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Evaluating the effects of forest fire on water balance using fire susceptibility maps
Ecological Indicators 110 (2020) 105856
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Evaluating the effects of forest fire on water balance using fire susceptibility maps
T
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Venkatesh K. , Preethi K., Ramesh H. Department of Applied Mechanics and Hydraulics, National Institute of Technology, Karnataka 575025, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire susceptibility map Forest fire SCS-CN Stream flow SWAT SWAT CUP
Sudden and long term changes in the landscape can be attributed to periodic wildfires which, is a cyclic occurrence at Kudremukh national forest in Western Ghats of India. These land-use changes influence the hydrology of landscape, causing disintegration of soil, loss of biodiversity, changes in stream and flooding. To understand and account for these land-use changes, a new approach was implemented by developing fire susceptibility map from topographic, climatic and human-induced factors and validating it with MODIS (Moderateresolution Imaging Spectro-radiometer) fire points for discretising accuracy. The fire susceptibility map can be used for studying the long-term (year or more) effects of fire on water balance systems. The fire susceptibility map generated for the years 2005 and 2017 was overlaid with MODIS LULC (Land Use Land Cover) for establishing the post-fire scenario whereas MODIS LULC MCD12Q1 (2005 and 2017) was considered as the no-fire scenario to analyse the intensity of the fire and its effect on streamflow and infiltration. These maps along with historical satellite hydro-climatic datasets, were used to assess the effect of forest fire on hydrological parameters using the SWAT (Soil and Water Assessment Tool) model. No-fire and post-fire conditions were established by modifying SCS-CN (Soil Conservation Service-Curve Number) based on previous works of literature to represent the catchment as unburnt and burnt area. The SWAT model was calibrated (2002–2008) and validated (2009–2012) for establishing a baseline scenario. The sensitive parameters obtained from SUFI-2 (Sequential Uncertainty Fitting) algorithm in SWAT-CUP (Calibration and Uncertainty Programs) were used to simulate stream flows till 2017 due to lack of observed streamflow data for the year 2017. It was inferred that the effect of wildfire on flows in recent years (2017) had increased radically when compared to the flows before a decade (2005), diminishing the rate of infiltration and causing the deficit in groundwater to energise. The methodology can further be executed in any forest area for distinguishing fire hazard zones and implementing prior actions in those areas for mitigation of forest fires and maintaining sustainable water balance.
1. Introduction Vegetation, an integral component of the earth’s surface, plays a crucial role in basins health. The quality and quantity of water contributing to streamflow mainly depend on the amount of vegetation cover. The loss of vegetation contributes to the decrease of soil moisture, infiltration rate, and rainwater percolation and strengthens soil erosion, thereby causing increased streamflow and floods. One of the significant destructive events causing the decrease of foliage is a forest fire. Forest fires are the most vulnerable incidents recurringly occurring throughout the forests worldwide. They contribute to abrupt changes in land use thereby increasing the chances of floods, soil and nutrient loss and deficit in groundwater availability. Parts of forests are eroded due to the forest fire and another part by forest fragmentation
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for proliferating agricultural systems due to increase in population growth and economic desires causing a tremendous ecological disturbance in tropical regions (Renard et al., 2012). It is of great importance for identifying and mitigating these forest fires for conserving bio-diversity and loss of habitat. For planning and reducing the risk of fire, analysis of fire occurrence has to be carried out at different spatial and temporal scales (You et al., 2017). Fire susceptibility maps should be generated based on localised observations and external explanatory variables aiming to assess and predict the occurrence and vulnerability of fire. Zonation of areas based on fire risks helps the planning and protection of forest management teams for identifying high fire-prone zones thereby enlarging the preventive measures in these zones by perpetuating high surveillance, laying roads for faster access, restricting the access to these sites and reshaping the management practices to
Corresponding author. E-mail address: [email protected] (K. Venkatesh).
https://doi.org/10.1016/j.ecolind.2019.105856 Received 28 April 2019; Received in revised form 8 August 2019; Accepted 21 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Location of Study Area.
prevent forest fire (Ribeiro et al., 2016). To discover the significance of fire and its effects on water balance, studies have been carried out in different regions of the world. Some conclusions were drawn identifying the reasons for the occurrence of fire, its behaviour and the effect on water balance systems. The reason maybe because of the differences in fire intensities, fire spread and duration and lack of ground truth data. Some studies have proved that the increase of fire spread and intensity decreases the infiltration and aggravates runoff (Batelis and Nalbantis, 2014; Soulis, 2018), estimated the effect of forest fire on streamflow either by increasing the CN (Curve Number) values to suit the post-fire scenario and modified the land cover type of pre-fire to represent the post-fire condition. (Nalbantis and Lymperopoulos, 2012; Versini et al., 2013; Livingston et al., 2005) has used SCS-CN method for estimating the effect of forest fire on vegetated areas and the hydrological losses associated due to the occurrence of fire. (Rodrigues et al., 2019) studied the effect of loss of vegetation due to a forest fire on the water balance of PERSM Park, Brazil, using the SWAT model. He identified that recurring fires causes floods and droughts in rainy and dry seasons, thereby reducing infiltration, groundwater storage, baseline flow, and increasing runoff. Even though fires are recurringly occurring in Indian forested regions, no study has been undertaken to study and understand the potential effects of forest fire on water balance components. Regarding this, the present study has been carried out in Tungabhadra river basin which comprises three significant forests (Kudremukh, Bhadra and Someshwara forests) within the catchment to assess the effects of forest fire on water balance parameters using SWAT model. The main objective of the present study is that 1. To generate susceptibility maps for two years (2005 & 2017) based on topographic, climatic and humaninduced factors. 2. To check the accuracy and validate the generated
susceptibility maps with MODIS burned points for an accurate representation of risk zones. 3. To calibrate and validate the SWAT model with no fire (MODIS LULC maps of 2005 and 2017) and postfire (susceptibility maps overlaid with MODIS LULC of 2005 and 2017) conditions. 4. To estimate and understand the potential effects of forest fire on water balance in the Tungabhadra river basin. The Novelty lies in the usage of secondary satellite datasets (CHIRPS (Climate Hazards Group Infrared Precipitation with Station data), MERRA-2 (Modern-Era Retrospective analysis for Research and Application) and GLEAM (Global Land Evaporation Amsterdam Model) datasets) for the generation of susceptibility maps and using these susceptibility maps for finding the effect on streamflow using the SWAT model which considers topographic, climatic and human-induced factors into account. Earlier studies (Wulder et al., 2009; Springer et al., 2015) have used Normalized Burn Ratio (NBR) for estimating and assessing the impact of forest fire on streamflow. In the case of NBR, if the regeneration of vegetation occurs in the burned areas between the pre and post-fire scenarios, they may not reveal the exact condition of water balance. Some studies (Batelis and Nalbantis, 2014; Havel et al., 2018) have altered the land uses by converting the forested lands to other land use which may not represent the actual scenario on the ground. Hence, in the present study, an attempt was made for estimating the long term effects of fire on water balance by considering fire susceptibility maps along with vegetation map for establishing realworld scenario that actually represents the land use for identifying future risk zones. 2. Study area Tungabhadra, a major tributary of river Krishna is a trans-boundary 2
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Table 1 Datasets Used in the Study. S.No
Dataset
Source
Resolution
1 2 3 4 5 6 7 8 9
LULC DEM Fire points Precipitation Evapotranspiration Wind speed Soil Stream flow Shape files (Roads, settlements)
MODIS MCD12Q1 (https://lpdaac.usgs.gov/) SRTM (https://earthexplorer.usgs.gov/) MODIS FIRMS NASA (https://earthdata.nasa.gov/) CHIRPS (ftp://ftp.chg.ucsb.edu/pub/org/chg/products/) GLEAM (https://www.gleam.eu/) MERRA2 (https://disc.gsfc.nasa.gov/) FAO (http://www.fao.org/) India-WRIS (https://india-wris.nrsc.gov.in/) DIVA-GIS (http://www.diva-gis.org/)
500 m 30 m 1 km 0.05˚ 0.25˚ 0.5˚ × 0.625˚ 1:5000000 ** **
Fig. 2. (a) Digital Elevation Model (b) Soil Map.
river flowing between the two states Andhra Pradesh, and Karnataka was formed due to the confluence of two rivers Tunga and Bhadra. These two rivers will rise in the Western Ghats at an altitude of 1198 m above M.S.L (Mean Sea Level) and unite at Kudli at an altitude of 610 m near Shimoga. The total length of the river is around 531 kms from its origin in Karnataka state till the joining of river Krishna at Sangameshwaram in Kurnool district, Andhra Pradesh. The total catchment area of Tungabhadra river basin is about 69552 km2 out of which 15393 km2 was considered in present study till Honnali gauging station. The current study area lies between 74°00′00″ to 76°30′00″ E and 13°00′00″ to 15°30′00″ N, as shown in Fig. 1. The topographic pattern is highly undulating with elevation ranging from 500 m to 1900 m. The Tungabhadra river region receives an average annual rainfall of about 1016 mm and average annual temperature of about 26.7 °C (Nandi and Reddy, 2018). Predominant crops in the study area are paddy, sorghum, sugarcane (Nesheim et al., 2010) and red soils, and sandy loamy soils are the significant soils found in this region.
model, i.e., SWAT (Soil and Water Assessment Tool). Topographic, climatic, fuel type, and human-induced factors are necessary for the generation of fire risk map whereas SWAT model requires land use, soil, topography and hydro-meteorological datasets for simulating the water balance of the study area. The details regarding input datasets were represented in Table 1. Digital Elevation Model (DEM) representing the terrain of the study area is represented in Fig. 2(a). SRTM (Shuttle Radar Topographic Mission) DEM having a spatial resolution of 30 m is downloaded from USGS earth explorer for the year 2000 for representing the terrain. The elevation values of each pixel are represented by DEM and is used for the delineation of the watershed using ARCSWAT and generation of slope, aspect and terrain ruggedness maps using ARC GIS. Soil map having a scale of 1:50,00,000 was obtained from the Food and Agricultural Organization (FAO), and it classified the watershed into two classes as shown in Fig. 2(b). Moderate-resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) was used to generate the International Geosphere-Biosphere Programme (IGBP) land cover type map for the years 2005 and 2017. Precipitation data were obtained from CHIRPS (Climate Hazards Group Infrared Precipitation with Station data) dataset developed by University of California and USGS (United States Geologic Survey) having a spatial resolution of 0.05° for the years 2000–2017 (Funk et al., 2015).
