Parameterisation and scaling of the land surface model for use in a coupled climate-hydrological model

Journal of Hydrology 426–427 (2012) 63–78

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Parameterisation and scaling of the land surface model for use in a coupled climate-hydrological model S.H. Rasmussen a,⇑, M.B. Butts b, S.M. Lerer b,1, J.C. Refsgaard c a

Danish Meteorological Institute, Lyngbyvej 100, DK2100 Copenhagen East, Denmark DHI, Agern Allé 5, DK2970 Hørsholm, Denmark c Geological Survey of Denmark and Greenland (GEUS), Øster Voldgade 10, DK1350 Copenhagen K, Denmark b

a r t i c l e

i n f o

Article history: Received 24 February 2011 Received in revised form 4 January 2012 Accepted 14 January 2012 Available online 26 January 2012 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Aiguo Dai, Associate Editor Keywords: Soil vegetation atmosphere transfer (SVAT) Upscaling FIFE MIKE SHE

s u m m a r y Climate impact studies in hydrology have traditionally neglected the land–atmosphere feedback. Hydrological models are forced with output from climate models but neglecting this feedback may lead to an inaccurate estimation of evapotranspiration (ET). Two-way coupling of a hydrological model and a climate model can overcome this problem by linking the two models through a shared land–atmosphere process description. In this study we analyse the hydrological model MIKE SHE using a two-layer energybased ET model for use in a coupling with a regional climate model (RCM). The value of coupling to MIKE SHE is that it makes it possible to include lateral transport of surface water and groundwater not generally treated in RCM’s and to represent human interventions like groundwater pumping, irrigation schemes, etc. for adaptation studies. The hydrological model is applied to the FIFE site to investigate the effects of model resolution and parameter scales. The area of interest corresponds to a RCM grid cell. The hydrological model is parameterized with effective parameters assessed directly from field data at the site and literature. Using only these observed data and literature estimates to parameterise the model, it is able to reproduce the observed ET, sensible heat flux and to some extent surface soil moisture content; over a whole growing season. Hydrological simulations carried out over a range of spatial grids from 240 m to 15 km show that, for this case, the areal average ET appears to be insensitive to model resolution. The model is able to reproduce some of the spatial variability within the area, but not the exact pattern. By running the hydrological model at the highest resolution with uniform atmospheric input we examined the effect of using coarser resolution climate forcing, for example from a RCM. The areal mean ET and soil moisture (SM) temporal variations are reproduced quite well, but the spatial variability in the hydrological response is substantially underestimated; mainly because of the uniform precipitation. Our results are therefore encouraging for using this type of energy-based model in a coupling between a regional climate model and a distributed hydrological model. As the FIFE area is a relatively homogeneous site, additional tests are needed at heterogeneous sites to validate whether our findings are in general valid. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction Traditionally, climate change impacts in hydrology, runoff or groundwater, for example, have been simulated by forcing hydrological models with output from general circulation models (GCMs) or regional climate models (RCMs). This uncoupled approach neglects feedback from the land surface to the atmosphere (Overgaard et al., 2007). Normally RCM’s have a land surface model (LSM); that represents only the vertical fluxes of moisture, sensible heat and ground heat between the atmosphere and the surface. The subsurface is therefore only modelled down to a depth of a few metres below land surface. In these upper soil layers quite ⇑ Corresponding author. Tel.: +45 39157453. 1

E-mail address: [email protected] (S.H. Rasmussen). Present address: Copenhagen Energy, Denmark.

0022-1694/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2012.01.014

simple representations of the recharge to the groundwater are often applied. The lateral movement of water at the surface and subsurface is usually not included. By using a hydrological model, the lateral movement of water at the surface and subsurface, including SM distribution, can be modelled. Proper representation of land surface conditions, in particular the root zone SM, is recognised as being crucial for describing the energy balance of the land– atmosphere interaction (Sellers and Hall, 1992). Seuffert et al. (2002) showed, using the weather prediction model Lokal Modell, that initialization with more realistic SM fields improves simulation of ET, but not the local weather prediction. By then coupling to TOPLATS, a LSM that includes groundwater, weather prediction was shown to improve as well. Furthermore, the ultimate goal in assessing the impact of climate change on water resources is to include climate change into water resources management scenarios and appropriate adaptation

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strategies. This requires models that can represent changes such as alternative groundwater pumping or the introduction or reservoirs or irrigation schemes, not found in RCM’s. Hydrological models that are able to simulate distributed hydrological conditions at the surface and subsurface are, e.g. MIKE SHE (Abbort et al., 1986; Graham and Butts, 2005), tRIBS (Ivanov et al., 2004; Vivoni et al., 2007), GEOtop (Rigon et al., 2006), PIHM (Qu and Duffy, 2007), HydroGeoSphere (Jones et al., 2008) and ParFlow (Maxwell et al., 2007; Maxwell and Kollet, 2008). These physically based models include unsaturated flow and lateral movement of surface water and groundwater. Research studies using fully coupled land–atmosphere models have investigated to what extent the uncoupled approach affects the simulation results (York et al., 2002; Overgaard, 2005; Maxwell et al., 2007; Maxwell and Kollet, 2008; Yuan et al., 2008). York et al. (2002) included a distributed hydrological model within a single column atmospheric model at GCM scale over Mill Creek Watershed, Kansas. They found that 5–20% of the ET was drawn from the groundwater. Overgaard (2005) developed a full coupling between the 3D nonhydrostatic atmospheric model code ARPS (Advanced Regional Prediction System) and the hydrological model code MIKE SHE and tested it against data from the 15 km  15 km FIFE site in Kansas (Sellers et al., 1992b). A hypothetical scenario of land use change from grassland to agriculture was modelled with and without the two-way coupled model system, and it was found that neglecting the feedback led to a 40% overestimation of ET. Maxwell et al. (2007) developed a full coupling between ARPS and the integrated model code ParFlow and applied it to a 45 km  32 km model domain around the Little Washita catchment. Through a series of idealised test cases run for both a fully coupled and an uncoupled model they found that SM conditions and lateral subsurface flow significantly affect the atmospheric boundary layer after a 36 h simulation period. In a follow-up study for the same area Maxwell and Kollet (2008) concluded that the groundwater depth, which results from lateral water flow, plays a critical role for the land– atmosphere feedback. Other studies use a coupling of ParFlow and the LSM Common Land Model to analyse the sensitivity of land–atmosphere feedback to climate change (Ferguson and Maxwell, 2010), hillslope heterogeneity (Rihani et al., 2010) and SM heterogeneity (Atchley and Maxwell, 2011). Yuan et al. (2008) implemented a groundwater model in the RegCM3 regional climate model. They found that including groundwater table dynamics significantly affected the vertical SM profile and surface atmospheric fluxes, which again influenced the boundary layer, the local precipitation and the wind. Furthermore, different winds affect the inflow and outflow of atmospheric moisture and thus the regional precipitation. In tests over the Asian monsoon area they found that the precipitation bias was reduced by 25–50% in four of five validation regions and increased by 6% in the last region. Anyah et al. (2008) used RAMS (Regional Atmospheric Model System) with a modified LSM to include more hydrological processes. By including ground water the soil become wetter in some areas and the same areas showed enhanced ET. In the arid western part of USA, this led to a greater convective precipitation and precipitation recycling. Jiang et al. (2009) included a simple groundwater model (SIMGM) and a canopy model into the NOAH LSM of WRF (weather research and forecasting) model. They found that the simulation of the diurnal cycle of precipitation over the Central US improved, indicating an improved persistence of seasonal precipitation. Maxwell et al. (2011) coupled WRF and ParFlow. They highlight the impact of SM on the boundary layer wind. Thus, existing studies indicate that including groundwater–atmosphere feedback in RCMs can be important in some areas. Consistent dynamic coupling between an atmospheric model and a hydrological model that includes subsurface water flows requires that they share the same land–atmosphere processes. These

