分布型流出モデルとwrf気象モデルのnoah地表面モデルとの双 … ·...

平成 22年 10月 22日 10:5

第 24 回数値流体力学シンポジウムE11-1

分布型流出モデルとWRF気象モデルのNoah地表面モデルとの双方向結合

Two-way coupling of a distributed hydrological model with the Noah land surface model of WRF modeling

system

© セレスタクンダンラル, 阪大, 大阪府吹田市山田丘 2-1, S4棟, E-mail：[email protected]

近藤　明，阪大, 大阪府吹田市山田丘 2-1, S4棟, E-mail：[email protected]

井上　義雄，阪大, 大阪府吹田市山田丘 2-1, S4棟, E-mail：[email protected]

Kundan Lal Shrestha, Osaka University, S4, 2-1 Yamadaoka, Suita, Osaka

Akira Kondo, Osaka University, S4, 2-1 Yamadaoka, Suita, Osaka

Yoshio Inoue, Osaka University, S4, 2-1 Yamadaoka, Suita, Osaka

Feedback processes and lateral movement of water in soil are important aspects for accurate prediction of the

different parts of water cycle; but they are usually neglected or simplified in current meteorological models likeWeather Research and Forecasting (WRF). To overcome these problems, we have two-way coupled a quasi-physicaldistributed runoff model (HM) with the Noah land surface model of WRF to improve the feedback of soil moistureon hydrometeorological variables. Notwithstanding the different representation of soil moisture in WRF and HM,

initial validation of the coupled model reveals a distinct advantage of transmitting the stratified soil moisturefrom distributed hydrological model to WRF over the simple water balance method used in Noah model of WRF.Hence, two-way coupling of WRF and HM provides further impetus to address the growing need to improve the

simulation of the entire water cycle and its feedback processes at high spatial resolutions.

1. Introduction

One of the main phenomena governing land-air inter-action is the movement of water between atmosphere,lithosphere, hydrosphere and biosphere. In globalas well as regional settings, global warming and cli-mate change are intricately related with water cycle (1).Basin-level regional modeling requires the considerationof water cycle at high resolution to accurately simu-late or forecast the regional hydro-climate (2). Recently,mesoscale meteorological models have been widely usedto predict and forecast high-resolution mesoscale vari-ables like precipitation needed for heavy precipitationevents and flood events (3,4,5,6,7). But only episode sim-ulation and few long-term results on basin scale areavailable. Most researchers have focused on how toeffectively use atmospheric forcings like precipitationin high spatial resolution settings of hydrological mod-els. One-way coupling of meteorological and hydrolog-ical models is mostly used to drive hydrological mod-els by the hydrometeorological variables like air tem-perature and precipitation generated by meteorologicalmodels (8). Due to high resolution of spatial and tempo-ral scales needed for coupling atmospheric and hydro-logical model, many computational problems arise dur-ing such integrated hydrometeorological studies. Thesecomputational limitations are also being overcome withthe use of more powerful computing resources and bet-ter atmosphere-hydrology coupling techniques.

Recently, the feedback from hydrological models tothe atmospheric models are also being studied withspecial attention given to atmosphere−land surface in-teractions related mostly to changes in precipitationand soil moisture fields. Feedback between atmosphere,land and soil are important when considering the studyof water cycle. The runoff processes on land surfacesand lateral movement of water in soil layers are impor-tant while considering the exchange processes relatedto water cycle at the atmosphere-land interface (9) be-cause their correct representation produces better sim-

ulation of soil moisture and soil temperature (3,6). Con-sequently, the representation of the effect of soil mois-ture on evapotranspiration improves (10); then, the sim-ulation of the effect of evapotranspiration on precipita-tion pattern improves (11); and the overall effect can befelt in the change in the formation of precipitation andcloud (3,12).The dominant spatial and temporal scales in the at-