3. Datasets used The present study deals with the development of fire susceptibility map and finding the effect of fire on water balance using hydrological 3
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Fig. 3. Flowchart Representing the Methodology Implemented in the Present Study.
MODIS fire product (MOD14) during 2005 and 2017 was downloaded from Fire Information for Resource Management System (FIRMS) NASA website which gives the information about active fire locations during the satellite overpass. These Fire points were used for the validation of fire risk maps generated. Road and settlements shapefiles were downloaded from DIVA-GIS to generate proximity maps.
Temperature (maximum and minimum) and wind speed having daily and monthly temporal resolution are obtained from atmospheric reanalysis dataset, i.e., MERRA-2 v5.12.4 (Modern-Era Retrospective analysis for Research and Application) developed from Goddard Earth Observing System (GOES-5) having a spatial resolution of 0.5°×0.625° (Bosilovich et al., 2015). GLEAM (Global Land Evaporation Amsterdam Model) v3.0a and 3.0b dataset developed by VU Amsterdam University having a spatial resolution of 0.25° × 0.25° was used for obtaining PET (Potential EvapoTranspiration) data at daily time step (Martens et al., 2017). The daily streamflow data for honnali gauging station was obtained from India-WRIS (India-Water Resources Information System) for the period 2000 to 2012 for calibrating and validating the SWAT model.
4. Methodology The methodology involves generation of fire susceptibility map, validation using MODIS fire points, SWAT model setup and calibration and validation of streamflow using SUFI-2 algorithm in SWAT-CUP. The entire methodology was explained in a flowchart represented in Fig. 3. 4
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Fig. 4. Base Maps of Various Factors Influencing the Forest Fire for the Year 2005.
Fig. 5. Base Maps of Various Factors Influencing the Forest Fire for the Year 2017.
4.1. Generation of fire susceptibility map
of all the factors are represented in Figs. 4 and 5, respectively. Topographic parameters such as elevation, aspect, slope, and terrain ruggedness are considered as they profoundly influence the ignition of fire (Jaiswal et al., 2002). The climate of a region changes with the increase in elevation. The intensity of wind increases and the area will be highly exposed to sunlight with the increase in altitude, thereby causing high proneness to fire. The direction and rate of spread of fire augment with
Fire susceptibility maps are generated using various factors influencing the occurrence and propagation of fire such as topographic, climatic, vegetation, and proximity to water bodies as well as humaninduced parameters to know the risk of fire danger (Pourtaghi et al., 2016; Tripathi et al., 2017). The base maps for the years 2005 and 2017 5
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effect of soil moisture, air temperature and solar radiation on the terrain. Digital Elevation Model was used for the calculation of elevation, slope aspect terrain ruggedness. Climatic parameters such as precipitation, temperature, wind speed, as well as evapotranspiration, were considered. High temperature encourages quick drying of biomass, in specific dry grass, dead leaves, tree needles, and little trees thereby causing the biomass more susceptible to fire (Vadrevu et al., 2006). Increase in precipitation elevates moisture content, thereby causing the suppression of fires. High Evapotranspiration induces water stress in vegetation, thereby decreasing the live fuel moisture content thus increasing the rate of fire. All these derived factors were classified into five classes such as very low, low, moderate, high and very high accordingly. Fuel is any organic matter such as litter, twigs and wood type on the forest floor that can ignite and burn. Hence fuel type is a significant factor contributing to the occurrence of fire. Fuel type map was generated using MODIS LULC. IGBP land cover type is generated from the MCD12Q1, and each class is assigned with risk level according to the historical fire points as shown in Table 3. Regions near human activities such as roads, settlements are at high risk, whereas areas near water bodies are less vulnerable to fire due to high moisture content. Roads and settlements were downloaded from DIVA-GIS whereas water bodies were extracted from the land cover map and small streams were delineated using ArcGIS. Proximity maps were generated using ArcGIS and risk levels were assigned accordingly. The reclassified maps for the years 2005 and 2017 are represented in Figs. 6 and 7, respectively. Validation is an important process to assess the accuracy of a model to real-world representation. The generated susceptibility maps were further validated using MODIS fire points to know the degree of accuracy.
Table 2 Fire Risk Levels Assigned to Terrain Ruggedness Map. Terrain Ruggedness Index
Class
Fire risk level
0–80 80–116 116–161 161–239 239–497 > 497
Level Nearly level Slightly rugged Intermediately rugged Moderately rugged Highly rugged
Very low Moderate Low High Very high Moderate
Table 3 Fire Risk Levels Assigned to Each Fuel Type. S.no
Land cover type
Fire risk level
1 2 3 4 5
Water bodies, barren, wetland Built-up, agriculture Evergreen forest, deciduous forest Range Lands Mixed forest, deciduous forest
Very low Low Moderate High Very high
the increase in slope. Majority of forest area in the study region is located at steep slopes thereby increasing the chances of ignition of the fire. Terrain heterogeneity can be well explained with terrain ruggedness. Riley’s classification (Riley et al., 1999) was used to classify the terrain into six levels, and the risk level of each class is based on the percentage of historical fire points in each class of terrain ruggedness (mentioned in Table 2). In this region, a high percentage of fire points were accumulated in moderately rugged terrain, and hence it was assigned with very high fire risk level. Less percentage of points were fell in level terrain, and hence it is assigned with very low-risk level class, and the intermediate terrain classes have varying minimum to maximum risk levels. Aspect is the direction that slopes are facing which determine the
Fig. 6. Reclassified Maps of (i) Settlement (ii) Evapotranspiration (iii) Aspect (iv) Elevation (v) Precipitation (vi) Roads (vii) Slope (viii) Temperature (ix) Terrain Ruggedness (x) Waterbody (xi) Wind speed (xii) Fuel type for the Year 2005. 6
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Fig. 7. Reclassified Maps of (i) Settlement (ii) Evapotranspiration (iii) Aspect (iv) Elevation (v) Precipitation (vi) Roads (vii) Slope (viii) Temperature (ix) Terrain Ruggedness (x) Waterbody (xi) Wind speed (xii) Fuel type for the Year 2017. Table 4 Attributes and Corresponding Curve Numbers for Post-Fire Scenario. S.NO
IGBP Classification
SWAT Code
SWAT LULC Description
CN2A
CN2B
CN2C
CN2D
1 2 3 4 5 6 7 8 9 10 11 12
Forest-Evergreen Forest-Deciduous Rangelands Pasture Forest-Evergreen Forest-Deciduous Forest-Mixed Rangelands Forest-Deciduous Rangelands Forest-Evergreen Forest-Mixed
FRLA FRLC FRLG FRLL FRMB FRMD FRMF FRMH FRHE FRLI FRHJ FRHK
Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire
30 50 54 36 35 55 46 59 60 64 40 36
60 71 74 64 35 76 70 79 81 84 70 64
75 82 84 77 80 87 83 89 92 94 85 77
82 88 89 84 87 93 89 94 98 98 92 84
forest, evergreen low burn forest, deciduous low burn range, grasses low burn Pasture low burn forest, evergreen moderate burn forest, deciduous moderate burn forest, mixed moderate burn range, grasses moderate burn forest, deciduous high burn range, grasses high burn forest, evergreen high burn forest, mixed high burn
4.2. Establishment of post fire scenario
LULC and sensitive parameters for the years 2005 and 2017 was established by changing the attributes and curve numbers of LULC classes.
Long term effects on streamflow induced due to forest fire cannot be studied using burnt area maps generated from NBR (Normalised Burn Ratio) method since they deal with a limited amount of time. Considering the burnt area maps for long term studies induces errors since the regrowth of vegetation occurs with time. For analysing the long term effects, Fire susceptibility maps generated from several factors (mentioned in Section 4.1) should be employed. To realistically represent the post-fire scenario, curve numbers were altered depending upon the intensity of risk. Two scenarios are established to understand the effect of fire on water balance parameters.
The attributes and curve numbers of LULC for the post-fire scenario are represented in Table 4 obtained from (Havel et al., 2018; Rodrigues et al., 2019). The change in water balance between no-fire and post-fire for the years 2005 and 2017 were assessed to understand the increase or decrease in proportions of area burnt and the amount of flows altered.
4.3. SWAT model description SWAT (Soil and Water Assessment Tool) is a semi-distributed, continuous-time and physically based catchment model used for simulating runoff, sediment yield, pesticides and nutrients at different spatial and temporal scales (Yang et al., 2006; Abbaspour et al., 2015).
1. No-fire or baseline scenario with sensitive parameters and vegetation was calibrated, which acts as a reference. 2. Postfire scenario using susceptibility maps overlaid with MODIS
7
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Fig. 8. MODIS Land Use Land Cover Maps for the Years 2005 and 2017.
calibrated and validated till 2012, and those sensitive parameters are used in SWAT model to extend the simulated flows till 2017 with MODIS LULC of the year 2017 using manual calibration in Arc SWAT. The model was run for the years 2005 and 2017 using MODIS LULC of years 2005 and 2017 respectively to represent no-fire condition by keeping all other inputs constant. A postfire scenario was established by running the model with fire susceptibility maps overlaid with MODIS LULC generated for the years 2005 and 2017.