processes are handled by LSMs. One type of LSM model is Surface vegetation atmosphere transfer (SVAT) model; that describes the energy and water exchange between the land surface, vegetation and atmosphere. Examples of SVAT models include SiB (Sellers et al., 1992a), Ex-BATS (Smith et al., 1993), or integrated in hydrological models TOPLATS (Famiglietti and Wood, 1994), VIC (Lakshmi and Wood, 1998) and MIKE SHE (Overgaard, 2005). The SVAT models listed have all been tested against data from the first international satellite land surface climatological project (ISLSCP) field experiment (FIFE). FIFE is an ideal case for testing SVAT models because it includes numerous observation sites for meteorological variables, surface fluxes, SM, leaf area index (LAI), vegetation height, albedo, etc. These tests have typically only been conducted for single column models for the campaign periods, each covering a few days, and no tests have been carried out for longer periods, such as a full growing season, with a distributed set up. In the fully coupled models reported to date a distributed physically-based hydrological model is coupled to a local or mesoscale atmospheric model that is run for the same model area and with the same resolution as the hydrological model. For climate change studies it would be relevant to couple a hydrological model with an RCM, where the hydrological model covers a smaller area than the RCM. In this way we can save computer time, only running the hydrological model at high resolution over the area of interest, resolving the important processes of land–atmosphere feedback. For the mesoscale climate outside the area of specific interest the LSM of the RCM is assumed to be sufficient to generate the correct weather system entering the area of interest. To our knowledge such coupling, in only a limited area of the RCM, has not been reported in the literature. The method allows detailed representation of the unsaturated zone for simulating more accurate SM. Furthermore the hydrological model includes surface water and groundwater processes; that typically are not included in RCM’s LSM today. The present study is part of a project (www.hyacints.dk) aiming at establishing such coupling between the HIRHAM RCM (Christensen et al., 2006) and the MIKE SHE hydrological model code (Abbort et al., 1986; Graham and Butts, 2005). While it appears evident that, for some purposes, there is a need for fully coupled modelling of the land–atmosphere interactions, it is less clear from the literature how the upscaling from the hydrological models typically using computational grids of a few hundred metres or less to the RCM grids of 10 km or more can be made. The most straightforward approach would be to use the hydrological model at the larger RCM grid assuming the existence of appropriate effective parameters. Alternatively, we might run the hydrological model at smaller grids than the RCM and then aggregate/disaggregate the energy fluxes in the coupling to exploit the higher resolution hydrological data and achieve better resolution in the hydrological response to climate change. An interesting question here is what would be an appropriate grid size in the hydrological model that still ensures accurate simulations of the energy fluxes from the land surface to the atmosphere? A more practical question is how to assess model parameter values from field data at that scale? The critical aspect in this respect is the effect of surface heterogeneity, which according to Giorgi and Avissar (1997) can be divided in two categories: ‘‘aggregation’’ and ‘‘dynamical’’. Aggregation is about sub-grid representation of surface heterogeneities for more accurate estimates of surface fluxes. The dynamical effect of surface heterogeneities can influence the microscale and mesoscale circulations and is important for, e.g. boundary layer structure, cloud formation and precipitation. The present study is confined to the aggregation effect. Sellers et al. (1992a) showed for the FIFE site that simple area averaging of atmospheric forcing, vegetation and SM can be used

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to calculate surface fluxes with a reasonable accuracy on a scale of 15 km. This is around the same scale as a high resolution RCM. This result was confirmed by Overgaard (2005) using a coupled model. Sellers et al. (1995) confirmed these results as well, but noted that averaging of SM is not straightforward. Wood (1997) found that significant errors in ET will arise from the use of spatially averaged SM. Different approaches to this problem have been tested. Giorgi and Avissar (1997) have identified two classes of aggregation methods: discrete methods, where each cell is divided into tiles or patches or methods using probability density functions (PDF), where the sub grid variability is described by statistical parameters. Crow and Wood (2002) tested different methodologies and found that even a crude statistical description of sub grid SM variability can improve prediction of surface fluxes. Normally, a SVAT model is run with atmospheric forcing from local meteorological observations. In a coupling of a hydrological model and a RCM the two sub-models need to exchange variables. If the hydrological model is run at a finer resolution than the RCM, disaggregation of data from the RCM is needed. The easiest way is to use the same values from the RCM in all subgrids of the hydrological model and its SVAT module. This appears to be a good approximation for atmospheric forcing like temperature and humidity as they are known to have a low spatial variability (Sellers et al., 1992a). However, this approximation may be less appropriate for the main driver in a SVAT model, namely net radiation, because albedo and surface temperature are known to vary with soil and vegetation characteristics. Furthermore, precipitation will often exhibit considerable spatial variation within the length scale of an RCM grid. To our knowledge no studies have been reported on how to disaggregate RCM data for use in a coupling with a finer resolution hydrological/SVAT model. The present study, where the analysis is confined to an area corresponding to an RCM grid cell, has following objectives: (1) To evaluate to what extent a physically-based combined SVAT and hydrological model is able to reproduce measured latent and sensible heat fluxes, based solely on parameter values assessed directly from field data or literature. (2) To assess the performance of the model simulations for longer continuous periods more appropriate for climate modelling. (3) To evaluate how well a distributed physically-based hydrological model is able to reproduce the spatial variability of SM, latent and sensible heat fluxes. (4) To evaluate the effect of using uniform atmospheric input, as from a coarser RCM, compared to local observed station data. The paper is divided into the following sections: Methods, describing the study area, the hydrological model including the SVAT model, model parameterisation with data processing and the model runs; Results, describes the model validation at local scale, upscaling to a single grid cell and the simulation of spatial variability; Conclusion and discussion, summarising the conclusions, with discussion and recommendations for further work.

