The Influence of Hydrologic Modeling on the Predicted ... - AMS Journals

VOLUME 3

JOURNAL OF HYDROMETEOROLOGY

OCTOBER 2002

The Influence of Hydrologic Modeling on the Predicted Local Weather: Two-Way Coupling of a Mesoscale Weather Prediction Model and a Land Surface Hydrologic Model G. SEUFFERT, P. GROSS,

AND

C. SIMMER

Meteorological Institute, University of Bonn, Bonn, Germany

E. F. WOOD Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey (Manuscript received 19 July 2001, in final form 10 March 2002) ABSTRACT A two-way coupling of the operational mesoscale weather prediction model known as Lokal Modell (LM; German Weather Service) with the land surface hydrologic ‘‘TOPMODEL’’-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS; Princeton University) has been carried out to investigate the influence of a ‘‘stateof-the-art’’ land surface hydrologic model on the predicted local weather. Two case studies are presented that quantify the influence of the combined modeling system on the turbulent fluxes and boundary layer structure and on the formation of precipitation. The model results are compared with ground-based measurements of turbulent fluxes, boundary layer structure, and precipitation. Furthermore, whether the initialization of the original LM with more realistic soil moisture fields would be sufficient to improve the weather forecast is investigated. The results of the two case studies show that, when compared with measurements, the two-way coupled modeling system using TOPLATS improves the predicted energy fluxes and rain amount in comparison with predictions from the original LM. The initialization of the LM just using soil moisture fields based on TOPLATS does not result in an improvement of the local weather forecast: although the simulation of the sensible and latent heat fluxes is improved, the representation of the boundary layer structure is not captured well. In the original LM, the surface processes are not modeled in sufficient detail, which resulted in significant overprediction of precipitation for one case study. The main reason for the improved performance of the two-way coupled modeling system on the basis of TOPLATS probably is the more accurate representation of vegetation and soil hydrologic processes. This results in more realistically simulated soil moisture fields and better simulation of the dynamic range of the surface temperature when compared with the other model configurations.

1. Introduction For dynamic atmospheric models (AMs), the knowledge of parameters describing the boundary conditions at the top and bottom of the atmosphere is absolutely necessary. While the upper boundary condition can be adequately formulated by introducing a ‘‘friction layer,’’ for the bottom, the fluxes of heat, momentum, and water have to be known. In AMs the processes in the soil and the modeling of the exchange between the soil and the atmosphere are usually parameterized by a soil–vegetation–atmosphere transfer (SVAT) model. Most SVAT models simulate the exchange of heat and water fluxes well, but many have the disadvantage of not sufficiently simulating soil hydrologic processes at catchment scales, essentially neglecting lateral water transport and Corresponding author address: G. Seuffert, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom. E-mail: [email protected]

q 2002 American Meteorological Society

its effect on the groundwater table. On regional scales, neglecting these hydrologic processes may result in incorrect soil moisture fields, which in return influence the atmospheric model-derived structure of the atmospheric boundary layer (Braun et al. 2001) and possibly the development of precipitation (Scha¨r et al. 1999; Mo¨lders and Raabe 1997). It is impractical to initialize numerical weather prediction (NWP) models with observed soil moisture fields because there are too few in situ soil moisture measurements and because satellite-based observations are at microwave frequencies that provide limited coverage and accuracy. Therefore, other meteorological variables, for example, near-surface air temperature and humidity, are used to adjust the soil moisture by variational methods (Mahfouf 1991; Rhodin et al. 1999). For cases of strong soil–atmosphere coupling, these methods improve the predicted heat fluxes and soil moisture. Even in cases in which the simulated surface radiation is in error, these methods result in reasonable

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estimates of the sensible heat flux and near-surface temperature but unrealistic estimates of the latent heat fluxes and soil moisture fields (Hu et al. 1999). An alternative approach is to couple the AM with a hydrologic model (HM) or land surface–hydrologic model (LSHM). When coupling an AM and an HM, one has to recognize that the models were originally designed for very different temporal and spatial scales. In general, NWP models simulate processes of up to 7 days with a spatial resolution of 1–25 km. On the other hand, HMs generally simulate the processes for longer time periods but usually using finer grids. Their typical spatial resolution is 1 km or less, while simulating the processes for timescales from days to years. Several studies (e.g., Kite et al. 1994; Hostetler and Giorgi 1993) investigate the use of AM output as inputs to the HM and LSHM for the simulation of catchment discharge. In these studies, the AM and HM are oneway coupled, which means that the atmospheric variables are only passed from the AM to the HM, with no feedback to the AM. In contrast, in two-way coupling, both models exchange the relevant parameters; that is, there is feedback from the land model to the atmospheric model that influences future AM-derived fluxes. The above studies focused on the influence of the spatial resolution of the AM on the predicted discharge (Kite 1997; Kite and Haberlandt 1999; Lakhtakia et al. 1999). In general, these and other papers show that AM output, used as an input to HM, results in realistically simulated discharge when compared with measurements. Furthermore, Benoit et al. (2000) showed that the comparison of simulated runoff with measured runoff is an additional useful parameter for the validation of an AM. A two-way coupling of an AM with an HM on a regional scale is presented by Mo¨lders and Raabe (1997). They coupled the Geesthacht Simulation Model of the Atmosphere (GESIMA); Kapitza and Eppel 1992) with the Neiderschlag Abluss Simulations Modell (NASMO) hydrologic model (Maniak 1996) to investigate the influence of coupling on the formation of clouds and precipitation. In their study, the problem of the different spatial resolutions of AM and HM is solved by including hydrologic model elements with a spatial resolution of 1 km inside a 5 km 3 5 km grid box of the AM. The necessary upscaling of the soil parameters is done by averaging the energy fluxes from the hydrologic to the atmospheric grid. A 24-h simulation of an idealized scenario shows that modeling runoff influences soil moisture and hence, indirectly, the formation of cloudiness and precipitation by changing the turbulent energy fluxes, when compared with the control run. In our study, an NWP model that is in operational use at the German Weather Service (DWD) and an LSHM, which simulates the land hydrologic processes and the exchange between soil and atmosphere, are coupled with two-way exchange. This paper investigates the influence of the LSHM-predicted surface exchanges on the predicted energy fluxes, structure of the atmo-

