Projects / Case Studies / Research & Development
Government Projects | Corporate Projects | Public Projects | Global Projects | Industry Projects | Research ProjectS
Landuse /Landcover maps helps us in detecting changes in terms of land use trends, green cover management and other changes over a period of time. Land cover data documents how much a region is covered by forests, wetlands, impervious surfaces, agriculture and other land and water types. Landuse shows how people use their landscape weather for development, conservation or mixed uses. The different types of land cover can be managed or used quite differently. Land use cannot be determined from satellite imagery.
Land Cover maps provide information to understand the current landscape . To see the changes over time, land cover maps for several different years are needed. With this information analyst can evaluate past management decisions as well as gain insight into the possible effects of their current decisions before they are implemented.
CHANGE DETECTION IN MARAT LONGRI WILDLIFE SANCTUARY USING GPS SURVEY AND GIS
Marat Longri Wildlife Sanctuary, spreading 451.00 sq. kms. is located in Karbi Anglong Autonomous District Council. It is an important component of Dhansiri-Lungding Elephant Reserve. The given data below shows the total area in (sq. km). for different land use patterns for the year 2020. We are making a future predictability land use mapping for the year 2030 trying to understand the change and the reason in land use pattern of the area.
Classification Area in sq.km
Agriculture – 556051.5806 km
Barren Land – 5680063.007 km
Built Up Areas – 27224983.18 km
Dense Forest – 188808433.9 km
Moderate Dense Forest – 80897158.85 km
Preface : The present era is increasingly being modernization and urbanization with rapid industrialization along with which there is a decrease in the forest cover, with the root cause of population explosion, which has led to a huge increase in urban cover, which in turn has made significant changes in the land surface. Thereby it has become important for us to increase our awareness about such conditions present. I hope this project will bring a better idea of the phenomena of the increasing land surface temperature and its variation across different places on Earth and the causes behind it. This project tries to answer some questions of the remote sensing with special reference to Guwahati. Guwahati, being one of the fastest growing commercial, educational and industrial hubs of the entire north eastern region experiences a subtropical and humid climate with less extremes in the temperature variation.
GIS and remote sensing techniques such as mapping, monitoring, evaluation and modeling have been done which gives an insight into the local climate in that area concerned. To establish the relationship between land use and land cover different some biophysical indicators like that of NDVI, NDWI and NDBI have been calculated. Landscape metrics have been used which further facilitated the cluster analysis which gives an insight into the patch density.
Urbanization changes the natural land surface into sophisticated structures such as settlements and transportation networks which leads to change in temperatures of land surface. It is common these days that there is a higher temperature in urban areas. The developing urban infrastructure and increasing urban population is putting more pressure on natural land since it is being converted into man-made grounds (Gill, Forest, & Ennos, 2007). This change increases the land surface temperatures (LST) because of the artificial structures which keeps and emits heat, which in turn causes the urban heat Islands (UHI) (Gill et al., 2007). Land Surface Temperature is the normal temperature we feel on the Earth surface which is calculated through the remote sensing (thermal bands). The estimation of this depends on albedo, the cover of vegetation and the soil moisture present. (https://land.copernicus.eu/global/products/lst).
These days, automobiles and industries have increasingly contributed to increase in the atmospheric Greenhouse Gases (GHGs) and Chlorofluorocarbons (CFCs), which has put a lot of pressure on the vegetation cover and belts, which has turned to increase in temperatures between the urban and rural areas.
Due to this the urban areas are constantly having increasing temperatures and can be studied with temperature parameters (Isotherms), that indicate the rapid changes compared to the rural areas. This modern phenomenon is also known as Urban Heat Island (USI). The land use and land cover (supervised classification), LST and NDVI techniques are used to calculate the UHI (Borthakur & Nath, 2012). In the comparative study between the Indian cities of Mumbai and Delhi, it was revealed that Mumbai has a higher intensity of UHI than Delhi because of the higher rate of urban development in Delhi which has led to decrease in the vegetation cover. LST is a dependent variable and the NDVI is an independent variable had been used to make the regression analysis to show the correlation between these components and shows how the NDVI directly affects the LST (Grover and Singh, 2015).
The occurrence of the LST has an increasing trend in the core of the urban area of the city Kunming in China. This had a very rapid urbanization along with the increase in built up area and had a drastic change in a short period of time. The regression is calculated by the LULC, LST, NDVI and NDWI (Zhou and Wang, 2010). In Lagos (Nigeria), vegetation cover has significantly reduced from 70.043% to 10.127% over 3 decades and this huge difference has increased the land surface temperature. LST has increased through the process of urban sprawl, growing urban population, degradation of agricultural land and vegetation cover. Regression analysis was taken between LST and LULC to know about the relationship between LST and LULC over the year (Babalo and Akinsanola, 2016).
Guangzhou of southern China experienced a rapid urbanization process due to economic growth and this rapid process has resulted in formation of the Urban Heat Island (UHI) in the area. Barren land, built-up
area and grass land were found in the inner part of the city which justifies the fact that the city has experienced a high LST (high surface temperature) zone because of low thermal inertia. Forest and water bodies were found in the outer part of the city, so the outer part of the city has experienced a low LST (low surface temperature) zone because of high thermal inertia. High NDVI (Normalized Difference Vegetation Index) zone area has experienced low LST and low NDVI zone area has experienced high LST in the different parts of the city (Sun, Wu, & Tan, 2012). In India, large numbers of metropolitan cities have experienced the phenomena of Urban Heat Island (UHI) in recent decades. Recently, Guwahati has experienced UHI because of rapid urbanization and population growth. Guwahati metropolitan area became UHI in the year of 2009. The reason being the construction of new expressway from Jalukbari to Khanapara which has changed the land use pattern along with the settling up of industries along the National Highway 37 (Brothakur and Nath, 2012).
Guwahati (Assam) has experienced rapid urban growth because of rapidly growing economic activities. There is a great pressure of increasing settlement putting a gigantic pressure on forest areas, agricultural land and wetland areas in and around the premises of the city, so that can be seen a change in LULC affects the change of LST. The aim of the study is to find out the temporal relationship between LULC and LST over 27 years (1991-2018).
GIS and Remote Sensing techniques have been used to find the relationship between LULC and LST. NDVI and NDWI is utilized as indicators of LULC and LST change using satellite images (Landsat dataset).The relationship between LULC and LST might help to provide some developmental plans in the city. If the developmental plans can be successfully articulated, the city development authority can implement some counter measures to minimize the local (micro) climatic change. This study may give significant inputs to the land use policy and planning in the upcoming satellite towns.
SIGNIFICANCE OF THE STUDY
The increasing development processes in the city of Guwahati have increased in the recent period and this has converted the natural land surface into man-made land surface in majority of the premises of the city and also led to urban sprawl. Deforestation and the subsequent changing Land use Land cover have been taken place because of the high rate of urbanization and modernization. As the vegetation is converted into built-up areas and since the built-up areas are made up with concrete and cement, land surface temperature is higher in the built-up areas and deforested areas. Due to this reason the regions of built-up areas have become the zones of urban heat Island. This phenomena is a major climatic condition specially in the urban areas since it has brought changes in the local climatic conditions which in turn has the capacity to hamper air quality and contribute to the problems in human health.