Table 5 Land Use Land Cover Characteristics. Class
Forest-Evergreen Forest-Deciduous Forest-Mixed Range Lands Wetlands Agriculture Barren Urban Water
Code
FRSE FRSD FRST RNGE WETL AGRL BARR URBN WATR
Percentage Occupancy (%) 2005
2017
12.64 7.52 6.07 41.10 0.35 31.31 0.01 0.43 0.57
16.27 8.87 7.59 36.22 0.12 29.17 0 0.45 1.31
4.4. Calibration and validation The output of Arc SWAT was used in SWAT CUP for calibrating and validating the streamflow using SUFI-2 algorithm with observed runoff data. The model was calibrated for the years 2002 to 2008 for simulating streamflow, and the sensitive parameters obtained from the calibration phase are used for validation from 2009 to 2012. Sensitive parameters are identified based on previous pieces of literature and were adjusted according to a set of ranges for analysing the effect of these parameters on streamflow. Global sensitivity analysis was carried out to identify the most sensitive parameters before calibration and validation. The sensitivity of parameters after 1000 iterations are assessed based on t-stat and p-value. Larger values of t-stat and smaller values of p-value indicate most sensitive parameter. The uncertainty of a parameter is assessed based on p-factor and r-factor. The value of pfactor near to 1 and r-factor near to 0 represents an exact match to observed data. The accuracy of the SWAT model was evaluated based on statistical parameters like N-S (Nash-Sutcliffe), PBias (Percentage Bias) and R2 (coefficient of Determination). The magnitude of variance between the simulated and observed data was established by N-S coefficient. The coefficient of Determination represents the collinearity between the observed and simulated discharge data where values near to one represent the high correlation. The tendency of the simulated streamflow data to be smaller or larger than the measured flows is obtained from PBias. The negative values indicate underestimation, and positive
The SWAT model is being applied for leaf size to large size watersheds under different climatic, land use and management practices for hydrologic and water quality assessments. The SWAT model mainly depends on the water balance equation, which combines the individual processes of the hydrologic cycle. Based on drainage patterns obtained from DEM, SWAT divides the catchment into sub-basins by defining a threshold to form a stream of minimum drainage area. Based on unique LULC (Land Use Land Cover), soil and slope characteristics, these subbasins are further discretized into small spatial units, i.e., Hydrologic Response Units (HRU). The processes within the land phase can be simulated at user-defined time steps at HRU level and can be summed up to the sub-catchment level. The output from each sub-catchment combining with climatic data will be routed through reaches to the outlet of the basin. In the present study, Arc SWAT was used for the initial setting of the model with topographic, climatic and spatial parameters for desired time spans of calibration and validation. The entire basin has been subclassified into 61 sub-basins and 3182 HRU’s. The SWAT model was calibrated from 2002 to 2008 and validated from 2009 to 2012 with a warm-up period of 2 years for no fire-condition. Since the measured streamflow data is available till 2012, the model was first 8
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Fig. 9. Fire Susceptibility Maps with MODIS Fire Points for the Years 2005 and 2017.
regions.
Table 6 Percentage of the Area Fell in Each Risk Level of Susceptibility Map. S.no
Fire risk level
5.2. Fire susceptibility map
Percentage of area
5. Results
All factors contributing to fire such as slope, elevation, aspect, terrain ruggedness, precipitation, evapotranspiration, wind speed, temperature, fuel type, roads, settlements, and water bodies were reclassified and integrated by taking the average of all layers to obtain fire susceptibility map. These fire susceptibility maps represent the areas that are vulnerable to the fire risk and the areas where the fire might spread quickly. The final fire susceptibility maps were overlaid with MODIS fire points for validation, as shown in Fig. 9. The percentage of fire points fell in high & very high-risk zones were computed to obtain the accuracy, as shown in Equation (1).
5.1. Land use land cover change
Accuracy =
MODIS LULC for the years 2005 and 2017 were employed in the present study. The LULC maps were reclassified into nine classes for 2005 and eight classes in 2017. The LULC map for the years 2005 and 2017 was represented in Fig. 8. Rangelands, followed by agriculture and forests, occupied most of the study area. Kudremukh, Bhadra and Someshwara forests majorly contribute the rangelands and forest areas in the study area. The agricultural lands are majorly occupied by coconut, areca nut, and rice. The water percentage is due to the existence of Gajanur dam and Bhadra reservoir. It can be inferred from Table 5 that forest and water percentages were increased in the study area in 2017 when compared to 2005. The rangelands and agricultural lands were decreased by 5% and 2% in 2017 respectively. The increase in the forest canopy is mainly due to forest protection acts implemented in these regions and the increase in water percentage can be accounted due to the termination of mining activities carried out by KIOCL (Kudremukh Iron Ore Company Limited) in Kudremukh
The fire susceptibility maps exhibited an accuracy of 76.38% and 81.74% for 2005 and 2017 respectively. The percentage of area occupied by each risk zone and their degree of risk are mentioned in Table 6. It can be inferred from these maps that the vegetation types that come under high and very high-risk zones are rangelands, deciduous forests. Parts of deciduous and evergreen forests cover the moderaterisk zones. Wetlands, water bodies, urban area, and agriculture come under low fire risk zone. The final fire susceptibility maps were overlaid with LULC for constructing post-fire scenario maps, as shown in Fig. 10.
1 2 3 4 5
Very Low Low Moderate High Very High
2005
2017
13.50 30.20 33.50 17.40 5.40
5.40 22.20 28.50 31.75 12.15
values specify overestimation and zero signifies perfect simulation.
(No.of points in high and very high−risk zone) x100 (Total no. of fire points)
(1)
5.3. Calibration and validation SUFI-2 (Sequential Uncertainty Fitting) algorithm in SWAT CUP was used for calibration and validation of streamflow. Sensitive parameters were identified, and the parameter ranges are adjusted for optimised solutions. The fitted values, minimum and maximum ranges of selected sensitive parameters are listed in Table 7. 11 parameters were found 9
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Fig. 10. Post-Fire Scenario Maps for the Years 2005 and 2017. Table 7 Sensitive Parameters and the Fitted Values for SWAT CUP. S.no
Sensitive Parameters
Allowable Range
Fitted Value
1 2 3 4 5 6 7 8 9 10 11
CN2 (SCS runoff curve number) Alpha-BF (Base flow alpha factor (days)) GW-Delay (Groundwater delay (days)) GWQMN (Threshold depth of water in the shallow aquifer required for return flow to occur (mm)) CH_N2 (Manning's “n” value for the main channel) Alpha_BNK (Base flow alpha factor for bank storage) CH_K2 (Effective hydraulic conductivity in main channel alluvium.) GW_REVAP (Groundwater “revap” coefficient) REVAPMN (Threshold depth of water in the shallow aquifer for “revap” to occur (mm)) SOL_AWC (Available water capacity of the soil layer) SLSOIL (Slope length for lateral subsurface flow)
−0.2 to 0.2 0 to 1 0 to 500 0 to 5000 −0.01 to 0.3 0 to 1 −0.01 to 500 0.02 to 0.2 0 to 500 0 to 1 10 to 150
0.077 0.287 21.382 4196.2 0.007 0.080 5.616 0.106 317.898 0.858 52.169
Table 8 Statistical Coefficient Values during Calibration and Validation Time Steps. Statistical Coefficients
Calibration
Validation
R2 N-S PBias
0.82 0.82 5.80
0.78 0.77 7.60
sensitive based on t-stat and p-value using global sensitivity analysis. The performance of the SWAT model was assessed during calibration and validation using statistical coefficients R2, N-S, and PBias. Based on the ranges suggested by (Moriasi et al., 2007), the R2 ≥ 0.75, N-S ≥ 0.75 and PBias ≤ 10% are categorized as very good performance of the model. The SWAT model was calibrated for the years 2002 to 2008, which produced R2 and N-S of 0.82, indicating high correlation and less variance between observed and simulated values. Validation has been carried out for the years 2009 to 2012, which yielded promising results having R2 of 0.78 and N-S of 0.77. The statistical coefficient values are represented in Table 8. Even though satellite rainfall and temperature datasets (CHIRPS and MERRA-2) were used in the
Fig. 11. Scatterplot Depicted for Simulated vs. Observed Stream Flow Values during the Calibration Phase.
study, fewer biases of 5.8 and 7.6 during calibration and validation are observed indicating that these datasets can be deployed as a replacement in scarce data regions. 10
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no-fire and post-fire scenarios. This can be accepted as the amount of area burnt in that year is comparatively less. In 2017, around 25% of runoff was increased during the post-fire scenario, decreasing the amount of infiltration. It was a proven result in many studies, that increase in percentage burnt area increases the runoff and decreases the infiltration (Inbar et al., 1998; Benavides-Solorio and MacDonald, 2001; Neary et al., 2003; Reddy et al., 2013). However, it should be identified that in a decade gap, a large amount of area has been subjected to fire, thereby causing a higher increase in the percentage of runoff. The Evapotranspiration in case of post-fire scenario has increased from 760.1 mm to 762.9 mm and 726.9 mm to 734.1 mm when compared to no-fire scenarios for the years 2005 and 2017. These changes profoundly affect the natural process of infiltration and groundwater recharge as the water is transported outside of the basin, thereby causing water deficit in the study area and floods in the downstream of the basin. The results are further analysed at the subwatershed scale to know the effect of fire on surface and lateral flows. To identify the outcome of these effects, five sub-basins which are highly prone to risk are selected along with entire watershed. It can be observed from Fig. 15 that the surface flow in all the post-fire cases has increased where the burnt areas are large. The entire study area is highly prone to fire as it indicates 43% of the watershed as burnt. The highest percentage of change has occurred in subbasin-61, which is a part of Kudremukh forest with an increase of 25% of streamflow compared to the no-fire scenario. Fig. 16 represents the average annual total runoff values for selected subbasins and the entire watershed for the year 2017. It can be observed that subbasin 51 and 44 are yielding more than a 50% increase in total annual runoff when compared to the no-fire condition. Around 30% increase in annual streamflow was identified in the post-fire scenario for the entire watershed. These results highlight the importance of vegetation for maintaining proper water balance in the watershed. Fire leaves a complex impact on the watershed functions. In the case of post-fire scenario, the increase in streamflow is primarily because of the decrease in infiltration caused due to the burning of vegetation cover. Loss of vegetation in the post-fire scenario decreased the evapotranspiration, contributing to the increase in streamflow (Rogger et al., 2017). As vegetation cover reduces, interception reduces and overland flow increases, which along with hydrophobicity of the soil induced by fire, increases streamflow in general, and flood magnitudes in specific (Woods et al., 2007). The loss of vegetation in post-fire case increases the contact between the precipitation and surface, thereby decreasing the rate of infiltration and causing soil deterioration (Martin and Moody 2001). The recurrent fire diminishes the plant species causing soil cohesion loss and degradation of root systems. Due to loss of cohesion, the soil particles form an impervious layer reducing the permeability and increasing the runoff. All these changes in land use, soil profile, loss of vegetation due to forest fire affects the groundwater recharge, water quality and quantity of the basin. Hence it is of utmost
Fig. 12. Scatterplot Depicted for Simulated vs. Observed Stream Flow Values during the Validation Phase.
