Investigating alternative techniques for incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework

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1 Investigating alternative techniques for incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Tim R. McVicar, Tom G. Van Niel and Albert Van Dijk July 2009 a water information R & D alliance between the Bureau of Meteorology and CSIRO s Water for a Healthy Country Flagship

2 Water for a Healthy Country Flagship Report series ISSN: X The Water for a Healthy Country Flagship aims to achieve a tenfold increase in the economic, social and environmental benefits from water by The work contained in this report is a collaboration between the Flagship and the Bureau of Meteorology. In April 2008, a joint initiative $50m over five years between the Bureau s Water Division and CSIRO s Water for a Healthy Country Flagship was formed to provide the core underpinning research to support the Bureau s role as the national water information provider. For more information about the partnership visit Citation: McVicar, T.R., Van Niel, T.G. and Van Dijk, A.I.J.M. (2009) Investigating alternative techniques for incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework. CSIRO: Water for a Healthy Country National Research Flagship, pp 19. Copyright and Disclaimer 2009 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. Important Disclaimers: CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. The Bureau of Meteorology advises that whilst this report was completed in 2009 it appears that comments from the Bureau on the draft were not provided to CSIRO at this time - and hence the Bureau's perspective is not well represented in the document. It should be noted that in 2013 this report can be considered to be background work but no longer reflects the current approach used in the AWRA modelling system projects of the Water Information Research and Development Alliance (WIRADA) between CSIRO and the Bureau. (The current version of the AWRA System is version 3.0 whilst this report makes reference to AWRA v0.5.) Cover Photograph: Photographer: Randall Donohue 2009 CSIRO

3 CONTENTS Executive Summary... v 1. INTRODUCTION FRAMEWORK CONS IDERATIONS DEFINING KEY HYDROLOGICAL TERMS DEFINING A SEMI-DISTRIBUTED FRAMEWORK APPROACHES OF USING GRIDDED DATA IN A S EMI-DISTRIBUTED FRAMEWORK INCORPORATING RS-BASED SURFACE TEMPERATURE AND GRIDDED METEOROLOGICAL DATA INTO THE EXISTING AWRA v0.5 MODEL STRUCTURE Current heat balance modelling making use of RS-derived Ts and reference surface temperatures (the NDTI processing pathway) Possible simplification to the current NDTI processing pathway Use one-layer model at all locations directly, reducing computational load and the need for interpolation: Define the required reference surface temperatures from the imagery, possibly removing the need for specific time-of-day net radiation and vapour pressure deficit: For measured T dry only: For measured T dry and T wet: CONCLUSIONS REFERENCES Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page iii

4 ACKNOWLEDGMENTS This report is funded by WIRADA, with thank Neil Viney and Warrick McDonald for helpful comments that improved an earlier version of this report. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page iv

5 EXECUTIVE SUMMARY This is a briefing report on Task 1c.1 of the Surface Water Model (Activity 1) of WIRADA project 3.1, Water Resource Assessment and Accounting. The briefing contains an investigation of alternative techniques for incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework. The difference between lumped and fully distributed modelling approaches is discussed, and the implications of a semi-distributed modelling framework are assessed in terms of the use of gridded data. A review of current techniques of the use of the various gridded datasets in semi-distributed frameworks was undertaken. Uses of gridded data are categorised as (FD) forcing data; (P) Parameter surfaces; and observed data for (C) calibration of model parameters; (E) evaluation of model results; or (R) regionalisation of model parameters. Examples of all uses are provided. The current prototype Australian Water Resources Assessment (AWRA version 0.5, denoted AWRA v0.5 herein) system contains a landscape hydrological model that uses AWAP Priestley-Taylor and SILO rainfall as gridded input data, and (among others) MODIS greenness observations as evaluation data. Satellite-observed land surface temperature (LST) contains valuable information about the water balance. Options to potentially improve on this relatively simple approach are discussed, specifically, incorporating LST and additional or alternative gridded meteorological data. A scoping study is recommended to assess the feasibility of some potentially computationally efficient approaches to incorporate LST in ET estimation. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page v

6 1. INTRODUCTION This report is the briefing report for the Task 1c.1 of the Surface Water Activity (Activity 1) of the Water Resource Assessment and Accounting Project (Project 3) of the water information R&D alliance (WIRADA) between the Bureau of Meteorology water division (BoM) and CSIRO Water for a Healthy Country Flagship Program. Initially the briefing scope was stated as Investigation of alternative techniques for incorporating gridded climate and terrain data (including rainfall, land cover, soils, geomorphology) into a semi-distributed modelling framework. Due to the growing use of remotely sensed data in concert with other forms of gridded datasets in semi-distributed hydrologic modelling the role of remote sensing is now also included in the expanded scope of this briefing report. Hence the revised scope of this briefing report is (with additional terms bolded) Investigation of alternative techniques for incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework. To assist investigating alternative techniques a review of current techniques of the use of the various gridded datasets in a semi-distributed framework needs to be undertaken. This is the main focus of this briefing report. Prior to this broader context considering use of a semi-distributed framework and working definitions are provided. The reader is also referred to 2 other WIRADA publications that provide relevant information to the current briefing report. These are: (1) a discussion paper on a national-level catchment and river streamflow model by Viney (2009); and (2) a journal paper about using LAI from remote sensing to define evapo-transpiration and ultimately improve rainfall-runoff modelling over the Murray- Darling Basin by Zhang et al. (In Press). Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 1