STUDY AREA (GUWAHATI) The study area, Guwahati, means “areca nut marketplace” in Assamese, (the gateway of North-East), is the capital city of Assam which is located in the north-eastern part of India (Desai, Mahadevia, & Mishra, 2014).The city is situated on the banks of Brahmaputra River and is 55 meters above the sea level. The total area of the study area is 375.781234 km2. The city is situated in the subtropical climate zone and the temperature ranges from 19°C to 26°C https://en.wikivoyage.org/wiki/Guwahati. In recent decades Guwahati has experienced some problems related to LULC change and the major problem being the increase of LST. Since Guwahati is gateway of north-east India, so the population growth has increased with the dramatic growth of urbanization. The population of Guwahati has increased from 809,895 in 2001 to 963,429 in 2011 and population density has also increased from 3736 persons per km2 in 2001 to 4445 persons per km2 in 2011. (Census of India, 2011).
- To show the changes in Land Surface Temperature (LST) changed over the period of time from 1990 to 2018?
- Depict the relationship between changing nature of LST and LULC over the period of time?
- What is the relationship among Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI) and Land Surface Temperature (LST) over the period of time?
- What are the factors responsible for LST change and UHI?
The main objectives of the project are given below:
- To examine the changes in Land Surface Temperature (LST) and Land use/Land cover (LULC) from the year 1990 to 2018
- To examine the relationship of Land Surface Temperature (LST) with Land use/Land cover (LULC) types, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built- up Index (NDBI) and Normalized Difference Water Index (NDWI).
To identify factors importance LST change and
DATA OBTAINED FROM:
Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) acquired from 1991, 2001, 2011 and 2018 are the main source of data for this project. The satellite images are accessible from USGS Earth Explorer which is developed by United States Geological Survey https://earthexplorer.usgs.gov/. The Landsat images have been taken for LULC classification, LST (Land Surface Temperature), NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index) and Normalized Difference Built-up Index (NDBI) calculation. Thermal infrared band of Landsat 5, 7 and 8 has special resolution of 120m, 60*(30) m and 100m respectively https://eos.com/landsat-7/. Thermal bands of Landsat satellite images have been resampled with a pixel size of 30 meter using nearest neighbour algorithm to match with the optical bands and to estimate the change in Land use/Land cover types and Land surface temperature (Guha, Govil, Dey, & Gill, 2018).
Table 4.1. Details of the satellite image used for Guwahati
PATH & ROW
Note: *Resampled to 30 meter
To interpret and understand the Land use classes in the satellite images, it is first converted into false colour composite bands that have been used in different satellite images. False colour composite bands (4, 3, and 2) are used in different satellite images to compare between the different land cover classes. Five Land use and Land cover have been used for Guwahati LULC classification are Urban, Water Bodies, Wetland, Open Land, Forest, Plantation and Agriculture. Supervised Classification has been done to obtain the Land use/Land cover classification in Erdas Imagine (2014) and Saga GIS. Spectral signature is used for this supervised classification which is used to define the training sets and these will apply to the entire image. In this we use the Maximum Likelihood Algorithm because that can help in calculation for each class’ statistics in the bands which are distributed and it also helps in calculation of a pixel for each class. Every pixel obtained is a part of some class and this helps in highest probability. This algorithm is default mode in this supervised classification. After this classification is done, many mixed classified pixels will be added. So to minimize and remove them we will ‘recode’ the tool in Erdas Imagine software (2014). ArcMap 10.3.1 software has been used for different purposes to find out the significant outputs in LST, NDVI, NDWI, and NDBI. The data of these four aspects have been given for the time period of 1991, 2001, 2011 and 2018.
TESTING ACCURACY FOR LULC CLASSIFICATION:
‘Google Earth Pro’ software has been used to find out the accuracy for the Land use/Land Cover classification by using GCP points, 150 and 210 GCP’s points have been obtained from the Google Earth Pro to do the classification for the testing of accuracy in Guwahati. Jacob Cohen’s Kappa accuracy test has been used to estimate for every year.
- K=Po-Pe/1-Pe…………………………………………… (i)(K represents Kappa test; Po represents observed Agreement; Pe represents Expected Agreement).
1.00-0.81 = Almost perfect agreement
0.40-0.21 = Fair agreement
0.80-0.61 = Substantial agreement
0.20-0.00 = Slight agreement
0.60-0.41 = Moderate agreement
<0 = Poor agreement or no agreement
The Kappa statistics helps in giving the result to make significant interpretations. The range of this statistics ranges from 0 to 1 and this is subdivided further into 5 different classes for better understanding. (R Nichols, M Wisner, Cripe, & Gulabchand, 2010)
- User’s Accuracy = (N𝑜. 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑎𝑝 / 𝑁𝑜.𝑐𝑙𝑎𝑖𝑚𝑒𝑑 𝑡𝑜 𝑏𝑒 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑎𝑝)…(ii)
- Producer’s Accuracy = (N𝑜.𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑖𝑛 𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑝𝑙𝑜𝑡𝑠 𝑜𝑓 𝑐𝑙𝑎𝑠𝑠/ 𝑁𝑜.𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦 𝑖𝑛 𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒
- Total Accuracy = (No.𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑝𝑙𝑜𝑡𝑠/ 𝑇𝑜𝑡𝑎𝑙 𝑛𝑜.𝑜𝑓 𝑝𝑙𝑜𝑡𝑠)…(iv)
LST EXTRACTION FROM THERMAL BAND:
LST is calculated with the Landsat thermal bands (Landsat 5- band 6, Landsat 7- band 6VCID (1) and Landsat 8- band 10) for 1991, 2001, 2011 and 2018. The thermal infrared band has resampled into a 30 meter spatial resolution to match the optical bands to find the relationship with LULC change. LST is calculated by the help of established formulas. (Dissanayake, Morimoto, Murayama, & Ranagalage, 2019; Grover & Singh, 2015; Kayet, Pathak, Chakrabarty, & Sahoo, 2016; Sannigrahi et al., 2018; Sannigrahi, Rahmat, Chakraborti, Bhatt, & Jha, 2017; Taylor, 2011; Yoo, 2018; Zhou & Wang, 2011).
- Conversion of the Digital Numbers to spectral radiance:
|All the objects on the Earth’s surface emits some electromagnetic thermal energy when its temperature is above zero (Kelvin). The satellite sensors receive the electromagnetic thermal energy (Kelvin) by the thermal sensors, with the principle and thermal energy can be converted to sensor radiance. The conversion of thermal energy to spectral radiance is calculated using the formula. (Ogunjobi, Adamu, Akinsanola, & Orimoloye, 2018; Pal & Ziaul, 2017; Taylor, 2011; Tran et al., 2017).|
LMAX represent the spectral radiance that is calculated to QCALMAX; LMIN represent spectral radiance that is scaled to QCALMIN; QCALMAX represent maximum quantized calibrated pixel value; QCALMIN represents minimum quantized calibrated pixel value; QCAL represents quantized calibrated pixel value in DN. QCALMAX and QCALMIN are 255 and 1 for Landsat 5 and 7 and 65535 for Landsat 8 respectively. For, TM (Landsat 5) LMAX and LMIN are 15.303 and 1.238 respectively; for ETM+ (Landsat 7) LMAX and LMIN are 17.040 and 0.000 (band- 6.VDID (1)) respectively and for OLI-TIRS (Landsat 8) LMAX and LMIN are 22.00180 and 0.10033 respectively.
|Conversion of spectral radiance to temperature in Kelvin:|
Where, TB represents satellite brightness temperature in kelvin; K1 represents calibration constant 1; K2 represents calibration constant 2; L represents spectral radiance. For, TM (Landsat 5) K1 and K2 are 607.76 and 1260.56 respectively, for, ETM+ (Landsat 7) K1 and K2 are 666.09 and 1282.71 respectively and for OLI-TIRS (Landsat 8) K1 and K2 are 774.8853 and 1321.0789 respectively.