The average annual runoff for observed and simulated streamflows are 216.8 mm and 204.3 mm during calibration period whereas 210.9 and 194.8 mm during validation. These average annual runoff values during both the time steps are nearly matching, indicating that the SWAT model was able to capture the flows. Scatter plots (Figs. 11 and 12) and line graphs (Fig. 13 and 14) were plotted for the calibration and validation phases. The SWAT model was unable to capture all the peaks during calibration and validation phases, which may be due to the uncertainties induced by the biases in satellite rainfall and temperature datasets and the larger resolutions of the input land use and soil datasets. The statistical coefficient values of R2 and N-S during calibration are slightly higher than the validation phase, which may be due to the shorter span of the time period during the validation time step. 5.4. Effect of wild fires on water balance After running the SWAT model for 13 years (2000–2012) for establishing a baseline scenario, the model was calibrated manually with the sensitive parameters to extend till 2017. The SWAT model was rerun for no-fire and post-fire scenarios using MODIS LULC in the initial step and susceptibility map (overlaid with LULC) in the post-fire scenario for the years 2005 and 2017 respectively. Analysis of the results indicated in Table 9 shows that the amount of infiltration is relatively larger when compared with runoff which is due to the presence of open lands (41%) when compared to the forest area (26.23%) in the study area. The susceptibility maps for the years 2005 and 2017 shows more substantial difference in the percentage of area burnt, which is because of the changes that occurred in land use, climate and topography in recent years. The amount of precipitation in the study region has decreased by around 19%, and there is an increase of temperature by 0.4 °C in 2017 when compared to the year 2005, demonstrating a broader climate change. The effect of fire on streamflow is very less in the year 2005 since there are no much differences observed between
Fig. 13. Monthly Time Series of Simulated vs Observed Runoff during the Calibration Phase. 11
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Fig. 14. Monthly Time Series of Simulated vs. Observed Runoff during the Validation Phase. Table 9 Average Annual Values of Water Balance Parameters during No-Fire and Post-Fire Scenarios. YEAR
Scenario/Variable Precipitation Runoff Infiltration Evapotranspiration
2005
2017
MODIS LULC
Susceptibility Map
MODIS LULC
Susceptibility Map
No-Fire 2076.80 108.53 1208.17 760.10
Post-Fire 2076.80 110.53 1203.37 762.90
No-Fire 1672.20 66.91 878.39 726.90
Post-Fire 1672.20 88.77 849.33 734.10
Fig. 15. Pie Charts Representing Surface and Lateral Flows during No-Fire and Post-Fire Events for Selected Subbasins and the Entire Watershed for the Year 2017.
Fig. 16. Percentage Change in Average Annual Runoff between No-Fire and Post-Fire Scenarios.
necessary for identifying the fire risk zones and implementing precautionary measures for mitigation of fires which affect the water balance of the study area. In conclusion, the present work implemented several static and
dynamic factors (Climatic, topographic and human-induced factors when combined acts a significant indicator for the identification of fire risk zones) for generating fire susceptibility maps. The generated fire susceptibility maps are integrated with MODIS LULC to develop post12
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fire susceptibility maps, which serves as a major indicator for identifying the zones associated with fire risk and occurrence. The novelty lies in the integration and development of these post-fire susceptibility maps which can be used for ecological monitoring and management since they furnish the information regarding the chance and occurrence of fire. The developed post-fire susceptibility maps can further be deployed in hydrological models for identifying the significant changes that occurred in water balance parameters like streamflow, sediment yield and evapotranspiration etc.
References Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., Kløve, B., 2015. A continental-scale hydrology and water quality model for europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J. Hydrol. 524, 733–752. Batelis, Stamatios Christos, Nalbantis, Ioannis, 2014. Potential effects of forest fires on streamflow in the enipeas river Basin, Thessaly, Greece. Environ. Process. 1 (1), 73–85. Benavides-Solorio, Juan, MacDonald, Lee H., 2001. Post-fire runoff and erosion from simulated rainfall on small plots, colorado front range. Hydrol. Process. 15 (15), 2931–2952. Funk, Chris, Peterson, Pete, Landsfeld, Martin, Pedreros, Diego, Verdin, James, Shukla, Shraddhanand, Husak, Gregory, Rowland, James, Harrison, Laura, Hoell, Andrew, Michaelsen, Joel, 2015. The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes. Sci. Data 2, 1–21. Havel, Aaron, Tasdighi, Ali, Arabi, Mazdak, 2018. Assessing the hydrologic response to wildfires in mountainous regions. Hydrol. Earth Syst. Sci. 22 (4), 2527–2550. Inbar, M., Tamir, M., Wittenberg, L., 1998. Runoff and erosion processes after a forest fire in Mount Carmel, a Mediterranean Area. Geomorphology 24 (1), 17–33. Jaiswal, Rajeev Kumar, Mukherjee, Saumitra, Raju, Kumaran D., Saxena, Rajesh, 2002. Forest Fire risk zone mapping from satellite imagery and GIS. Int. J. Appl. Earth Obs. Geoinf. 4 (1), 1–10. Livingston, Russell K., Andrew Earles, T., Wright, Kenneth R., 2005. In: Los Alamos PostFire Watershed Recovery: A Curve-Number-Based Evaluation, pp. 1–11. Martens, Brecht, Miralles, Diego G., Lievens, Hans, Van Der Schalie, Robin, De Jeu, Richard A.M., Fernández-Prieto, Diego, Beck, Hylke E., Dorigo, Wouter A., Verhoest, Niko E.C., 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 (5), 1903–1925. Martin, Deborah A., Moody, John A., 2001. Comparison of soil infiltration rates in burned and unburned mountainous watersheds. Hydrol. Process. 15 (15), 2893–2903. Bosilovich, Michael G., Akella, Santha, Coy, Lawrence, Cullather, Richard, Draper, Clara, Gelaro, Ronald, Kovach, Robin, Liu, Qing, Molod, Andrea, Norris, Peter, Wargan, Krzysztof, Chao, Winston, Reichle, Rolf, Takacs, Lawrence, Vikhliaev, Yury, Bloom, Steve, Collow, Allison, Firth, Stacey, Labow, Gordon, Partyka, Gary, Pawson, Steven, Reale, Oreste, Schubert, Siegfried D., Suarez, Max, 2015. MERRA-2: initial evaluation of the climate. Technical Report Series on Global Modeling and Data Assimilation. September. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50 (3), 885–900. Nalbantis, Ioannis, Lymperopoulos, Spiridon, 2012. Assessment of flood frequency after forest fires in small ungauged basins based on uncertain measurements. Hydrol. Sci. J. 57 (1), 52–72. Nandi, S., Reddy, M.J., 2018. Assessing suitability of satellite rainfall data for estimation of daily streamflows of a small tropical catchment In India. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 5278–5281. Neary, Daniel G., Gottfried, Gerald J., Ffolliott, Peter F., 2003. Post-wildfire watershed flood responses. June In: Second International Fire Ecology and Fire Management Congress, Orlando, Florida, 16-20 November 2003, Paper 1B7, pp. 1–8. Nesheim, Ingrid, Desmond McNeill, K.J., Joy, S. Manasi, Nhung, Dang Thi Kim, Portela, Maria Manuela, Paranjape, Suhas, 2010. The challenge and status of IWRM in four river basins in Europe and Asia. Irrigat. Drainage Syst. 24 (3–4), 205–221. Pourtaghi, Zohre Sadat, Pourghasemi, Hamid Reza, Aretano, Roberta, Semeraro, Teodoro, 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol. Ind. 64, 72–84. Reddy, Veerabasawant, Kiran, M.C., Bonell, Michael, Jagdish Krishnaswamy, K.N., Rakesh, Basappa Venkatesh, Lele, Sharachchandra, Badiger, Shrinivas, Purandara, Bekal K., 2013. The groundwater recharge response and hydrologic services of tropical humid forest ecosystems to use and reforestation: support for the ‘infiltrationevapotranspiration trade-off hypothesis’. J. Hydrol. 498, 191–209. Renard, Quentin, Ṕlissier, Raphal, Ramesh, B.R., Kodandapani, Narendran, 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int. J. Wildland Fire 21 (4), 368–379. Ribeiro, Guido Assunção, Banhos, Áureo, dos Santos, Aderbal, da Silva, Gomes, Fiedler, Nilton Cesar, Paneto, Greiciane Gaburro, Eugenio, Fernando Coelho, dos Santos, Alexandre Rosa, Schettino, Vitor Roberto, 2016. Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil. J. Environ. Manage. 173, 65–71. Rodrigues, Evandro Luis, Jacobi, Claudia Maria, Figueira, José Eugênio Côrtes, 2019. Wildfires and their impact on the water supply of a large neotropical metropolis: a simulation approach. Sci. Total Environ. 651, 1261–1271. Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J.C., Bodner, G., Borga, M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L., Horn, R., Kiss, A., Quinton, J.N., Leitinger, G., Lennartz, B., Parajka, J., Peth, S., Robinson, M., Salinas, J.L., Santoro, A., Szolgay, J., Tron, S., Viglione, A., 2017. Land use change impacts on floods at the catchment scale: challenges and opportunities for future research. Water Resour. Res. 53, 5209–5219. Riley, Shawn, DeGloria, Stephen D., Elliot, Robert, 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain J. Sci. 5 (1–4), 23–27. Soulis, Konstantinos X., 2018. Estimation of SCS curve number variation following forest fires. Hydrol. Sci. J. 63 (9), 1332–1346. Springer, Johanna, Ludwig, Ralf, Kienzle, Stefan, 2015. Impacts of forest fires and climate variability on the hydrology of an alpine medium sized catchment in the Canadian Rocky mountains. Hydrology 2 (1), 23–47. Tripathi, D.K., Sahdev, Seema, Kumar, Manish, 2017. Forest fire risk zone mapping using geographic information system: a case study. IPASJ Int. J. Comput. Sci. 5 (7), 9–15.