7 2. FRAMEWORK CONSIDERATIONS The style of framework considered here primarily aims to support the National Accounting and Assessment goals of BoM s Water Division. A lens of appropriate modelling has been applied to the plethora of the hydrological models and hence frameworks currently available. Slightly altering the definition of Rosbjerg and Madsen (2005), but noting the value if more than one model is used (e.g., Viney, Bormann et al. 2009), here appropriate modelling means the development or selection of a model (or models) with a degree of sophistication that reflects the actual needs for modelling results. For many of the National Accounting and Assessment goals of BoM s Water Division this most likely means coupling surface water models running at daily to weekly time-step with ground water models running at weekly to monthly time-step, with (as appropriate) results amalgamated to seasonal or annual time-step for reporting purposes. Van Dijk (2009, his Section 2) provides reasons why different surface water and ground water models, with different degrees of connectivity, are needed for different landscapes to underpin the National Accounting and Assessment goals. For example, different hydrological models will likely need to be used in the upland catchments compared to the major river system alluvial floodplains (Van Dijk 2009). Furthermore, the idea of appropriate modelling would also apply to long term water resource planning considering climate change (e.g. including behaviour of slow responding storages, increases of atmospheric CO 2 concentrations and its feedback to vegetation and hence hydrological processes), which would also be assessed using models that operate at monthly to annual time-steps. This consideration of appropriate modelling means that sub-daily models (i.e., 12-hour to 1-minute time-steps) or steady state models (i.e., annual average responses) are precluded, and hence will not be considered in the following discussion. The availability of sub-daily data and the computational capacity to process data with such fine temporal resolution over such a large geographic area are the primary constraints to appropriate semi-distributed hydrological modelling (Van Dijk 2009). Also the following discussion does not aim to suggest which models are suited to particular tasks, model names are only mentioned to provide examples of the encapsulation of concepts that readers are likely familiar with to assist with the discussion. 3. DEFINING KEY HYDROLOGICAL TERMS In this briefing report we are discussing hydrological models, where a model here means a set of algorithms (or equations) to predict the dynamics of a hydrological system. Hydrological models vary hugely in complexity, as dynamics may be assessed: (i) only temporally; (ii) only spatially; or (iii) as a combined spatio-temporal response. The temporal dimension may be either retrospective or prospective, and models can be driven by both Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 2

8 thematic and/or continuous data, may be stochastic, deterministic or conceptual in nature, and different temporal and spatial domains are represented across the range of model uses. An example of a very simple model is when recharge to groundwater systems are estimated as being 2% of annual rainfall. On the other hand, an extremely complex ecohydrological model may model all the linked components of the energy, water and carbon balances of a catchment allowing for changing soil properties (in space and time) following re-afforestation or clearing of vegetation. There are five components most modelling approaches require; these are identified with working definitions provided. They are: 1) Forcing data (FD) are the key input data to hydrological models, these vary temporally and may vary spatially (depending on the spatial extent of the modelling exercise to be undertaken). A primary example is meteorological data. This may be the data for one station for a small experimental catchment, or the average of several stations, or the catchment average from a spatially varying surface, or individual grid-cells extracted from the surface. Another example is land cover data when vegetation dynamics are incorporated. 2) Parameters (P) are also key inputs to hydrological models, but they generally do not vary temporally and may or may not vary spatially. Examples include terrain attributes defined from a digital elevation model (DEM), and soil attributes obtained from a soils database, and model parameters these may be measurable or may be mathematical coefficients that govern the rates of transfer or storage sizes in more conceptual models. If vegetation dynamics are not considered important then in such cases only one instance of land cover data are used in the modelling, and as defined here, in this case land cover data would be considered a parameter (as it does not vary with time). 3) Calibration (C) involves altering the tunable model parameters so that model estimates optimally replicate the dynamics of an observed variable(s). In stream flow modelling the observed variable is usually the observed stream flow at catchment outlet (noting they can be nested). Stream flow observations are an integrated response from many interactions between the meteorological conditions and landscape (slope, aspect, vegetation, soil and geology) attributes found in the contributing area (i.e., the upslope area assuming no intercatchment ground water transfer) to the location of the stream flow gauging station. As the response is spatially integrated, recently there is a growing use of spatially varying remotely sensed observation of variables such as soil moisture, surface temperature and vegetation characteristics to assist with the spatial calibration (see examples in Kalma, McVicar et al. 2008). To do this requires that the hydrological model provides some estimate of the Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 3