Correction of emissivity of surface temperature: (3)
Where, Ts represents LST in kelvin; 𝜆 represents the emitted radiance wavelength which is 11.5 µm; TB represents the satellite brightness temperature in kelvin;𝘢 represents 1.438*10-2 mk
(𝘢 calculated as 𝘢=hc/𝜎, where h represents planck’s constant 6.626*10-34 Js, represents the velocity of light 3*108 and 𝜎 represents Stefan-Boltzmann constant 1.38*10-23 J/K); Ɛ represents the surface emissivity (Yoo, 2018; Zhou & Wang, 2011).
|Conversion of surface temperature in Kelvin to Celsius:|
NDVI is commonly and widely used for vegetation extraction which is applied in Guwahati. NDVI is used to map for estimation of vegetation coverage and health of vegetation in both the study areas from 1991 to 2018 to find out temporal change. The NDVI is generated from near-infrared and red band (Landsat 5 & 6 – near-infrared band (4) and red band (3), Landsat 8 – near-infrared band (5) and red band (4)). NDVI value ranges from +1 to -1. The positive value represents healthy vegetation cover and high density green vegetation cover, while negative value represents non-vegetative cover.
Where, NDVI represents normalized difference vegetation index; NIR represents near- infrared band of electromagnetic spectrum; RED represents red band of electromagnetic spectrum.
Where, NDWI represents normalized difference water index; NIR represents near-infrared band of electromagnetic spectrum; GREEN represents green band of electromagnetic spectrum. NDWI index is commonly used for extraction of water body. NDWI index is used to map the extent of water body areas in Guwahati and temporal changes of water body in both the study areas from 1991 to 2018. The NDWI is generated from near-infrared and green band (Landsat 5 & 6 – near-infrared band (4) and red band (2), Landsat 8 – near-infrared band (5) and green band (3)). NDWI is used to map the extent of water body coverage area. NDWI value ranges from +1 to -1.
NDBI Index is used for the extraction of built-up areas. In the study areas NDBI Index has been applied to detect the built-up areas. NDBI is used to map the extent of built-up area in both the study areas. The NDBI is generated from the near-infrared and green band (Landsat 5 & 6- near infrared band (4) and SWIR band (5), Landsat 8- near infrared band (4) and red band (6). NDBI values range from +1 to -1.
Where, NDBI represents normalized difference built-up index; NIR represents near infrared band of electromagnetic spectrum; SWIR represents short wave infrared band of electromagnetic spectrum.
Landscape Matrices had been developed for categorical map patterns and sometimes it is also used for topographic measures. These have different algorithms which quantifies the special pattern and characteristics of classes, patches and the entire landscape. (Evelin, Marc, Juri, Riho, & M, 2009; McGarigal, Cushman, Neel, & Ene, 2012; Navarro-Cerrillo, Guzmán-Álvarez, Clavero-Rumbao, & Ceaceros, 2012). The Landscape metrics is mostly used by the Fragstats software (Evelin et al., 2009). The use of these Landscape metrics have been increasing since 1999 and this is a part of broad metrics, ND which can be grouped into seven categories. These are namely;
- Biodiversity and Habitat Analysis,
- Use and Misuse/Selection of metrics,
- Evaluation of landscape pattern and changes,
- Estimation water quality,
- Aesthetics of landscape,
- Urban landscape pattern, road network and,
- Management, Planning and (Evelin et al., 2009).
The landscape metrics is a combination of eight different metrics are: Area and edge metrics, Shape metrics, Core area metrics, Contrast metrics, Aggregation metrics, Subdivision metrics, Isolation metrics and Diversity metrics (Liu et al., 2017; McGarigal et al., 2012).
As urbanization is increasing over the years, urban sprawl has influenced urban growth and fragmentation. To show the spatial pattern of urban growth and fragmentation. The Landscape metrics have been used. In case of evolution of landscape pattern, the changing pattern of landscape is also used. (Evelin et al., 2009).
Spatial clustering shows the different variables in a significant manner and is an important method of spatial data analysis. The data of proximal spatial data somehow contribute a role in urbanization. The scenario is simplistic to describe the pattern of urban growth, hence it misleads to policy perspective at local level. The data provides a spatio-temporal variability of driving forces by using these statistical models. A selection of an appropriate model and technique for the spatial data analysis fundamentally depends on the objectives of study. The spatial autocorrelation technique and spatial regression model have been selected to understand mutual agreements of local spatial variables, and urban pattern of growth and LULC Change.
To show the mutual understanding of the spatio-temporal urban growth or the LULC Change, spatially clustered analysis will be carried out. The Moran’s Index is an indicator of the spatial autocorrelation is performed in the study. All the analyses were executed in the GeoDa software to depict the LULC changes for each study area.
Using the formula of the Moran’s Index, we can classify those under four classes, namely:
- High-High (HH) is the quadrant exemplifies with the positive value of all the observations in this zone and positive average of the contiguous neighbor of that location.
- Low-Low (LL) is that quadrant exemplifies with a negative value of all the observations in that zone and negative average value of adjacent neighbors of that location.
- Low-High (LH) is that quadrant exemplifies with a negative value of all the observations in that zone and positive average value of a contiguous neighbor of that location.
- High-Low (HL) is that quadrant exemplifies with the positive value of all the observations in that zone and negative average value of the contiguous neighbor of that location.
Moran’s and Quadrant analysis gives an individual measure of the whole spatial pattern over the map, they are reported as Global statistics because single value describes the overall spatial pattern of the entire map. Other hand, “Local statistics” is also very much important which is used to find out the high or low locational cluster map. Local statistics gives the opportunity to test the significant location of the new cluster. LISA (Local indicator of spatial association) and Global Moran is different in their capacity to decomposition of the global indicator. LISA allows users to map out green shaded “Significance map” and blue-red shade “Clustered map.”
During the last 27 years (1991-2018), Guwahati had undergone a huge phenomenal change in terms of LULC and LST, as well as the bio-physical indicators have resulted in the change in natural surface to man-made surface. There is a difference in Land Surface Temperature (LST) of Guwahati city which is much higher and at the same time contradictory because there is difference between the location, urban growth, natural surface and climatic condition. The city has experienced a different surface temperature because of having different physiography. Guwahati has experienced a high LST in the inner city area because of having high vegetation cover in the outer part of the city and the inner part of the city is covered by the Settlements. That is why outer part of the city has experienced lower surface temperature because of having high NDVI. In Guwahati, the Brahmaputra River is flowing in the northern side of the city which becomes a cooling factor in the urban micro climate. The concrete surface is higher in case of Guwahati. The agricultural practices in case of Guwahati agricultural practices do persist. Difference also exists between economic activities where economic development is higher in Guwahati metropolitan area. These factors reflect the UHI phenomenon. Strong UHI exists in Guwahati city.
Monitoring Urbanization in Jabalpur city using Landsat 8 Images from 2016-2020.