2. Methods 2.1. Study area The analysis is performed on data from the first international satellite land surface climatological project (ISLSCP) field experiment (FIFE) (Sellers et al. (1992b). The FIFE dataset is ideal for testing a SVAT model, because of the high density of station data, soil and vegetation data and the availability of airborne measurements. The FIFE area covers an area of 15  15 km2, located near Manhattan on the tallgrass prairie of Kansas. The topography varies between 350 and 450 m.a.s., Fig. 1a. During the period 26th May– 16th October, 1987 the monitoring programme included 10 meteorological stations, 22 surface flux stations, 32 SM stations, air-

borne measurements and other observations. Four intensive field campaigns (IFCs) were conducted, each of roughly two weeks duration. A full overview of the site and the FIFE data is given by Sellers et al. (1992b). Data from the meteorological, surface flux and SM stations were processed to site averages and analyzed by Betts and Ball (1998). No correction of the variables has been made, e.g. wind correction of precipitation or energy balance closure on surface fluxes. An overview of the water budget is given by Duan et al. (1996). A surface classification was made by Davis et al. (1992). The five soil types within the FIFE area are all either silty loam or silty clay loam and a profile has been made for each (Huemmrich and Levine, 1994; Kanemasu, 1994), Table 1. 2.2. The hydrological model MIKE SHE is an integrated distributed numerical modelling system (Abbort et al., 1986; Graham and Butts, 2005). MIKE SHE is able to simulate the key hydrological processes at the surface and subsurface including snow melt, surface runoff, river flow, infiltration, groundwater and ET. MIKE SHE is ideal for a coupling with an atmospheric model because it uses a process-based approach and it can handle all surface and subsurface water cycle processes. In the present study, the model consists of modules for Overland Flow (2D kinematic wave), Unsaturated Flow (1D Richards’ equation) and ET (SVAT; a two layer Shuttleworth Wallace scheme). As the hydrological information available for the FIFE area is very sparse, e.g. no groundwater data, and the groundwater is, in most of the area, located several metres below the surface it is assumed to be unimportant. Therefore the groundwater and river routing modules are not included in this study. In MIKE SHE the model grid can be defined independently of the grids used in the input data. MIKE SHE incorporates a re-gridding routine for converting input classification grid data to the chosen model grid. 2.3. The two-layer evapotranspitation model (SVAT) The ET model used here, developed by Overgaard (2005), is an energy-based land surface model based on the widely used structure for two layer models proposed by Shuttleworth and Wallace (1985). This model was extended by Overgaard (2005) to include evaporation and sensible heat flux from water ponded on the soil and on the leaf. Conceptual ET models are predominant in hydrological modelling, in part because they require less data. The advantage of energy-based formulations in the context of this work is that they allow a stronger direct link to atmospheric and climate modelling. In contrast to one layer or ‘‘big leaf’’ models, like the wellknown Penman–Monteith model, which do not distinguish between soil evaporation and transpiration, the two-layer model consists of a semi-transparent canopy layer above the soil surface through which the heat and moisture fluxes must enter or leave and allows the soil surface and vegetation fluxes to interact. Sensible and latent heat fluxes are calculated according to the network resistance, Fig. 2. The fluxes between nodes can be written in the general form (Overgaard, 2005):

Hx ¼ qc

Ti  Tj rx

LEx ¼ k

ei  ej rx

where H is sensible heat flux (W/m2), LE is latent heat flux (W/m2), T is temperature (K), e is absolute humidity of air (kg/m3), r is resistance (s/m), q is air density (kg/m3), c is the specific heat of air at constant pressure (J/kg/K), k is the latent heat of vaporisation (J/ kg) and x is an index of resistance. Subscript i and j is index of nodes; as Ti and Tj are the temperature on each side on the resistance rx.

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Fig. 1. (a) Digital elevation model, 25  25 m2 (Strebel et al., 1994). (b) Weather stations with Thiessen polygons and flux stations. Accumulated ET and precipitation over the FIFE period (Dabberdt, 1994). (c) Surface classification (Davis et al., 1992). (d) Soil map (Strebel et al., 1994). Map a–d is covering the whole FIFE area.

Table 1 Soil properties. Average measured texture, bulk density (BD), saturated hydraulic conductivity (Ks), depth of horizon A and porosity (Huemmrich and Levine, 1994; Kanemasu, 1994). Saturated water content (bs ) is assumed equal observed porosity. Van Genuchten parameter residual water content (br ), a and n; estimation based on texture and BD. Hor. A

Dwight

Tully

Flor.-Ben.

Clime

Silty clay loam

Silty loam

Mean

Silt

Clay (%) Silt (%) Sand (%) BD (kg/m3) Ks (cm/s) bs (%) br (%)