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spheric boundary layer, and precipitation, when compared with observed data. Two case studies, both with a forecast time of 3 days, are presented, for a measurement site and a river catchment. The DWD operational mesoscale weather prediction model known as Lokal Modell (LM; Doms and Scha¨ttler 1999) and the land surface hydrologic model known as the ‘‘TOPMODEL’’-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS; Famiglietti et al. 1992; Famiglietti and Wood 1994a,b; Peters-Lidard et al. 1997) are used. To avoid down- and upscaling difficulties, both models are run on the same grid with a mesh size of 1 km. The simulation results are evaluated for the twoway coupled model system (LM–TOPLATS) and for the ‘‘stand-alone’’ LM, which in its original version is coupled to the ‘‘TERRA’’ SVAT model. The paper is structured as follows: First the basic characteristics of both models and the two-way coupling strategy are described (section 2). In section 3, information regarding the application of the coupled system and its calibration are presented. The results for the two case studies, in which the different model runs are compared with measurements, are in section 4. One case study represents a calm summer weather situation, whereas in the second case the modeling domain was influenced by a frontal system. Section 5 has a discussion of the results and conclusions. 2. Model description a. Lokal Modell atmospheric model Since December of 1999, the LM is part of the operational weather prediction system of the DWD. In this study, we use a preoperational version (V1.17) of the LM. The LM is a nonhydrostatic model based on the primitive atmospheric state equations. Hence it is suitable for the prediction of atmospheric flows down to the meso-g scale. The prognostic variables are the wind vector, temperature, specific humidity, perturbation pressure, cloud water content, and cloud ice content. Diagnostic variables are total density and precipitation fluxes of rain and snow. For the horizontal grid, a rotated geographical grid (Arakawa C) is used. When run operationally, the LM is currently run with a mesh size of 7 km, whereas in our study a 1-km horizontal mesh is used. In the vertical dimension, the variables are computed on generalized terrain-following levels. The model has a total of 35 layers, 15 of which represent the lower 1500 m. The diagnostic subgrid-scale turbulence is parameterized with a diagnostic second-order closure for vertical turbulent fluxes (Mellor and Yamada 1974). The surface-layer parameterization follows a stabilitydependent drag-law formulation for momentum, heat, and moisture fluxes according to similarity theory (Louis 1979). A mass flux scheme for moist-convective processes based on Tiedke (1989) is used, and cloud microphysics is implemented. When the model is run at

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spatial scales of a few kilometers, the convection parameterization is used, based on analyses of the scales of convective processes in the atmosphere and some sensitivity studies that are not reported here in detail. The calculations of short- and longwave radiation fluxes and heating rates are based on the d-two-stream approximation of the radiative transfer equation according to Ritter and Geleyn (1992), using eight spectral intervals. A much more detailed description of the LM can be found in Doms and Scha¨ttler (1999). As stated earlier, the operational version of LM uses the TERRA SVAT module. This module is described in some detail because of its special interest in the context of this paper. TERRA provides surface temperature and soil moisture, which are used to calculate the turbulent fluxes and to determine the energy balance. The soil temperatures at the surface and at the interface between the upper and lower soil layers are predicted by solving the equation of heat conduction in a two-layer system. The soil layer thickness and heat fluxes at the interface between the layers are calculated using the extended force–restore method (Jacobsen and Heise 1982). As a lower boundary condition, the temperature at the transition between the lower soil layer and the ‘‘deep’’ soil is kept constant. Soil texture based on soil parameters such as heat capacity and thermal conductivity is used in the model and is prescribed for eight soil types (sand, sandy loam, loam, loamy clay, clay, ice, rock, and peat). The hydrologic element of TERRA provides a prediction of soil moisture and evapotranspiration. Evaporation is calculated as a function of soil texture (air dryness point, field capacity), soil moisture, and potential evaporation as proposed by M. Budyko (Doms and Scha¨ttler 1999). The calculation of plant transpiration is based on soil water content, rooting depth, soil texture, and potential evaporation. A more comprehensive description of vegetation, taking, for instance, the leaf area index into account, is not implemented. Thus the transpiration strongly depends on the soil water content in both soil layers and on soil texture. Latent and sensible heat fluxes are parameterized by a resistance-based formulation. The water content and the water fluxes are calculated for two stores at the surface (interception and a snow store) and for two soil layers. The top and bottom soil layers have a thickness of 10 and 90 cm, respectively. The soil gains water from precipitation, snow, rime, and dew, whereas evaporation, transpiration, and runoff deplete water from the stores and soil layers. The vertical water transport in the soil is based on the Darcy equation. Processes such as capillary ascent, percolation, and infiltration are parameterized to describe the vertical exchange and transport of water between the different stores and soil layers. Runoff is calculated for each layer in the individual columns but does not allow for lateral transport between neighboring soil columns. To provide the initialization and the lateral boundary values on an hourly basis, the LM (at a 1-km spacing)

is one-way nested into another LM version with a horizontal spacing of 2.8 km. The 2.8-km LM simulation is initialized with an interpolated analysis of the hydrostatic ‘‘Deutschland Modell’’ (DM). For the lateral boundary conditions, subsequent DM analyses are used. The DM was the operational weather prediction model of the DWD for the meso-b scale until December of 1999. b. TOPLATS land surface hydrologic model The TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (version 3.1 from 1999) used as an LSHM was developed at Princeton University (Famiglietti et al. 1992). Since then TOPLATS has been continuously improved (Famiglietti and Wood 1994b; Peters-Lidard et al. 1997) and has been applied to different sites and different investigations (Drusch et al. 1999; Pauwels and Wood 1999a,b; Crow et al. 2001). TOPLATS is typically used for horizontal grid spacings from 30 m to 1 km. In this study TOPLATS is applied with a 1-km mesh. The atmospheric forcing needed by the model includes short- and longwave radiation, nearsurface air temperature and humidity, horizontal wind speed, precipitation, and surface pressure. The main parameters predicted by TOPLATS are the surface energy fluxes, soil and surface temperatures, runoff, and soil moisture. The formulation of the subsurface hydrologic behavior, and the resulting spatial variability in soil moisture and depth to the water table, are based on the TOPMODEL approach (Beven and Kirkby 1979). Its main feature is the topographic index (TI), defined as TI 5 ln