Urbanization in context of Indian Cities
Rapid urbanization is the standout socio-ecological phenomenon in present-day Asia and Africa, with these regions slated to experience about 90 % of the world’s urban population growth by the middle of this century (United Nations, 2018). Indian cities are likely to house an additional 416 million people by 2050 (United Nations, 2018), mostly in its burgeoning and continually expanding metropolitan cities and large urban agglomerations (UAs) Such swift city growth adversely impacts the natural environment through loss of fertile agricultural lands or water bodies, and its impacts are felt well beyond city and the decline/loss of valuable ecosystem services. To ameliorate such impacts and foster sustainable urbanization, enhanced understandings of the spatial patterns of urban growth are therefore
In many parts of the world, rapid urbanization over the last several decades has created a strong shift towards a more urbanized population and currently, the majority of the global population live in urban areas. According to United Nations estimates, more than 50% of the world’s population now live in urban areas, and it is projected to increase 72% by 2050 (UNFPA, 2016), particularly in developing countries. If the existing trend continues, the land converted to urban areas will nearly triple in the next 20 years . This tremendous urban growth and the land use changes in these countries, which resulted from rapid expansion of large cities and suburban areas, have caused serious challenges especially in metropolitan areas due to massive rural-urban migration and intensive human activities. These challenges seriously threaten the human and natural environment such as loss of agricultural lands and natural ecosystem, continuous environmental degradation, loss and fragmentation of natural resources and considerable changes in urban landscape, the loss of fertile lands, open space and biodiversity spoiling water quality, increasing runoff and flood potential, and an increase in energy consumption.
Studying Land use/Land change for analyzing urban growth sprawl
Changes in landscape patterns are more likely observed through changes in land use/land cover Therefore, one of the ways to understand landscape changes and their reactions is to be aware of the dynamics of land use patterns. Because land use changes lead to changes in landscape patterns; hence, the analysis of land use changes can help to understand the changes in landscape patterns (Nagendra et al., 2004). As landscape pattern changes, natural driving factors along with human activities have an important role in the shaping of landscape (Yang et al., 2014) Since today human culture and politics can play an essential role in the structuring of landscape pattern human use of the land plays a fundamental role in the landscape heterogeneity. The results of changes in landscape patterns through human activities (population increase, urbanization, urban sprawl, creation of sub-divisions and settlements, agricultural mechanization), can affect ecological processes within these landscape and transform the relationships between humans and the natural environment. The change of landscape types and proportions has been featured in the conversion of ecological lands such as forest land and grassland into agricultural land and built-up land, and in some areas, agricultural land has largely been transformed into the built-up land. At the same time, landscape patterns in rapidly urbanizing areas have presented a remarkable, highly fragmented feature. The single, continuous natural patches have become complex, heterogeneous and discontinuous mosaics. Thus, studying the characteristics of spatial-temporal changes in urban landscape patterns is an important issue
- Landsat 8 Data collection
- The data from March 2016 March 2018 andMarch 2020 was collected from USGS Earth Explorer.
- Extracting Area of interest using Shapefile
- The study area was extracted from the whole Landsat 8 Image using shapefile of Jabalpur district.
- Supervised Classification
- Supervised classification is conducted on the multispectral image with 4 classes- Builtup, agricultural land, barren land and water body.
- Extracting Built-up
- From the classified image , the built-up area is extracted
- Change Detection
- Change detection analysis is performed on the images from to monitor the urbanization trend in Jabalpur.
Image- March 2016
Urban Builtup- Black
Agricultural Land- Green
Water body- Blue
Barren Land- Yellow
The built-up area expands gradually in all direction as we move ahead in timeline .The area of the built-up is calculated and a graph is plotted to display the difference in all the three images.
Image- March 2018
- Builtup – Black
- Water body- Blue
- Agricultural land- Green
- Barren land- Yellow
Image- March 2020
- Builtup – Black
- Water body- Blue
- Agricultural land- Green
- Barren land- Yellow
The graph of Areas of Built-up and agricultural land is plotted for 2016-2018-2020 period.
Deduction from Graph.
- From the graph it is clear that built-up area is increasing rapidly between 2016 and 2020 due to increase in population and movement of masses from Villages to City for economic
- The Agricultural line shows a linear growth trend as Madhya Pradesh has major parts of its economy attributed to agricultural activities. Jabalpur being a major centre has carefully planned agricultural expansion schemes thus the graph shows a linear trend unlike the built-up
The trend from built-up graph and the classified image clearly shows the rapid expansion of city. The reason for this is various economic activities which is propelling growth of the city and attracting population from nearby small cities and villages. The agricultural land has also shown a linear rising trend indicating carefully planned agricultural expansion taking place in the city.
The urban growth pattern is random in nature expanding spirally towards the edges of the city. This has also reduced the barren lands inside the districts boundary as new townships are being built. The water resources of the city will thus be affected as there will be increased usage of surface and underground water. This may in turn affect the agricultural growth in the region.
Thus proper planning has to be done and implemented to tackle such issues way in advance as all the current metro cities in our country face issue of water availability. Proper connectivity between two spots with road and rail infrastructure has also increased the urban agglomeration within some areas. In essence Jabalpur is on the path of becoming a Tier 1 city , but this should be done in an environmental friendly and sustainable manner. The driving factors should not affect the growth adversely. Furthermore studies of census data
,infrastructure data and agricultural data can be also used to predict more precise trends for planning purposes.
AIR POLLUTION MAPPING USING GIS: A CASE STUDY OF KARNATAKA
AOI : Karnataka
Author : Madhura Kulkarni | GIS Professional (GVI® Trainee id: GVI-73-12-20)
Air pollution is a growing problem in the world today and the WHO ranks air pollution as the 13th leading cause of worldwide mortality (Promoting Healthy Life, 2015). It is believed that pollution due to air quality is more harmful as opposed to pollution caused by either water or land. Breathing polluted air can considerably decrease the lifespan of humans. Air pollution may have mild effects on health, such as itchy eyes, sore throat, etc., or severe effects such as breathing problems, lung cancer, etc., and inhaling severely polluted air may also result in death. Currently developing countries face the major brunt of air pollution problems in the world. Asia reportedly is the worst affected with a large number of Asian cities having critical levels of pollution. Asia also houses the world’s worst polluted cities and it is estimated that 65% of deaths occurring in all of Asia are due to air pollution (Conserve-Energy-Future, 2013). India is a fast-growing economy but is burdened by increasing levels of air pollution that pose a serious threat to the environment and the wellbeing of its citizens.
Air quality monitoring (AQM) is essential to assess levels of air quality and impact on health. The goal of AQM is to protect human health, the environment and the overall welfare of animal and plant lives (Stockholm Environment Institute, 2008). A map of air pollution levels for an entire region can assist in the management of air quality and its subsequent effects.
Based on the data obtained from Karnataka State Pollution Control Board(KSPCB)for the year 2020, the aim of this project is to find out the most polluted area in Karnataka state based on PM2.5 and Air Quality Index (AQI) using GIS software.
PM is also called Particulate Matter or particle pollution, which is a mixture of solid particles and liquid droplets present in the atmosphere. The particles present in the air are so minute that you cannot even view through naked eyes. Some particles are so small that they can only be detected by using electron microscope. Particle pollution consists of PM2.5 and PM10 which are very dangerous.
PM2.5 refers to the atmospheric particulate matter that has a diameter of less than 2.5 micrometres, which is about 3% of the diameter of human hair.
The particles in PM2.5 category are so small that they can only be detected with the help of the electron microscope. These are smaller than PM10 particles. PM10 are the particles with a diameter of 10 micrometers and they are also called fine particles. An environmental expert says that PM10 is also known as respirable particulate matter.
Health effects of PM2.5 and PM10– Due to small in size both PM2.5 and PM10 particles act as gas. When you breathe, these particles they penetrate into the lungs, which can lead to cough and asthma attacks. High blood pressure, heart attack, stroke etc. serious diseases may occur and as a result of which premature death can occur. The worst effect of these particles in the air is on children and the elderly people.