mosphere, land and soil parts of the water cycle is dif-ficult to be included in a single coupled model. Therehave been several attempts to circumvent this problemwhile coupling the atmospheric, land surface and hydro-logical models. One method is to couple the air-land-soil interaction at regional scale for large domains (13),and for finer scales, downscaled and upscaled exchangeof hydrometeorological variables between different me-dia or models can be used (14), such as the mosaic andexplicit subgrid scaling method (3,9). Another straight-forward method for two-way coupling between differ-ent parts of water cycle is to use the same high spa-tial resolution in all the parts so that the spatial scaleof the integrated model satisfies the standalone modelhaving highest spatial scale requirements (6). Using thesame spatial scale for atmospheric model and the landand sub-surface models, the intricacies involved in inter-media communication and scaling of hydrometeorolog-ical variables are avoided but the simulation time in-creases due to high-spatial resolution required by theintegrated model.Many research works have used the model coupling

techniques in the studies of water cycle that focuses onthe atmosphere-land interface, with emphasis on air-soil-vegetation interaction (15), air-land interaction atmesoscale with data assimilation (12), and so on. Therehave been other attempts at improving the representa-tion of hydrology in the land surface and atmosphericmodels by coupling a distributed hydrological modelhaving interception, infiltration and runoff processeswith the atmospheric or land surface model (16,17). We

1 Copyright c© 2010 by JSFM


Fig. 1: Nesting of WRF domains.

have used this explicit two-way coupling technique inthis presented research work to couple the widely usedNoah land surface model of Weather Research and Fore-casting (WRF) mesoscale model with a distributed hy-drological runoff model so that the both models run atsame spatial resolution. The presented two-way cou-pled atmospheric-hydrological model provides scope forbetter representation of hydrology in atmospheric mod-els, and better simulation of waterflow in terrestrial andsub-surface regions.

2. Method

2.1 WRF mesoscale modelingSome important options selected for WRF model are

shown in Table 1. Precipitation field is mainly affectedby the microphysics and cumulus parameterization. Dy-namic or stratiform precipitation is obtained from mi-crophysics scheme and convective precipitation is ob-tained from cumulus parameterization scheme. Cumu-lus parameterization generally has greater impact in theprecipitation over landmass, and microphysics optionhas greater effect in the diurnal precipitation pattern inoceans (18). WRF Single-Moment 5-class microphysicsscheme is suitable for long-term simulation with gridsizes less than 3-km. Similarly, Kain-Fritsch cumulusparameterization scheme is a deep and shallow convec-tion subgrid scheme that can be efficient and suitablefor mesoscale modeling. But in 1-km grid, cumulusparameterization is not used. Since sea surface tem-perature (SST) also affects the precipitation events inlong-term simulations (19), SST was daily updated us-ing the 0.5-degree NCEP real-time global analysis SST(http://polar.ncep.noaa.gov/sst/ophi/).NCEP Global Analyses data was chosen as boundary

condition and initial condition for WRF simulation be-cause real-time global analysis SST data was also avail-able in case of NCEP data. NCEP Global Analyses datawas dynamically downscaled to 1-km grid domain con-taining the Yodo River basin using one-way nesting of1-km gridded Domain-3 inside 4-km gridded Domain-2,which was again nested inside 16-km gridded Domain-1(Fig. 1).

Tab. 1: WRF parameters and options

Parameter/option Value

Nesting One-way with 3 domains

Domain-1 size 90 × 90 mesh with

16-km mesh size

Domain-2 size 93× 93 mesh with 4-km

mesh size

Domain-3 size 141 × 177 mesh with

1-km mesh size

Vertical grid 35 full eta levels with

top level at 1 kPa

Microphysics WSM 5-class scheme

Cumulus Kain-Fritsch scheme

(not used for Domain-3)

Planetary boundary

layer

YSU scheme

Land surface Noah land surface model

Longwave radiation RRTM scheme

Shortwave radiation Dudhia scheme

2.2 Hydrological modelingYodo River basin is a mesoscale type of river basin.

Basin area of mesoscale river basins can reach fromseveral hundreds to several thousands of square kilo-meters (20) and high-resolution hydrological modeling isrequired to study the impact of meteorological and cli-mate changes on water resources of such mesoscale riverbasins. The mesoscale hydrological modeling requiresfiner spatial and temporal resolutions than the large-scale hydrological modeling. The land-use and precipi-tation data are also required to have high resolution.Physically-based distributed hydrological modeling is

suitable for assessing hydrology of mesoscale basins.The important model components of such distributedhydrological models consist of evapotranspiration, sur-face runoff, sub-surface water flow, groundwater flow,river channel flow, reservoir operations, water intake,sewerage disposal, interflow in the soil, and so on.Yodo River basin is one of the major basin areas in

