IMPACT OF LAND USE CHANGE ON WEATHER RESEARCH AND FORECASTING (WRF) OUTPUT: A CASE STUDY FOR THE PHILIPPINES

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1 IMPACT OF LAND USE CHANGE ON WEATHER RESEARCH AND FORECASTING (WRF) OUTPUT: A CASE STUDY FOR THE PHILIPPINES Robert B. Badrina 1 Jay Samuel L. Combinido 2 Gay Jane P. Perez 2 1 Philippine Atmospheric Geophysical Astronomical Services Administration, Diliman, Quezon City, Metro Manila, Philippines rbbadrina@gmail.com 2 University of the Philippines, Diliman, Quezon City, 1001, Metro Manila, Philippines, combinido@gmail.com, gpperez1@up.edu.ph KEY WORDS: MODIS, Land Use Change, Numerical Weather Prediction Model ABSTRACT: The interaction between land and the atmosphere is important in every weather prediction model. Changes in land use can affect the output of weather model because the incoming and outgoing radiations are altered by the modification of biophysical properties on the earth's surface. In this research, the Weather Forecasting and Research Model (WRF) was used to produce numerical weather prediction (NWP) output for June 2015 with the 2001 and 2010 land use from the Moderate Resolution Imaging Spectroradiomater (MODIS). Comparisons of the resulting forecast outputs were conducted between the two time periods of to evaluate the impact of land use changes that happened in the country. The study utilized the biophysical parameters of various land use in order to determine the difference brought by land use change, particularly it looked in the Albedo, Emissivity, Surface Roughness and Soil Moisture availability. The research proved the feasibility of using available land use data from MODIS in order to update the land use input in the model. The changes in land use brought variation on these biophysical parameters which have caused the general alteration in meteorological variables such as changes in minimum and maximum temperature, wind speed and precipitation. The result of this study proved that MODIS is one of the land use data feasible to be utilized in NWP model. The impacts brought by updated land use data could be used to understand the land and atmospheric interaction including the improvement of weather and climate model for future simulations. 1. INTRODUCTION Land use, as part of the complex climate system, can significantly influence meteorological parameters such as precipitation, temperature and wind. Changes in the land use may brought these impacts on the atmospheric processes due to the modification of storage and transfer of heat including water and air flow at the surface (Mahmood et al., 2013). The biogeophysical processes related to land use change affect atmospheric processes because it influences the physical parameters of the land surface such as albedo, surface roughness, root depth and leaf area index (LAI). There has been added interest in the study of biogeophysical components because of the availability of tools and technology to quantify these parameters. Modelling studies demonstrated that land use change impinges on the atmospheric parameters such as temperature, precipitation and wind as a consequence of the changes in the perturbation in the partition of sensible and latent heat (Narisma and Pitman, 2003; De Fries et al., 2006; Pielke Sr. et al., 2002). Additional observational studies using climate records and remotely sensed data showed that changing land cover correlates to changes in temperature and precipitation (Mahmood et al., 2014). Numerical Weather Prediction (NWP) Models are used to mathematically represent numerically the different components of atmospheric processes. Researchers try to utilize models using a set of mathematical equations to understand the interactions of these components. It is also a way of reducing the complexity of the atmospheric processes. With the advent of technology, remotely sensed data can be integrated in the NWP models in order to better represent the land use component in the atmospheric process. In this study, we utilized the integration of updated MODIS data to an NWP model to test its feasibility and to determine the impact of the changes brought by land use change on biogeophysical parameters and meteorological variables. 2. METHODOLOGY 2.1 WRF Model The Weather Research and Forecasting (WRF) is a type of numerical weather prediction model which has mesoscale spatial characteristics and can be used both for operational and atmospheric research purposes. It has been widely utilized in the meteorological modeling community. It was developed in the late 1990 s with a partnership among the