Approaches to pollution mapping- Traditionally, two general approaches to air pollution mapping can be identified: spatial interpolation and dispersion modelling (Briggs 1992). The former uses statistical or other methods to model the pollution surface, based upon measurements at monitoring sites. With the development of GIS and geostatistical techniques in recent years, a wide range of spatial interpolation methods have now become available. Recently, particular attention has tended to focus on kriging in its various forms Nevertheless, despite a number of studies comparing this with other techniques, there is no clear consensus to suggest that any one approach is universally optimal. Instead, performance of the various methods tends to vary depending upon the character of the underlying spatial variation being modelled, and the specific characteristics of the data concerned. A number of these interpolation methods have found applications in pollution mapping. Linear interpolation, for example, has been widely used to derive contour maps of pollution surfaces on the basis of point measurements. Kriging in its various forms has been used to map national patterns of NO2 concentrations, acid precipitation and ozone concentrations. Also reports the use of kriging to estimate and map exposures to groundwater pollution and microwave radiation. Within an urban area, emissions may derive from a large number of intersecting line sources. The distance decay of pollution levels away from these sources is also rapid, and greatly affected by local meteorological and topographical conditions. monitoring networks provide only a limited picture of spatial patterns of urban air pollution, potentially biased estimates of trends, and poor indications of human exposure.
AIMS AND OBJECTIVES
AIM– To find out the most polluted area in Karnataka state based on PM2.5 and Air Quality Index (AQI) using GIS software for the year 2020
- To map the pollution data on the Karnataka shapefile using ArcMap
- To find out the mostly polluted area in the Karnataka state for the year 2020 based on PM5
- To map Air Quality Index (AQI) and find out which district has less
GEOGRAPHY OF THE STUDY AREA
- Study Area– In the present study, pollution data of different cities of Karnataka states is
collected and mapped. The Indian State of Karnataka is located 11°30′ North and 18°30′ North latitudes and 74° East and 78°30′ East longitude. It is situated on a tableland where
the Western and Eastern Ghat ranges converge into the complex, in the western part of the Deccan Peninsular region of India. The State is bounded by Maharashtra and Goa States in the north and northwest; by the Arabian Sea in the west; by Kerala and Tamil Nadu States in the south and by the States of Andhra Pradesh and Telangana in the east. Karnataka extends to about 750 km from north to south and about 400 km from east to west.
Figure 1 shows the location of the study area.
- Climate- Karnataka witnesses three types of climate. The state has a dynamic and erratic weather that changes from place to place within its territory. Due to its varying geographic and physiographic conditions, Karnataka experiences climatic variations that range from arid to semi-arid in the plateau region, sub-humid to humid tropical in the Western Ghats and humid tropical monsoon in the coastal
More than 75 percent of the entire geographical area of Karnataka, including interior Karnataka, witnesses arid or semi-arid climate. Karnataka has about 15 percent of the total semi-arid or 3 percent of the total arid areas marked in India.
- Analysis of PM5
Airborne particulate matter (PM) is a suspension of a heterogeneous mixture of solid and liquid particles of various sizes and chemical compositions (Brook, 2004). PM comprises primary and secondary particles. Primary particles result from direct emissions, such as diesel soot, into the atmosphere. Secondary particles result from a physicochemical transformation of gas such as nitrate and sulphate (Limaye and Salvi, 2010). Due to its complex nature, PM is measured and regulated based on mass and has been distinguished into three groups based on size ranges (Kim et al., 2015):
PM10 – < 10 μm in diameter, referred to as thoracic particles
- 5 to 10 – coarse particles
- 5 – < 2.5 μm in diameter, referred to as fine particles.
As mentioned above, primary PM consists of carbon (soot) emitted from cars, trucks, heavy equipment, burning waste, material from unpaved roads, stone crushing, construction sites and metallurgical operations. In urban areas, PM is composed of carbon and hydrocarbons. A major part of PM existing in the air also comes from natural sources, including ground, oceans and volcanoes (Limaye and Salvi, 2010). Furthermore, PM can travel over long distances and even remain suspended in the atmosphere over time (Londahl et al., 2007). The WHO described the way particulate matter affects health (WHO, 2003).Pollutant PM2.5 was mapped.
The districts Bagalkot, Chikamagalur, Kolar have least concentration of PM2.5 indicated by red colour in the map. Districts Chikaballapur, Chamrajanagar, Yadgir, Vijaywada, Gulbarga Bangalore has concentration between 19 to 40 μg/m3 which is in the limit recommended by CPCB. Raichur 40 μg/m3 District has the most concentration of PM2.5 which is 64 40 μg/m3.
In Karnataka for the year 2020, PM2.5 concentration was found the most in Raichur district (64(μg/m3) while least in Bagalkot District (2 (μg/m3).
The main reason for the more concentration in Raichur district is more industrial area and Thermal Power Plant. The major industries in this area are gold mining, power generation, solvent extraction, oilseeds and oil refining, petrochemical products and cotton spinning.
Anitha K. Chinnaswamy et.al (2016) Air pollution in Bangalore, India: an eight-year trend analysis
The data for different cities has been collected from Central Pollution Control Board (CPCB) website. The data collected is an annual average of each pollutant for year 2020.
Air pollutants were measured over the study period in the above locations using respirable dust samplers (RDS) by conventional methods. The sites were identified on the basis of their representation of the various characteristics of the city, i.e., industrial, commercial, residential and sensitive areas. Four air pollutants, namely SO2, NOx, suspended particulate matter (SPM) and respirable suspended particulate matter (RSPM/PM10), are regularly monitored at all locations. The table shows the data collected for different cities.
As the objective of the project is to find out most polluted city and AQI for the state, Air pollution concentration data on SO2, NO2, and SPM were collected from an air quality monitoring network system for year 2020. The collected data was then tabulated in excel file.
Analysis of Air Quality Index
Air Quality Index for the year 2020 was higher for Bangalore Air quality is ranked as Good – 0 to 50
satisfactory – 51 to 100
moderate – 101 to 200
poor – 201 to 300
very poor- 301 to 400
AQI is calculated by measuring eight criteria of air pollutants (Particulate matter, Sulphur Dioxide, Carbon Monoxide, Nitrogen Dioxide, Ozone, Ammonia and Lead). The city’s increasing reliance on private modes of transport may lead to an increase in particulate matter pollution by more than 70% by 2030, states a study that has a dire prediction for air pollution in Bengaluru.The city’s immense growth, particularly in vehicles and construction activity that produces copious amount of dust, is reflected in the increase in air pollution in the past few years. Air Quality Index of Bangalore is highest which indicates Bangalore air pollution is the worst in Karnataka. Whereas Air Quality Index of Mysore is lowest which indicates it is good quality air.
As the aim of the project was to map the pollutants on the map to find out the most polluted area in the Karnataka state, the most concentration of PM2.5 which is the most pollution causing pollutant is found in Raichur District. The main reason for this is the thermal power plant present in the area.
