Observing catchments, rivers and wetlands from space: assimilating hydrologic information into distributed models Peter A. Troch

Outline of presentation Data needs for (surface) water resources management Satellite based observations of rivers and wetlands Satellite based observations of soil moisture and latent heat fluxes Satellite based observations of rainfall Satellite based observations of catchment storage changes Data assimilation into distributed models Recommendations/conclusions

Data needs for Water Resources Management Sound water resources management is hampered by uncertainties in quantifying the water balance components at the catchment scale. Water balance components at the catchment scale are traditionally estimated by means of in-situ measurements and distributed hydrological models. A wide variety of distributed hydrological models has been developed over the past decade. A major problem plaguing distributed modelling is parameter identifiability, owing to a mismatch between model complexity and the level of data which is traditionally available to parametrize, initialize, and calibrate models, and to uncer- tainty and error in both models and observational data. New data sources for observation of hydrological processes (ENVISAT, MSG, SMOS) can alleviate some of the problems facing the validation and operational use of hydrological models. Data assimilation provides a means of integrating these data in a consistent manner with model predictions.

Lack of Q?

Lack of Q and  S Measurements: An example from Inundated Amazon Floodplain 100% Inundated! Singular gauges are incapable of measuring the flow conditions and related storage changes in these photos whereas complete gauge networks are cost prohibitive. The ideal solution is a spatial measurement of water heights from a remote platform. How does water flow through these environments? (L. Mertes, L. Hess photos)

Example: Braided Rivers It is impossible to measure discharge along these Arctic braided rivers with a single gauging station. Like the Amazon floodplain, a network of gauges located throughout a braided river reach is impractical. Instead, a spatial measurement of flow from a remote platform is preferred.

Resulting Science Questions –How does this lack of measurements limit our ability to predict the land surface branch of the global hydrologic cycle? Stream flow is the spatial and temporal integrator of hydrological processes thus is used to verify predicted surface water balances. Unfortunately, model runoff predictions often do not agree with observed stream flow during validation runs.

Solutions from Radar Altimetry Water surface heights, relative to a common datum, derived from Topex/POSEIDON radar altimetry. Accuracy of each height is about the size of the symbol. Topex/POSEIDON tracks crossing the Amazon Basin. Circles indicate locations of water level changes measured by T/P radar altimetry over rivers and wetlands. Presently, altimeters are configured for oceanographic applications, thus lacking the spatial resolution that may be possible for rivers and wetlands.

0 km 20 Solutions from Interferometric SAR for Water Level Changes JERS-1 Interferogram spanning February 14 – March 30, “A” marks locations of T/P altimetry profile. Water level changes across an entire lake have been measured (i.e., the yellow marks the lake surface, blue indicates land). BUT, method requires inundated vegetation for “double-bounce” travel path of radar pulse. These water level changes, 12 +/- 2 cm, agree with T/P, 21 +/ cm.

River Velocity & Width & Slope Measurements Example of measurement of the radial component of surface velocity using along-track interferometry Measure +Doppler Velocity Measure -Doppler Velocity Measure Topography Concept by Ernesto Rodriguez of JPL Basic configuration of the satellite

Global Wetlands Wetlands are distributed globally, ~4% of Earth’s land surface Current knowledge of wetlands extent is inadequate

Saturated extent from RADARSAT - Putuligayuk River, Alaska = wet = dry a. b. c. d.e.

Variable source areas detected from ERS-1/2 Verhoest et al. (1998)

Hillslope-storage dynamics

European contribution to GPM (SRON) 1 core satellite (dual frequency 13.6 / 35 GHz imaging pulsed radar, TMI-like radiometer) 8 constellation satellites (passive microwave radiometers)

River basin storage changes through gravity GRACE: Gravity Recovery and Climate Experiment –Schatten van bergingsverandering in grote stroomgebieden –Horton Research Grant (AGU) AIO onderzoek

Sensitivity of gravity changes to water storage changes 1 Gal = 9,807 m/s 2 Time (days)

Existing Instruments Water Surface Area: –Low Spatial/High Temporal: Passive Microwave (SSM/I, SMMR), MODIS –High Spatial/Low Temporal: JERS- 1, ERS 1/2 & EnviSat, RadarSat, LandSat Water Surface Heights: –Low Vertical & Spatial, High Temporal (> 10 cm accuracy, 200+ km track spacing): Topex/POSEIDON –High Vertical & Spatial, Low Temporal (180-day repeat): ICESat Water Volumes: –Very Low Spatial, Low Temporal: GRACE –High Spatial, Low Temporal: Interferometric SAR (JERS-1, ALOS, SIR-C) Topography: –SRTM (also provides some information on water slopes)

Motivation for Data Assimilation Continued progress in our scientific understanding of hydrological processes at the regional scale relies on making the best possible use of advanced simulation models and the large amount of environmental data that are increasingly being made available. The objective of data assimilation is to provide physically consistent estimates of spa- tially distributed environmental variables. Geophysical data assimilation is a quantitative, objective method to infer the state of the land-atmosphere-ocean system from heterogeneous, irregularly distributed, and temporally inconsistent observational data with differing accuracies, providing at the same time more reliable information about prediction uncertainty in model forecasts. Data assimilation is used operationally in oceanography and meteorology, but in hydrology it is only recently that international research activities have been deployed.

Data assimilation of remote sensing observations (26) (31) (28) (27) (32) (18) (30) (22) (23) (14) (38) (13) ETact SIMGRO ETact SEBAL

Data assimilation of remote sensing observations Rivierenland-project (ICES-KIS3) –Soil moisture measurements and scintillometer to validate RS

Open Research Issues (1) Remote sensing technology provides many types of data that are related to land surface variables of interest to hydrologists. However, very little of this information is available in a form that can be used directly for hydrological purposes. Data assimilation research in hydrology should focus on producing data products that are directly useful for water management. Such products need to be carefully designed to meet the needs of potential users: resolution and spatial configuration of data products; quantitative measures of data product reliability; quality control issues; sensitivity of data products to “hidden” model properties. There is a need to bridge the gap between continental scale data sets (GLDAS) and catchment scale applications (downscaling and parameterization issues).

Open Research Issues (2) Classical hydrological models that have been optimized for use with sparse in situ observations are inadequate for extension to work with remote sensing data. There is a need for developing more appropriate distributed models at catchment scale. More research is needed to develop data assimilation algorithms that can handle the specific problems encountered in hydrological applications: subsurface processes are hard to “observe”; high degree of heterogeneity of physical system; hydrologic systems function over a wide range of temporal scales. Geostatistical techniques for describing multi-scale spatial heterogeneity need to be incorporated into algorithms that account for the multi-resolution nature of different but complementary hydrologic measurements. Case studies are needed to introduce and demonstrate the potential of data assimilation in operational water resources management (e.g. improved flood predictions).

What is needed? Continued investment and coordination of data assimilation initiatives at the European level is urged: wide range of research topics relevant to data assimilation; strong need for innovation in each of these areas; clear potential for water resources modelling and management; transboundary nature of catchments and river basins; need for common algorithms, models, tools, data standards, etc. leading role already demonstrated by European researchers. Expertise from many disciplines will be needed to meet the challenge of data assimilation for improved river basin water resources management: hydrology meteorology remote sensing ecology mathematics (systems theory, statistics) information technology water management etc.