[

]

flow accumulation . tan(slope)

(1)

With the help of the catchment- (or area) average TI, an average water-table depth is calculated. The depth to the water table for each pixel within the catchment (or area) is determined as a deviation from the mean water-table depth using the difference between the average TI and the local TI. This local water-table depth gives the lower boundary values for the unsaturatedzone vertical water transport calculations. The soil is divided into two layers: a root zone and a transmission zone, the thickness of which depends on the depth to the water table. The link with the atmosphere is parameterized with precipitation and dew processes as sources of water and evaporation, transpiration, and runoff as sinks of water. Because the roots can extend also into the bottom soil layer, transpiration also affects the water content in this layer, with the vegetation transpiration based on a Penman–Monteith formulation. The vertical water transport in the soil is solved using a numerical approximation of the Richards (1931) equation. Furthermore, infiltration and drainage are included. The soil texture is characterized by Brooks–Corey (1964) param-

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FIG. 1. Scheme of the two-way coupling of LM and TOPLATS.

eters, which are used to estimate the saturated hydraulic conductivity, heat capacity, and bubbling pressure. The lateral water transport from one soil column to its neighbors is taken into account implicitly by the adjustment of the average water-table depth (based on the areally averaged local water balance computations), thus influencing the local water-table depth through the topographic index. A snow and a lake module complete the hydrologic part. The energy balance and the heat storage equations are iteratively solved with regard to surface and a middle soil temperature. The ground heat flux is described as a function of the damping depth of the diurnal temperature variation with a constant temperature for the lower (deep soil) boundary condition. The sensible and latent heat fluxes are parameterized with bulk formulations depending on aerodynamic and canopy resistance. The two resistances are calculated using representative parameters for different vegetation types (e.g., leaf area index, albedo, rooting depth, stomatal resistance, critical and wilting points, displacement height, and roughness length). In this way, the vegetation is implicitly accounted for. Evapotranspiration is calculated for wet and dry vegetation and for bare soil. TOPLATS can be applied in a statistical version or a distributed version, with the former version using a statistical distribution for the TI within a catchment and spatially averaged forcing, and the latter version using an explicit spatial representation of the TI and distributed vegetation and forcing. In this study, the distributed version was used. For more detailed information about the model the reader is referred to Famiglietti et al.

(1992), Famiglietti and Wood (1994b), Peters-Lidard et al. (1997), and Pauwels and Wood (1999a). c. Two-way coupling strategy In the two-way coupling of the LM and TOPLATS, TOPLATS replaces the original TERRA soil module of the LM. An interface was developed that controls the online coupling of both models, so as to have a coupling as flexible as possible with regard to future improvements to both models, to the transfer of the communicated parameters (e.g., from specific to relative humidity), and to the inclusion of down- and upscaling effects. The interface synchronizes the call of the LM and TOPLATS and organizes the exchange of the parameters for each model. Polcher et al. (1998) proposed a general interface based on physical and numerical arguments that offers plug compatibility between GCM and land surface schemes. As compared with the more complicated coupling approach suggested by Polcher et al. (1998), the method used in this study is simple and is strongly related to both models. Figure 1 shows the two-way coupling scheme. Both LM and TOPLATS are run on the same grid with a horizontal spacing of 1 km and a time step of 10 s. Therefore, no spatial and temporal down- or upscaling is necessary. The meteorological variables of temperature, relative humidity, surface pressure, wind speed, turbulent diffusion coefficient, and rain rate, as well as solar and longwave radiation, are provided by the LM as input to TOPLATS (Fig. 1). TOPLATS cal-

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FIG. 2. (left) Location of the 2.8-km LM area (shaded domain) in which the 1-km LM area is nested; (right) topography of the 1-km area. Point KLA (Klein Altendorf ) indicates the location of the field measurement site, and the marked line indicates the catchment of the Sieg River.

culates the soil parameters of soil moisture, surface temperature, and evapotranspiration (Fig. 1). Two basic features were changed in the models to make the coupling more consistent. Under the assumption that the LM calculates more realistically the turbulent diffusion coefficients of heat and momentum, these coefficients are used for the calculation of fluxes in TOPLATS in the coupled system. Thus, the aerodynamic resistance for TOPLATS is determined at the interface by turbulent diffusion coefficients of the LM. The calculation of the aerodynamic resistance in TOPLATS is switched off. To assure consistency in solar net radiation in both models, the dependency of the surface solar albedo on soil moisture in the LM is switched off and is replaced by the TOPLATS albedo field, which takes the spatial variability of vegetation into account. For most of the parameters passed through the interface, only a simple transformation of units is necessary. This applies to the meteorological parameters of surface pressure and rain rate and to the soil parameters of moisture, surface temperature, and evapotranspiration. No changes have to be made for incoming shortwave and longwave radiation. For TOPLATS, the input height of meteorological data depends on the height of vegetation and varies between 2 and 12 m. Therefore, horizontal wind speed (for the calculation of the aerodynamic re-

sistance), relative humidity, and temperature are extrapolated from the lowest LM vertical level (;12.5 m above ground) to the heights of 2, 6, and 12 m, depending on the vegetation type. The extrapolation of the wind speed is done using the assumption of a logarithmic wind profile. For the extrapolation of temperature and relative humidity, we assumed a well-mixed layer (constant specific humidity and potential temperature). We are aware of the fact that this might introduce larger errors under very stable conditions. 3. Applications a. Model area The geographic location for the case studies is situated in the German federal state of Nordrhein-Westfalen (NRW) and includes parts of Niederrheinische-Bucht and Bergisches-Land (Fig. 2, right). The area is characterized by hills of up to 700-m height covered with meadows, forest, a variety of agricultural land cover, and urbanization in the Rhine valley. The model domain has an extent of about 12 000 km 2 , which includes the catchment of the river Sieg (2000 km 2 ) that flows into the Rhine near Bonn (see Fig. 2, right). To provide the boundary values for the LM calculations, the 1-km LM