9 variables that can be observed remotely, and this is more in the realm of a land-surface model as opposed to, for example, a lumped rainfall-runoff model (see for example Van Dijk 2009). Other forms of calibration exist such as spatially isolated observations of soil moisture and other variables such as yield data mainly from crops and forests may be used (assuming conversions between harvested biomass and water use can be developed with required accuracy). Here we consider optimisation to be a subset of calibration, where optimisation means numerically and objectively minimising the difference (or maximising the agreement) between a modelled and (usually the same) observed variable. An additional distinction can be made for multi-objective optimisation. This is the process of optimising against a number of objective functions in order to better calibrate the parameters by using the trade-offs between the various objectives. The idea being that by using multiple objectives, the calibration should not be overly influenced by a single characteristic related to the single objective function, but rather more balanced by being influenced by many objective functions, each emphasising different catchment behaviours. 4) Evaluation (E) is an independent assessment from the data used for calibration: evaluation assesses how well the model estimates and replicates the dynamics of an observed variable. When using a time series of stream flow observations, it is common to use part (e.g., the first 5 to 10 years) of the series for model calibration and the remaining part for model evaluation. A common concern for hydrological modelling is that due to changes in variables that impact the hydrological processes the results from model calibration may not be representative for the period of model evaluation. Examples of such changes include, but are not limited to: (i) a variable climate, meaning model calibration was performed in a wet or dry period only; (ii) a changing climate; and (iii) varying landuse and land management practices. For accounting and assessment of retrospective and current conditions this issue can be avoided, in part, by performing the calibration on part 1 of the series and evaluation on using part 2, and vice versa. With differences in evaluation statistics and calibration parameters for the two different options provided information on model stability, in a sensitivity analysis style framework. The concern about non-stationarity being adequately captured in model calibration is particularly acute when considering scenario (or prospective) modelling (e.g., for water resource planning with 30-, 50-and 100- year time frames) where issues such as structural (e.g., canopy cover) and physiological (e.g., water use efficiency) changes are likely to reduce the confidence in the applicability of the long-term scenario. 5) Regionalisation (R) is the process of spatially and/or temporally extending the hydrological model parameters. As noted previously (when defining 'calibration') observed stream flow data are the primary form of data used for calibration and evaluation of stream flow models. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 4

10 However not all streams are gauged, and hence there is the need to extend the required model components to areas outside the area covered by the contributing areas of stream gauges. Considering the spatial extent of the goals of the Water Division, forcing data and parameters derived from spatial data (e.g., terrain attributes and soil parameters) are usually continental and hence do not have to be made regional. However, the 'tuneable' model parameters are not known everywhere and hence need to be made regional and there are a variety of approaches to extend these beyond the catchments where they were derived. Several of the regionalisation approaches can use the spatial variation in gridded datasets from one or more of the following: remote sensing; climate; and terrain. While this task has sometimes been called parameter estimation we avoid this term as to some readers it may mean inverting model parameters in catchments where stream flow is measured, and here we wish to make the clear distinction that regionalisation means extending model parameters to catchments where stream flow is not observed. 4. DEFINING A SEMI-DISTRIBUTED FRAMEWORK As there is no agreed definition of what a 'semi-distributed framework' means, its working definition is provided after discussing lumped and distributed approaches, which have clearer definitions. In some semi-distributed approaches the modelling engine may be a lumped model, rarely is a fully distributed model run in a semi-distributed framework. Lumped models are those working at the catchment scale, where the explicit spatial representation below that of the catchment resolution is not considered. These models incorporate rainfall loses (e.g., interception) and use a series of stores connected with transfer functions to move water to the stream. Common rainfall-runoff models that fit into this category are SimHYD, Sacramento and AWBM. Other than using catchment boundaries (usually defined from a DEM) to calculate catchment-average forcing meteorological data (usually precipitation and some formulation of potential evapotranspiration from spatially extensive surfaces) this class of model has little reliance on spatial gridded data. It should be noted that in many instances this type of model was developed using point observations of forcing meteorological data for small experimental catchments. By being small it was usually assumed that there was low spatial variability in the forcing meteorological data and hence the catchment-average could be represented by this observation. However this is not always the case. Some lumped frameworks, such as the Budyko model have their conceptual basis intrinsically linked to large processes and attempt to model steady-state conditions (Donohue, Roderick et al. 2007). Other lumped models, such as those simulating the hydrological impact of re-vegetation packaged in the ReVegIH decision support system (McVicar, Li et al. 2007), rely on information (i.e. current fraction of the catchment forested) Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 5

11 primarily derived from remote sensing data, again the sub-catchment spatial arrangement is not considered. On the other end of the range of model complexity lie the distributed hydrological models. In such models, parameters and variables are needed for every discrete element across the catchment, and the spatial arrangement of these elements is considered. Discrete elements can be either: (i) an arrangement of uniform grid-cells projected horizontally over the catchment; or (ii) vector based areas such as those delineated on topographic attributes only or hydrological response units (HRUs). A series of HRUs for a catchment is defined by the spatial intersection of various landscape attributes such as topography, soils, vegetation and climate (or depending on data availability a subset of these attributes). For each attribute the within-class variation is assumed to be smaller than the between-class variation. Distributed models include TOPOG and TOPMODEL (the elements in both are represented using vectors and are primarily determined, as the model name in each case implies, from topographic assessment of the catchment) and the MIKE-SHE model where the modelling is performed on a grid-cell basis, with attributes for each grid-cell usually stored in raster format in different datasets. A key feature distinguishing distributed models from other types of models is that the processes governing water movement within a hill-slope are explicitly described. This means detailed assessment of flow paths and degree of connectedness between surface water and ground water processes down a hill-slope are explicitly modelled in such systems they are computationally expensive and are best suited to small (< 10 km 2 ) catchments. To optimally parameterise these models topographically, this means that topographic information (usually in the form of a DEM) needs to be higher resolution than the average hill-slope length in a study area. Also good quality soil parameters and vegetation related parameters are desired to improve the confidence in the modelling results. Semi-distributed models fill the gap between lumped models and the distributed models. In semi-distributed models, while spatial variability below the resolution of the catchment is considered the spatial arrangement may, or may not, be considered. The following two cases are examples where the spatial arrangements below the resolution of the catchment are not considered. A simple way of using a lumped model in a semi-distributed framework is to use the model with catchment-averaged forcing meteorological data this averaging may be performed on a network of stations (or points ) located in the catchment or calculated from an extensive surface of gridded meteorological data intersected with the catchment boundary. Another relatively straight forward manner or applying a lumped model across a catchment is using the model at each grid-cell within an extensive surface of gridded meteorological data, with the grid-cell resolution being defined by the resolution of forcing meteorological data. In this approach, flow from one grid-cell to another is not Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 6

