Water resource monitoring systems and the role of satellite observations

Abstract. Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources assessment (AWRA) system aims to achieve this by coupling landscape models with models describing surface water and groundwater dynamics and water use. A review of operational and research applications demonstrates that satellite observations can improve accuracy and spatial detail in hydrological model estimation. All operational systems use dynamic forcing, land cover classifications and a priori parameterisation of vegetation dynamics that are partially or wholly derived from remote sensing. Satellite observations are used to varying degrees in model evaluation and data assimilation. The utility of satellite observations through data assimilation can vary as a function of dominant hydrological processes. Opportunities for improvement are identified, including the development of more accurate and higher spatial and temporal resolution precipitation products, and the use of a greater range of remote sensing products in a priori model parameter estimation, model evaluation and data assimilation. Operational challenges include the continuity of research satellite missions and data services, and the need to find computationally-efficient data assimilation techniques. The successful use of observations critically depends on the availability of detailed information on observational error and understanding of the relationship between remotely-sensed and model variables, as affected by conceptual discrepancies and spatial and temporal scaling.

[1]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[2]  P. Döll,et al.  A global hydrological model for deriving water availability indicators: model tuning and validation , 2003 .

[3]  A. Pitman,et al.  The Relationship between Intermodel Differences and Surface Energy Balance Complexity in the Rhône-Aggregation Intercomparison Project , 2006 .

[4]  Wolfgang Wagner,et al.  Using ENVISAT ASAR Global Mode Data for Surface Soil Moisture Retrieval Over Oklahoma, USA , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Lu Zhang,et al.  Response of mean annual evapotranspiration to vegetation changes at catchment scale , 2001 .

[6]  Keith Beven,et al.  Changing ideas in hydrology — The case of physically-based models , 1989 .

[7]  M. Steven Tracton.  On the impact of radiometric sounding data upon operational numerical weather prediction at NMC , 1977 .

[8]  Peter A. Troch,et al.  Catchment-scale hydrological modeling and data assimilation , 2003 .

[9]  A. S. Ramamoorthi.  Snow-melt run-off studies using remote sensing data , 1983 .

[10]  Ralf Ludwig,et al.  Editorial Remote sensing in hydrological sciences , 2009 .

[11]  F. Castelli,et al.  Use of multi‐platform, multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin, Italy , 2006 .

[12]  Peter Droogers,et al.  Calibration of a distributed hydrological model based on satellite evapotranspiration , 2008 .

[13]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[14]  Wade T. Crow,et al.  A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals , 2008 .

[15]  F. Chiew.  Lumped Conceptual Rainfall‐Runoff Models and Simple Water Balance Methods: Overview and Applications in Ungauged and Data Limited Regions , 2010 .

[16]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[17]  Niko E. C. Verhoest,et al.  Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[18]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[19]  Charlotte Bay Hasager,et al.  Incorporating remote sensing data in physically based distributed agro-hydrological modelling , 2004 .

[20]  T. Gan,et al.  Semi-distributed, physically based, hydrologic modeling of the Paddle River Basin, Alberta, using remotely sensed data , 2001 .

[21]  J. Thielen,et al.  The European Flood Alert System – Part 1: Concept and development , 2008 .

[22]  Ann Henderson-Sellers,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1995 .

[23]  Lifeng Luo,et al.  Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System Project , 2004 .

[24]  Zong-Liang Yang,et al.  The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red–Arkansas River basin experiment:: 1. Experiment description and summary intercomparisons , 1998 .

[25]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[26]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[27]  M. Bierkens,et al.  Assimilation of remotely sensed latent heat flux in a distributed hydrological model , 2003 .

[28]  R. Freeze,et al.  Blueprint for a physically-based, digitally-simulated hydrologic response model , 1969 .

[29]  W. Bastiaanssen,et al.  Constraining model parameters on remotely sensed evaporation: Justification for distribution in ungauged basins? , 2008 .

[30]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[31]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[32]  Stephen J. Burges,et al.  A framework for classifying and comparing distributed hillslope and catchment hydrologic models , 2007 .

[33]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[34]  A. Weerts,et al.  An operational drought forecasting system using coupled models for groundwater, surface water and unsaturated zone , 2011 .

[35]  Roger A. Pielke,et al.  A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology , 1989 .

[36]  Günter Blöschl,et al.  Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria , 2008 .

[37]  Naota Hanasaki,et al.  GSWP-2 Multimodel Analysis and Implications for Our Perception of the Land Surface , 2006 .

[38]  T. Jackson,et al.  Improving Satellite-Based Rainfall Accumulation Estimates Using Spaceborne Surface Soil Moisture Retrievals , 2009 .

[39]  Ray Leuning,et al.  Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia , 2009 .

[40]  Albert Rango,et al.  Operational applications of remote sensing in hydrology: success, prospects and problems , 1998 .

[41]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[42]  Niko E. C. Verhoest,et al.  Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values , 2002, Hydrological Processes.

[43]  A. Dijk,et al.  Selection of an appropriately simple storm runoff model , 2009 .

[44]  A. Dijk,et al.  Climate and terrain factors explaining streamflow response and recession in Australian catchments , 2010 .

[45]  Paul Poli.  Assimilation of satellite observations of the atmosphere , 2010 .

[46]  M. Rodell,et al.  Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin , 2008 .

[47]  A. Dijk,et al.  Satellite mapping of areas evaporating river and groundwater flows , 2010 .

[48]  Lifeng Luo,et al.  A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part I: Implementation and Synthetic Experiment , 2009 .

[49]  Damian Barrett,et al.  Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters , 2008 .

[50]  M. Watkins,et al.  GRACE Measurements of Mass Variability in the Earth System , 2004, Science.