Also Air Quality Index is highest in Bangalore district as it has the more industrial area and traffic. For Bangalore, the critical level of pollution is likely to have a damaging effect on the health of the citizens that may result in a tremendous burden on the public health system. Additionally, if this were to affect the skilled young human resource, it would pose a threat to the city’s economy. Therefore it is of vital importance that the government addresses the issue of health impact due to air pollution by adhering to stringent measures of pollutants
Runoff Potential Analysis of a Watershed using HEC HMS in Netravati Basin located in south of India in the Sahyadri Ranges
AOI : Netravati River Basin, Karnataka
Authors : Ankush Kumar NIT Silchar (GVI ® Candidate id GVI-76-2-21)
Estimation of design flood one of the usual tasks assigned to hydrologists. Design flood data is required for protective measures in a watershed or for designing water related structures. For ensuring efficient watershed management suitable model selection is required. In this project HEC HMS is used for hydrologic modelling in order to estimate runoff potential of the basin. HEC HMS is a hydrologic modelling tool which is programmed to simulate the hydrologic process of dendritic watershed system.The project topic is Runoff Potential Analysis of a Watershed using HEC HMS. The Nethravathi Basin located in south of India in the SAHYADRI ranges. It has elevation about 2000m and ranges from 75033’N and 76038’E and watershed area is about 3580km2. The daily rainfall data of the 36 rain gauge station located in the catchment were collected for the study year which is 2011 to 2015 and runoff data for same duration. Digital elevation model of the Nethravathi catchment to delineate basins and sub basins. Land use and land cover data for Nethravathi catchment is required for initial estimations of curve number and other basin parameters.
The basin is divided into five sub basins for each sub basin theission polygon method is applied considering all the rain gauge stations in that sub basin. This reduces the data entry load for the simulation. For the loss analysis SCS CN method is used and for transform analysis SCS UH is used. From the various available routing techniques muskingum routing method is used for routing reaches. Shape file of the catchment is obtained using ArcGIS. Shape file is required to calculate area of basin and sub basins. Shape file is also used in conjunction with HEC HMS as background map for fixing the relative positions of the basin components.
Once the model is setup, trial run simulation is done and model is calibrated and parameters were optimised. The calibrated model is validated with observed runoff values of two years. The analysis done for the five years is compared with the existing model for the same catchment. The values obtained from the HEC HMS model are within four percent variation with the observed flow for the study carried out from 2011 to 2015.
Water related challenges are ever increasing and highly diversified. Current and future water related problems are location and time specific and have great impact on economy and population growth. Hydrologic models help us in process understanding and scenario analysis to effectively counter such challenges. Estimation of design flood is one of the usual tasks assigned to a hydrologist. Design flood data is required for protective measures in a watershed or for designing water related structures. A model represents real time situations, build to study and understand the response in the prototype. Number of rainfall and runoff simulations are available. Choice of and appropriate model is essential for any watershed to have an efficient planning and solving watershed related problems. For ensuring efficient watershed management suitable model selection is required.
One of the such effective tools developed under U S Army Corps of Engineers in 1998 named HEC HMS abbreviated as Hydrologic Engineering Centre – Hydrologic Modelling System, it is graphical user interface-based model which has free access on internet and it is a useful tool in solving hydrologic problems around the world. HEC HMS The hydrological modelling system is programmed to simulate the hydrologic process of dendritic catchment system. Many hydrologic modelling methodologies are included. They are event – infiltration, unit hydrograph and also routing reaches.
It includes methodologies required in simulation it also includes evapotranspiration, melting snow along with moisture conditions in soil. This program supports an integrated work interface with data entry tools, computation system and result display tools. The GUI allows easy movements among the various segments of model. Results of simulation will be stored at system for data storage which can be utilised along with different software to carry out various research work like availability of water, drainage in urban areas, predicting flood, impact of urbanisation, design of spillway and reservoir, damage control from flood.
To estimate runoff potential of a basin using HEC HMS and to study the applicability of HEC-HMS to Western Ghats.
- Hydro meteorological description of Western Ghats
- Studying models available and used for Western Ghats
- HEC-HMS tool & its salient features
- Case study: Solution using HEC-HMS tool for Nethravathi river basin
- Collection of daily rainfall data from 2011-2015 of Western Ghats
- Comparatively studying the models available for the Western Ghats region.
- Mapping and delineation of the catchment and streams of Nethravathi river basin using ArcGIS software.
- Land-use and Land-cover for the selected catchment.
- Determining total outflow using HEC-HMS
- Comparison of estimated outflow with observed outflow along with outflow of SAHYADRI model and MODCUR-PQR model.
Organization : In this research study, Chapter 1 comprises of the introduction, objectives and scope of project. Chapter 2 contains literature review and gap areas relevant to this study. Chapter 3 illustrates the theory and procedure. Chapter 4 describes features of Nethravathi Catchment. In chapter 5, The results and conclusions are discussed.
Dilip and Rajib (2011) developed an inverse model and solved for estimating the curve numbers for the lumped and distributed models. They concluded that if spatial variations of rainfall and catchment characteristics are significant then the lump approach will not give accurate results.
Apoorva and Yusuf (2014) used Curve Number (CN) method for estimating infiltration features of the catchment depending on the properties of soil and land use property using HEC-HMS and compared results with the models existing in that area. Observation shows that outcomes of the model are within satisfactory limits for given conditions.
Dweependra developed flood hydrographs for North Brahmaputra and South Brahmaputra using HEC HMS and compared results with CWC reports. It was found that the HEC-HMS models are useful with the equal reliability and viability as compared with CWC reports.
Girma et al. (2009) studied hydrologic response of a watershed to climatic changes in The Upper Blue Nile river using predictor variables derived from NCEP. They showed a projected decrease in annual future runoff.
Meenu et al. (2013) Compared present and future runoff and loss estimates over the sub basin of the study area. From results of water balance studies carried out, it was concluded that increasing precipitation increases runoff and decreases actual evapotranspiration.
Majidi and Shahedi (2012) used Green-Ampt transform methodology and initial results reflected that there are differences between observed and estimated peak flows. From the results it was concluded that the lag time is one of the sensitive parameters. Therefore, optimization methods used to calibrate model and sensitivity analysis was done.
Roy et al. (2013) studied hydrologic response for the Subarnarekha catchment, starting at Ghatsila extending up to Bhosraghat. The results show that there are two different sets of parameter accounting for changes in hydrologic conditions and corresponding simulation results that are more accurate compared to set of single parameters applied for an entire year.
Nasri et al. (2011) studied about the simulation of rainfall and runoff model with HEC- HMS. He used SCS CN loss method and lag-time transform method used to find flood process. The model was calibrated and optimised values of CN and other parameters were included. The outflow from each sub basin was calculated.
Bruce and McEnroe (2010) studied about the guidelines provided for simulating streamflow at Kansas in Johnson County, with the help of HEC-HMS. The study explores the hydrologic features of USGS – gauged reaches in Johnson County considering soil moisture accounting procedures to simulate direct runoff from rainfall and neglecting interflow, groundwater flow, snowpack accumulation and snowmelt.
Yener et al. studied about modelling in HEC-HMS and its applicability in Yuvacik basin. The research is based on the choice of system supporting the decision which is applied in the management and operation in dam reservoir at Yuvacik, which is used for supplying water and controlling flood reservoir. The study uses, the frequency storm method in meteorological model to simulate a frequency storm from available statistical precipitation data.
Mazumdar et al. studied about water resources management in India under changed climatic scenarios using HEC-HMS. Input of data like daily or hourly rainfall, soil condition at the micro-watershed level and hydro-meteorological parameters has been given in this study.
Joolien et al. (2010) studied about mitigation of flood in the Muda river, Malaysia. The study explores important aspects related to Southeast Asia and comparative study of hydrologic and hydraulic models. It is observed about 25% difference between the flood frequency analysis of field measurements and hydrologic model results.
Gap areas and Motivation
It was observed that a very few conceptual models are applied for the region of Western Ghats. These models are developed for a particular region and are bounded by large number of parameters.