27 65 8 1140 54.5  105 53 9 0.0065 1.58 15

36 57 7 1105 73.2  105 52 10 0.0105 1.45 23

38 51 11 993 105  105 56 10 0.0126 1.42 33

53 39 8 1055 181  105 55 11 0.0202 1.30 41

41 50 9 1058 112  105 53 10 0.0135 1.40 31

37 54 9 1050 91.3  105 56 10 0.0116 1.43 25

40 51 9 1055 104  105 54 10 0.1028 1.40 29

1700 50.6  105 46 3 0.034 1.37 29

39 52 9 1450 0.27  105 47 9 0.0101 1.44

43 51 6 1478 0.52  105 47 3 0.0112 1.39

45 40 15 1328 1.07  105 48 10 0.0136 1.37

36 56 7 1466 1.07  105 46 9 0.0091 1.46

42 50 8 1462 1.20  105 47 9 0.0113 1.40

39 48 12 1442 0.54  105 50 9 0.0106 1.43

41 49 10 1455 0.93  105 47 9 0.0110 1.41

As Hor. A – – – – –

a n Depth (cm) Hor. B Clay (%) Silt (%) Sand (%) BD (kg/m3) Ks (cm/s) bs (%) br (%)

a n

The numerical formulation of the two-layer model is developed by linking the expressions for fluxes of latent and sensible heat through a surface energy balance. The various resistances are parameterised according to expressions found in the literature. The resulting system of equations is linearised at each time step to reduce the computation load and therefore relatively small time steps are required. Further details concerning the formulation and numerical solution of this Shuttleworth–Wallace based model can be found elsewhere (Overgaard, 2005). This two layer Shuttleworth–Wallace based model combined with the unsaturated zone model simulates the spatial distribution of evaporation from vegetation and bare soil, sensible heat flux, surface temperature and SM content used in this study.

2.4. Model parameterization and data processing The approach adopted here was to estimate parameter values directly from field data wherever possible and to assess any remaining parameter values from the literature, as outlined below, to avoid calibration. However some of the literature values are from a previous study of the FIFE area and therefore are not independently derived parameters. Atmospheric variables were measured each half hour at 10 automated meteorological stations (Dabberdt, 1994). All missing values are filled with data from the mean climate dataset of FIFE (Betts, 1994). The Betts (1994) data set has some small gaps (one to four time steps). These where filled by interpolation, except

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Fig. 2. MIKE SHE SVAT model structure, network of resistances (Overgaard et al., 2007).

for precipitation and wind speed, where zero values were assumed because of the large temporal variation. Each station is assumed to be representative for areas according to the nearest neighbour (Thiessen polygon) principle, Fig. 1b. The surface classification is based on Davis et al. (1992). Davis et al. (1992) has classified the areal with all combinations of either burned or unburned and either uplands or bottom lands or moderate slopes or steep slopes, see Fig. 1c. The slopes are further divided into aspect classes for eight directions. We assumed aspect not to be important for our purposes and therefore did not include this in our classification. The classification is made on a grid of 30 m  30 m cells. Leaf area index, vegetation heights and albedo are measured on different days during the FIFE period (Nelson et al., 1994; Blad and Walter-Shea, 1994), Fig. 3. Within each vegetation class the average of measurements at a station within this class is calculated. Burned bottomland and unburned steep slopes were not measured. For burned bottomland a mean of all other classes of burned vegetation is applied. For unburned steep slopes a mean of burned steep slopes is used. Root depth is assumed to be 1.48 m for all classes, corresponding to the mean measured soil depth.

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For all soils, the A horizon is less compact than the horizons below, Table 1. The soils are therefore parameterised with two horizons A and BC. A map of soil types is available in the FIFE database; containing 45 classes, some soil types are divided into classes according to the magnitude of slope and stone content. Vertical observations of the soil are only available at the five soil profiles: Dwight, Tully, Clime, Florence and Benfield. We wanted to use the soil profile data wherever possible and to not rely on parameters from the literature. Therefore we began by relating soil types from the soil map to the profiles based on their names, e.g. ‘‘Clime silty clay loam (20–60% slopes, very stony)’’ and ‘‘Clime-Sogn silty clay loams (5–20% slopes)’’ were classified as ‘‘Clime’’. Florence and Benfield are the same class in the soil map, e.g. ‘‘Florence–Benfield complex’’. The rest of the soils are classified according to their texture: Silty clay loam, silty loam and coarser. The soil classification used is shown at Fig. 1d. Texture (sand, silt and clay fraction), bulk density, porosity, saturated conductivity and depth of horizon A for the eight soil classes are taken from soil surveys based on the five profiles, Table 1 (Huemmrich and Levine, 1994; Kanemasu, 1994). For the classes Dwight, Tully, and Clime parameters are taken directly from their soil profile. Florence–Benfield is mean of the Florence and the Benfield profile. For the class Silty clay loam the mean of three silty clay loam soils (Tully, Clime, and Florence) is used. Similarly, Silty loam is the mean of Dwight and Benfield. The last coarser texture class is taken as silt, parameters from Loll and Moldrup (2000) are used. Van Genuchten parameters for retention curves and unsaturated hydraulic conductivity functions are estimated using the software RETC (Van Genuchten et al., 1991), Table 1. The relation between the two empirical parameters n and m is held constant through m = 1–1/n, the Mualem based formulation. The residual water content and the two empirical constants n and a, are calculated by RETC based on texture and bulk density of each of the eight classes. Saturated water content is assumed to be equal to the measured soil porosity. The default shape factor l is 0.5 in RETC when using the Mualem formulation. The groundwater table was not measured. Famiglietti and Wood (1994) estimated initial water table conditions for the different IFCs to be between 2.1 m and 3.0 m on the basis of the

Fig. 3. Leaf area index, vegetation height and albedo.

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probability distribution of water table depth from the soil-topographic index of TOPLATS (Wood, 1997). We have, as in Overgaard (2005), assumed the groundwater table to be located 3 m below the surface over the entire FIFE area. Values for minimum stomata resistance, coefficient for radiation through canopy, leaf width, substrate roughness length, root mass distribution, top soil heat capacity and top soil heat conductivity are taken from Overgaard (2005), Table 2. As in Overgaard (2005), all of these are literature values and not calibration parameters. These parameters are assumed uniform either because of lack of data or limitations in the current model code. 2.5. Model runs The model is run continuously from May 1st to October 16th, 1987. The observations at the meteorological stations started at May 1st allowing the model to ‘‘warm up’’ in 25 days before the FIFE period starts at May 26th. The initial condition for SM is assumed to correspond to equilibrium conditions, with field capacity at the top and increasing SM with depth according to the specified moisture–pressure relationship. The soil is divided into 11 calculation layers with a thickness of 2 cm at the top, slowly increasing to 100 cm at the bottom. The model is run with seven different grid resolutions: 240, 480, 960, 1920, 3840, 7680 and 15360 m. Using the same grid dimensions in the x- and y-direction, then doubling the cell size results in four times less cells in the horizontal plane. The vegetation and soil classification are available on a 30 m grid, the model grid is then an integer multiple of the classification grid cell size. For the different grids MIKE SHE can re-grid the vegetation and soil types while preserving the statistical distribution found in the observed spatial data (DHI, 2009, p. 216). This method ensures the same spatial statistical distribution after the re-gridding. The classification of a cell is depends on the classes found in the source data within the cell and the statistical distribution of the source data. For example, to re-grid to a 2  2 cell model where the statistical distribution of land use is random in space with 75% grassland and 25% forest, then three cells become grassland and one forest. Using a majority method, all four cells would become grassland. All runs have also been made with a simple majority method, to analyse the sensitivity to re-gridding methods. The effect of using atmospheric input at the RCM scale is addressed by a model run using uniform atmospheric input and global rather than net radiation. The extent of the hydrological model domain is the same as a RCM cell and therefore areal mean atmospheric input is used for the whole domain but within this domain the highest hydrological model resolution, 240 m, is used. In this run global radiation is used, because global radiation is unlike net radiation not affected by the surface properties. The SVAT model calculates net radiation from global radiation, based on a specified albedo:

Rn ¼ SW in ð1  aÞ þ rðea T 4a  es T 4r Þ

Table 2 Parameter values for the SVAT model. Parameter

Value

Unstressed stomata resistance Root depth Root mass distribution Canopy interception Coefficient for radiation through the canopy Leaf width Substrate roughness length Depth of ground water table Top soil heat capacity Top soil heat conductivity

110 m/s 1.48 m 1 0.1 0.7 0.02 m 0.03 m 3m 0.9 0.17

where SWin is short wave incoming radiation, a is albedo, r is Stefan–Boltzmann constant, ea and es are emissivity of the atmosphere and land-surface, Ta is air temperature and Tr is the radiometric surface temperature. The first term is net short wave radiation calculated from the specified local albedo. The second term is net long wave radiation according to Stefan–Boltzman’s law. The Thiessen polygons for the atmospheric stations are remapped to the different model grids by MIKE SHE’s built-in gridding tool. Some polygons may then be excluded because of grid size. The model grid at 7680 m contains only four cells and therefore only uses four atmospheric stations as input. For the largest 15360 grid, mean atmospheric inputs from Betts (1994) were used with the exception of precipitation. To ensure the same water input areal mean precipitation calculated on 240 m grid is used. The areal mean dataset from Betts (1994) has 404 mm of accumulated precipitation. Whereas the areal mean estimated from the 240 m grid run is 495 mm, closer to the estimate for FIFE by Duan et al. (1996) of 488 mm. Observations within the FIFE area have been most intensive in the north-west, see the locations of meteorological stations and flux stations in Fig. 1b. The soil profiles are also made in that area. Spatial variability may therefore be better represented by the observations for that area. For validation of the simulated spatial variability the analysis is made on grid cells corresponding to a station and not the total model domain. In this way the analysis accounts for the potential spatial sampling errors.

3. Results 3.1. Model validation at local scale In order to test the capability of the model to simulate the land– atmosphere processes at the local scale, model simulations from the finest resolution (240 m) have been compared to observations from different stations. The performance of simulated surface fluxes compare to observed can be assessed from Figs. 4 and 5 by the time series plots and by four performance indicators: (a) Mean error; (b) root mean square error, RMSE; (c) correlation coefficient; and (d) explained variance or Nash Sutcliffe coefficient (Nash and Sutcliffe, 1970). Four stations (4439, 4609, 6912, 8639) in the FIFE area have recorded meteorological data (input to the model) and surface fluxes (validation data) at the same site, but only during the IFCs. At these stations all components of the surface energy balance are observed. Fig. 4 shows simulated and observed surface fluxes from station 6912 during IFC3. Station 6912 is the one with the most complete records of observations during the IFC’s. As indicated by the high values of correlation and low RMSE values, the diurnal cycle of the surface fluxes and the magnitudes are simulated well. Fig. 5 shows latent heat fluxes at the same station for IFC1, 2 and 4. It is seen that the diurnal cycle and level at each time step is simulated well in all IFCs. Simulation results at the same station with same grid size but forced with uniform atmospheric data and global radiation are plotted as well on Figs. 4 and 5, in grey. In typical observation data sets, net radiation is not available and the density of the climate observation stations is low. Therefore, we have made this comparison to analyse the effect of using coarser atmospheric data and global radiation. As shown, Figs. 4 and 5, these results are not significantly different. The small differences that are seen, e.g. midday August 15th and May 31st are related to differences in precipitation, events that occur in the FIFE area but not at this station. The measured and simulated SM content for three depths at station 6912 are presented in Fig. 6. The water content appears to be consistently overestimated by the model at 2.5 cm and 100 cm depths, while there is no bias at 50 cm depth. The temporal

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Fig. 4. Surface fluxes at station 6912 during the first 10 days of IFC 3, simulated at 240 m grid cell. The statistics shown cover the entire IFC 3 (August 6th–21st), black and grey are for simulations with distributed and uniform atmospheric forcing, respectively.

dynamics are reasonably well simulated at all three depths. The higher simulated SM content may be partly explained by the fact that the saturated SM content is assumed equal to the measured soil porosity. Soil porosity is in general larger than saturated moisture content, as the saturated soil may also contain some residual air. The saturated SM content used in horizon A is around 0.1 (m3/ m3) larger than other studies (Sellers et al., 1992a; Famiglietti and Wood, 1994; Lakshmi and Wood, 1998). Another possible explanation is the initial equilibrium pressure condition. In particular, the continuous decrease of the simulated water content at 100 cm depth over the whole period could be an effect of the initial conditions (assumed equilibrium pressure), the short warm up period of the model (25 days) or an incorrect estimate of the water table depth. Furthermore, in the model, the soil is 3 m deep, down to the ground water, but in reality the soil is around 1.5 m deep with limestone below; which may also affect the soil water dynamics. However, the surface fluxes are well simulated even with these biases in SM content. The results for three other stations are similar; two of them also have simulated SM contents that are too high at 50 cm depth. Only four flux stations operated continuously during the FIFE period (1246, 1445, 2655, 6340). Daily mean values of observed and simulated ET are plotted in Fig. 7 for the two stations with the highest and lowest ET (1445 and 2655). There is good agreement between the simulation and the observations. The largest differences occur during the dry period from mid July to beginning of August, where station 1445 is underestimated. This is related to