6. Conclusions Our results gave an insight and advantages associated with the applicability of fire susceptibility map in hydrological modelling. The key aspects were dealt with the usage of satellite datasets for the generation of base maps and susceptibility maps, establishing no-fire and post-fire scenarios using susceptibility maps, applying SWAT model for baseline and post-fire scenario for hydrological parameter estimations and finding the effects of fire on water balance parameters. Fire susceptibility maps were generated by considering human, climate and topographic factors. Validation of generated maps was performed using MODIS fire points, which gave an accuracy of around 76% and 81%. These maps which indicate the risk of fire were overlaid with LULC for creating the post-fire scenario. The SWAT model was employed with no-fire and post-fire scenarios by changing the CN values and attributes for estimating water balance parameters. Good correlation and fewer biases were observed between observed and simulated values during calibration and validation phases. The effect of fire on streamflow, infiltration and E.T were studied. The results demonstrated that in recent years, the intensity of the forest fires was increased compared to the previous decade. Around 21.16% increase in burnt area was estimated from the results between the years 2005 and 2017. The increase in the percentage change of runoff from no and postfire scenarios in 2005 is around 1.84% whereas in case of 2017 it is around 32.67%. It can be inferred that around 30.83% increase in the percentage change of streamflow was observed from 2005 to 2017. There were no significant differences observed in hydrological parameters between no-fire and post-fire in 2005 since the percentage of area burnt was less. In 2017, the burnt area for the entire watershed was around 43%, which caused an increase of 25% of annual runoff, making the watershed prone to floods. Estimations were further carried out at sub-basin scale to find out the effect of forest fire on surface and lateral flows. The subbasin-61 under Kudremukh forest region has contributed high stream flows having a burnt area of 86%. These results demand the need for developing and deploying fire mitigation strategies and methods for maintaining healthy water balance in the study area. High precautions and preventive measures should be implemented in Kudremukh National Park of the study area as it is highly prone to fire and is majorly affecting the infiltration thereby causing high runoff in low-level areas. The adopted methodology can be implemented for developing susceptibility maps and finding the effects on water balance in any forested regions across the world for identifying fire risk zones and maintaining sustainable water resource management. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105856. 13
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Characterizing boreal forest wildfire with multi-temporal landsat and LIDAR data. Remote Sens. Environ. 113 (7), 1540–1555. Yang, Jing, Maximov, Ivan, Srinivasan, Raghavan, Zobrist, Juerg, Mieleitner, Johanna, Abbaspour, Karim C., Siber, Rosi, Bogner, Konrad, 2006. Modelling hydrology and water quality in the pre-alpine/alpine thur watershed using SWAT. J. Hydrol. 333 (2–4), 413–430. You, Weibin, Lin, Li, Liyun, Wu., Ji, Zhirong, Yu, Jian’an, Zhu, Jianqin, Fan, Yunjian, He, Dongjin, 2017. Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecol. Ind. 77, 176–184.
Vadrevu, Krishna P., Eaturu, Anuradha, Badarinath, K.V.S., 2006. Spatial distribution of forest fires and controlling factors in Andhra Pradesh, India using spot satellite datasets. Environ. Monit. Assess. 123 (1–3), 75–96. Versini, P.A., Velasco, M., Cabello, A., Sempere-Torres, D., 2013. Hydrological impact of forest fires and climate change in a Mediterranean Basin. Nat. Hazards 66 (2), 609–628. Woods, Scott W., Birkas, Anna, Ahl, Robert, 2007. Spatial variability of soil hydrophobicity after wildfires in Montana and Colorado. Geomorphology 86 (3–4), 465–479. Wulder, M.A., White, J.C., Alvarez, F., Han, T., Rogan, J., Hawkes, B., 2009.
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Evaluating the effects of forest fire on water balance using fire susceptibility maps
T
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Venkatesh K. , Preethi K., Ramesh H. Department of Applied Mechanics and Hydraulics, National Institute of Technology, Karnataka 575025, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire susceptibility map Forest fire SCS-CN Stream flow SWAT SWAT CUP
Sudden and long term changes in the landscape can be attributed to periodic wildfires which, is a cyclic occurrence at Kudremukh national forest in Western Ghats of India. These land-use changes influence the hydrology of landscape, causing disintegration of soil, loss of biodiversity, changes in stream and flooding. To understand and account for these land-use changes, a new approach was implemented by developing fire susceptibility map from topographic, climatic and human-induced factors and validating it with MODIS (Moderateresolution Imaging Spectro-radiometer) fire points for discretising accuracy. The fire susceptibility map can be used for studying the long-term (year or more) effects of fire on water balance systems. The fire susceptibility map generated for the years 2005 and 2017 was overlaid with MODIS LULC (Land Use Land Cover) for establishing the post-fire scenario whereas MODIS LULC MCD12Q1 (2005 and 2017) was considered as the no-fire scenario to analyse the intensity of the fire and its effect on streamflow and infiltration. These maps along with historical satellite hydro-climatic datasets, were used to assess the effect of forest fire on hydrological parameters using the SWAT (Soil and Water Assessment Tool) model. No-fire and post-fire conditions were established by modifying SCS-CN (Soil Conservation Service-Curve Number) based on previous works of literature to represent the catchment as unburnt and burnt area. The SWAT model was calibrated (2002–2008) and validated (2009–2012) for establishing a baseline scenario. The sensitive parameters obtained from SUFI-2 (Sequential Uncertainty Fitting) algorithm in SWAT-CUP (Calibration and Uncertainty Programs) were used to simulate stream flows till 2017 due to lack of observed streamflow data for the year 2017. It was inferred that the effect of wildfire on flows in recent years (2017) had increased radically when compared to the flows before a decade (2005), diminishing the rate of infiltration and causing the deficit in groundwater to energise. The methodology can further be executed in any forest area for distinguishing fire hazard zones and implementing prior actions in those areas for mitigation of forest fires and maintaining sustainable water balance.
1. Introduction Vegetation, an integral component of the earth’s surface, plays a crucial role in basins health. The quality and quantity of water contributing to streamflow mainly depend on the amount of vegetation cover. The loss of vegetation contributes to the decrease of soil moisture, infiltration rate, and rainwater percolation and strengthens soil erosion, thereby causing increased streamflow and floods. One of the significant destructive events causing the decrease of foliage is a forest fire. Forest fires are the most vulnerable incidents recurringly occurring throughout the forests worldwide. They contribute to abrupt changes in land use thereby increasing the chances of floods, soil and nutrient loss and deficit in groundwater availability. Parts of forests are eroded due to the forest fire and another part by forest fragmentation
⁎
for proliferating agricultural systems due to increase in population growth and economic desires causing a tremendous ecological disturbance in tropical regions (Renard et al., 2012). It is of great importance for identifying and mitigating these forest fires for conserving bio-diversity and loss of habitat. For planning and reducing the risk of fire, analysis of fire occurrence has to be carried out at different spatial and temporal scales (You et al., 2017). Fire susceptibility maps should be generated based on localised observations and external explanatory variables aiming to assess and predict the occurrence and vulnerability of fire. Zonation of areas based on fire risks helps the planning and protection of forest management teams for identifying high fire-prone zones thereby enlarging the preventive measures in these zones by perpetuating high surveillance, laying roads for faster access, restricting the access to these sites and reshaping the management practices to
Corresponding author. E-mail address: [email protected] (K. Venkatesh).
https://doi.org/10.1016/j.ecolind.2019.105856 Received 28 April 2019; Received in revised form 8 August 2019; Accepted 21 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Location of Study Area.