12 considered and both of these examples of semi-distributed frameworks are closely related to the lumped model end of the spectrum (indeed a lumped model is the engine). In the second example, a major issue is how to amalgamate the runoff modelled from each grid-cell in the catchment to stream flow from the catchment. Thresholds for accuracy of this amalgamation process are application specific. For example, if looking at flooding in an urban context would require this amalgamation process to be modelled more accurately than if the application is modelling inflow to large storages for use in annual (or seasonal) time-step water accounts. The semi-distributed HBV model, for example, addresses this issue by incorporating a lag response via a triangular unit hydrographic response. In other applications of semi-distributed frameworks, the spatial arrangement of elements below the catchment resolution is explicitly considered by modelling the hydrological response considering the connectedness between spatial elements. An example of this is the Macaque model (Watson, Vertessy et al. 1999) where the catchment is divided into elements of similar topographic wetness index (essentially an index of contributing area for each grid-cell) for each unique hill-slope. At each model time-step lateral flow between the elements is considered based on total saturated water of the hill-slope and the wetness index. This is an example of a semi-distributed model that has close links to distributed models, in that within hill-slope processes are modelled. Another example of a semidistributed model where the spatial arrangement of elements below the catchment resolution is considered is the LASCAM model (Viney and Sivapalan 2001) here the smaller spatial elements are sub-catchments, with the stream flow from the sub-catchments routed through the river network to define stream flow from the catchment. In these two examples of semidistributed frameworks, the contrasting level of connectedness of the spatial arrangement of elements below the catchment resolution should be noted as: (i) for Macaque hill-slope processes are parsimoniously modelled; whereas in (ii) LASCAM sub-catchments are modelled in a lumped manner with routing between these elements via the river network providing the connectivity. 5. APPROACHES OF USING GRIDDED DATA IN A SEMI- DISTRIBUTED FRAMEWORK As seen in the above section, there are many potential ways that gridded data can be used in a semi-distributed framework, in this section we provide generic examples structured within the context of the 5 previously introduced modelling steps (outlined above in Section 4) of how this can be performed. Here remembering that gridded data means remotely sensed, gridded meteorological surfaces and terrain data. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 7

13 Table 1. Listing of approaches illustrating how gridded data can be used in a semidistributed rainfall runoff modelling framework. The abbreviations for the above defined model components are: Forcing data (FD); Parameters (P); Calibration (C); Evaluation (E); and Regionalisation (R). Case Model Component Details 1 FD remotely sensed estimates of precipitation (and associated error surfaces) used as input to lumped models. Allows the propagation of arguably the most important variable to hydrological models to be quantified and spatially assessed. 2 FD remotely sensed estimates of albedo (used in shortwave calculations) and / or fractional vegetation cover (used in longwave calculations of emissivity) used to drive surfaces of net radiation which are used to calculate potential evapotranspiration in lumped models. 3 FD remotely sensed estimates of vegetation dynamics such as persistent (~ deeper rooted) and recurrent (~ shallower rooted) vegetation linked to a landscape ecohydrological model. 4 FD using a lumped model with catchment-averaged meteorological data acquired from more extensive surfaces of meteorological data 5 FD using a lumped model in a grid-cell basis from more extensive surfaces of meteorological data 6 P classifying remotely sensed data to provide land cover information of trees and non-trees, and then determining the proportion of the catchment that is in each land cover type 7 P intersect the catchment boundary with soils data and run a lumped model with catchment-averaged soil properties and catchment-averaged meteorological data 8 P intersect the catchment boundary with soils, terrain and vegetation data to develop HRUs and run a lumped model for each unique combination of attributes using catchment-averaged meteorological data 9 P intersect the catchment boundary with soils, terrain and vegetation data to develop HRUs and for each resultant polygon run a lumped model using polygonaveraged meteorological data 10 FD/P - using a lumped model in a grid-cell basis from more extensive surfaces of meteorological data with parameters (where possible and relevant) extracted from soils, terrain and vegetation data. While seemingly distributed the lumped model does not allow for lateral flow of water (or energy). 11 P/C linking dynamics of modelled state variables that are also observable using remote sensing. Likely allowing for refining of selected parameter sets by optimising model calibration (to raster data only, or to raster data and observed stream flow data). Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 8