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is nested within the 2.8-km LM model domain, as shown by the shaded area in the left-hand portion of Fig. 2. b. Calibration and initialization of TOPLATS Before coupling TOPLATS to LM, it was first calibrated for the Sieg catchment (Fig. 2) and its subcatchments, the Agger (750 km 2 ) and Wahnbach (65 km 2 ). Discharge measurements [provided by the project SFB 350; sponsored by Deutsche Forschungsgemeinschaft (DFG)] and meteorological forcing data (DWD and Meteorological Institute Bonn, Germany) are available for 1997–99. All necessary land surface characteristics, such as the digital elevation model (DEM)–derived topographic index, vegetation, and soil parameters, were extrapolated to the 1-km modeling grid. To derive the TI, a 50-m high-resolution DEM was used (Landesvermessungsamt NRW). The soil parameters were derived using a dataset from Geologisches Landesamt NRW and distinguish five soil types (rock, sandy loam, loam, clay, and clay loam). Eight vegetation classes (urban, natural grass, agricultural crop, orchard, shrub, deciduous, coniferous, and mixed woodland forest) are specified through analyzing long-term Advanced Very High Resolution Radiometer satellite data. This classification was carried out by the European Commission’s Coordination of Information on the Environment (CORINE) land cover project (CEC 1993). Typical vegetation parameter values that characterize each class were estimated from the literature (Bormann et al. 1996; Rutter 1975). In TOPLATS, the flow Q reaching the channel from the subsurface soil store is parameterized as being dependent on the catchment mean water-table depth z (Beven and Kirkby 1979): Q 5 Q 0 e zf ,

z # 0.

(2)

The two parameters that have to be calibrated are the base flow Q 0 at saturation and the parameter f , which is related to the decay of saturated hydraulic conductivity with soil depth. Notice in (2) that the depth to the water table is expressed as being less than or equal to 0. The parameters f and Q 0 were found by comparison between the simulated and measured discharge, using short-term hydrograph recession curves and the total annual runoff. The calibration was done for 1997 and then tested against data from 1998 and 1999. The best values of Q 0 and f were found to be 200 m 3 s 21 and 3.5 m 21 , respectively, for the Sieg River catchment. Hydrologic models are usually calibrated using longer datasets. Discharge and meteorological measurements were unfortunately only available for the 3-yr period of 1997–99. A sensitivity check, in which Q 0 and f were varied from 150 to 250 and from 3 to 4, respectively, showed that the results were not very sensitive, however, with the maximum change in the annual discharge being 1.5%. Overall the modeled runoff matched the measurements well for the three years, even though some spe-

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cific differences were noticed (not shown). For example, because river routing was not implemented, the simulated flow peaks occur slightly earlier than in the measurements. Also, related to this result, after rainfall ceases the direct runoff ceases, whereas in the measurements a hydrograph recession is observed from the attenuation through the river network. Furthermore, during the summer a lower base flow is predicted, which might be due to the fact that the model is run on a somewhat coarse 1-km spacing. Also, the sparse coverage by meteorological stations (especially rain gauges; see section 3c) may result in a failure to observe summertime convective rain events. On the basis of the data described above, the initialization for the model domain was carried out by making a model run with the calibrated TOPLATS for 1997 and 1998, using measured meteorological forcing data, that provided the initial soil moisture fields for the case studies. c. Comparison data An extensive measurement campaign took place in the summer of 1998 in the ‘‘Obstversuchsgut Klein Altendorf’’ orchard, located about 20 km southwest of Bonn (Braun et al. 2001; for location, see Fig. 2, right). The focus of the campaign was investigation of the relationship between evapotranspiration and the structure of the boundary layer. In this area, the orchards with apple trees of similar size and similar leaf area index are the dominant land use category and were deemed representative of the larger area (at least 25 km 2 ; B. Maurer 2000, personal communication). The conditions, in the surface layer, up to 6-m height were continuously measured with a profile station from June to September of 1998. The instrumentation consisted of psychrometers, anemometers, a wind vane, and a net radiation sensor. In addition, direct turbulence measurements were made with an ultrasonic anemometer. During 7 days, tethersondes were used to observe the boundary layer up to a height of 200 m. The measurements were completed by sap flow measurements, soil temperature sensors, and a rain gauge. More detailed information about the measurements and the results can be found in Braun et al. (2001). For comparison with the model simulation, two 3day periods were chosen. For 6–8 August 1998, profile and tethersonde measurements are available from 6 August to 1100 UTC 7 August. For 27–29 August, measurements of the profile station are available. For comparison with the measurements, the model results were averaged for the 20 grid points (20 km 2 ) closest to the measurement site (see the box in Fig. 2, right-hand panel). This averaged area is representative of the measurement location because the vegetation type is uniform across the area and the orography is smooth. The influence of the built-in SVAT model (TERRA) and the LSHM model (TOPLATS) on the simulated

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precipitation is investigated for the larger Sieg catchment. For comparison, daily data from 39 rain gauges, provided by the DWD, were used to calculate areal mean precipitation. The daily area average sums were interpolated to hourly values using the hourly measurements at the rain gauge at Bonn. Westerly winds are usually present in this area. Therefore, the rain gauge in Bonn may observe precipitation somewhat earlier (30 min–2 h) than it may rain in the Sieg catchment, which is located 20–70 km east of Bonn. d. Model runs To test the two-way coupling, a set of four different model runs was performed. The first one is a control run without any changes to the original LM coupled to its built-in SVAT model TERRA (ctrl run). For the second model run, the initial soil moisture fields for TERRA from the operational runs are exchanged by using the TOPLATS-generated soil moisture fields and substituting these into TERRA (LMinit run). This model run investigates whether more realistic initial values of soil moisture are already sufficient to improve the forecast. The third model run represents the two-way coupling of LM and TOPLATS (2way run), which is also initialized with the soil conditions produced by the TOPLATS stand-alone version. For the fourth model run (2waypcwtd run), the lateral water transport (i.e., spatial variability in soil moisture based on the TI) is turned off within the two-way coupling of LM and TOPLATS. This means that the local water-table depths are kept spatially constant (cwtd 5 constant water-table depth) for the model run. This model run investigates whether the spatial patterns of soil moisture are important for short-term weather forecasts at the mesoscale. 4. Results a. Calm summer weather situation (case study 1) The first case study investigates the influence of the different land surface models and their simulated soil moisture fields on the boundary layer structure and the energy fluxes for a dry weather period, for which the highest variations in the energy fluxes are expected. We choose a calm summer weather situation with strong shortwave forcing during daytime. Such a situation occurred from 6 to 8 August 1998, related to a high pressure ridge expanding from the high pressure area west of the English Channel to central Europe after the passage of a cold front over Germany on 4 August. Only weak sea level pressure gradients were present in the investigated area. 1) SOIL