prevent forest fire (Ribeiro et al., 2016). To discover the significance of fire and its effects on water balance, studies have been carried out in different regions of the world. Some conclusions were drawn identifying the reasons for the occurrence of fire, its behaviour and the effect on water balance systems. The reason maybe because of the differences in fire intensities, fire spread and duration and lack of ground truth data. Some studies have proved that the increase of fire spread and intensity decreases the infiltration and aggravates runoff (Batelis and Nalbantis, 2014; Soulis, 2018), estimated the effect of forest fire on streamflow either by increasing the CN (Curve Number) values to suit the post-fire scenario and modified the land cover type of pre-fire to represent the post-fire condition. (Nalbantis and Lymperopoulos, 2012; Versini et al., 2013; Livingston et al., 2005) has used SCS-CN method for estimating the effect of forest fire on vegetated areas and the hydrological losses associated due to the occurrence of fire. (Rodrigues et al., 2019) studied the effect of loss of vegetation due to a forest fire on the water balance of PERSM Park, Brazil, using the SWAT model. He identified that recurring fires causes floods and droughts in rainy and dry seasons, thereby reducing infiltration, groundwater storage, baseline flow, and increasing runoff. Even though fires are recurringly occurring in Indian forested regions, no study has been undertaken to study and understand the potential effects of forest fire on water balance components. Regarding this, the present study has been carried out in Tungabhadra river basin which comprises three significant forests (Kudremukh, Bhadra and Someshwara forests) within the catchment to assess the effects of forest fire on water balance parameters using SWAT model. The main objective of the present study is that 1. To generate susceptibility maps for two years (2005 & 2017) based on topographic, climatic and humaninduced factors. 2. To check the accuracy and validate the generated
susceptibility maps with MODIS burned points for an accurate representation of risk zones. 3. To calibrate and validate the SWAT model with no fire (MODIS LULC maps of 2005 and 2017) and postfire (susceptibility maps overlaid with MODIS LULC of 2005 and 2017) conditions. 4. To estimate and understand the potential effects of forest fire on water balance in the Tungabhadra river basin. The Novelty lies in the usage of secondary satellite datasets (CHIRPS (Climate Hazards Group Infrared Precipitation with Station data), MERRA-2 (Modern-Era Retrospective analysis for Research and Application) and GLEAM (Global Land Evaporation Amsterdam Model) datasets) for the generation of susceptibility maps and using these susceptibility maps for finding the effect on streamflow using the SWAT model which considers topographic, climatic and human-induced factors into account. Earlier studies (Wulder et al., 2009; Springer et al., 2015) have used Normalized Burn Ratio (NBR) for estimating and assessing the impact of forest fire on streamflow. In the case of NBR, if the regeneration of vegetation occurs in the burned areas between the pre and post-fire scenarios, they may not reveal the exact condition of water balance. Some studies (Batelis and Nalbantis, 2014; Havel et al., 2018) have altered the land uses by converting the forested lands to other land use which may not represent the actual scenario on the ground. Hence, in the present study, an attempt was made for estimating the long term effects of fire on water balance by considering fire susceptibility maps along with vegetation map for establishing realworld scenario that actually represents the land use for identifying future risk zones. 2. Study area Tungabhadra, a major tributary of river Krishna is a trans-boundary 2
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Table 1 Datasets Used in the Study. S.No
Dataset
Source
Resolution
1 2 3 4 5 6 7 8 9
LULC DEM Fire points Precipitation Evapotranspiration Wind speed Soil Stream flow Shape files (Roads, settlements)
MODIS MCD12Q1 (https://lpdaac.usgs.gov/) SRTM (https://earthexplorer.usgs.gov/) MODIS FIRMS NASA (https://earthdata.nasa.gov/) CHIRPS (ftp://ftp.chg.ucsb.edu/pub/org/chg/products/) GLEAM (https://www.gleam.eu/) MERRA2 (https://disc.gsfc.nasa.gov/) FAO (http://www.fao.org/) India-WRIS (https://india-wris.nrsc.gov.in/) DIVA-GIS (http://www.diva-gis.org/)
500 m 30 m 1 km 0.05˚ 0.25˚ 0.5˚ × 0.625˚ 1:5000000 ** **
Fig. 2. (a) Digital Elevation Model (b) Soil Map.
river flowing between the two states Andhra Pradesh, and Karnataka was formed due to the confluence of two rivers Tunga and Bhadra. These two rivers will rise in the Western Ghats at an altitude of 1198 m above M.S.L (Mean Sea Level) and unite at Kudli at an altitude of 610 m near Shimoga. The total length of the river is around 531 kms from its origin in Karnataka state till the joining of river Krishna at Sangameshwaram in Kurnool district, Andhra Pradesh. The total catchment area of Tungabhadra river basin is about 69552 km2 out of which 15393 km2 was considered in present study till Honnali gauging station. The current study area lies between 74°00′00″ to 76°30′00″ E and 13°00′00″ to 15°30′00″ N, as shown in Fig. 1. The topographic pattern is highly undulating with elevation ranging from 500 m to 1900 m. The Tungabhadra river region receives an average annual rainfall of about 1016 mm and average annual temperature of about 26.7 °C (Nandi and Reddy, 2018). Predominant crops in the study area are paddy, sorghum, sugarcane (Nesheim et al., 2010) and red soils, and sandy loamy soils are the significant soils found in this region.
model, i.e., SWAT (Soil and Water Assessment Tool). Topographic, climatic, fuel type, and human-induced factors are necessary for the generation of fire risk map whereas SWAT model requires land use, soil, topography and hydro-meteorological datasets for simulating the water balance of the study area. The details regarding input datasets were represented in Table 1. Digital Elevation Model (DEM) representing the terrain of the study area is represented in Fig. 2(a). SRTM (Shuttle Radar Topographic Mission) DEM having a spatial resolution of 30 m is downloaded from USGS earth explorer for the year 2000 for representing the terrain. The elevation values of each pixel are represented by DEM and is used for the delineation of the watershed using ARCSWAT and generation of slope, aspect and terrain ruggedness maps using ARC GIS. Soil map having a scale of 1:50,00,000 was obtained from the Food and Agricultural Organization (FAO), and it classified the watershed into two classes as shown in Fig. 2(b). Moderate-resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) was used to generate the International Geosphere-Biosphere Programme (IGBP) land cover type map for the years 2005 and 2017. Precipitation data were obtained from CHIRPS (Climate Hazards Group Infrared Precipitation with Station data) dataset developed by University of California and USGS (United States Geologic Survey) having a spatial resolution of 0.05° for the years 2000–2017 (Funk et al., 2015).
3. Datasets used The present study deals with the development of fire susceptibility map and finding the effect of fire on water balance using hydrological 3
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Fig. 3. Flowchart Representing the Methodology Implemented in the Present Study.
MODIS fire product (MOD14) during 2005 and 2017 was downloaded from Fire Information for Resource Management System (FIRMS) NASA website which gives the information about active fire locations during the satellite overpass. These Fire points were used for the validation of fire risk maps generated. Road and settlements shapefiles were downloaded from DIVA-GIS to generate proximity maps.
Temperature (maximum and minimum) and wind speed having daily and monthly temporal resolution are obtained from atmospheric reanalysis dataset, i.e., MERRA-2 v5.12.4 (Modern-Era Retrospective analysis for Research and Application) developed from Goddard Earth Observing System (GOES-5) having a spatial resolution of 0.5°×0.625° (Bosilovich et al., 2015). GLEAM (Global Land Evaporation Amsterdam Model) v3.0a and 3.0b dataset developed by VU Amsterdam University having a spatial resolution of 0.25° × 0.25° was used for obtaining PET (Potential EvapoTranspiration) data at daily time step (Martens et al., 2017). The daily streamflow data for honnali gauging station was obtained from India-WRIS (India-Water Resources Information System) for the period 2000 to 2012 for calibrating and validating the SWAT model.
4. Methodology The methodology involves generation of fire susceptibility map, validation using MODIS fire points, SWAT model setup and calibration and validation of streamflow using SUFI-2 algorithm in SWAT-CUP. The entire methodology was explained in a flowchart represented in Fig. 3. 4
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Fig. 4. Base Maps of Various Factors Influencing the Forest Fire for the Year 2005.
Fig. 5. Base Maps of Various Factors Influencing the Forest Fire for the Year 2017.
4.1. Generation of fire susceptibility map
of all the factors are represented in Figs. 4 and 5, respectively. Topographic parameters such as elevation, aspect, slope, and terrain ruggedness are considered as they profoundly influence the ignition of fire (Jaiswal et al., 2002). The climate of a region changes with the increase in elevation. The intensity of wind increases and the area will be highly exposed to sunlight with the increase in altitude, thereby causing high proneness to fire. The direction and rate of spread of fire augment with
Fire susceptibility maps are generated using various factors influencing the occurrence and propagation of fire such as topographic, climatic, vegetation, and proximity to water bodies as well as humaninduced parameters to know the risk of fire danger (Pourtaghi et al., 2016; Tripathi et al., 2017). The base maps for the years 2005 and 2017 5
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effect of soil moisture, air temperature and solar radiation on the terrain. Digital Elevation Model was used for the calculation of elevation, slope aspect terrain ruggedness. Climatic parameters such as precipitation, temperature, wind speed, as well as evapotranspiration, were considered. High temperature encourages quick drying of biomass, in specific dry grass, dead leaves, tree needles, and little trees thereby causing the biomass more susceptible to fire (Vadrevu et al., 2006). Increase in precipitation elevates moisture content, thereby causing the suppression of fires. High Evapotranspiration induces water stress in vegetation, thereby decreasing the live fuel moisture content thus increasing the rate of fire. All these derived factors were classified into five classes such as very low, low, moderate, high and very high accordingly. Fuel is any organic matter such as litter, twigs and wood type on the forest floor that can ignite and burn. Hence fuel type is a significant factor contributing to the occurrence of fire. Fuel type map was generated using MODIS LULC. IGBP land cover type is generated from the MCD12Q1, and each class is assigned with risk level according to the historical fire points as shown in Table 3. Regions near human activities such as roads, settlements are at high risk, whereas areas near water bodies are less vulnerable to fire due to high moisture content. Roads and settlements were downloaded from DIVA-GIS whereas water bodies were extracted from the land cover map and small streams were delineated using ArcGIS. Proximity maps were generated using ArcGIS and risk levels were assigned accordingly. The reclassified maps for the years 2005 and 2017 are represented in Figs. 6 and 7, respectively. Validation is an important process to assess the accuracy of a model to real-world representation. The generated susceptibility maps were further validated using MODIS fire points to know the degree of accuracy.