14 12 P/C/E linking dynamics of multiple modelled state variables that are also observable using remote sensing. For example, a suitably complex land system model could allow for modelling of vegetation amount, evaporative fractions and upper layer soil moisture. Given these can be sensed using reflective, thermal and microwave remote sensing opportunities exist to use a subset of these to parameterise and calibrate the model, while simulating the withheld data to validate the model with (dynamically using spatial and temporal variability) (e.g., Barrett and Renzullo 2009). Performing this simulating one data source in a structured approach allows the information content in the model of each of the remotely sensed data types to be assessed. 13 R using similarity indices of functional properties observed from remote sensing (such as seasonally specific integration of evaporative fraction and, possibly lagged, vegetation response) to interpolate similar functional properties away from gauged catchments to ungauged catchments. Following this generic discussion of approaches to incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework, the following section provides specific discussion regarding how to best use land surface temperature in semidistributed water balance models. 6. INCORPORATING RS-BASED SURFACE TEMPERATURE AND GRIDDED METEOROLOGICAL DATA INTO THE EXISTING AWRA v0.5 MODEL STRUCTURE The AWRA v0.5 system currently has been evaluated against, and can potentially be updated with, remotely sensed optical vegetation status (EVI, NDVI, FPAR), active or passive microwave observations of soil surface wetness (AMSR-E, TRMM, ENVISAT ASAR GM) and/or total water storage (GRACE). Currently the ET model does not explicitly make use of the heat balance and therefore does not estimate nor incorporate RS-based surface temperature (T s ). Instead it uses Potential ET as defined by Priestley & Taylor (P-T PET) and scales this depending on moisture availability. In this section we discuss ways this can be optimally achieved considering the context of the applicable modelling introduced previously. If the ET model was to make use of the heat balance via T s, a number of developments would be required: 1. The current P-T PET product would probably not be used, but meteorological fields would need to be sourced instead. These include some or all of net radiation (R n ), Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 9

15 ground heat flux (G), air temperature (T a ), vapour pressure deficit (D) and wind speed (u) (noting that R n would already be used to define P-T PET). 2. A suitable background model should be able to estimate ET from meteorological inputs alone, but would be required to make use of various spatial and temporal estimates of terms in the heat balance. Most importantly for this discussion, it should ingest RS-based estimates of T s when they are available. Additionally, if soil moisture or vegetation density and structure data exist, these should also be able to be used. 3. Likewise, the model should be tunable to multiple sources of heat balance observations such as observed latent or sensible heat at flux towers (although given how few flux towers there are in Australia, tuning a model with flux tower data is currently fairly limited). 4. A suitable and close to operational RS-based T s product would be needed. 5. The ET model would need to be extended to consider the full heat balance to provide forward estimates of T s and other heat balance terms. 6. Special consideration to Time of Day (ToD) will be required as RS thermal data are acquired instantaneously. This will impact on the use of daily meteorological variables and on how the RS T s data are ingested into the background model, this topic has been researched previously (McVicar and Jupp 1999). Of these issues, the one we will discuss in detail is the incorporation of RS-based Ts observations into an ET model (the second point above). Kalma et al. (2008) describe several options for incorporating thermal RS data into an ET model. These include, but are not limited to: 1. Direct use of satellite observed T s into a full heat balance model. Most commonly, a resistance energy balance model (REBM) is used. If a two-layer REBM is used, e.g., (Friedl 2002; Jupp, Tian et al. 1998; McVicar and Jupp 1999; McVicar and Jupp 2002) then there is a high computational cost. If a one-layer REBM is used, the computational cost is much lower, but there is a risk of large errors when the surface temperature decouples from the heat balance, which can happen in mixed soil and vegetation areas where the soil temperature is very different from the vegetation or when the skin temperature is very different to the mid canopy temperature (Friedl 2002). 2. Optimisation of key heat or water balance parameters from the remote sensing data. For example, empirical estimation of spatial aerodynamic resistance (r a ) properties using a variational method over a longer time period (that is, finding optimal spatial resistance values that agree with observed T s (see Raupach, Briggs et al. 2008). 3. Using RS-derived T s, some form of reference surface temperature, and gridded T a. These methods include heat balance inversion approaches, which can make use of Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 10

16 theoretical reference surface temperatures (McVicar and Jupp 1999) or purely empirical methods that define hot /cool end members in combination with a RSderived VI (Carlson, Capehart et al. 1995). These approaches could be combined with a simple statistical method of nudging the ET model towards improvement. From the above list, we concentrate our discussion on numbers 1 and 3 and defer #2 until some other time. It is mentioned here for completeness as it could be a useful method to inspect in the future. As far as AWRA v0.5 is concerned, given similar results, the most relevant ET model should be the simplest one available, while still maintaining a physical basis in the heat balance. Approach #1 may be too computationally and parameter intensive for the two-layer method to be implemented at every location across an image, however this has not been tested. Likely, the required data exist to perform a complete one-layer REBM for the AWRA v0.5 scheme without too much computational cost. It should be noted that the 3 approaches above are not mutually exclusive and the resultant ET model could likely merge some ideas of each of the three (and others not specifically mentioned). For example, the Normalised Difference Temperature Index (NDTI) is actually a result of combining a full implementation of a two-source REBM (#1 above) at meteorological station locations with an inversion of the heat balance using theoretical reference surface temperatures and RS-derived T s (#3 above). Although the current processing pathway is not likely appropriate for AWRA v0.5, it could be possible that either developing an enhanced system through software engineering support or some form of simplification of this process could be well suited. The latter is discussed here. We describe these possible adjustments in a general sense below and define some potential benefits and costs involved. Firstly, we describe generally, the current NDTI processing Current heat balance modelling making use of RS-derived Ts and reference surface temperatures (the NDTI processing pathway) The theory behind the use of reference surface temperatures in the heat balance is summarised by (Jackson, Idso et al. 1981; Jones 1999; Jupp, Tian et al. 1998; McVicar and Jupp 1999; McVicar and Jupp 2002). The main difference is whether the reference temperature(s) are derived through thermodynamic theory (Jupp, Tian et al. 1998) or by direct measurement (Jones 1999). When directly measuring the reference temperature, the surface must be representative of the surface which is being modelled (Leinonen, Grant et al. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 11