MOISTURE DEVELOPMENT

Figure 3 shows in the first column the spatial variability in soil moisture for the top soil layer (10-cm

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thickness) on 6 August for the ctrl run, the LMinit run, and the 2way run. The changes in soil moisture for the following two days at 1200 UTC are shown by the differences in the middle and right-hand columns. First, it is obvious that the initializations of the soil moisture fields of the original LM and TOPLATS differ considerably (left-hand column). The soil moisture simulated with TERRA (ctrl run) is considerably lower than that simulated with TOPLATS (2way run; mean area value of ctrl and 2way runs are 23% and 33% volumetric, respectively; see also Fig. 5). The areas close to the Rhine, Sieg, and Agger rivers (the last two are within the marked catchment) are evident with high soil moisture values in the TOPLATS simulation, whereas the soil moisture pattern simulated by TERRA (ctrl run) follows more or less a different soil-type pattern. Over the course of the days, the 2way run simulates a drying on the hilltops, whereas in the valleys the soil moisture is only slightly changed. In some parts of the valleys, one can even observe an increase in soil moisture due to the lateral flow, which is taken into account only by TOPLATS. When the lateral flow within the two-way coupling is turned off (2waypcwtd run), the soil moisture values in both soil layers change in some areas by up to 10% (volumetric) when compared with the original 2way run for 8 August 1998 (Fig. 4). Most of the areas lose water because of runoff and drainage, which is not refilled by redistribution of subsurface moisture from adjacent soil columns. This is especially significant for the valley areas. The amount of change also depends on the soil texture. For soil textures with a high water-holding capacity, the change is smaller and can even be positive. The changes affect the simulated latent heat flux at the single-pixel scale from 20 to 300 W m 22 , which is large enough possibly to influence the predicted precipitation [see section 4b(3)]. In Fig. 3, the LMinit run shows almost the same pattern as the 2way run for the top soil moisture field at 1200 UTC 6 August, which means that during the first 12 h for both TERRA and TOPLATS, the change in the soil moisture fields is small. For the next two days, the LMinit run predicts also some drying on the hilltops, but, in contrast to the 2way run, the valleys are much more depleted. There are also a few areas in which the simulated soil moisture increases. These areas gain water during the night, when the evapotranspiration is low and the bottom layer diffuses water up to the top layer. In all model runs, a general drying occurs over the three days. Figure 5 shows the temporal development of the average soil moisture fields for the Sieg catchment for the top (Fig. 5a) and bottom (Fig. 5b) soil layers. In both layers, TERRA (ctrl run and LMinit run) simulates a stronger decrease in soil moisture (difference of up to 2% volumetric) than does TOPLATS (2wayrun). Corresponding results are also found for areas surrounding the Klein Altendorf measurement site (not shown).

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FIG. 3. (left) Simulated soil moisture fields (top layer, 0.1-m depth) at 1200 UTC 6 Aug for the LM model area without any modifications (ctrl run), with the two-way coupling (2way run), and with the TOPLATS soil moisture initialization of the LM (LMinit run). Differences of the top soil moisture fields between (middle) 7 and 6 Aug and (right) 8 and 6 Aug at 1200 UTC for the ctrl, 2way, and LMinit runs. The dotted lines indicate contour lines for 200, 400, and 600 m (see also Fig. 2). The marked line indicates the catchment of the Sieg River.

2) ENERGY

FLUXES

The diurnal cycles of modeled net radiation and latent, sensible, and ground heat fluxes for the three model runs are compared with the measurements (Fig. 6). On all three days, the observed net radiation and the heat

fluxes show typical diurnal cycles for an almost cloudfree sky. During the day, net radiation reaches values of up to 550 W m 22 , decreasing at night to 25 W m 22 . In general, simulated net radiation values for all three model runs are in good agreement with measured net radiation and differ only slightly from each other. Dur-

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FIG. 5. Time series of soil moisture development in the (a) top (0.1-m depth) and (b) bottom layers (0.9-m depth) on 6, 7, and 8 Aug, for area means of the Sieg catchment. The different model runs are the ctrl, 2way, and LMinit runs.

FIG. 4. Scatterplots of simulated soil moisture in the (a) top and (b) bottom layers and (c) of simulated latent heat flux for the 2waypcwtd run vs 2way run at 1200 UTC 8 Aug 1998.

ing the night, the 2way run simulates considerably lower surface temperatures (Fig. 7b, temperature differences up to 10 K, respectively) as compared with the other two model runs, which is consistent with the measured lower outgoing longwave radiation. As compared with the measured 2-m temperature (Fig. 7a), a lower surface temperature during nighttime is more realistic, but the 2way run underestimates the surface temperature by 2– 3 K in this case. Figure 6 shows that the ctrl run considerably underestimates latent heat flux and, therefore, overestimates sensible heat flux when compared with the measurements. This is because the soil is very dry in both soil layers (Figs. 3, 5; see also Braun et al. 2001). In contrast, both the 2way and LMinit runs are in good agreement with the measurements for latent and sensible heat fluxes, mainly because of the higher soil moisture content (Figs. 3, 5). The simulated heat fluxes of these two model runs are both within the assumed measurement error of about 50 W m 22 (B. Maurer

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FIG. 6. Time series of (a) net radiation and (b) latent, (c) sensible, and (d) ground heat flux measured at Klein Altendorf compared with the simulated area mean values of the ctrl, 2way, and LMinit runs for 6, 7, and 8 Aug 1998. Measured data were provided by B. Maurer (2000, personal communication).

FIG. 7. Same as Fig. 6 but for (a) 2-m temperature and (b) surface temperature.

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FIG. 8. Time–height cross sections of the potential temperature for 6 and 7 Aug 1998: (a) measured and (b) simulated with ctrl run. Also shown are time–height cross sections of potential temperature differences of (c) 2way run minus ctrl run and (d) LMinit run minus ctrl run. Measured data were provided by B. Maurer (2000, personal communication).