Hydrology community should utilise the advantages of fast computer programs than following the traditional spreadsheet technique. Model should be developed on regional scale satisfying necessary conditions and considering major parameters.
Theory and Procedure
Hydro Meteorological Description of Western Ghats. More than 245 million population is supported by Western Ghats for drinking water, irrigation and power etc (WGEEP,2011). The wet mountainous Western Ghats regions of Karnataka are usually called as SAHYADRI RANGES shown in FIGURE 3.1. Sahyadri ranges are the one of the most significant topographic feature in southern part of India. The western face is extremely steep, whereas eastern slope is gently descending. Deep and vertical road cuts stand unsupported despite of heavy rain, a major portion of which on the slopes gets infiltrated (infiltration leads to increased subsurface storage and to increased chances of landslides). Laterites, red loam, black soils, hilly soil, red gravel, coastal alluvium and mixed red and black soil are the seven main soil types in the area. These soils are generally acidic in nature. Primary investigations show that the response of pipe flow is slow and it has great significance in case of long duration and low intensity rain. Rate of infiltration was found to be high in forest areas (Putty and Prasad,2000). Forests occupy only the Appalachian slopes and the valleys, where they are called Sholas, and the rounded crests are usually covered with grass.
Stream flow and catchment response
In the experiments carried out in the experimental watersheds show that rising limbs are steep with multiple peaks and negligible baseflow characterizes the hydrographs (Putty and Prasad, 2000). In the upland lower order streams draining the grass cover catchment. It has been observed that quick flow in streams draining grassy blanks is a combination of
- Infiltration excess overland flow from limited areas,
- Overland flow from surface saturated areas (Dunne and Black, 1970) riparian to the stream channel and
- Surface runoff from those portions of the slope which have been rendered wet by outflow from pipes, opening out away from the channel.
HEC HMS has wide range applicability to solve diversified problems. Some of them are large scale projects to supply water hydrology of flood also small and urbanised and naturally occurring watershed runoffs. The results of simulation can be used in conjunction with other software for analysing water related problems (Charley et al. ,1995). Some major problems which can be addressed are water availability, urban drainage, flood forecasting, urbanisation impact, spillway design, flood mitigation and reservoir design studies.
HEC HMS features There are mainly three input data components in HEC HMS. These are model for basin, meteorological model for basin and control specifications for the basin. Entry of data can be done for each basin components separately. Optimisation of data is not mandatory but it may be done based on the need and application of project. Results are in the form of summary table.
In the basin model there are seven elements. They are routing reach, junction, reservoir, diversion, sub basin, source and sink. The basin model is a representation of the physical scenario. FIGURE 3.2 Shows schematic methodology to execute HEC HMS basin model.
It is a set of data which defines precipitation. This precipitation may be hypothetical or historical. This data is used along with basin model. Frequency – based storms requires rainfall depths for various durations which have to be provided by the user
Case study on Nethravathi River
The Nethravathi river originates from the Chikmagaluru district of the Karnataka. It is one of the holy rivers of the India and flows through the Dharmasthala a famous pilgrimage. Kumardhara another stream joins Nethravathi at Uppinangadi. It finally flows into Arabian sea at the south of Mangalore city. Its length is approximately one hundred three kilometres and average width is two hundred meters. The catchment area for the river is about three thousand and six hundred square kilometres. FIGURE 4.1Shows map of Nethravathi river basin.
- Daily precipitation data from 2011 to 2015
- Daily observed runoff from 2011 to 2015
- DEM data of Nethravathi catchment Methodology used in HEC-HMS software
Nethravathi catchment boundary was delineated using ArcGIS. Total area of the basin was sub divided into five sub basins and area of each sub basin was calculated and net precipitation of each sub basin calculated using theission polygon method. FIGURE 4.2 shows the basin shape file used in conjunction to HEC HMS with primary model setup
Results and Discussion
The calibration and validation carried out for the reference years 2011 to 2015. The optimised parameter values for models is given in the Table 5.1 and Table 5.2. The optimization is carried out for the sensitive parameters and the same optimized parameters are used for prediction of flood data of entire year.
The entire basin is divided into five sub basins and area of the basin calculated using ArcGIS. The estimation of curve number is based on land use and land cover of the basin. Lag time is delay between the maximum rainfall amount and the peak discharge. It is estimated considering basin gradient and drainage density of the basin. Muskingum K and x are dependent on channel characteristics used for routing reaches.
The model output displays results in the form of Graph, Summary Tables, Time Series Table, Outflow, Precipitation, Cumulative Precipitation, Soil Infiltration, Precipitation Loss etc. sample results are shown in the following figures.
Land use and Land cover
With high rainfall regime, the slope towards western face of the Ghats are naturally covered with evergreen forests, whereas when we consider eastern slopes it shifts to moist and gradually to dry deciduous types. The vegetation cover has its dense development towards the southern tip in Kerala covered with rich tropical rain forests (Usha et al.,2014). There are number of horticultural and tuber crops in the area. TABLE 3.1 shows the land use pattern of the Nethravathi catchment.
TABLE 3.1: Land use pattern of Nethravathi catchment
Percentage area covered
3.1 SAHYADRI Model
According to SAHYADRI model runoff of a day consists of mainly three components. They are:
Saturated area quick flow
Soil zone lateral seepage
Saturated ground water
The maximum interception capacity of the catchment is taken as the first parameter. Rain falling in excess of this capacity, throughfall, is divided between the variable source area quick flow and infiltration into the soil, assuming that the infiltration capacity always exceeds the rainfall intensity. The water absorbed by the ground remains temporarily in the upper soil zone. Flow occurring out of this storage above the field capacity, is taken as the outflow from a linear reservoir and is called the drainage rate. This quantity of the soil zone outflow is divided between two components – the vertical percolation into the ground water zone and the lateral throughflow, in a constant proportion. Evaporation is allowed from the interception store at the potential rate and the remaining demand is met out of the soil store at a rate proportional to the fraction of saturation of the zone considered above the wilting point. The delayed ground water discharge from the lower zone is taken as outflow from a non-linear reservoir.
The eleven parameters of the model out of which four related to the soil and land use characteristics, namely, interception capacity, the pore capacity, the field capacity and the wilting point, are determined, at least in principle, using data from toposheet and soil maps (Bourgeon, 1989). The rest are to be calibrated by optimisation.
The procedure of optimisation by trial and error should start with those parameters which control the long duration flow volume (James, 1972) and the dry period flow recession. As has been discussed earlier, flow from the ground water zone matters the most in the regions of the Western Ghats. Four parameters govern the ground water zone in the model SAHYADRI. While SZRK and SZROK determine the recharge, GWZK and GWZE govern the outflow from the groundwater zone. Hence, in the optimisation procedure, these parameters are first dealt with. Since the records pertaining to the dry periods are not usually much reliable, it is not possible to obtain the values of GWZK and GWZE by regression. Hence, it becomes necessary to obtain them by trial and error, so as to make discharge volume during dry periods comparable with the measured.
The parameters in the functional expression for calculating the source area are dealt with only after obtaining satisfactory values of all the other parameters. The initial value of the parameter weighting the soil zone wetness, SZWK, is taken as unity. In this case, the value of SAE can be taken as equal to the maximum amount of water retained in the catchment, determined using the sample data, through a water balance study. Once, SZWK and SAE are fixed, the initial value of SAK can be calculated so as to obtain realistic values of the saturated area. The final values of these parameters will have to be determined solely by trial and error.
