Updated Land Use Research Articles

Evapotranspiration increment was underestimated in China due to underrepresented land cover changes

Numerous evapotranspiration (ET) products have been produced using various approaches and diverse forcing data even as the magnitude and trends of ET show divergence. We simulated ET using updated land use and cover change (LUCC) data in China from 1900 to 2020. We found that China’s ET increased slightly from 1900 to 1980, but it increased rapidly after 1980 due to LUCC characterized by forest expansion (2.05 mm yr−1, P < 0.01). We also found that the ET trends derived from our simulation were significantly higher than other ET products (−0.70–1.47 mm yr−1, P < 0.01), implying that existing, long-term ET products might have underestimated ET trends in China during the post-1980 period because of underrepresented LUCC. These underestimated ET trends could introduce biases in the regional water budget and water resources management. We advocate for future studies to take into account the impacts of LUCC in global ET simulations.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Evaluation of site selections for solid waste landfill by using GIS in Phu Quoc island, Kien Giang province, Vietnam.

Selecting the location for domestic waste disposal and waste treatment plant is a crucial issue as it directs impacts on health-related, social and environmental factors. One of the effective tools for solving this problem is Geographic Information System (GIS) which employs Multi-Criteria Analysis (MCA). This research aims to identify the optimal locations to evaluate the existing solid waste landfill site and waste treatment plant for land use planning in Phu Quoc island in Kien Giang province for reducing the plastic waste leaking into the ocean. The island is categorized as the urban at small scale with population of 100,000 to 500,000; construction of waste treatment facilities (middle scale) with the capacity of 500 tons per day is now planned. These contents of this study are the new contributions to the methodology for data processing and data collection. The main research methods were applied to geo-spatial data collection derived from maps, satellite imagery, and existing local land use planning information. The criteria for site selections for solid waste landfill were chosen under Vietnamese construction standards and Ministry of Natural Resources and Environment, Ministry of Construction, international regulation, and the provincial policy. Then Analytic Hierarchy Process (AHP) and GIS processes were based on 9 main factors such as (1) distance to an urban area, (2) distance to a rural village, (3) distance to the water surface, (4) distance to roads, (5) distance to electrical supply stations, (6) distance to industrial zones, (7) distance to waste disposal, (8) compliance with the local land use planning, and (9) agreement for approval for site locations for solid waste landfill from the local community. National standard requirements for waste landfill site selection are adjusted to meet the local context. The updated land use data source is applied, and the recommendation for the suitability of the waste landfill sites is formulated. Study results showed that there are four potential sites located in Cua Can, Bai Thom, and Ham Ninh communes with a total area of 84.5 hectares. These areas are identified to be very suitable for solid waste landfill locations and location of waste treatment plants; and corresponding to the land use planning of the local government. The results of the study also recommend policies and solutions for environmental management in the coastal area of Phu Quoc island.

Impact of different land use data on WRF model short range forecasts during pre-monsoon and monsoon seasons in India

This study focuses to analyse the impact of land use changes on short range weather forecasts over Indian region. Weather Research and Forecasting (WRF) model simulation experiments are conducted by using land use data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Indian Space Research Organization (ISRO) satellites for pre-monsoon and monsoon season. MODIS 2001 land use is used in control (CNT) experiment and updated land use with recent urban class from MODIS (EXP1) and ISRO (EXP2) for the year 2019 is used to generate model lower boundary conditions in other two experiments. Quantitative error measures and skill score computed for rainfall forecast showed that model skill is better with the use of realistic recent land use data from MODIS and ISRO during pre-monsoon and monsoon period. Extreme Dependency Index score computed also revealed that model skill in predicting extreme rare rainfall events is improved with recent landuse data. Model simulated surface meteorological variables and profiles at lower levels also found to be improved with the inclusion of realistic land use class from MODIS and ISRO. Between the two experiments, the one which used ISRO based land use showed larger improvement particularly during the monsoon season.