the description and parameterisation of the soil and depth to ground water in the model, which will affects the water holding capacity of the soil and the availability of water drawn from the ground water in dry periods. The results of the two other continuous flux stations are similar. Station 1246 is similar to 2655 and station 6340 similar to 1445 with respect to simulations of the dry period and the performance statistics. When comparing the simulations to observed data, the scale discrepancies need to be considered. The model is run at 240 m grid, where the soil and vegetation parameters are aggregated from maps with 30 m resolution to the a 240 m grid, while the atmospheric input data are point scale values. The flux observations are from a mast 2 m above the vegetation with a foot print scale of the order of 10 m. Thus, the soil and vegetation classification in a 240 m grid is not necessarily identical to the actual soil and vegetation within 10 m from a station located within the grid. In spite of this, the simulated fluxes compare quite well to the observations. This indicates that the difference in scales between the observations and the simulations is a minor problem in this case, perhaps because the fluxes are dominated by the point scale climate data or because the area is rather homogeneous. The model performance is similar to other studies of the FIFE area, Table 3. Our error in mean area fluxes is similar to smaller than other studies. For single stations we have some stations with larger error than other studies, but we use the full FIFE period with all available flux stations. The largest errors occur at stations where no meteorological observations are made and where the stations

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Fig. 5. Latent heat flux at station 6912 during the first 10 days of IFC 1 (top), 2 (middle) and 4 (bottom), simulated at 240 m grid cell. The statistics shown cover the entire IFC periods (1: May 26th–June 6th, 2: June 25th–July 15th, 4: October 5th–16th). The legend is the same as Fig. 4.

Fig. 6. SM content at station 6912 during the FIFE period, simulated at 240 m grid cell.

are not operated continuously. This shows that the ability of the model to reproduce ET and sensible heat flux is comparable to other studies, even though the model parameters were assessed directly from field data and literature rather than by calibration. 3.2. Upscaling to a single (15 km) grid cell Fig. 8 shows the water balance averaged over the FIFE area and accumulated over the five months simulation period for simulations using a 240 m grid with 68  68 cells and a single square cell of 15,360 m compared to the area mean of the observations. In

order to ensure the same total precipitation for both runs, the precipitation in the 15,360 m run, uses the areal mean of the precipitation of the 240 m grids. It is seen that both the ET and the SM changes are simulated quite well both for the 240 m and the 15,360 m runs, and that the simulations for the two grid sizes are similar. Fig. 6 also shows that the relative temporal changes are simulated well, but the absolute content was not right. The main differences occur during the dry period in July and the beginning of August and can be explained by differences in ground water recharge. The groundwater recharge over the whole FIFE period is 21 mm in the 15,360 m run and 39 mm in the 240 m run. For the

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Fig. 7. ET at station 1445 and 2655.

Table 3 Model performance in other studies for the FIFE area. LH is latent heat flux and SH is sensible heat flux. Error range is range over the different periods IFCs, cloud free days or stations. Latent heat flux is also given in mm/d. Study

Area mean/stations

Periods

Error type

Flux

Error (W/m2)

(mm/d)

Overgaard (2005)

Area mean, whole days

IFC 1, 2, 3 and 4

Mean average error

LH SH

12–25 20–30

(0.42–0.87)

Lakshmi and Wood (1998)

Area mean, whole days

IFC 1–4 together

Standard deviation

LH SH

47 48

(1.64)

Famiglietti and Wood (1994)

Area mean, daylight hours

IFC 1, 2, 3 and 4

RMSE

LH

34–48

(1.19–1.68)

This study, 240 m grid

Area mean, whole days

FIFE period

RMSE

LH SH

25 29

(0.88)

Smith et al. (1993)

Single station, daylight hours

IFC 1, 2 and 3

RMSE

LH SH

39–63 34–44

(1.36–2.20)

Famiglietti and Wood (1994)

Single stations, daylight hours

Cloud free days

RMSE

LH

14–48

(0.49–1.68)

Sellers et al. (1992)

Single stations, whole days

One day in each IFC

Standard deviation

LH SH

4–47 5–52

(0.14–1.64)

This study, 240 m grid

Single stations, whole days

FIFE period

RMSE

LH SH

27–95 18–56

(0.95–3.33)

run at 15,360 m, a single cell, precipitation is uniform whereas for the 240 m run precipitation is distributed. Therefore, in the 240 m case greater than average rain may fall in some parts and where this rain falls on areas with higher SM content more downward flow of soil water will occur. Also in the 240 m run with spatial variability in soil types and precipitation, some soils dry out faster than others generating upward movement of water from the groundwater. Upward movement of water is not seen in the 15,360 m run. A comparison of simulated area mean ET and SM change for seven different resolutions is shown in Fig. 9. Overall, the simulations with different resolutions show only small differences. With higher resolution the results approach the results for the 240 m grid run. For grid cells of 3840 m or less, the simulated ET is in practice identical. The simulated SM dynamics are almost identical (within ±2 mm) for the 1920 m grid and higher resolutions. This coincides

with how well the observed spatial distribution of vegetation and soil properties are preserved by the distributed model. For square grid cells of 1920 m or smaller the model spatial distributions match the observed Fig. 10. The two re-gridding methods appear to give similar results. That the resolution only has a small impact on the ET is in line with Sellers et al. (1992a, 1995) who suggested that area averages of atmospheric data, topography, vegetation and soil parameters can be used for calculating surface fluxes within the FIFE area. Other studies show the importance of resolving spatial variability (Giorgi and Avissar, 1997; York et al., 2002; Crow and Wood,2002; Patton et al., 2005) and it is subject of current research (e. g. Vereecken et al., 2010). Therefore our findings maybe site dependent. The FIFE site may be too homogeneous. Further tests are needed at more heterogeneous sites to assess whether our findings are more generally valid.

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Fig. 8. Mean area water balance.

The simulations using uniform atmospheric input and global radiation shows only small differences in the SM and ET, Fig. 11. The differences in accumulated SM change and ET are 4 mm and 25 mm, respectively. On any particular day there are in general only small differences in ET.