Japan and it influences and controls the water cycle ofthe Kinki region of Japan, which contains large citieslike Osaka, Nara and Kyoto. Seta River originates fromLake Biwa and flows through Kyoto Prefecture as UjiRiver. Katsura River from Kyoto Prefecture and KizuRiver from Mie Prefecture merge with Uji River to formYodo River in Osaka Prefecture, which then finally flowsinto Osaka Bay.In Yodo River basin, high-resolution distributed hy-

drological modeling approach is used to simulate thewater flow and river discharge in response to the chang-ing hydrometeorological variables in the basin. Riverflow, surface runoff, sub-surface groundwater flow, wa-ter intake, and dam reservoir operations in Yodo Riverbasin is modeled using a high-resolution distributed hy-drological model based on a rainfall-runoff model knownas Hydrological River Basin Environment AssessmentModel (HydroBEAM) (21). This hydrological model willbe called simply as “HM” henceforth.



Fig. 2: Dams and hydrological observation stations in

Yodo River basin.

Tab. 2: Different parts of runoff model (HM).

No. Zones Modeling technique

1 Surface runoff Kinematic routing

2 Unsaturated

vadose

Effective porosity and

hydraulic conductivity

3 Saturated

soil-water

Kinematic routing in

sloping aquifer

4 Groundwater Tank cascade with linear

storage elements in series

Yodo River basin is gridded into 8242 square gridshaving 1 km x 1 km resolution (Fig. 2). Each grid inthe basin contains terrain data and river network flowdirection that channels water flowing in the rivers. Theevapotranspiration model is used to solve surface energybalance in each of the basin grids. In Lake Biwa, lakemodel has not been used to simulate the lake hydrody-namics. So, in the rainfall-runoff model, only 7557 grids,excluding those of Lake Biwa, are used in the simula-tion. The input streams into Lake Biwa are directly ledout from the outlet of the lake.The heterogeneity in orography of Yodo River basin

requires a high spatial resolution in precipitation resultsfrom WRF. The spatial scale in the order of 1-km isgenerally required in these types of mesoscale studies.To avoid downscaling and upscaling operations, grid sizeof both the HM and WRF were set at 1-km.HM avoids the complexities of fully physical models

but better than the conceptual lumped models. Somesalient features of HM are shown in Table 2.In the hydrological model (Fig. 3), the basin is di-

Fig. 3: Hydrological model framework.

vided into terrain grids that act as a single unit basinhaving 1 km x 1 km horizontal resolution. The grids alsoact as river flow network (Fig. 2) in which correspond-ing upstream grids are allocated for each of the down-stream grids. The surface runoff in each grid flows intothe corresponding downstream river grid. Each terraingrid is divided into five land-use categories (fields, for-est, urban, paddy and water) (Fig. 4). The basin unitsor the grids are vertically divided into four soil layers (A,B, C, and D from top to bottom respectively). Surfaceenergy balance model is applied at each grid to esti-mate rainfall, snowmelt and evapotranspiration. Thesurface runoff, runoff from paddy fields, ground waterflow, water intake and release, and lateral water flowsare simulated by HydroBEAM. The lateral movementof water in soil layers, except layer D, can flow into theriver channel. The sewerage network, wastewater andirrigation canals in paddy fields are also included in themodel.Precipitation and evapotranspiration quantities sim-

ulated by WRF are used as input data in the Hy-droBEAM runoff model. Water intake and release forwater supply, water use, hydroelectric plants, and irri-gation purposes are also used as input into the runoffmodel. The sewerage and wastewater flow data are alsoused for the corresponding grids. Dam operation rulesare used to control the water level and storage in thesix majors dams (Fig. 2).