Monitoring Land Cover Changes in Tehran City over 5 years (2018 to 2022) using Remote Sensing based Spatial Information

Rapid economic growth has increased the speed of resource changes and many of these changes have rapid and harmful effects Natural environment such as agriculture, forest, water resources, value Cultural things such as historical landscape and health Humans have put Land use changes directly It changes the resources of the earth, which affects the temperature and humidity causes changes in the climate and weather of the region as well It reduces cultivated areas [1]. Considering that one of the main prerequisites for the optimal use of land, obtaining information One of the patterns of land use over time is map design. Related specializations are one of the most important goals in management it is considered natural resource [2]. In recent years, preparing land use maps by digital classification of remote sensing data have been adverted as appropriate alternative for using this type of maps. Remote sensing is a modern and useful technique in updating land use maps and detecting new changes. In this research ArcGIS pro used for classification that is one of the most accurate and updated software for remote sensing’s process for detecting 4 main type of classes in Tehran city in IRAN. standard accuracy in satellite image processing is important criteria in this study with standard kappa coefficient accuracy, and overall accuracy of data calculated for each maps, by considering 4 essential classes in a major city and converted to maps of changes in linear regression concluded that build-up class have a significant slope increase 3422/3 (hectares), plant class is improving during the study period as 2821/71 (hectares) but these increment are inhomogeneous, water class has sharp drop as 443.52 (hectares), Then the most of decrement is for the barren area which named soil class as 5800.48 (hectares). Part of accuracy in this research depends on severity of the numbers of test samples which given for classification that are more than5000 pixels to assessment reliable results. According to the standards of kappa coefficient that provided in USGS earth data site all off maps are acceptable.

Coastal-urban meteorology: A sensitivity study using the WRF-urban model

The air quality of a city can be assessed by applying a computational modelling approach. However, one of the most critical issues faced while running air quality models is based on the meteorological model setup, which can significantly affect simulations over a coastal urban area with complex terrain. Thus, this study evaluates the performance of the Weather Research and Forecasting (WRF) model using urban canopy model schemes considering three different model heights (4, 10, and 20 m), and updated land use and cover data (ESA-CCI). The one-year simulation results, based on the observed surface parameters (wind speed and direction, temperature, humidity, and precipitation) over the Metropolitan Region of Vitória (MRV), Southeast of Brazil, showed that the WRF model systematically overpredicted temperature and wind speed, and underestimated humidity, especially during the autumn and winter seasons. However, a significant improvement was obtained using the building effect parameterisation (BEP) formulation to emulate meteorological variables over urban surfaces, especially for wind speed. For temperature and relative humidity, the use of BEP did not show appreciable improvement because the BULK scheme (the control experiment) had a similar performance. The land-use dataset changes showed minimal impact on the representation of urban surfaces; however, significant differences were observed in natural/vegetated areas. The different land covers impacted the surface meteorological parameters, such as temperature and precipitation. However, due to the low coverage of monitoring stations in the countryside area, it was insufficient to determine whether the performance of the WRF model is favoured by the land-use update.

Tropospheric Ozone Perturbations Induced by Urban Land Expansion in China from 1980 to 2017.

Urbanization perturbs air pollutants from a dynamic and thermodynamic perspective, which has inspired extensive investigations in China due to rapid urban land expansion in the past four decades. However, knowledge gaps remain in the long-term and nationwide responses to air pollutants to urbanization. The evolution of tropospheric ozone associated with urban land expansion across China was assessed from 1980 to 2017 using a coupled WRF-Chem model based on a recently updated land use change (LUC) data set. The results revealed that urban-land expansion drove growing ozone trends for this period and contributed about 3-9% to its summer maximum concentrations during the 2010s in major urban agglomerations across China. The association between a long-term change in summer O3 concentrations and LUC after excluding the effect of precursor emissions and meteorological conditions and causes of interannual (short-term) variations in O3 concentrations induced by urban-land expansion were also explored by examining the relationships between ozone fluctuations and meteorological variables.

Impacts of Urbanization, Aerodynamic Roughness, and Land Surface Processes on the Extreme Heavy Rainfall Over Chennai, India