3.3. Spatial variability To analyse the ability of the model to simulate the spatial variability within the FIFE area model simulations for 240–7680 m grids are compared to the observations. Across all measurement stations the mean and standard deviation of daily latent heat flux is calculated. For all the grid cells corresponding to a measurement station the same is done and the results for the 240 m run are shown in Fig. 12. The number of available stations of observations varies (highest in IFC periods), but all grid cells corresponding to a station are used in calculating the mean and standard deviations of the simulations. In general, there is a good agreement between simulated and observed area mean values, while the variability, represented as the standard deviation, is in general smaller in the simulations than in the observed data. This is further illustrated in Fig. 13, where the observed and simulated standard deviations are plotted against each other for different resolutions. At all resolutions the variability is underestimated. The single day with the largest overestimation is on the day with the largest simulated ET. The simulated variability at all resolutions is around the same. Note that the stations representing the daily highest and lowest ET are not the same for the different resolutions. This is because the stations do not fall within the same classification of vegetation and soil at each resolution. Some of the underestimation may be explained by uncertainties in the observational data, but the main part is likely to be caused by lack of representation of some of the spatial heterogeneity in the model, e.g. related to grouping of vegetation and soil classes, and neglecting small scale heterogeneity within each vegetation and soil class. A third explanation could be the assumption of a uniform depth of the groundwater table. Maxwell et al. (2007) found that the surface heat flux may vary with up to 100 W/m2 for some soil types as a consequence of variations in groundwater table depths.

When the model is forced with uniform atmospheric input, the spatial variability in the simulated ET is further underestimated. This is mainly a result of uniform precipitation input. An additional run with uniform atmospheric input but with distributed precipitation exhibited nearly the same spatial variability as when all atmospheric input is distributed. It appears that most of the spatial variability in the simulation is lost when the model is driven with uniform atmospheric input, but as shown above the area mean fluxes are still simulated well. The pattern of precipitation influences the pattern of ET and SM. This may be an issue in coupled RCM and hydrological modelling. The spatial variability of ET and SM may be limited by the resolution of the RCM. Similar analyses for the sensible heat flux are shown in Figs. 14 and 15. The area mean sensible heat flux is overestimated in the dry period, in contrast to latent heat flux but otherwise simulated quite well. In the dry period, the soil dries too quickly and the availability of ground water for ET is too low in the model. Outside the dry period, the SM is able to accurately capture the partitioning between latent and sensible heat flux. The variability of sensible heat flux is better represented in these simulations than for the latent heat flux, Figs. 13 and 15. The observed spatial variability of latent heat flux has a larger range than the sensible heat flux. The range of simulated latent and sensible heat fluxes is similar. It is mainly days with large variability of ET that are not well simulated. This might be due to assumed uniform ground water depth, neglecting wetter soils in valleys and therefore the spatial variability of SM. It could also be related to the low spatial variability in the observed precipitation data which depends on the density of meteorological stations. For all SM stations the means and standard deviations were calculated, Fig. 16. The SM measurement campaigns had typical durations of three to four days, so not all stations were visited on the same day. The lengths of the red lines in Fig. 16 indicate the period, over which observations were made. For the simulations, the areal mean SM is taken at noon, and the standard deviations are calculated for all stations over the same days as the observations are made. The area mean values show the same picture as the station data shown in Fig. 6, namely an overestimation of the SM level (likely due to under-estimation of the saturated soil water content, bs ) and a good agreement between simulated and observed

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Fig. 9. ET and SM change simulated at different grid resolution.

dynamics. Furthermore, the spatial variability, represented by the standard deviations, is simulated quite well. For the four IFCs, the mean ET rates are calculated from the observations and the simulations, Fig. 17. A simple mean of observations within same grid cell is compared with the simulations. For all IFCs the scatter is a cloud, around the ‘‘1:1’’ line implying that the model more or less catches the range of variability within the FIFE area, but the exact spatial pattern is not captured. Underestimation of spatial variability of latent heat flux is also seen in Fig. 13. The range of variability is slightly underestimated in IFCs 1–3, but around the same as the observed for IFC 4. The variability is similar for resolutions from 240 m to 1920 m, while both for simulated and observed values it is reduced for 3840 m and 7680 m as a result of the aggregation to larger grids. 4. Conclusion and discussion In order to test model performance for locations where no flux data exist for model calibration we have parameterised a SVAT model based on the MIKE SHE code using a two layer energy-based model for ET with parameter values assessed from field data and

from literature. The simulation results are compared with observational data from the First International Satellite Land Surface Climatological Project Field Experiment (FIFE) where a comprehensive data set of surface energy fluxes is available for a 15  15 km2 area in Kansas for the growing season of 1987. The test results are encouraging, because the model, parameterised in this way was able to reproduce observed values of ET, sensible heat flux and to some extent SM at local scale at a performance level similar to previous FIFE studies (Famiglietti and Wood, 1994; Smith et al., 1993; Lakshmi and Wood, 1998; Overgaard, 2005). While previous FIFE studies (Famiglietti and Wood, 1994; Smith et al., 1993; Lakshmi and Wood, 1998; Overgaard, 2005) have made simulations for shorter periods such as the four intensive field campaigns (IFCs) with typically two weeks durations, we have made continuous simulations for the full, five months, growing season, i.e. without adjusting initial conditions with measured SM data prior to each of the IFCs. We found that our model simulations for the long continuous period have similar accuracies as the previous FIFE studies focusing on the IFCs. This is encouraging, because continuous periods, as opposed to event simulations, are more appropriate for hydrological modelling of climate change.

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Fig. 10. Spatial distribution of vegetation and soil classes. Legend for vegetation classes see Fig. 3. Top: preserve statistical distribution. Bottom: majority.

Fig. 11. ET and SM change simulated at 240 m grid. Distributed atmospheric forcing and net radiation compared to forcing with uniform atmospheric forcing.

The model has been run with seven different resolutions with grid size ranging from 240 to 15,360 m. Areal average SM is simulated equally well when using grid sizes between 240 m and 1920 m, while the performance for larger grids is slightly poorer.

For simulation at 3840 m and 7680 m, the spatial variability of the rainfall, soil and vegetation properties are not well captured and this causes the difference in average SM, Fig. 9. For the run at 15,360 m the SM change is similar to the highest resolutions,

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Fig. 12. Latent heat flux area mean and standard deviation; observed and simulated on 240 m grid.

Fig. 13. Standard deviation of daily area mean latent heat flux, observations against simulations at different resolutions. All days within the FIFE period.