2.3 Two-way coupling methodIn the two-way coupling between the Noah land sur-

face model of WRF and HM, the hourly precipitationand surface moisture flux from Noah are used as inputto HM and the soil moisture simulated by HM is passedback to WRF.The soil layers in WRF and HM have different thick-

ness. WRF soil layers have thickness of 0.1 m, 0.3 m,0.6 m, 1 m from top to bottom layers respectively; andHM soil layers have thickness of 0.3 m, 1.0 m, 2.5 m,10 m from top to bottom layers respectively (Fig. 5).Since the soil moisture is not directly calculated in HM,we have derived soil moisture content (m3 m−3) as wa-ter depth (m) in HM divided by soil layer thickness (m).Since the field capacity of soil layers is not explicitly in-cluded in HM, the minimum water depth is assumed to



(a) Urban (b) Forest

(c) Paddy (d) Field

Fig. 4: Land use percentage of different land use cate-

gories in Yodo River basin.

have soil moisture equal to the field capacity. The soiltype in Yodo River basin is predominantly loam andclay loam in WRF model. So the field capacity is pa-rameterized as 0.36, which is the average of these twosoil types. The first and second soil layer of WRF wasset to receive soil moisture from the first soil layer ofHM, third layer of WRF was set to receive moisturefrom second layer of HM and the bottommost layer ofWRF was set to receive soil moisture from the third soillayer of HM.After the one-way nesting in WRF between Domain-

1 and Domain-2 was completed, the two-way couplingbetween Domain-3 of WRF and HM was carried out.Hourly feedback between WRF and HM was imple-mented with a small time difference between the ex-change steps. The following sequence of operation arecarried out hourly for two-way coupling between WRFand HM:

(1) Call Noah land surface model

(2) Write WRF meteorological variables (Precipita-tion, evapotranspiration)

(3) Call hydrological runoff model (HM) from Noahmodel

(4) Read meteorological variables in HM

(5) Write hydrological variables from HM (Soil mois-ture)

(6) Read hydrological variables in Noah land surfacemodel

WRF HM

−14

−12

−10

−8

−6

−4

−2

0

Soillayerdepth

(m)

0.3

1.0

2.5

10.0

Layer 1 (top)

Layer 2

Layer 3

Layer 4

−2.0

−1.5

−1.0

−0.5

0.00.1

0.3

0.6

1.0

WRF (magnified)

Fig. 5: Soil depths in WRF and hydrological model

(HM).

The one-day simulation for the two-way coupled model-ing of Yodo River basin took nearly 1 hour of user timewhen using 11 parallel processes in two 2.67 GHz 6-core Intel Xeon processors. It should be noted that theinclusion of HM at every hour of simulation does notsignificantly increase the WRF simulation time. Usu-ally the standalone HM takes only about 2 to 3 hoursfor the completion of 1-year simulation of Yodo Riverbasin.

3. Results

3.1 Observation dataFor evaluation of the simulated meteorological vari-

ables, the observation data were obtained from the “AirPollution Continuous Monitoring Network Data Files”provided by Environmental Pollution Control Center,Osaka Prefecture. The air pollution monitoring net-work of Osaka covers the Osaka prefectural area with 20air monitoring stations, and 11 auto exhaust monitor-ing stations. The monitored items are SO2, particulate,



Fig. 6: Meteorological observation stations.

oxidants, CO, NO, NO2, hydrocarbons, meteorologicalparameters and traffic volume. So the Osaka prefec-ture has 31 air monitoring stations. Similarly, there are94 stations in the various cities of Osaka. The onlinerepository of Osaka prefecture and Osaka City (Osaka-shi) contains a well-managed set of observation data formore than 100 station points spread across the prefec-ture. Fig. 6 and Table 3 show the meteorological ob-servation points used in this study for the purpose ofevaluating the model performance.TheWater Information System database provided on-

line by Ministry of Land, Infrastructure and Transport,Japan (http://www1.river.go.jp/, in Japanese) con-tains many hydrological observation stations in YodoRiver basin. But, at present, most of the observationstations have missing data in 2006. Hence, only twoobservation stations, viz., Hirakata and Takahama (Fig.2) are validated with the observed hydrological data. InHirakata station, only the high discharge peak in July2006 was available in the observed river discharge data.