AbstractIn this study, the impacts of urban land surface processes on the extreme heavy rainfall event on December 01, 2015 over Chennai, located in north coastal Tamil Nadu, India are analyzed using convection permitting WRF simulations. A series of numerical experiments are conducted using different land cover data (USGS‐1992, NRSC‐2004, NRSC‐2015), aerodynamic roughness, and land surface models (LSM) to assess their sensitivity on the predicted rainfall. Results suggest that experiments with NRSC‐2015 with increased urban extent improved the rainfall prediction in terms of rainfall intensity and its distribution. Though temperatures, sensible heat, and planetary boundary layer height (PBLH) increased due to urbanization in both dry and wet phases, the humidity and convective available potential energy (CAPE) reduced during the dry phase suggesting thermal convection played a secondary role in rainfall. Considerable increase of surface drag, momentum transport, wind shear, and turbulent kinetic energy is found in simulations with updated land use and roughness, which determined the location of the cyclonic circulation, convergence, and maximum precipitation. LSM sensitivity experiments indicated that while the five‐layer model substantially increased the sensible heat, temperature, and PBLH, it reduced the moisture convergence and CAPE relative to Noah and Noah‐MP thus resulting in low rainfall. The simulation with Noah‐MP enhanced the low‐level shear and convergence over other LSMs thus produced a wide spread rainfall along the coast. Our results demonstrated that the momentum transport due to urban drag played a vital role by strengthening the low‐level convergence and moist convection, which caused heavy precipitation over Chennai.

The Utilization of Supervised Classification Sampling for Environmental Monitoring in Turin (Italy)

In a world threatened by climate change, the need to observe the land transformation is crucial to set environmental policies. One of the most prominent issues of environmental monitoring is the availability of updated and reliable land use data. The last land-use release in Piedmont Region (Italy) is in 2010, while the most updated Normalized Difference Vegetation Index is in 2016. To overcome this limit, in this study, a supervised classification sampling has been applied on a Sentinel-2 image produced by the Copernicus Program on 29 September 2020, using Esri ArcGIS (ver.10.8 Redlands, California, US) by accessing via ONDA-DIAS services to L2A products. After land classification, three maps were generated—the Habitat Quality, the Habitat Decay, and the Normalized Difference Vegetation Index. This study aimed at classifying the environmental status in five classes ranging from “critical” to “health” with a double perspective—(i) to make a comparative metropolitan assessment between municipalities and (ii) to evaluate the quality of urban public green areas in the city of Turin while defining a different kind of intervention. Results indicate that products derived from supervised classification sampling can be applied in a wide range of applications while reaching seasonal monitoring of the environmental status and delivering just-in-time solutions.

High Resolution WRF Simulations for the Tel‐Aviv Metropolitan Area Reveal the Urban Fingerprint in the Sea‐Breeze Hodograph

AbstractWe implement and verify for the first time four Weather Research and Forecasting model urban configurations, focused on the coastal metropolitan area of Tel‐Aviv (MTA) using updated landuse and morphological maps. We analyze the mesoscale summertime flow and the urban canopy (UC) role in the occurrence of different hodograph dynamics observed within MTA at night. These events may be significant in air quality research. The four configurations—bulk (MM), single‐layer (SLUCM), multilayer (BEP), and BEP coupled with the building energy model—reproduce the observed diurnal temperature and wind cycles, with similar 10‐m wind direction bias and RMSE (15° and ∼30°, respectively), with preference for MM and SLUCM at night. However, the SLUCM shows the lowest skill for the 10‐m wind speed (WS) (bias and RMSE 1 m s−1), and the BEP shows the largest underestimation of the 2‐m temperature, ∼−2.5 °C. In the SLUCM, the WS increases over an UC and with increasing building heights. These results call for a reexamination of the SLUCM WS parameterization. The simulations show that at night, a convergence line (CL) builds up with the urban heat island, downstream of the NW flow. West of the CL, the wind continues flowing from the sea, and rotates anticlockwise to form a nonelliptical sea‐breeze hodograph. Removing MTA UC restores an elliptical hodograph. East of the CL, the UC supports an elliptical hodograph with a clockwise rotation through the NE sector, previously reported as dynamically unstable. We expect such wind hodograph dynamics within similar coastal metropolitan areas.

Impacts of urbanization on atmospheric circulation and aerosol transport in a coastal environment simulated by the WRF-Chem coupled with urban canopy model

This study examines the impacts of urbanization on the local atmospheric circulation and the dispersion of air pollutants over the populated city of Ulsan, South Korea, located in the coastal region. Two experiments are conducted using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the urban canopy model (UCM) at 1-km horizontal resolution. The model experiments are conducted in August for three consecutive years of 2016–2018, with an updated land use category and diurnally-varying surface emission and anthropogenic heat flux. The impacts of urbanization are isolated by comparing the control run (CTRL) with realistic land use conditions and the sensitivity run (NOURB) that replaces the urban surfaces with non-urban grasslands. CTRL reproduces the urban heat island (UHI) and associated wind patterns realistically with reliable local land-sea breeze circulations. The positive temperature anomalies develop over the urban area due to enhanced surface heat flux at the urban surfaces, driving low-level convergence and secondary circulation. Enhanced heating by UHI changes the ground-level aerosol concentration differently. While the concentration does not change significantly by UHI in the daytime, it is reduced considerably at night in the urban areas due to enhanced vertical mixing. The dominant process that the urbanization modifies the aerosol concentration is the thermodynamical effects, which are also supported by the observation.