[51]  Micha Werner,et al.  On model integration in operational flood forecasting , 2007 .

[52]  N. Crawford,et al.  DIGITAL SIMULATION IN HYDROLOGY' STANFORD WATERSHED MODEL 4 , 1966 .

[53]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[54]  Z. Su,et al.  Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation , 2008, Sensors.

[55]  Peter Droogers,et al.  Estimating actual irrigation application by remotely sensed evapotranspiration observations , 2010 .

[56]  F. Aires,et al.  Estimation of the aerodynamic roughness length in arid and semi-arid regions over the globe with the ERS scatterometer. , 2005 .

[57]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[58]  J. M. Van Der Knijff,et al.  LISFLOOD : a GIS-based distributed model for river basin scale water balance and flood simulation , 2008 .

[59]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[60]  J. Famiglietti,et al.  Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data , 2010 .

[61]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[62]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[63]  J. Durkin,et al.  GROWING PERIOD AND DROUGHT EARLY WARNING IN AFRICA USING SATELLITE DATA , 1986 .

[64]  Damian Barrett,et al.  On the efficacy of combining thermal and microwave satellite data as observational constraints for root-zone soil moisture estimation. , 2009 .

[65]  E. Small,et al.  A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land‐surface models , 2010 .

[66]  Matthew F. McCabe,et al.  Influence of cracking clays on satellite estimated and model simulated soil moisture. , 2010 .

[67]  D. Lettenmaier,et al.  Assimilating remotely sensed snow observations into a macroscale hydrology model , 2006 .

[68]  IMPACT OF REMOTELY SENSED LEAF AREA INDEX ON A GLOBAL LAND DATA ASSIMILATION SYSTEM , 2001 .

[69]  Albert I. J. M. van Dijk,et al.  TRMM‐TMI satellite observed soil moisture and vegetation density (1998–2005) show strong connection with El Niño in eastern Australia , 2007 .

[70]  David Molden,et al.  Accounting for water use and productivity , 1997 .

[71]  V. Haverd,et al.  CSIRO Marine and Atmospheric Research Component: Final Report for Phase 3 , 2008 .

[72]  Gert A. Schultz,et al.  Remote Sensing in Hydrology and Water Management , 2000 .

[73]  N. DiGirolamo,et al.  MODIS snow-cover products , 2002 .

[74]  D. Vidal-Madjar,et al.  Estimation of Land Surface Temperature with NOAA9 Data , 1992 .

[75]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[76]  Andreas Güntner,et al.  Improvement of Global Hydrological Models Using GRACE Data , 2008 .

[77]  J. D. Tarpley,et al.  Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project , 2003 .

[78]  Edward A. King,et al.  Operational Delivery of Hydro-Meteorological Monitoring and Modeling Over the Australian Continent , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[79]  Paul R. Houser,et al.  An Integrated Hydrologic Modeling and Data Assimilation Framework , 2008 .

[80]  Thomas J. Jackson,et al.  Microwave remote sensing of soil moisture for estimation of profile soil property , 1998 .

[81]  April Spisak.  Grace (review) , 2010 .

[82]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[83]  J. Smith,et al.  Hydrologic modeling of extreme floods using radar rainfall estimates , 2003 .

[84]  D. Lettenmaier,et al.  Hydrologic Sensitivity of Global Rivers to Climate Change , 2001 .

[85]  Taikan Oki,et al.  Assessment of Annual Runoff from Land Surface Models Using Total Runoff Integrating Pathways (TRIP) , 1999 .

[86]  Guillaume Ramillien,et al.  Detection of Continental Hydrology and Glaciology Signals from GRACE: A Review , 2008 .

[87]  Catherine Ottlé,et al.  Sequential Assimilation of ERS-1 SAR Data into a Coupled Land Surface–Hydrological Model Using an Extended Kalman Filter , 2003 .

[88]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[89]  C. Heunks,et al.  Land cover characterization and change detection for environmental monitoring of pan-Europe , 2000 .

[90]  A. Hollingsworth,et al.  Some aspects of the improvement in skill of numerical weather prediction , 2002 .

[91]  Y. Knyazikhin,et al.  Validation and intercomparison of global Leaf Area Index products derived from remote sensing data , 2008 .

[92]  Wolfgang Wagner,et al.  Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa , 2007 .

[93]  John D. Vona,et al.  Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets , 2004 .

[94]  D. Vidal-Madjar,et al.  Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region , 1994 .

[95]  Klaus Scipal,et al.  Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale , 2005 .

[96]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[97]  Giovanni Laguardia,et al.  On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates , 2008 .

[98]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[99]  Peter Droogers,et al.  Comparing evapotranspiration estimates from satellites, hydrological models and field data , 2000 .

[100]  Tim R. McVicar,et al.  Climate‐related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006 , 2009 .

[101]  D. Jones,et al.  High-quality spatial climate data-sets for Australia , 2009 .

[102]  P. Xie,et al.  Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs , 1997 .

[103]  Richard de Jeu,et al.  Analytical derivation of the vegetation optical depth from the microwave polarization difference index , 2005, IEEE Geoscience and Remote Sensing Letters.

[104]  W. J. Shuttleworth,et al.  Integration of soil moisture remote sensing and hydrologic modeling using data assimilation , 1998 .

[105]  Wade T. Crow,et al.  The added value of spaceborne passive microwave soil moisture retrievals for forecasting rainfall‐runoff partitioning , 2005 .

[106]  M. Budyko,et al.  Climate and life , 1975 .

[107]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[108]  W. G. M. Bastiaanssen,et al.  Assimilation of satellite data into agrohydrological models to improve crop yield forecasts , 2009 .