Finer adjustments of the parameters may be required at the end of the above two steps. While the major adjustments of the parameters are done by matching the hydrographs, the value of the residual sum square alone is looked into while carrying out the fine tuning. The usual experience is that a number different set of parameters would yield the same results while minimising the residual sum square of the daily runoff. In such a case, the best fitting set is obtained by calculating the error in runoff of longer durations, either weeks or fortnights.
The watershed model SAHYADRI has been calibrated using the records of the Netravathi basin for five years of data from 2011 to 2015. Since, the values of the individual parameters, obtained by optimisation, do not carry much meaning; the calibrated values are not discussed here. Also, the following observations are worth mentioning:
The value of the parameter SZPC (noted as physically based), which controls the amount of evapotranspiration, had to be optimised, since the values calculated as explained previously led to erroneous magnitudes of evapotranspiration. This implies that the number of parameters needed to be optimised is one more than what has been mentioned
The procedure of trial and error, used for optimising, has shown that the value of the parameters determined physically, from land use and soil characteristics, have little influence on the performance of the model, since any alteration in them requires a corresponding change in the parameters required to be optimised for the fit to be good However, calculating a few of the parameters by using the knowledge of catchment characteristics has rendered the optimisation procedure a little less cumbersome.
Despite the fact that different sets of parameter values hold good equally, the information concerning the components of streamflow, simulated by the optimised model, seems to be unique (an evidence to this is available by noting that the magnitude of the ground water flow component, simulated by the model branded unrealistic earlier is almost equal to the magnitude being estimated by the present model).
3.2 MODCUR-PQR Model
The particular interest of the conceptual parameter model presented (with the acronym MODCUR -PQR) has been to study the importance of subsurface flow contributions to the stream quickflow. The model was developed starting with the application of the popular CN-Method to some catchments in the region and then modifying the model in steps in accordance with the inferences obtained at each stage (Harish et. al., 1996). The model uses CN as a basis for estimating the quickflow components – the variable source area runoff (SARO) and the subsurface stormflow (SSRO). Since rainfall occurs in the region almost daily and for most part of the day during the Monsoon, a continuous moisture accounting procedure using the method suggested by Hawkins (1978) is used. While the model estimates the SARO component as being proportional to the CN, it uses the CN expression
for determining the quick subsurface runoff (SSRO). Pipe flow being the dominating SSRO process in the region, the quick SSRO has been termed Pipe quickflow (PRO).
Rainfall on the catchment, in excess of interception, gets bifurcated into two parts at the surface –
That falling on the saturated zone becomes Saturated Area Surface Runoff (SARO)
That falling on the other portions gets infiltrated completely (INFL).
A part of the infiltrated water flows quickly through the pipe-net to become Quick Pipe flow (PRO). The remaining part of INFL enhances the catchment store, which drains slowly to the stream as Delayed Flow (DRO). The intercepted water gets evaporated at the potential rate, which is always more than the maximum rate of interception. Evapotranspiration also occurs from the catchment store and is dependent on the availability of moisture.
There were four parameters used in the model (Lag time, Curve Number, Muskingum k, Muskingum x) and they influence the runoff from the basin to a great extent hence their optimization is important aspect.
The model is validated for two consecutive years and it was observed that estimated and observed flow differ by 3.02% and 2.81%.
The result for five consecutive years measured and compared with SAHYADRI Model and MODCUR-PQR Model. The values obtained from the HEC HMS Model are within 4% variation with observed flow.
By studying the features of Western Ghats and attributes of the HEC HMS it can be concluded that HEC HMS is applicable to the Western Ghats. HEC HMS model yields better results considering lesser parameters.
Very simplified models can be developed using HEC-HMS for runoff predictions and hydrologic response of watershed.
Future Scope for Studies
1.) There are two major objectives of hydrologic modeling.
- Runoff potential analysis
- Peak flood analysis
For peak flood analysis the precipitation data of the storm duration is required. With availability of appropriate data peak flood analysis can be done.
2.) There are various loss, transform and routing methods available in HEC HMS. Similar models can be setup with different methods to have a comparative analysis about sensitivity parameters and respective models.
- Dilip Kumar and Rajib Kumar Bhattacharjya (2011) – Distributed Rainfall Runoff Modeling International Journal of Earth Sciences and Engineering ISSN 0974-5904, Volume 04.
- Apoorva V. and Yusuf Javeed (2014) – Rainfall – Runoff Simulation Using Hec-Hms Tool (A Case Study of Nethravathi River Basin) International Conference on Emerging Trends in Engineering (ICETE -2014) @ NITTE
- Dweependra Nath Kalita-A study of basin response using HEC HMS and subzone reports of CWC, Office of the Chief Engineer (P&D) Brahmaputra Board, Guwahati- 781029, India
- Meenu,1 S. Rehana1 and P. P. Mujumdar1 (2013) – Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM Hydrol. Process. 27, 1572–1589 (2013) Published online 11 May 2012 in Wiley Online Library
- Majidi, K. Shahedi- Simulation of Rainfall-Runoff Process Using Green-Ampt Method and HEC-HMS Model (Case Study: Abnama Watershed, Iran) , International Journal of Hydraulic Engineering 2012, 1(1): 5-9 DOI: 5923/j.ijhe.20120101.02
- Girma yimer, Andreja Jonoski and Ann Griensven (2009), Hydrological Response of a Catchment to Climate Change in the Upper Beles River Basin, Upper Blue Nile, Ethiopia, Nile Basin Water Engineering Scientific Magazine, Vol.2, 2009
- Roy, S. Begam, S. Ghosh and S. Jana (2013) – Caliberation and validiation of HEC HMS for a river basin in eastern India. ARPN Journal of Engineering and Applied Sciences, Vol. 8, No. 1, January 2013.
- Hydrologic Modelling System (HEC-HMS) applications guide: version 1.0. (2008).
U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA.
- Hydrologic Modelling System (HEC-HMS) technical reference manual: (2000). U.S. Army Corps of Engineers, Institute for Water Resources, Hydrologic Engineering Center, Davis, CA.
- Hydrologic Modelling System (HEC-HMS) user’s manual: version 3.1.0. (2006). U.S. Army Corps
- V.T, Maidment.d.r, Mays.L.W.(1988). Applied Hydrology, New York: McGraw Hill Book.
- Ghanshyam Das (2009). Hydrology and soil conservation engineering: Including watershed management, PHI Ltd., New Delhi
- Masoud Nasri, Farshad Soleimani Sardoo and Mehdi Katani (2011) Simulation of the Rainfall-Runoff Process Using of HEC-HMS Hydrological Model (A Case Study of Sheikh Bahaei Dam Basin) World Academy of Science, Engineering and Technology 54
- Mysooru Yadupathi Putty, Salient Features of the Hydrology of the Western Ghats in Karnataka
- Pankaj kr Roy, Asis Mazumdar (2013) Water resources in india under changed climatic scenario
- Thomas Lloyd, Clark Barlow, Oliver Obregon, Kyle Sanford, Adam Birdsall (2007) HEC-HMS Long Term Simulation for the El Cajon Watershed
- Yener, Şorman, A.Ü. Şorman, A.A. Şensoy, A Gezgin, T. Modeling Studies With Hec- Hms And Runoff Scenarios In Yuvacik Basin, Turkiye
- In Johnson County, Kansas, with HEC-HMS Report to Johnson County Public Works and Infrastructure Stormwater Management Program
- Francisco Olivera and David Maidment GIS Tools for HMS Modeling Support Center for Research in Water Resources University of Texas at Austin Austin, Texas