17 2006). If this is the case, then a great number of simplifications can be performed, while modelling within the heat balance. When the reference surface is not a good representation of the surface to be modelled, then the simplifications are less valid. Defining representative surfaces is rather easier when the surface is a single leaf (Jones 1999) and more complicated when composite surfaces across a wide geographic extent covering various terrain slopes and aspects, elevations, vegetation types and amounts, and air temperature and wind speeds, to name just a few are being considered from satellite remote sensing. For satellite remote sensing applications concerned with catchment scale ET modelling, two solutions prevalent in the literature to this problem are: (1) define the reference surface temperature(s) theoretically at locations where meteorological data exist and then spatially interpolate the results across the catchment (McVicar and Jupp 2002); or (2) define the reference surface temperature(s) from the imagery. The NDTI process uses the first and is discussed below, but we discuss the second option further on. The current NDTI processing relies on 4 general steps: Two-layer REBM calculations at meteorological stations at the time-of-day that a remote sensing thermal image is acquired. Given that many meteorological stations record daily extremes and integrals, methods have been developed to allow the specific time-of-data to be calculated from the daily data (McVicar and Jupp 1999). This provides a greater spatial density of stations where the REBM calculations are performed, and is critical to providing enough observations to allow the NDTI to be spatially interpolated. These results are inverted through the heat balance to define two reference surface conditions at the meteorological points: o T wet ( C) is the hypothetical surface temperature if the surface was saturated with water or wet ; and o T dry ( C) is the hypothetical surface temperature if the surface (and by implication, the root zone) was totally dry and therefore unable to accommodate ET. T wet and T dry is combined with the T s defined by the two-layer modelling to determine the NDTI, which is equivalent to the evaporative fraction (still only at meteorological points). The evaporative fraction (i.e., the NDTI) is then spatially interpolated mainly using the Ts derived from the remote sensing image, Ta spatially and temporally interpolated across the landscape at the time of the image, and vegetation condition also determined from the satellite imagery. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 12

18 o o The evaporative fraction (NDTI) is interpolated because it is a normalised index between 0 and 1, and is thus more suitable for interpolation than surface resistance, which is exponential in nature. If a suitable estimate of PET exists, then the evaporative fraction (NDTI) can be inverted again to define the various heat balance terms, such as surface resistance, or it can simply be multiplied by PET to give the estimate of actual ET across the landscape. These clever, but somewhat indirect and computationally expensive processing steps were implemented in favour of a more direct and computationally inexpensive one-layer model (that would calculate surface resistance directly at every point in the landscape) because it was deemed that at various times, the discrepancies between the soil and vegetation components of the surface would cause a significant decoupling between the composite surface temperature observed from the remote sensing data and that which solves the sensible heat term of the heat balance (Jupp and Kalma 1989). In theory the interpolated NDTI data should not suffer from this de-coupling effect as much as the direct implementation of the one-layer model would. However, as stated above, this comes at both a computational cost, and less process control away from the meteorological points. The basic steps are given below for defining reference surface temperatures by theoretical inversion of the heat balance (Jones 1999; Jupp and Kalma 1989; McVicar and Jupp 2002). For the current NDTI procedure, after the two layer model is run at the meteorological points at the time of the satellite overpass, the reference surface temperatures are defined. Firstly, the temperature associated with a saturated or wet surface ( T, C) wet is defined as (Jones 1999) T wet ( ) p ( γ + ) r r γ R G r DρC HR aw ni HR p = + ρc r r aw HR T a (1) where R ni (W m -2 ) is the isothermal net radiation, G (W m -2 ) is the ground heat flux, or the change in the heat energy stored in the ground, D (kpa) is the vapour pressure deficit, ρ ( kg m -3 ) is the mean air density at constant pressure, and C p (J kg -1 C -1 ) is the specific heat of air, Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 13

19 T a ( C) is the near-surface air temperature, Δ (kpa C -1 ) is the slope of the temperature-saturation vapour pressure relationship, γ (kpa C -1 ) is the psychrometric constant, r aw (s m -1 ) is the resistance to transport of water between the surface and the reference height, and is assumed to be equal to r ah, r HR (s m -1 ) is the parallel resistance to the transfer of sensible heat and radiative transfer and is given by r ah r HR rahrr = rah + r R, (s m -1 ) is the resistance to transport of sensible heat between the surface and the reference height, and r R (s m -1 ) is the resistance to long-wave radiative transport. The hypothetical surface condition when the surface is completely dry ( T, dry C) is also known as the infinite state because the effective surface resistance ( r sw ) for this situation is infinite (Jupp, Tian et al. 1998). The temperature associated with this totally dry surface is defined as (Jones 1999) rhr ( Rni G) (2) Tdry = + Ta ρcp Jupp et al (1999) show that the ratio of these dry and wet temperatures along with an estimate of the surface temperature can be written in the following form to represent the evaporative fraction Tdry Ts λe (3) NDTI = T T λe where λ E (W m -2 ) is the latent heat flux (evapo-transpiration), and dry wet λ E 0 (W m -2 ) is the latent heat flux under water saturated surface conditions (PET). 0 For the current NDTI processing, the NDTI is spatially interpolated across the entire image using a thin plate spline using three remotely sensed based co-variates; they are: (i) net radiation at the time of the overpass; (ii) T s -T a, again at the time of the overpass; and (iii) vegetation cover. The last covariate only uses remotely sensed information, the second only uses remotely sensed data and estimates of air temperature at the time of the overpass Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 14