2 National Center for Atmospheric Research (NCAR), the National Oceanic for Environmental Prediction (NCEP) and the (then) Forecast System Laboratory (FSL), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, the University of Oklahoma and the Federal Aviation Administration (FAA). Currently, it has a worldwide community of registered users according to the user s support website (as of 2015, over 25,000 in over 30 countries). The development of this model is continuously being done with the contribution of various users since this is an open source model. The WRF version 3.4 was used in the experiment; the USGS is the default land use while a version of MODIS 2001data is also available for the user s option. 2.2 Landuse Data According to Friedl et al. (2002) the main objective of the MODIS land use product is for regional and global modeling studies by using the biophysical information embedded to it. The International Geosphere Biosphere Programme Data and Information System (IGBP-DIS) with a set of 17 land use classes was used for MODIS land use product since IGBP system of units was developed for similar use of biophysical parameterization for modeling (Loveland and Belward, 1997). The MODIS based land use has a resolution of 1 kilometer which can be updated at a 96 day interval or quarterly. The WRF version 3.4 model has two available land use options for the users, the USGS and MODIS. The MODIS option is based on the data retrieved in Since this research dealt with an updated MODIS land use, additional processing was done to utilize the 2010 data retrieved for MODIS. The raw MODIS land use product (MCD12Q1) was downloaded from These data are in sinusoidal projection; hence, additional files were needed to cover the specified model domain. The MODIS raw data was reprojected to be compatible to a geographic projection. The collated and reprojected files were converted to a single binary file following the resolution required by the WRF model. 2.3 Simulation Experiment Two simulation experiments were run using the Advanced Research WRF (ARW) dynamic solver of the WRF system Version The experiments were run using the same configurations but with different land use MODIS 2001 and MODIS For both experiments, the physical parameterization schemes are shown in Table 1 and the model configurations are shown in table 2. Table 1: Physical Parameterization Schemes Vertical Levels 35 Model top 50 hpa Map projection Lambert conformal Horizontal grid distribution staggered Arakawa C-grid Horizontal grid distance 12 km Integration time step 72 seconds Nesting none Table 2: WRF model configuration Long-wave radiation Rapid Radiative Transfer Model (RRTM) Short-wave radiation Surface layer Revised Planetary boundary layer Microphysics Cumulus Dudhia MM5 Monin-Obukhov Yonsei University (YSU) Ferrier (new Eta) Kain-Fritsch The initial condition boundary condition (ICBC) used as input in the model is the NCEP FNL, this is the Final Operational Global Analysis data which are on a 1-degree by 1-degree grids prepared operationally every six hours. In the two experiments the simulation date started from May UTC up to June UTC, however, only the output for the month of June were used in the analysis since the May run is used for the spin up of the model.

3 Figure 1: Domains of the Simulation Experiments In this experiment, we run the simulation using two domains. Domain 1 is the larger area with 12 km resolution while Domain 2 is a smaller area with higher resolution of 4 kilometer. We decided to run two simulations using different domains to look on how the changes can affect at different spatial scale, Domain 1 is the synoptic while Domain 2 is the mesoscale. Figures 2 and 3 show the land use map derived from the MODIS data, changes in land use particularly the decrease of forestland and increase in urban areas are observable in the updated land use. Figure 2: Comparison of MODIS Land Use in Domain 1 Figure 3: Comparison of MODIS Land Use in Domain

4 3. Result The result of the simulations is divided into two parts, first is the impact on biophysical parameters and the effect on meteorological variables such as precipitation, minimum and maximum temperature and wind speed. The former explains the impact on the latter since the only difference between the two simulations is the land use. 3.1 Biophysical Parameters Albedo Figure 4: Difference of the two land use for Albedo in Domain 1 (left), Domain 2 (right) Figure 4 shows the result of the difference between the MODIS 2012 and MODIS 2001 land use for albedo. Generally, in Domain 1 there is a decrease in albedo in majority of the areas in the country while the Domain 2 shows an increase of albedo particularly in Metro Manila and adjacent areas. Decrease in albedo would result to decrease in the amount of shortwave radiation reflected back in the atmosphere; hence, this results to increasing absorption of shortwave radiation. Figure 5: Difference of the two land use for Soil Moisture Availability in Domain 1 (left), Domain 2 (right) Figure 5 shows that updated land use has a general decrease in soil moisture availability in the northern part of the country while increase in the southern part (Domain 1). Domain 2 shows majority of the areas with decrease in soil moisture availability. A lower soil moisture availability may cause in decrease in heat capacity of the surface hence it

5 may increase the temperature at the surface. Lower moisture may also cause a change in evaporation rate of the model. However, in depth study is still needed to establish the said relationship. Figure 6: Difference of the two land use for Emissivity in Domain 1 (left), Domain 2 (right) The emissivity is considered as the outgoing longwave radiation since it measures the capacity of the material to emit energy after absorption. An updated land use shows a general decrease of emissivity in the Domain 1, while in the Domain 2, areas in Metro Manila experienced an increase in emissivity. The outgoing longwave radiation usually happens in the evening thus increasing emissivity could affect the minimum temperature. Figure 7: Difference of the two land use for Roughness Length in Domain 1 (left), Domain 2 (right) The roughness length is an important parameter affecting wind speed because it influences the frictional force cause by the surface drag as the air moves in the land. Figure 7 shows that updated land use shows decreasing roughness length, this could be due to the decrease of forested areas in the domain upon using the MODIS 2012 data. Higher value for the updated land use can cause more frictional force effect hence can lower the value of wind speed.