3.2 Validation of coupled model by statistical

analysis of meteorological variablesThe two-way coupling of WRF and HM was vali-

dated indirectly by statistical analysis of near-surfaceair temperature and near-surface relative humidity, withthe assumption that these variables are affected by thechanging soil moisture, surface moisture flux and evap-otranspiration. Since the measurements of soil moistureand evapotranspiration were not readily available, thismethod has been selected for initial validation of thecoupled model. Though we are aware that most of theobservation stations are in downstream urban region ofOsaka, this initial assessment will certainly be helpful inascertaining the impact of coupling between WRF andHM on the highly populated urban region.The overall mean bias and mean average gross er-

ror of hourly air temperature of the first 99 days of2006 show an improvement in the prediction of near-surface air temperature when using two-way couplingbetween WRF and HM (Table 4). Compared with one-

Tab. 3: Observation stations used for statistical analy-

sis.

Station Name

104 Moriguchifuminkenkoupuraza

107 Kokusetsuoosaka

109 Ibarakishiyakusho

110 Neyagawashiyakusho

115 Ikedashiritsuminamihatakaikan

116 Daitoushiyakusho

168 Kokusetsushijounawate

201 Seibishougakkou

203 Hiraoshougakkou

210 Setsuyouchuugakkou

602 Sennari

649 Ueno

651 Senri

701 Suitashinishishoubousho

703 Suitashikitashoubousho

759 Kyoto

761 Hikone

772 Osaka

780 Nara

802 Higashioosakashiasahimachichousha

2101 Takatsukiminami

2202 Yaofuminkenkoupuraza

2301 Narita

way coupling, two-way coupling reduces mean bias by22.8% and mean average gross error by 2.4%. Over-all correlation coefficient remains the same for both thecoupling modes. On the other hand, near-surface rela-tive humidity shows mixed results when using the twomodes of coupling (Table 5). Overall mean bias of near-surface relative humidity decreases by 28.1% but theoverall mean average gross error slightly increases by0.5%. In the case of relative humidity also, the over-all correlation coefficient remains the same for both thecases. It should be noted here that precipitation is alsoan important variable that has been validated in previ-ous studies of one-way coupling of WRF and HM (22).Examples of air temperature, relative humidity and pre-cipitation time series are shown for a typical station inOsaka is shown in Fig. 7.

The initial validation results show that the simula-tion of meteorological variables may be improved bytwo-way coupling the WRF mesoscale meteorologicalmodel and a distributed hydrological model such as HM.The validation results also show that we can reliably as-sume some improvement in the other hydrometeorolog-ical variables like evapotranspiration and soil moisturecontent, when we use two-way coupling between WRFand HM.



Tab. 4: Statistical analysis of near-surface air temper-

ature.

MB MAGE COR

Sta-

tion

One

way

Two

way

One

way

Two

way

One

way

Two

way

104 0.82 0.66 1.32 1.26 0.92 0.92

107 0.77 0.63 1.48 1.43 0.89 0.89

109 0.50 0.35 1.66 1.62 0.88 0.87

110 0.80 0.65 1.38 1.33 0.92 0.92

115 -0.07 -0.17 1.33 1.35 0.92 0.92

116 0.70 0.55 1.82 1.78 0.82 0.82

168 0.69 0.49 1.52 1.45 0.90 0.90

201 1.49 1.35 1.73 1.63 0.92 0.92

203 1.20 1.06 1.49 1.42 0.92 0.92

210 0.79 0.66 2.98 2.96 0.49 0.49

602 0.77 0.62 1.53 1.49 0.91 0.91

649 0.50 0.12 1.64 1.66 0.89 0.89

651 0.49 0.36 1.75 1.72 0.87 0.87

701 0.11 -0.03 1.20 1.21 0.92 0.92

703 0.95 0.82 1.63 1.59 0.90 0.90

759 0.64 0.51 1.36 1.32 0.93 0.93

761 1.59 1.49 1.82 1.78 0.89 0.89

772 -0.17 -0.33 1.44 1.48 0.91 0.90

780 1.80 1.66 2.28 2.20 0.87 0.87

802 -0.38 -0.49 1.58 1.62 0.89 0.89

2101 0.66 0.51 1.44 1.40 0.91 0.91

2202 0.54 0.37 1.35 1.33 0.91 0.91

2301 0.81 0.66 1.87 1.80 0.87 0.87

Total 0.70 0.54 1.64 1.60 0.88 0.88

(1) Underlined values represent those cases where one-

way model gives better metrics than the two-way

model. In all other cases, two-way model fares

equal or better than the one-way model.