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.

Classification of Urban Area Using Multispectral Indices for Urban Planning

An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.

RECALCULATION OF FOREST INCREMENT, MORTALITY AND HARVEST RATE IN LATVIA ACCORDING TO UPDATED LAND USE DATA

National forest inventory (NFI) is an efficient way to monitor forest resources, including increment, mortality, and harvest rate. It provides accurate statistical information about land use data, land use change and biomass stock change and enables to tie the specific increment, mortality and harvest data to defined land use category, dominant tree species, site types etc. Recently there have been introduced changes in land use calculation method using NFI data in Latvia. New calculation method takes into account present land use data and land use data from two previous cycles considerably reducing uncertainty of the estimates and takes into account possible land management practices which may alter the land use category in long-term, also usage of auxiliary data has been introduced to increase accuracy of determination of final land-use category. Changes in land use calculation method directly affects distribution of data on increment, morality and harvests between land use categories. The aim of the study is to determine increment, mortality and harvest rate in Latvia between the first and third NFI cycle (2004-2018) using updated land use data and to compare obtained result with data from the most recent greenhouse gas (GHG) inventory report.

SWAT‐LUT: A Desktop Graphical User Interface for Updating Land Use in SWAT

AbstractLong‐term simulations of agricultural watersheds have often been done assuming constant land use over time, but this is not a realistic assumption for many agricultural regions. This paper presents the soil and water assessment tool (SWAT)‐Landuse Update Tool (LUT), a standalone, user‐friendly desktop‐based tool for updating land use in the SWAT model that allows users to process multi‐year land use data. SWAT‐LUT is compatible with several SWAT model interfaces, provides users with several options to easily prepare and incorporate land use changes (LUCs) over a simulation period, and allows users to incorporate past or emerging land use categories. Incorporation of LUCs is expected to provide realistic model parameterization and scenario simulations. SWAT‐LUT is a public domain interface written in Python programming language. Two applications at the Fort Cobb Reservoir Experimental Watershed located in Oklahoma and pertinent results are provided to demonstrate its use. Incorporating LUCs related to implementation of recommended conservation practices over the years reduced discharge, evapotranspiration, sediment, total nitrogen, and total phosphorus loads by 59%, 9%, 68%, 53%, and 88%, respectively. The user’s manual is included in this article as Supporting Information. The SWAT‐LUT executable file and an example SWAT project with three land use rasters and the user’s manual are available at the United States Department of Agriculture‐Agricultural Research Service Grazinglands Research Laboratory website under Software. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

The Weather Research and Forecasting (WRF) model is one of the regional climate models for dynamically downscaling climate variables at finer spatial and temporal scales. The objective of this study was to evaluate the performance of WRF model for simulating temperature and rainfall over Lake Tana basin in Ethiopia. The WRF model was configured for six experimental setups using three land surface models (LSMs): Noah, RUC and TD; and two land use datasets: USGS and updated New Land Use (NLU). The performances of WRF configurations were assessed by comparing simulated and observed data from March to August 2015. The result showed that temperature and rainfall simulations were sensitive to LSM and land use data choice. The combination of NLU with RUC and TD produced very small cold bias (0.27 °C) and warm bias (0.20 °C) for 2m maximum temperature (Tmax) and 2m minimum temperature (Tmin), respectively. WRF model with RUC and NLU captured well the observed spatial and temporal variability of Tmax, while TD and NLU for Tmin. Moreover, rainfall simulation was better with NLU; especially NLU and Noah configuration produced the smallest mean bias (2.39 mm/day) and root mean square error (6.6 mm/day). All the WRF experiments overestimated light and heavy rainfall events. Overall, findings showed that the application of updated land use data substantially improved the WRF model performance in simulating temperature and rainfall. The study would provide valuable support for identifying suitable LSM and land use data that can accurately predict the climate variables in the Blue Nile basin.