6 3.2 Meteorological Parameters Minimum and Maximum Temperature Figure 8: Difference of the two land use for Minimum (left) and Maximum (right) Temperature in Domain 1 Figure 9: Difference of the two land use for Minimum (left) and Maximum (right) Temperature in Domain 2 Figures 8 and 9 show the impact of the updated land use on the Minimum and Maximum temperature for both domains. For this analysis, the values are taken from the average daily minimum and maximum temperature for the complete month of June. The minimum and maximum temperatures are affected by changes in biophysical parameters as shown by the reviews and researches conducted by Pielke Sr. et al (2002) and Mahmood et al. (2014). In this experiment, updated land use has an increasing impact on the minimum temperature particularly in the southern part of the domain while a decreasing effect on the maximum temperature has shown in this region. The impact of increase in albedo affected the maximum temperature which shows decreasing effect. The increased in emissivity affected the minimum temperature by having a higher value after utilizing the updated land use. Urbanization on the other hand played a significant role as shown in the impact of updated land use in Domain 2. The increase in emissivity and decrease of soil moisture content caused the increase in minimum temperature in Domain 2. There are also areas that showed decreasing maximum temperature which could be attributed in the

7 3.2.2 Wind Speed Figure 10: Difference of the two land use for Wind Speed in Domain 1 (left) and Domain 2 (right) Figure 10 shows the impact of updated land usethe updated land use brought a generally increase in wind speed in both of the domains. The impact on the wind speed is to be attributed in the decreasing surface roughness using the MODIS 2012 data Precipitation Figure 11: Difference of the two land use for Accumulated Rainfall in Domain 1 (left) and Domain 2 (right) Figure 11 shows the impact of updated land use data on the total accumulated rainfall in the two domains. The values of the total accumulated monthly rainfall are quite high compared to the normal actual rainfall in the domain during the time of simulation. Narisma and Pitman (2002) found that precipitation is a more complex variable compared to other meteorological parameters; biophysical parameters are not the sole factor that can affect the changes in precipitation. Although generally, increase in temperature may lead to the decrease in the intensity of moisture convergences unbalances reduction of evapotranspiration leading to drier atmosphere.

8 Figure 12: Wilcoxon test of Precipitation for Domain 1 (left) and Domain 2 (right) Given the complexity of the precipitation, we tested if the difference in precipitation is to be attributed in the land use change. The Wilcoxon is a non parametric test for variables that has a non normal distribution. We determined using this test that there are certain areas in the model where significant difference on precipitation is observable. Hence, these are the areas that may warrant further studies to determine the interaction of biophysical parameters on the precipitation variable. 4. Conclusion The study showed that using an updated MODIS land use on the WRF model is feasible. The changes in land use caused an impact to the output particularly on meteorological variables such as minimum and maximum temperature, wind speed and precipitation. The changes on these variables are to be attributed on the differences on the values of biophysical parameters between the two land use inputs. The impacts brought by the updated land use data could be used to further understand land and atmospheric interaction. It could also serve part of the development of weather and climate model for future simulations by integration of land use change dynamics. 5. References Anderson, J. R., Hardy, E. E., and Roach, J. T. (1972). A land-use classification system for use with remote-sensor data. U.S. Geol. Survey Cire.,671:16. Chase, N. T., Pielke Sr., R. A., Kittel, T. G. F., Nemani, R. R., and W., R. S.(2000). Simulated impacts of historical land cover changes on global climate in northern winter. Climate Dynamics, 16: Cheng, F.-Y., Hsu, Y.-C., Lin, P.-L., and Lin, T.-H. (2013). Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the taiwan area. J. Appl. Meteor. Climatol., 52: DeFries, R. S., Pongratz, J., Bounoua, L., Morton, D. C., and Anderson, L. O. (2006). The impact of land cover

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