2000, personal communication). There is a slight tendency of the LMinit run to overestimate latent heat fluxes (30 W m 22 ) and to underestimate sensible heat fluxes (20 W m 22 ), however. For the time integral of ground heat flux measurements, the 2way-run results are in good agreement, which is in contrast to both the ctrl and LMinit runs. Comparing the diurnal cycles of modeled and measured temperatures at 2-m height, Fig. 7a shows that the ctrl run and especially the LMinit run do not reproduce the dynamic range of the measured temperature. During the day the simulated temperature of the ctrl run is in good agreement with the measurements, but during the night the temperature is too high by up to 5 K. In contrast, the 2way run captures the amplitude of the measured 2-m temperature well, although the modeled temperature is systematically too low by up to 2 K. In general, one can conclude that the simulation of the 2m temperatures is improved by the two-way coupling. A reason for the better simulation of 2-m temperature by the 2way run, when compared with the LMinit run,

could be the better vegetation parameterization in TOPLATS as compared with TERRA. Furthermore, TERRA and TOPLATS use different methods to calculate soil temperature (see sections 2a and 2b). Figure 7 shows an upward trend over the 3-day forecast period of the simulated 2-m temperature for the ctrl and LMinit runs, which is not represented in the results of the 2way run. There unfortunately are no measurements at Klein Altendorf to prove which model results are best, but the nearest SYNOP (synoptic observation) station (Cologne) reported also an increase in temperature (1200 UTC) during the three days. However, this station is 30 km away and has a different vegetation cover (partly grass and black top). 3) BOUNDARY

LAYER DEVELOPMENT

The differences described above for the soil moisture development and the energy fluxes have an influence on the simulated structure of the boundary layer. Figures 8, 9, and 10 show time–height cross sections of potential

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FIG. 9. Same as Fig. 8 but for wind speed.

temperature, wind speed, and specific humidity, respectively, for the measurements (panel a) and the ctrl run (panel b) and differences of 2way run minus ctrl run (panel c) and LMinit run minus ctrl run (panel d) for a 2-day simulation time (6–7 August 1998). During daytime, measured potential temperature values show the development of a well-mixed boundary layer (Fig. 8a) along with an increase of horizontal wind speed (Fig. 9a). At night, a surface inversion developed up to a height of 100 m, connected with a decrease of wind speed. In the early morning (around 0700 UTC) the stable stratification broke up. During the following hours, the potential temperature strongly increased from 158 to 258C, maintaining a well-mixed boundary layer. In general, during the daytime, all three model runs simulate the well-mixed boundary layer during 6 August 1998 (Figs. 8b–d). For the LMinit run, the increase of soil moisture results in a decrease in the simulated surface temperature and sensible heat flux and an increase of latent heat flux and leads to a considerable decrease in boundary layer heating (Fig. 8d). In contrast, the 2way run with almost the same soil moisture values

simulates no significant decrease in surface temperature when compared with the ctrl run. Therefore, for the LMinit run, the mixing during the daytime is less developed than in the other simulations. Furthermore, the wind speed is greatly underestimated (Fig. 9d). At night, none of the three model runs reproduce the development of the surface inversion (Figs. 8b–d). The simulated height of the inversion of 30 m is too low. This failure is mainly related to the turbulence parameterization implemented in the LM, because even the lower surface temperatures simulated by the 2way and LMinit runs, and hence the lower net radiation, do not increase the height of the inversion. Higher simulated values of soil moisture, resulting in higher values of latent heat fluxes, for the 2way and LMinit runs increase the simulated moistening of the near-surface air (Figs. 10c,d). In comparison with the measurements (Fig. 10a), the near-surface humidity is slightly underestimated by the ctrl run, is overestimated by the 2way run, and is overestimated even more by the LMinit run.

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FIG. 10. Same as Fig. 8 but for specific humidity.

b. Rainfall event (case study 2) The influence of the different modeling systems on simulated precipitation is of special interest in the second case study. During 27–29 August 1998, Germany was influenced by a stationary extensive low pressure system over Scandinavia, which, in combination with a high pressure system over Ireland, transported cold sea air into Germany. On 27 August, a smaller low pressure system crossed Germany accompanied by mainly convective rain. The overpass of an occlusion on 28 August yielded some rain showers. Because one can expect a change in simulated precipitation only for areas larger than the area representative of our measurement site or one precipitation station, the influence of different soil moisture fields is investigated for the Sieg catchment. 1) SOIL

MOISTURE DEVELOPMENT

In Fig. 11, the temporal development of simulated soil moisture in the two layers is shown as an average of the Sieg catchment. In this case, the soil moisture in the bottom layer of the ctrl run is lower than that sim-

ulated by TOPLATS (2way run) by a factor of 2, but in the top layer the soil moisture of the ctrl run is larger than that simulated by TOPLATS. The DM analysis (not shown) shows that a rain event after a drier period yields a wet top soil layer, because the infiltration of water down to the bottom layer has just started. As a result, in the ctrl run the soil in the top layer dries rapidly by 6% (Fig. 11a), despite the cloudy and rainy situation during the 3-day simulation. Three-day precipitation in the ctrl run sums to about 6.1 mm, as shown in Fig. 14. In contrast, the soil moisture simulated by TOPLATS in both layers slightly increases during the 3-day period. The corresponding temporal development for each of the three model runs is also predicted for the Klein Altendorf measurement site and its vicinity (not shown). The results found for the 2waypcwtd run are very similar to the ones found for the first case study [section 4a(1)]. In this second case study, the changes in the soil moisture values are not so pronounced (up to 5% volumetric, as shown in Fig. 12) when compared with the 2way run, because the water content is refilled by precipitation and evapotranspiration is low. Therefore, dif-

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FIG. 11. Same as Fig. 5 but for 27–29 Aug 1998.

ferences in soil texture have the strongest impact on the changes. Nonetheless, the turning off of the lateral water flow leads to evapotranspiration changes of up to 50 W m 22 (Fig. 12) and, hence, to small changes in simulated precipitation when compared with the 2way run (Fig. 14). 2) ENERGY

FLUXES

A comparison of the modeled and measured energy fluxes at Klein Altendorf is shown in Fig. 13. Simulated net radiation values of the three model runs differ only slightly from each other, but in all three model runs the net radiation on 27 August is underestimated (100 W m 22 ), and on 28 August it is overestimated by up to 200 W m 22 by the ctrl run and the LMinit run. These discrepancies in measured and simulated net radiation yield differences between observed and modeled heat fluxes. For all three days, the latent heat flux is underestimated by 30 W m 22 , on average, and on 27 August it is underestimated by up to 150 W m 22 (ctrl run and 2way run). The LMinit run captures the measured latent

FIG. 12. Same as Fig. 4 but at 1200 UTC 29 Aug 1998.