(2) MB is mean bias (K), MAGE is Mean average gross

error (K), and COR is correlation coefficient.

Tab. 5: Statistical analysis of near-surface relative hu-

midity.

MB MAGE COR

Sta-

tion

One

way

Two

way

One

way

Two

way

One

way

Two

way

107 -3.44 -2.81 10.14 10.17 0.67 0.67

110 3.22 3.87 9.84 10.06 0.74 0.74

168 2.98 3.90 9.38 9.59 0.73 0.73

201 -7.36 -6.75 11.24 11.01 0.74 0.73

203 -0.76 -0.18 8.60 8.75 0.71 0.71

210 -9.00 -8.45 16.13 15.86 0.29 0.30

602 -10.26 -9.64 17.19 16.91 0.68 0.68

649 6.15 7.86 10.62 11.34 0.72 0.73

701 -2.23 -1.64 9.14 9.24 0.73 0.72

703 -7.44 -6.92 13.13 13.10 0.69 0.68

759 0.55 1.05 8.91 9.01 0.71 0.71

761 -6.45 -6.45 10.31 10.34 0.66 0.66

772 5.56 6.24 10.37 10.81 0.71 0.70

780 -6.52 -5.83 12.01 11.84 0.66 0.66

802 2.44 2.97 10.15 10.42 0.67 0.66

2101 -5.99 -5.33 11.39 11.22 0.71 0.71

2202 -1.94 -1.21 8.75 8.77 0.73 0.72

2301 -1.76 -1.09 9.93 9.92 0.73 0.72

Total -2.35 -1.69 10.96 11.02 0.68 0.68

(1) Underlined values represent those cases where one-

way model gives better metrics than the two-way

model. In all other cases, two-way model fares

equal or better than the one-way model.

(2) MB is mean bias (%), MAGE is Mean average gross

error (%), and COR is correlation coefficient.



0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

265

270

275

280

285

290

295

300

305

Airtemperature

(K)

Observed One-way Two-way

(a) Air temperature.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

10

20

30

40

50

60

70

80

90

100

Relativehumidity(%

)

Observed One-way Two-way

(b) Relative humidity.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0

2

4

6

8

10

12

14

Precipitation(m

mhr−

1)

Observed

One-way

Two-way

(c) Precipitation rate.

Fig. 7: Near-surface air temperature, relative humidity

and precipitation at Station No. 107.

3.3 Soil moisture contentSoil moisture content is an important hydrometeoro-

logical variable that affects the surface and sub-surfaceparts of water cycle. It is generally accepted that someimportant feedback exists among the subsurface, ter-restrial and atmospheric parts of the water cycle. Thisstrong coupling between the various compartments ofwater cycle is also manifested in the development ofsoil moisture at various depths of soil from the surfacelevel (17,3,8,16).To investigate the actual change in soil moisture due

to the coupling of HM with the land surface modelof WRF, the soil moisture content at four soil layersof WRF were observed at various observation stations.Temporal changes of soil moisture content at four sta-tions are shown as example in this section (Fig. 8 to11).At the first and second soil layers from the surface

level, soil moisture content from HM was found to bewell-correlated with WRF results at the stations nearto the river streams (Fig. 8 and 9). But the soil mois-ture content was different at some stations relatively farfrom the river streams. It seems that the use of a dis-tributed runoff model is beneficial for providing the soilmoisture content to the land surface model in WRF.HM tends to show larger variation in soil moisture con-tent near the river streams and water bodies due tothe explicit representation of waterflow along the riverstreams. The results also suggest that the default hy-drological model in the Noah land surface model mayhave overestimated the soil moisture content in the ur-ban regions. At station no. 649, which is situated in themountainous region, feedback effect of two-way modelcoupling on the soil moisture content can also be ob-served with the decrease in soil moisture content after85 days of the start of simulation when two-way couplingis used. A few days of dry periods before the 85th dayof 2006 could have contributed to this feedback effecton the soil moisture content, which could also potentialchange the evapotranspiration and cloud formation inthe atmosphere. Eventually, the pattern of precipita-tion may also be affected due to the two-way couplingprocess.In the third soil layer from the surface level (Fig