Table 2 Fire Risk Levels Assigned to Terrain Ruggedness Map. Terrain Ruggedness Index
Class
Fire risk level
0–80 80–116 116–161 161–239 239–497 > 497
Level Nearly level Slightly rugged Intermediately rugged Moderately rugged Highly rugged
Very low Moderate Low High Very high Moderate
Table 3 Fire Risk Levels Assigned to Each Fuel Type. S.no
Land cover type
Fire risk level
1 2 3 4 5
Water bodies, barren, wetland Built-up, agriculture Evergreen forest, deciduous forest Range Lands Mixed forest, deciduous forest
Very low Low Moderate High Very high
the increase in slope. Majority of forest area in the study region is located at steep slopes thereby increasing the chances of ignition of the fire. Terrain heterogeneity can be well explained with terrain ruggedness. Riley’s classification (Riley et al., 1999) was used to classify the terrain into six levels, and the risk level of each class is based on the percentage of historical fire points in each class of terrain ruggedness (mentioned in Table 2). In this region, a high percentage of fire points were accumulated in moderately rugged terrain, and hence it was assigned with very high fire risk level. Less percentage of points were fell in level terrain, and hence it is assigned with very low-risk level class, and the intermediate terrain classes have varying minimum to maximum risk levels. Aspect is the direction that slopes are facing which determine the
Fig. 6. Reclassified Maps of (i) Settlement (ii) Evapotranspiration (iii) Aspect (iv) Elevation (v) Precipitation (vi) Roads (vii) Slope (viii) Temperature (ix) Terrain Ruggedness (x) Waterbody (xi) Wind speed (xii) Fuel type for the Year 2005. 6
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Fig. 7. Reclassified Maps of (i) Settlement (ii) Evapotranspiration (iii) Aspect (iv) Elevation (v) Precipitation (vi) Roads (vii) Slope (viii) Temperature (ix) Terrain Ruggedness (x) Waterbody (xi) Wind speed (xii) Fuel type for the Year 2017. Table 4 Attributes and Corresponding Curve Numbers for Post-Fire Scenario. S.NO
IGBP Classification
SWAT Code
SWAT LULC Description
CN2A
CN2B
CN2C
CN2D
1 2 3 4 5 6 7 8 9 10 11 12
Forest-Evergreen Forest-Deciduous Rangelands Pasture Forest-Evergreen Forest-Deciduous Forest-Mixed Rangelands Forest-Deciduous Rangelands Forest-Evergreen Forest-Mixed
FRLA FRLC FRLG FRLL FRMB FRMD FRMF FRMH FRHE FRLI FRHJ FRHK
Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire Post-fire
30 50 54 36 35 55 46 59 60 64 40 36
60 71 74 64 35 76 70 79 81 84 70 64
75 82 84 77 80 87 83 89 92 94 85 77
82 88 89 84 87 93 89 94 98 98 92 84
forest, evergreen low burn forest, deciduous low burn range, grasses low burn Pasture low burn forest, evergreen moderate burn forest, deciduous moderate burn forest, mixed moderate burn range, grasses moderate burn forest, deciduous high burn range, grasses high burn forest, evergreen high burn forest, mixed high burn
4.2. Establishment of post fire scenario
LULC and sensitive parameters for the years 2005 and 2017 was established by changing the attributes and curve numbers of LULC classes.
Long term effects on streamflow induced due to forest fire cannot be studied using burnt area maps generated from NBR (Normalised Burn Ratio) method since they deal with a limited amount of time. Considering the burnt area maps for long term studies induces errors since the regrowth of vegetation occurs with time. For analysing the long term effects, Fire susceptibility maps generated from several factors (mentioned in Section 4.1) should be employed. To realistically represent the post-fire scenario, curve numbers were altered depending upon the intensity of risk. Two scenarios are established to understand the effect of fire on water balance parameters.
The attributes and curve numbers of LULC for the post-fire scenario are represented in Table 4 obtained from (Havel et al., 2018; Rodrigues et al., 2019). The change in water balance between no-fire and post-fire for the years 2005 and 2017 were assessed to understand the increase or decrease in proportions of area burnt and the amount of flows altered.
4.3. SWAT model description SWAT (Soil and Water Assessment Tool) is a semi-distributed, continuous-time and physically based catchment model used for simulating runoff, sediment yield, pesticides and nutrients at different spatial and temporal scales (Yang et al., 2006; Abbaspour et al., 2015).
1. No-fire or baseline scenario with sensitive parameters and vegetation was calibrated, which acts as a reference. 2. Postfire scenario using susceptibility maps overlaid with MODIS
7
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Fig. 8. MODIS Land Use Land Cover Maps for the Years 2005 and 2017.
calibrated and validated till 2012, and those sensitive parameters are used in SWAT model to extend the simulated flows till 2017 with MODIS LULC of the year 2017 using manual calibration in Arc SWAT. The model was run for the years 2005 and 2017 using MODIS LULC of years 2005 and 2017 respectively to represent no-fire condition by keeping all other inputs constant. A postfire scenario was established by running the model with fire susceptibility maps overlaid with MODIS LULC generated for the years 2005 and 2017.
Table 5 Land Use Land Cover Characteristics. Class
Forest-Evergreen Forest-Deciduous Forest-Mixed Range Lands Wetlands Agriculture Barren Urban Water
Code
FRSE FRSD FRST RNGE WETL AGRL BARR URBN WATR
Percentage Occupancy (%) 2005
2017
12.64 7.52 6.07 41.10 0.35 31.31 0.01 0.43 0.57
16.27 8.87 7.59 36.22 0.12 29.17 0 0.45 1.31
4.4. Calibration and validation The output of Arc SWAT was used in SWAT CUP for calibrating and validating the streamflow using SUFI-2 algorithm with observed runoff data. The model was calibrated for the years 2002 to 2008 for simulating streamflow, and the sensitive parameters obtained from the calibration phase are used for validation from 2009 to 2012. Sensitive parameters are identified based on previous pieces of literature and were adjusted according to a set of ranges for analysing the effect of these parameters on streamflow. Global sensitivity analysis was carried out to identify the most sensitive parameters before calibration and validation. The sensitivity of parameters after 1000 iterations are assessed based on t-stat and p-value. Larger values of t-stat and smaller values of p-value indicate most sensitive parameter. The uncertainty of a parameter is assessed based on p-factor and r-factor. The value of pfactor near to 1 and r-factor near to 0 represents an exact match to observed data. The accuracy of the SWAT model was evaluated based on statistical parameters like N-S (Nash-Sutcliffe), PBias (Percentage Bias) and R2 (coefficient of Determination). The magnitude of variance between the simulated and observed data was established by N-S coefficient. The coefficient of Determination represents the collinearity between the observed and simulated discharge data where values near to one represent the high correlation. The tendency of the simulated streamflow data to be smaller or larger than the measured flows is obtained from PBias. The negative values indicate underestimation, and positive
The SWAT model is being applied for leaf size to large size watersheds under different climatic, land use and management practices for hydrologic and water quality assessments. The SWAT model mainly depends on the water balance equation, which combines the individual processes of the hydrologic cycle. Based on drainage patterns obtained from DEM, SWAT divides the catchment into sub-basins by defining a threshold to form a stream of minimum drainage area. Based on unique LULC (Land Use Land Cover), soil and slope characteristics, these subbasins are further discretized into small spatial units, i.e., Hydrologic Response Units (HRU). The processes within the land phase can be simulated at user-defined time steps at HRU level and can be summed up to the sub-catchment level. The output from each sub-catchment combining with climatic data will be routed through reaches to the outlet of the basin. In the present study, Arc SWAT was used for the initial setting of the model with topographic, climatic and spatial parameters for desired time spans of calibration and validation. The entire basin has been subclassified into 61 sub-basins and 3182 HRU’s. The SWAT model was calibrated from 2002 to 2008 and validated from 2009 to 2012 with a warm-up period of 2 years for no fire-condition. Since the measured streamflow data is available till 2012, the model was first 8
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Fig. 9. Fire Susceptibility Maps with MODIS Fire Points for the Years 2005 and 2017.
regions.
Table 6 Percentage of the Area Fell in Each Risk Level of Susceptibility Map. S.no
Fire risk level
5.2. Fire susceptibility map
Percentage of area
5. Results
All factors contributing to fire such as slope, elevation, aspect, terrain ruggedness, precipitation, evapotranspiration, wind speed, temperature, fuel type, roads, settlements, and water bodies were reclassified and integrated by taking the average of all layers to obtain fire susceptibility map. These fire susceptibility maps represent the areas that are vulnerable to the fire risk and the areas where the fire might spread quickly. The final fire susceptibility maps were overlaid with MODIS fire points for validation, as shown in Fig. 9. The percentage of fire points fell in high & very high-risk zones were computed to obtain the accuracy, as shown in Equation (1).
5.1. Land use land cover change
Accuracy =
MODIS LULC for the years 2005 and 2017 were employed in the present study. The LULC maps were reclassified into nine classes for 2005 and eight classes in 2017. The LULC map for the years 2005 and 2017 was represented in Fig. 8. Rangelands, followed by agriculture and forests, occupied most of the study area. Kudremukh, Bhadra and Someshwara forests majorly contribute the rangelands and forest areas in the study area. The agricultural lands are majorly occupied by coconut, areca nut, and rice. The water percentage is due to the existence of Gajanur dam and Bhadra reservoir. It can be inferred from Table 5 that forest and water percentages were increased in the study area in 2017 when compared to 2005. The rangelands and agricultural lands were decreased by 5% and 2% in 2017 respectively. The increase in the forest canopy is mainly due to forest protection acts implemented in these regions and the increase in water percentage can be accounted due to the termination of mining activities carried out by KIOCL (Kudremukh Iron Ore Company Limited) in Kudremukh
The fire susceptibility maps exhibited an accuracy of 76.38% and 81.74% for 2005 and 2017 respectively. The percentage of area occupied by each risk zone and their degree of risk are mentioned in Table 6. It can be inferred from these maps that the vegetation types that come under high and very high-risk zones are rangelands, deciduous forests. Parts of deciduous and evergreen forests cover the moderaterisk zones. Wetlands, water bodies, urban area, and agriculture come under low fire risk zone. The final fire susceptibility maps were overlaid with LULC for constructing post-fire scenario maps, as shown in Fig. 10.