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FIG. 13. Same as Fig. 6 but for 27–29 Aug 1998.

heat flux best on 27 August but considerably overestimates the flux on the other two days. At night, the LMinit and ctrl runs simulate a positive latent heat flux of about 30 W m 22 , whereas the 2way run is in agreement with the measurements, which show a slightly negative latent heat flux. For the first two days of the investigated time period, the modeled sensible heat flux from the ctrl and 2way runs are about 50 W m 22 higher than that simulated by the LMinit run. On the morning of 29 August, all three model runs are in good agreement with the measurements. For this period, the measurement errors for sensible and latent heat are considered to be about 100 W m 22 (B. Maurer 2000, personal communication). As a consequence, the differences in the sensible and latent heat fluxes outlined before do not seem as substantial when these errors are taken into account. As in the first case study, the measured ground heat flux varies considerably. During the day and night the ctrl run and the LMinit run over- and underestimate the ground heat flux by up to 50 W m 22 . On average, the ground heat flux simulated with the 2way run captures the measurements best. To summarize, the three model runs do not simulate correctly the cloudiness at

the measurement site on 27 and 28 August, leading to the deviations shown in Fig. 13 and discussed above. In addition, the different soil moisture fields are important. These two influences can be only separated in the first case study when net radiation is correctly simulated. 3) DEVELOPMENT

OF PRECIPITATION

Daily sums of rain gauge data (DWD) were used to calculate the area-averaged precipitation over the Sieg catchment for the three days from 27 to 29 August 1998 (see section 3c). The precipitation accumulates to 3.8 mm (Fig. 14), with 3.5 mm of convective precipitation during 27 August and 0.3 mm on 28 August. In general, all three modeling systems reproduce the temporal development. Most of the precipitation is simulated in the afternoon of 27 August (mainly convective rain) and in the morning of 28 August. In contrast to the measurements, the modeling systems simulate precipitation on 29 August (0.1 mm). The precipitation amounts simulated by the three modeling systems differ considerably from each other, and all three systems overestimate the

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c. Current limitations

FIG. 14. Time series of precipitation sums over the forecast time (27–29 Aug 1998) for an area average over the Sieg catchment measured by rain gauges and simulated by the ctrl, 2way, and LMinit runs. For 29 Aug 1998 all model runs simulate a small precipitation amount of 0.1 mm.

measured precipitation. The 2way run reproduces the measured amount best (5.1 mm), whereas the ctrl and LMinit runs simulate 6.1 and 7.7 mm, respectively. In addition, a second 2way run was performed in which the initialized soil moisture values were reduced by a factor of 0.7 [2way run (dry)]. This run results in a reduction in simulated rain amount (3.8 mm). Turning off the lateral water transport in the 2way run (2waypcwtd run) generally results in higher soil moisture values and a different distribution [see section 4b(1); also shown in Fig. 12]. These differences result in the simulation of slightly higher precipitation amounts (Fig. 14) for the 2waypcwtd run when compared with the 2way run. The phenomenon of the simulated precipitation being higher with increased soil moisture has been stated already by other authors (e.g., Scha¨r et al. 1999; Mo¨lders and Raabe 1997). When compared with TERRA in the LMinit run, TOPLATS in the 2way run reduces the deviation from the precipitation measurements (Fig. 14) from a factor of 2 to a factor of 1.34, despite both cases having started from identical initial soil moistures values (Fig. 11). These results show that not only adequate modeled soil moisture fields but also other parameters (e.g., temperatures, evapotranspiration) transferred from the different SVAT modules (TOPLATS, TERRA) to the atmospheric parts of the LM are important for the simulation of precipitation. Which of these parameters have the largest influence has not been investigated thoroughly in this study. The case study demonstrates that the amount of evapotranspiration has a significant influence on simulated precipitation, however, because TOPLATS (2way run) simulates lower evapotranspiration amounts when compared with TERRA (ctrl and LMinit runs) for the afternoon of 27 August.

As pointed out in section 2c, horizontal wind speed, temperature, and specific humidity have to be extrapolated from the lowest level (;12.5 m above ground) of the LM down to the height of the vegetation in TOPLATS. To cover different vegetation heights, three levels (2, 6, and 12 m) are chosen for the extrapolation. Figures 15a–c show scatterplots of measured temperature, wind speed, and absolute humidity versus the extrapolated parameters for heights of 2 and 6 m on 6, 27, 28, and 29 August 1998. The differences between measured and extrapolated temperature primarily occur because of differences simulated by the LM above 12.5 m. At 2-m height, measured absolute humidity shows a slight diurnal variation (drier during daytime), because at low levels even the absolute air humidity is strongly influenced by the ground and the structure of the boundary layer. These pronounced diurnal variations in the near-surface atmosphere cannot be captured by a simple adiabatic extrapolation. The differences in temperature and absolute humidity yield a bias of 210.6% and an rms error of 10.5% for relative humidity. If this bias is subtracted from the original input values, the simulation of the latent and sensible heat flux would have a maximum change of only 20 W m 22 . This means a change of 5% and 15%, respectively, from the total amounts [see the energy flux discussions in sections 4a(2) and 4b(2)]. Figure 15b shows a systematic overestimation of wind speed of 1.17 m s 21 with an rms error of 1.41 m s 21 . This bias primarily occurs because of differences in modeled and measured wind speed already existing above 12.5 m. As a conclusion from the above findings, more sophisticated models should be available for the near surface and the atmosphere to a height of about 100 m. In addition, the parameterization of hydrologic processes, like incorporating more soil layers, might improve the surface parameter simulation and, hence, the boundary layer structure. Corresponding model improvements (e.g., prognostic turbulent kinetic energy scheme, more layers in the soil module) are currently implemented in the LM (U. Scha¨ttler, DWD, 2002, personal communication). 5. Summary and conclusions In this study, the influence of a state-of-the-art LSHM, within a mesoscale atmospheric model, on the prediction of local weather has been investigated. Two initialization techniques of the LM and a two-way coupling of LM and TOPLATS are compared. In current NWP models, the bottom boundary layer values are provided by SVAT modules, which neglect the lateral water transport between neighboring grid boxes. In general, this yields unrealistically simulated soil moisture fields and, hence, influences the predicted turbulent fluxes, the boundary layer structure, and the formation of precipitation. To investigate the influence of soil moisture on the predicted local weather, the results of the original LM and