20 (temporally interpolated from daily extremes and then spatially interpolated), the first uses a combination of remotely sensed data, temporally and spatially interpolated meteorological fields and radiative process modelling). In order to retrieve the heat balance information, after interpolation, the surface resistance can be determined as r sw + γ 1 = raw 1 γ NDTI (4) This surface resistance can be used directly in the Penman-Monteith (P-M) equation, or otherwise, if a spatial version of an appropriate measure of potential ET exists, the latent heat flux can be directly derived as ( λ ) λe = NDTI E 0 (5) 6.2. Possible simplification to the current NDTI processing pathway Use one-layer model at all locations directly, reducing computational load and the need for interpolation: A possible modification of the NDTI approach might be possible and more suitable to the AWRA v0.5 scheme. All previous applications of the NDTI method have only applied the heat balance inversion from a two-layer REBM at meteorological points and then made the estimates spatial by interpolating them across the landscape. A very large computational simplification would be to invert the heat balance via a one-layer REBM everywhere spatially instead. This would allow for the extension of the full heat balance model to be applied across space thus eliminating the need for spatial interpolation of the two-layer REBM. However, the down side of doing this is that in the one layer model, T s decouples when soil temperature is much higher than vegetation temperature (or when skin temperature is much different to mid-canopy temperature) and this is what causes the large difference between RS-derived T s and the T s that solves the heat balance. Some other way to identify and correct for this de-coupling would need to be defined. We suggest that the use of the surface reference temperatures could be used to identify these areas. Without this identification and correction of the de-coupling, it would not be a modification of the NDTI approach, but rather just simply a one-layer REBM. The minimum forcing data requirements (besides T s from the remote sensing data) for this model that would need to be available in some form at every pixel and be representative of the time-of-day would be: (1) R ni ; (2) G; (3) T a ; (4) u; and (5) D. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 15

21 Define the required reference surface temperatures from the imagery, possibly removing the need for specific time-of-day net radiation and vapour pressure deficit: Furthermore, additional simplifications can be obtained if the reference surface temperatures could be acquired from the imagery itself with consideration to geographic and land cover conditions. By deriving the reference surface conditions on the imagery itself, it may remove the requirement of some gridded meteorological data. One problem with this approach is that it adds subjectivity to the processing, which could be propagated through the model. Though these would be defined in a repeatable manner, and they would be programmatically developed. In particular, the major concern is that when identifying the reference surface temperatures on the image, they must be representative of the surface which is being modelled (Leinonen, Grant et al. 2006). As stated above, defining representative surface temperatures is complicated from satellite remote sensing. For the case of spatial data it is probably quite important to considering geographic and land surface conditions. The endmember approach is often used to define the reference surface temperatures from within the image itself (Carlson, Capehart et al. 1995) by investigation of the histogram in (T s T a ) vs. VI data space. There are problems with this logic as cool or hot temperatures in one area over a certain vegetation type at one elevation and a certain aerodynamic resistance will not likely be relevant to surface temperatures elsewhere and especially over a different vegetation type or elevation. This method also loses the direct link to the heat balance, which is important for theoretical rigour and model transferability and transparency. If an automated method for defining reasonable reference surface temperatures could be implemented, then it would considerably simplify the processing and be quite suited to AWRA v0.5. We consider two cases below For measured T dry only: When T dry is obtained from the imagery somehow, then R ni can be obtained from it (Guilioni, Jones et al. 2008) R ni p ( dry a ) ρc T T G = r HR (6) Once R ni and G are acquired, then the surface resistance can be defined as r sw = r HR ( ) T T + D γ s a ( Tdry Ts ) (7) Again, surface resistance can be used to calculate latent heat flux from the P-M equation. The minimum forcing data requirements (besides T s from the remote sensing data) for this Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 16

22 model that would need to be available in some form at every pixel and be representative of the time-of-day would be: (1) T a ; (2) u; and (3) D For measured T dry and T wet : When T dry and T wet are obtained from the imagery somehow, then R ni can be obtained from equation (6) and D can be obtained as raw D= γ T T + T T rhr ( dry wet ) ( a wet ) (8) Once R ni is obtained, G is either ignored or modelled, and D is obtained as above, then the surface resistance can be defined as in equation (7). The minimum forcing data requirements (besides T s from the remote sensing data) for this model that would need to be available in some form at every pixel and be representative of the time-of-day would be: (1) T a ; and (2) u. The benefits of requiring so many less input forcing datasets may warrant inspection of how to define T dry and T wet from the imagery in an appropriate manner. 7. CONCLUSIONS In order to ensure that we can test the viability and value of using T s for the ET model of AWRA v0.5 within a year s time, we propose that some or all of the following major steps be inspected: Scope implementation of a non-interpolated 2-layer model everywhere using Friedl (2002), and if viable, implement the full 2-layer model. Apply one-layer REBM to estimate ET a using 5 meteorological forcing data sets. Compare one-layer REBM to current interpolated 2-layer NDTI ET a, and 2-layer noninterpolated Friedl (2002) model if it is implemented. o From this comparison, define recommendations. There are also a number of more targeted steps that would need to be inspected with respect to the time-of-day meteorological variables. From the above discussion, it is evident that there are a few core data sets that would be required for specific time of day ET modelling using T s from remote sensing. We propose that the 5 data sets to implement the full REBM should be researched: (1) R ni ; (2) G; (3) T a ; (4) u; and (5) D, but priority should be given to T a, R ni, and D: Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 17