On the uncertainties introduced by land cover data in high-resolution regional simulations

We investigate the impact of implementing an up-to-date and detailed land cover dataset in high-resolution regional climate simulations. We used the Weather Research Forecast (WRF) model version 3.6.1 on a high horizontal resolution of 5 km × 5 km, with 29 vertical levels, covering mainland Europe. We performed simulations within the year 2050, using future Representative Concentration Pathway 8.5 mid-century projections, for 2 winter (January, February) and the 2 summer months (June, July) to investigate the seasonal dependency of the impact of the land cover datasets on and their interaction with the different meteorological conditions prevailing in summer and winter. We compare simulations using the CORINE Land Cover dataset (100 × 100 m) and the standard United States Geological Survey (USGS) (~ 1 × 1 km) land use data for the same periods. Our analysis shows that simulated meteorological variables (temperature at 2 m, wind speed, sensible and latent heat fluxes and PBL heights) differ significantly between the WRF simulations, linked to the land cover parameterization. We quantify and discuss the modelling uncertainties arising from surface-type classifications and motivate the use of high resolution, and continuously updated land use inventories in climate modelling, especially for future projections. Our findings are particularly important for the summer season and over large urban centers, and we strongly recommend the use of high-quality resolution land use data in modelling experiments studying heat waves in synergy with the urban heat island phenomenon and land–surface interactions.

Application for developing countries: Estimating trip attraction in urban zones based on centrality

This paper introduced a network centrality-based method to estimate the volume of trip attraction in traffic analysis zones. Usually trip attraction volumes are estimated based on land use characteristics. However, executing of land use-based trip attraction models are severely constrained by the lack of updated land use data in developing countries. The proposed method used network centrality-based explanatory variables as “connectivity”, “local integration” and “global integration”. Space syntax tools were used to compute the centrality of road segments. GIS-based kernel density estimation method was used to transform computed road segment-based centrality values into traffic analysis zone. Trip attraction values exhibited significant high correlation with connectivity, global and local integration values. The study developed and validated model to estimate trip attraction by using connectivity, local integration and global integration values as endogenous variables with an accepted level of accuracy (R2 > 0.75). The proposed approach required minimal data, and it was easily executed using a geographic information system. The study recommended the proposed method as a practical tool for transport planners and engineers, especially who work in developing countries and where updated land use data is unavailable.

Non-chemistry coupled PM10 modeling in Chiang Mai City, Northern Thailand: A fast operational approach for aerosol forecasts

The Weather Research and Forecasting (WRF v. 3.7) model was applied to model PM10 data in Chiang Mai city for 10-days during a high haze event utilizing updated land use categories from the Moderate Resolution Imaging Spectroradiometer (MODIS). A higher resolution meteorological lateral boundary condition (from 1 degree to 0.25 degree) was also used from the NCEP GDAS/FNL Global Tropospheric Analyses and Forecast Grid system. A 3-category urban canopy model was also added and the Thompson aerosol-aware microphysics parameterization scheme was used to model the aerosol number concentrations that were later converted to PM10 concentrations. Aerosol number concentration monthly climatology was firstly used as initial and lateral boundary conditions to model PM10 concentrations. These were compared to surface data obtained from two stations of the Pollution Control Department (PCD) of Thailand. The results from the modeled PM10 concentrations could not capture the variability (r = 0.29; 0.27 for each site) and underestimated a high PM10 spike during the period studied. The authors then added satellite data to the aerosol climatology that improved the comparison with observations (r = 0.45; 43). However, both model runs still were not able to capture the high PM10 concentration event. This requires further investigation.

Detect irregularities of master plan by comparison with land use, using GIS and remote sensing techniques for Falujah city

Most of Iraqi Cities suffering from delaying of the update of Master plan, especially in the period between 1980 and 2003 the main reasons this delay are the Gulf War and the Economic Blockade. Increasing of population is the major factor causes changing in urban land use due to the human demand. These changes cause differences between master plan and real situation. To mention the spatial irregularities in Falujah City, the comparison between the master plan layers and updating land use map layers has achieved in this paper to determine the spatial change and irregularities in the city, that represent the reality of situation in case study . The changes were remarked; the areas of changes were calculated in table and thematic map were produced in our paper to illustrate the goal. This data processed using combination of GIS technique, and global positioning system GPS and geo media software.