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FIG. 15. Scatterplots of measured versus extrapolated (a) temperature, (b) wind speed, and (c) absolute humidity at heights of 2 and 6 m for 6, 27, 28, and 29 Aug 1998. Measured data were provided by B. Maurer (2000, personal communication).

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of a two-way coupled modeling system based on TOPLATS are compared for two case studies. In addition, the initialization of the TERRA SVAT module of the LM with soil moisture fields from TOPLATS is investigated to determine whether providing only soil moisture fields is sufficient to improve the forecast. The influence of the spatial patterns of soil moisture from TOPLATS lateral water transport is investigated by turning it off in some of the two-way coupled model runs. The simulation results are compared with ground-based measurements of the energy fluxes and the boundary layer structure and with the measured area-mean precipitation fields. The case studies indicate that the two-way coupling leads to the most promising results. The improved simulation of the soil moisture fields in conjunction with improved simulation of the dynamic range of the surface temperature leads to better agreements with the measured energy fluxes, boundary layer structure, and precipitation. For the weather situation that has a strong solar forcing, the large errors between measured and modeled sensible and latent heat fluxes were reduced by the two-way coupling. Only slight differences in simulated boundary layer structure between the original LM and the two-way coupled model system of LM and TOPLATS are found. Because of more realistically simulated soil moisture and a better parameterization of vegetation, the two-way coupled system is able to reproduce the observed precipitation better than the original LM did for one case study. These results emphasize the importance to weather forecasting models of accurately modeled soil moisture, especially its spatial patterns, and a state-of-the-art representation of the surface hydrological processes. The simulations in which the LM is only initialized with the TOPLATS soil moisture fields improve the representation of the sensible and latent fluxes. The higher soil moisture values result in a decrease of the diurnal amplitude of the surface temperature, however, and, hence, in an unsatisfactory reproduction of the boundary layer structure. This result means that the initialization of the SVAT module TERRA in the LM with TOPLATS soil moisture fields is not sufficient to improve the weather forecast on a local scale. The possible reason for this finding is that the evapotranspiration in the LM strongly depends on soil texture because of the simple, ‘‘indirect’’ representation of vegetation in TERRA. This representation leads to a fast drying of the upper soil layer, which is enhanced because there is no lateral flow between neighboring soil columns. Furthermore, the crude parameterization of the vegetation yields an underestimation of mean diurnal surface temperatures in the case of high soil moisture values. When the lateral water transport in the two-way coupling is turned off, the horizontal redistribution of simulated soil moisture results in different simulated spatial evapotranspiration patterns and, therefore, in different simulated precipitation amounts, when compared with the original two-way coupled run. These results suggest

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that the representation of lateral water transport in the soil model could be important for weather prediction. The two-way coupling was performed so that both models run with the same horizontal spacing (1 km) and use the same time step to avoid up- and downscaling effects in this first approach. In the future, the DWD plans to run the LM operationally on a grid with a mesh width of 2.8 km. Therefore, to represent better the typical temporal and spatial scales of the hydrological processes, investigations are needed to determine the appropriate spatial and temporal scale for running TOPLATS and the LM within a two-way coupled system. Down- and upscaling methods need to be developed to account for the subscale variability of the meteorological forcing and its interaction with the land surface heterogeneity. Acknowledgments. The work was supported by the Deutschen Forschungsgemeinschaft (DFG) as part of the SFB 350 project (TP A4), which also made the discharge measurements available, and the German Exchange Service DAAD. We gratefully acknowledge the support by the German Weather Service (DWD) by providing the ‘‘Lokal Modell’’ mesoscale model and the rain gauge measurements. The model runs were partly performed on the Cray J90 and T3E of the DWD and partly on an alpha PC of the NASA Goddard Space Flight Center (Hydrological Science Branch and Data Assimilation Office, Greenbelt, Maryland). The authors thank B. Maurer and G. Heinemann (Meteorological Institute, University of Bonn, Germany), who were responsible for the measurement campaign, for the processing of the data and their helpful discussions. This work benefited from valuable discussions with M. Drusch (Meteorological Institute, University of Bonn, Germany). We also thank the anonymous reviewers for their constructive comments, which improved the quality of the paper. REFERENCES Benoit, R., P. Pellerin, N. Kouwen, H. Ritichie, N. Donaldson, P. Joe, and E. D. Soulis, 2000: Toward the use of coupled atmospheric and hydrologic models at regional scale. Mon. Wea. Rev., 128, 1681–1706. Beven, K. J., and M. J. Kirkby, 1979: A physically based variable contributing area model of basin hydrology. Hydrol. Sci. Bull., 24, 43–69. Bormann, H., R. Conrad, J. Onigkeit, and R. Seppelt, 1996: Modellanwendung: Simulation des Gebiets-Wasserhaushaltes fu¨r das Untersuchungsgebiet Nienwohlde sowie der Stickstoff- und Bestandesdynamik fu¨r das Untersuchungsgebiet Neuenkirchen (Model application: Simulation of area-water balance for the Nienwohlde study area as well as the nitrogen and crop dynamics for the Neuenkirchen study area). Wasser- und Stoffdynamik in Agraro¨kosystemen. O. Richter, D. So¨ndgerath, and B. Diekkru¨ger, Eds., Vol. 24, Landschaftso¨kologie und Umweltforschung, 268–277. Braun, P., B. Maurer, G. Mu¨ller, P. Gross, G. Heinemann, and C. Simmer, 2001: An integrated approach for the determination of regional evapotranspiration using mesoscale modelling, remote

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