10), the results of soil moisture content in WRF andHM in one-way mode, as well as the soil moisture con-tent from two-way coupled mode show similar results,though more soil moisture was estimated by Noah landsurface model of WRF in one-way mode. Though ac-tual measurements of soil moisture at subsurface layersare not available, the use of two-way coupling is foundto be able to reliably reproduce the soil moisture thatis comparable with the WRF moisture predictions instandalone mode.In the fourth soil layer from the surface (bottommost

layer in WRF), very slow variation in temporal devel-opment of soil moisture content is observed when usingHM (Fig. 11). This can be partially attributed to thefact that the actual layer thick in HM for this layerreaches the depth of 3.8 m, while the WRF soil layerreaches the depth of only 2 m. Thus the deeper soilmoisture may have been changed slowly with time inHM.

3.4 River discharge and dam outflowDam network in Yodo River basin is highly regulated

using several complex operation rules such as ordinaryoperation, preliminary release operation, emergency op-eration, peak attenuation operation and post-flood op-eration (23). The microscale studies of reservoir effectson the water resource of river basins require exhaustive



0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way

(a) Station No. 201.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way

(b) Station No. 602.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way

(c) Station No. 649.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way

(d) Station No. 2101.

Fig. 8: Top soil layer (Layer 1) moisture content at four

stations in Yodo River basin.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


Fig. 9: Second soil layer (Layer 2) moisture content at

four stations in Yodo River basin.



0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


Fig. 10: Third soil layer (Layer 3) moisture content at

four stations in Yodo River basin.

0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0.35

0.40

0.45

0.50

Soilmoisture

content(m

3m

−3)

One-way (in WRF)

One-way (in HM)

Two-way


Fig. 11: Bottom soil layer (Layer 4) moisture content

at four stations in Yodo River basin.



classification of dam operation rules to simulate the ac-tual practices in dam control. In this study, the damoperations have been simplified to focus on the overallresponse of mesoscale Yodo River basin to atmosphericforcings. Since very few hydrological observation data isavailable for Yodo River basin in 2006, some major damoutflows and river discharge in a downstream station areonly discussed (Fig. 12).At Amagase dam (Fig. 12a), some large peaks are

observed in simulated outflow, which are not present inthe observed outflow. The highly regulated nature ofthe Amagase dam and Nangoaraizeki weir in the up-stream near Lake Biwa are mostly responsible for theabsence of high peak flows at this site. The complexnature of dam regulation is very difficult to incorporatein the simplified dam model used presently.At Takahama observation station (Fig. 12d), overall

simulation of the river discharge is comparable to theobserved outflow. Takahama station is situated down-stream of Yodo River basin and the outflow in Taka-hama suggests a good estimation of the total accumu-lated outflow in the upstream regions.When two-way coupling of WRF and HM is used,

usually some change is observed in the outflow in thestations under study. Some decrease in the outflow ratecan be observed after 85 days, which again can be at-tributed to several dry periods preceding this decreasein outflow rate. All in all, the effect of two-way couplingbetween WRF and HM on river discharge is apparentlyindicated by the results.

4. ConclusionWRF mesoscale meteorological model was two-way

coupled at 1-km grid resolution with the quasi-physicaldistributed rainfall-runoff hydrological model by usingthe Noah land surface model as the interface betweenthe two models. Rate of evapotranspiration and pre-cipitation were passed from Noah land surface model ofWRF to the distributed hydrological model and the soilmoisture at different soil layers calculated by the hy-drological model was passed back to Noah land surfacemodel in WRF.The preliminary validation of the hydrometeorolog-

ical variables showed that the two-way coupling tech-nique applied to integration of WRF and HM may bebeneficial in improving the performance of both the me-teorological and hydrological model. The two-way inte-gration of WRF and HM also indicates the advantage ofusing the distributed hydrological model over the simplewater balance model used in Noah land surface modelof WRF. The lateral water movement and streamflowrepresentation in HM may be reliably used in place ofthe simple surface runoff parameterization used in Noahmodel of WRF.All in all, this investigation into the high-resolution

coupling between the atmospheric and hydrologicalmodels further corroborates the need to represent thefeedback processes involved in the water cycle.

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0 10 20 30 40 50 60 70 80 90 100Days since 2006-01-01 00:00 UTC

0

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