Fig. 15. Standard deviation of daily area mean sensible heat flux, observations against simulations at different resolutions. All days within the FIFE period.

but the precipitation used as input was the area average from the highest resolution run to ensure exactly the same input of water. ET is less sensitive to resolution than SM but SM change is very similar at the different resolutions. Thus it appears that little is gained by decreasing the grid resolution below 2 km and even a 15 km grid provides almost as good results of energy fluxes as the finer resolutions. Using two different re-gridding methods (preserve the statistical distribution or assign the majority) does

not influence our simulation results. Our findings therefore suggest that it is possible to use a distributed physically-based SVAT model parameterised from field data and literature information and upscale the results by simple aggregation to simulate the energy balances at a scale relevant for regional climate modelling. The main difference between the area average water budget at the 240 m resolution and 15,360 m resolution is the groundwater recharge. There is nearly zero runoff, SM is similar and ET only differs by

Fig. 14. Sensible heat flux area mean and standard deviation; observed and simulated on 240 m grid.

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Fig. 16. SM content area mean and standard deviation; observed and simulated on 240 m grid. Right 2.5 cm depth and left 50 cm.

Fig. 17. ET rate during the four IFC’s, observed against simulated.

1%, Fig. 8. The simulated groundwater recharge is larger by a factor of two higher for 240 m grid. This is a critical issue when we want our coupled model system to predict future groundwater resources. Unfortunately, the FIFE database does not have any observations concerning groundwater from which we could validate the predictions of groundwater recharge. The simulation with uniform atmospheric input shows similar model results at local scale and for area mean ET and SM change. On any particular day there are in general only small differences in ET. These results indicate that uniform atmospheric forcing can be used in a coupling for areas with similar spatial variability in the rainfall, soil and vegetation characteristics. The spatial variability is underestimated mainly because of uniform precipitation. Nevertheless, the mean fluxes and SM are simulated well. The underestimation of variability could be an issue in cases where areal mean fluxes are not captured by the model because of lack of variability in the atmospheric input. This is not an issue in this case. This highlights an important issue in coupling RCM’s and hydrological models. The spatial variability of precipitation will affect SM and ET variability. With coarse RCM model input of precipitation some of the variability may be lost because of the resolution in

the RCM. This is also a critical factor for generalisation of our method to a scale of GCM resolution. Our scaling analysis may also be affected by limited spatial coverage of the meteorological input in the south-eastern part of the domain, Fig. 1a. For studies of climate change impact on hydrology this is an issue; does the climate model provide the right spatial precipitation input that is needed for the scope of the hydrological modelling study and the processes included? Our findings are in good accordance with other studies of the FIFE area, namely that the ET can be simulated well by aggregating soil and vegetation data to 15 km grids, while SM is more sensitive to the spatial resolution (Sellers et al., 1992a, 1995; Famiglietti and Wood, 1994). Therefore, the focus in the literature has been more on SM than ET; sub grid averaging SM and implication of SM variability (Sellers et al., 1995; Famiglietti and Wood, 1995; Crow and Wood, 2002; Sellers et al., 2007). Nevertheless, we see that the resulting differences in groundwater recharge can be significant and this requires further investigation. In the present study the assumption of uniform ground water depth may be a limitation. This could be tested by including spatially distributed ground water. Resolving features such as wetter soil in river valleys may then become important, which may result in a different scaling

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effect than found in this study. Our findings indicate that there is no need to carry out high resolution land–atmosphere modelling over homogeneous area like FIFE if spatially distributed ground water is unimportant for the land–atmosphere feedback and ground water recharge is not important for the study. It is important to keep in mind that the FIFE area is a homogeneous site and therefore the local variability may be small compared to the general model and parameterisation uncertainty. Therefore resolution may not be important for the FIFE area. Additional tests at other more heterogeneous sites are needed to test if our hypothesis that ET and SM can be simulated at RCM scale is generally valid. Crow and Wood (2002) conclude in their study that even a crude statistical description of sub grid SM variability can improve prediction of surface fluxes. In our study we do not see any improvement in our ET with higher resolution and thereby more SM variability. Our simplification of soil types may also contribute to lack of spatial variability and insensitivity to resolution. Unfortunately, more detailed vertical distributions of soil data are not available. If we had included more soil types we might have had to rely on literature parameters that may not be representative for the FIFE area. We could also have based the classification on US General Soil Map (STATSGO) (Schwarz and Alexander, 1995), it has similar classification as used here. The soil types in FIFE are very similar to each other and the variability may therefore be dominated by parameter uncertainty in literature values or lookup tables. In this study, field data is used to ensure the most representative site parameters but this may not capture the actual variability. While our results may not be valid at more heterogeneous sites, our findings are appropriate for homogeneous sites like FIFE. We could have made hypothetical changes to the vegetation and soils and then rerun some of the experiments, in this way testing the influence of heterogeneity. Such hypothetical studies, under idealised conditions with high resolution hydrological modelling, have shown that land–atmosphere feedback is important (e.g. Crow and Wood, 2002; York et al., 2002; Maxwell et al., 2007; Patton et al., 2005). We have carried out these investigations in an actual field site. The simulated spatial variability of latent heat flux, sensible heat flux and SM within the FIFE area is similar for the different grid sizes. The spatial variability of latent heat flux is underestimated, while the spatial variability of the simulated sensible heat flux is almost as large as in the observations. The spatial variability of SM at 2.5 cm is simulated with the same range as observed. A comparison of the simulated (240 m grid) and observed (stations) ET rates at different locations shows that although ranges of spatial variability are reproduced, the model is not able to simulate the pattern of ET rates within the FIFE area. Thus, although the model appears to have predictive capability at 15 km scale, which is important in the perspective of regional climate modelling, it has limited predictive capability with the current parameterisation at smaller scales within the large 15 km grid. The results of the present study are encouraging with respect of using MIKE SHE with the SVAT module described here in coupling to a RCM. High resolution hydrological land–atmosphere modelling may not be important over a homogeneous area like the FIFE site. In this study dynamical ground water with lateral flow was not included, and the effect of this needs further investigation. Including ground water and coupling to a RCM could lead to different scaling effects than found in this study. An open question is to what extent similar results can be expected in other areas, because some parameter values originate from previous FIFE studies and FIFE is relatively homogeneous area. Using MIKE SHE in coupling to an RCM also allows ground water to be fully included and can be used in the future for the analysis of ground water – atmosphere feedbacks.

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Acknowledgements The present study was funded by a grant from the Danish Strategic Research Council for the project HYdrological Modelling for Assessing Climate Change Impacts at differeNT Scales (HYACINTS – www.hyacints.dk) under Contract No: DSF-EnMi 2104-07-0008. The authors would like to acknowledge the early work of Dr. Jesper Overgaard on which work this study is based.

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