurements (whether radar or TDR ) Conclusion: The spatial correlation is a function of permanent properties of the catchment (e.g. soil, vegetation) rather than temporally uncorrelated fns such as rainfall. Implications: We can (in principle) predict catchment scale soil moisture from single site TDR measurements (but short correlation scale => permanent sites required not hand held)
19	Results from a synthetic data assimilation study using stream runoff (for heavy veg sites) Root zone soil moisture well assimilated Surface soil moisture also well simulated but more sensitive to noise
20	Climate Model Initialisation
21	Soil moisture and climate Koster (NASA) showed that global climate dynamics/forecasts (months-years) sensitive to soil moisture (through energy partitioning – ET) Entekhabi (MIT) showed bimodal continental climates as a result of rainfall feedback Eltahir (MIT) showed Sahel had three stable climate/vegetation states due to feedbacks.
22	Continental feedbacks Relative strength of ET to ocean moisture determines the local feedback
23	How much latent heat transfer from vegetation?
24	Potential role of TDR and RS Vegetation extracts from deeper layers so raw remote sensing will not capture full behaviour … profile modelling necessary. TDR ground truth soil moisture … potentially calibratable to regional averages. Potential for a network attached to meteorology stations.
25	Conclusions Point monitoring and telemetering of soil moisture now possible and economic. Not easy to use upcoming RS data (concentrated on surface response). TDR point scale data appears to be regionalisable. Profile data would complement surface imaging.