23 Enable specific time-of-day estimates of T a using BAWAP/SILO data using NDTI temporal interpolation logic, and assess options for extending this using higher temporal resolution BoM stations. Finalise writing and testing of specific time-of-day R ni code. Compare utility of NDTI vapour pressure estimate based on T dew = T min and the BAWAP vapour pressure surface and BAWAP T max and T min saturated vapour pressure calculations. Finally, given the potential computational benefits and simplifications specified above by deriving reference surface temperatures from the imagery, it may also be advisable to: Investigate methods for estimating T dry and/or T wet from RS-based thermal imagery. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 18

24 8. REFERENCES Barrett DJ, Renzullo LJ (2009) On the efficacy of combining thermal and microwave satellite data as observational constraints for root-zone soil moisture estimation. Journal of Hydrometeorology In Press. Carlson TN, Capehart WJ, Gillies RR (1995) A New Look at the Simplified Method for Remote Sensing of Daily Evapotranspiration. Remote Sensing of Environment 54, Donohue RJ, Roderick ML, McVicar TR (2007) On the importance of including vegetation dynamics in Budyko's hydrological model. Hydrology and Earth System Sciences 11, Friedl MA (2002) Forward and inverse modeling of land surface energy balance using surface temperature measurements. Remote Sensing of Environment 79, Guilioni L, Jones HG, Leinonen I, Lhomme JP (2008) On the relationships between stomatal resistance and leaf temperatures in thermography. Agricultural and Forest Meteorology 148, Jackson RD, Idso SB, Reginato RJ, Pinter Jr. PJ (1981) Canopy temperature as a crop water stress indicator. Water Resources Research 17, Jones HG (1999) Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant, Cell & Environment 22, Jupp DLB, Kalma JD (1989) Distributing Evapotranspiration in a Catchment Using Airborne Remote Sensing. Asian-Pacific Remote Sensing Journal 2, Jupp DLB, Tian G, McVicar TR, Qin Y, Fuqin L (1998) 'Soil Moisture and Drought Monitoring Using Remote Sensing I: Theoretical Background and Methods.' CSIRO Earth Observation Centre, Canberra. Kalma J, McVicar TR, McCabe M (2008) Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data. Surveys in Geophysics 29, Leinonen I, Grant OM, Tagliavia CPP, Chaves MM, Jones HG (2006) Estimating stomatal conductance with thermal imagery. Plant, Cell and Environment 29, McVicar TR, Jupp DLB (1999) Estimating one-time-of-day meteorological data from standard daily data as inputs to thermal remote sensing based energy balance models. Agricultural and Forest Meteorology 96, McVicar TR, Jupp DLB (2002) Using covariates to spatially interpolate moisture availability in the Murray-Darling Basin A novel use of remotely sensed data. Remote Sensing of Environment 79, McVicar TR, Li LT, et al. (2007) Developing a decision support tool for China s re-vegetation program: Simulating regional impacts of afforestation on average annual streamflow in the Loess Plateau. Forest Ecology and Management 251, Raupach MR, Briggs PR, Haverd V, King EA, Paget M, Trudinger CM (2008) 'Australian Water Availability Project (AWAP) CSIRO Marine and Atmospheric Research Component: Final Report for Phase 3.' Canberra. Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 19

25 Rosbjerg D, Madsen H (2005) Chapter 10: Concepts of Hydrological Modeling. In 'Encyclopedia of Hydrological Sciences'. (Ed. MG Anderson). (Wiley and Sons, Ltd) Van Dijk AIJM (2009) 'Landscape hydrology model for an Australian Water Resources Assessment system. A report for the Bureau of Meteorology (In Review).' Water for a Healthy Country Flagship, Canberra, Australia. Viney NR (2009) 'Development of a catchment and river streamflow generation model for national water resources assessment: Discussion paper prepared for the Bureau of Meteorology.' CSIRO Water for a Healthy Country Flagship. Viney NR, Bormann H, et al. (2009) Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions. Advances in Water Resources 32, Viney NR, Sivapalan M (2001) Modelling catchment processes in the Swan-Avon river basin. Hydrological Processes 15, Watson FGR, Vertessy RA, Grayson RB (1999) Large-scale modelling of forest hydrological processes and their long-term effect on water yield. Hydrological Processes 13, Zhang YQ, Chiew FHS, Zhang L, Li H (In Press) Use of remotely sensed actual evapotranspiration to improve rainfall-runoff modeling in southeast Australia. Journal of Hydrometeorology doi: /2009JHM Incorporating remote sensing, gridded climate and terrain data into a semi-distributed modelling framework Page 20

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