1 2 3 4 5
Very Low Low Moderate High Very High
2005
2017
13.50 30.20 33.50 17.40 5.40
5.40 22.20 28.50 31.75 12.15
values specify overestimation and zero signifies perfect simulation.
(No.of points in high and very high−risk zone) x100 (Total no. of fire points)
(1)
5.3. Calibration and validation SUFI-2 (Sequential Uncertainty Fitting) algorithm in SWAT CUP was used for calibration and validation of streamflow. Sensitive parameters were identified, and the parameter ranges are adjusted for optimised solutions. The fitted values, minimum and maximum ranges of selected sensitive parameters are listed in Table 7. 11 parameters were found 9
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Fig. 10. Post-Fire Scenario Maps for the Years 2005 and 2017. Table 7 Sensitive Parameters and the Fitted Values for SWAT CUP. S.no
Sensitive Parameters
Allowable Range
Fitted Value
1 2 3 4 5 6 7 8 9 10 11
CN2 (SCS runoff curve number) Alpha-BF (Base flow alpha factor (days)) GW-Delay (Groundwater delay (days)) GWQMN (Threshold depth of water in the shallow aquifer required for return flow to occur (mm)) CH_N2 (Manning's “n” value for the main channel) Alpha_BNK (Base flow alpha factor for bank storage) CH_K2 (Effective hydraulic conductivity in main channel alluvium.) GW_REVAP (Groundwater “revap” coefficient) REVAPMN (Threshold depth of water in the shallow aquifer for “revap” to occur (mm)) SOL_AWC (Available water capacity of the soil layer) SLSOIL (Slope length for lateral subsurface flow)
−0.2 to 0.2 0 to 1 0 to 500 0 to 5000 −0.01 to 0.3 0 to 1 −0.01 to 500 0.02 to 0.2 0 to 500 0 to 1 10 to 150
0.077 0.287 21.382 4196.2 0.007 0.080 5.616 0.106 317.898 0.858 52.169
Table 8 Statistical Coefficient Values during Calibration and Validation Time Steps. Statistical Coefficients
Calibration
Validation
R2 N-S PBias
0.82 0.82 5.80
0.78 0.77 7.60
sensitive based on t-stat and p-value using global sensitivity analysis. The performance of the SWAT model was assessed during calibration and validation using statistical coefficients R2, N-S, and PBias. Based on the ranges suggested by (Moriasi et al., 2007), the R2 ≥ 0.75, N-S ≥ 0.75 and PBias ≤ 10% are categorized as very good performance of the model. The SWAT model was calibrated for the years 2002 to 2008, which produced R2 and N-S of 0.82, indicating high correlation and less variance between observed and simulated values. Validation has been carried out for the years 2009 to 2012, which yielded promising results having R2 of 0.78 and N-S of 0.77. The statistical coefficient values are represented in Table 8. Even though satellite rainfall and temperature datasets (CHIRPS and MERRA-2) were used in the
Fig. 11. Scatterplot Depicted for Simulated vs. Observed Stream Flow Values during the Calibration Phase.
study, fewer biases of 5.8 and 7.6 during calibration and validation are observed indicating that these datasets can be deployed as a replacement in scarce data regions. 10
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no-fire and post-fire scenarios. This can be accepted as the amount of area burnt in that year is comparatively less. In 2017, around 25% of runoff was increased during the post-fire scenario, decreasing the amount of infiltration. It was a proven result in many studies, that increase in percentage burnt area increases the runoff and decreases the infiltration (Inbar et al., 1998; Benavides-Solorio and MacDonald, 2001; Neary et al., 2003; Reddy et al., 2013). However, it should be identified that in a decade gap, a large amount of area has been subjected to fire, thereby causing a higher increase in the percentage of runoff. The Evapotranspiration in case of post-fire scenario has increased from 760.1 mm to 762.9 mm and 726.9 mm to 734.1 mm when compared to no-fire scenarios for the years 2005 and 2017. These changes profoundly affect the natural process of infiltration and groundwater recharge as the water is transported outside of the basin, thereby causing water deficit in the study area and floods in the downstream of the basin. The results are further analysed at the subwatershed scale to know the effect of fire on surface and lateral flows. To identify the outcome of these effects, five sub-basins which are highly prone to risk are selected along with entire watershed. It can be observed from Fig. 15 that the surface flow in all the post-fire cases has increased where the burnt areas are large. The entire study area is highly prone to fire as it indicates 43% of the watershed as burnt. The highest percentage of change has occurred in subbasin-61, which is a part of Kudremukh forest with an increase of 25% of streamflow compared to the no-fire scenario. Fig. 16 represents the average annual total runoff values for selected subbasins and the entire watershed for the year 2017. It can be observed that subbasin 51 and 44 are yielding more than a 50% increase in total annual runoff when compared to the no-fire condition. Around 30% increase in annual streamflow was identified in the post-fire scenario for the entire watershed. These results highlight the importance of vegetation for maintaining proper water balance in the watershed. Fire leaves a complex impact on the watershed functions. In the case of post-fire scenario, the increase in streamflow is primarily because of the decrease in infiltration caused due to the burning of vegetation cover. Loss of vegetation in the post-fire scenario decreased the evapotranspiration, contributing to the increase in streamflow (Rogger et al., 2017). As vegetation cover reduces, interception reduces and overland flow increases, which along with hydrophobicity of the soil induced by fire, increases streamflow in general, and flood magnitudes in specific (Woods et al., 2007). The loss of vegetation in post-fire case increases the contact between the precipitation and surface, thereby decreasing the rate of infiltration and causing soil deterioration (Martin and Moody 2001). The recurrent fire diminishes the plant species causing soil cohesion loss and degradation of root systems. Due to loss of cohesion, the soil particles form an impervious layer reducing the permeability and increasing the runoff. All these changes in land use, soil profile, loss of vegetation due to forest fire affects the groundwater recharge, water quality and quantity of the basin. Hence it is of utmost
Fig. 12. Scatterplot Depicted for Simulated vs. Observed Stream Flow Values during the Validation Phase.
The average annual runoff for observed and simulated streamflows are 216.8 mm and 204.3 mm during calibration period whereas 210.9 and 194.8 mm during validation. These average annual runoff values during both the time steps are nearly matching, indicating that the SWAT model was able to capture the flows. Scatter plots (Figs. 11 and 12) and line graphs (Fig. 13 and 14) were plotted for the calibration and validation phases. The SWAT model was unable to capture all the peaks during calibration and validation phases, which may be due to the uncertainties induced by the biases in satellite rainfall and temperature datasets and the larger resolutions of the input land use and soil datasets. The statistical coefficient values of R2 and N-S during calibration are slightly higher than the validation phase, which may be due to the shorter span of the time period during the validation time step. 5.4. Effect of wild fires on water balance After running the SWAT model for 13 years (2000–2012) for establishing a baseline scenario, the model was calibrated manually with the sensitive parameters to extend till 2017. The SWAT model was rerun for no-fire and post-fire scenarios using MODIS LULC in the initial step and susceptibility map (overlaid with LULC) in the post-fire scenario for the years 2005 and 2017 respectively. Analysis of the results indicated in Table 9 shows that the amount of infiltration is relatively larger when compared with runoff which is due to the presence of open lands (41%) when compared to the forest area (26.23%) in the study area. The susceptibility maps for the years 2005 and 2017 shows more substantial difference in the percentage of area burnt, which is because of the changes that occurred in land use, climate and topography in recent years. The amount of precipitation in the study region has decreased by around 19%, and there is an increase of temperature by 0.4 °C in 2017 when compared to the year 2005, demonstrating a broader climate change. The effect of fire on streamflow is very less in the year 2005 since there are no much differences observed between
Fig. 13. Monthly Time Series of Simulated vs Observed Runoff during the Calibration Phase. 11
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Fig. 14. Monthly Time Series of Simulated vs. Observed Runoff during the Validation Phase. Table 9 Average Annual Values of Water Balance Parameters during No-Fire and Post-Fire Scenarios. YEAR
Scenario/Variable Precipitation Runoff Infiltration Evapotranspiration
2005
2017
MODIS LULC
Susceptibility Map
MODIS LULC
Susceptibility Map
No-Fire 2076.80 108.53 1208.17 760.10
Post-Fire 2076.80 110.53 1203.37 762.90
No-Fire 1672.20 66.91 878.39 726.90
Post-Fire 1672.20 88.77 849.33 734.10
Fig. 15. Pie Charts Representing Surface and Lateral Flows during No-Fire and Post-Fire Events for Selected Subbasins and the Entire Watershed for the Year 2017.
Fig. 16. Percentage Change in Average Annual Runoff between No-Fire and Post-Fire Scenarios.
necessary for identifying the fire risk zones and implementing precautionary measures for mitigation of fires which affect the water balance of the study area. In conclusion, the present work implemented several static and
dynamic factors (Climatic, topographic and human-induced factors when combined acts a significant indicator for the identification of fire risk zones) for generating fire susceptibility maps. The generated fire susceptibility maps are integrated with MODIS LULC to develop post12
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fire susceptibility maps, which serves as a major indicator for identifying the zones associated with fire risk and occurrence. The novelty lies in the integration and development of these post-fire susceptibility maps which can be used for ecological monitoring and management since they furnish the information regarding the chance and occurrence of fire. The developed post-fire susceptibility maps can further be deployed in hydrological models for identifying the significant changes that occurred in water balance parameters like streamflow, sediment yield and evapotranspiration etc.
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6. Conclusions Our results gave an insight and advantages associated with the applicability of fire susceptibility map in hydrological modelling. The key aspects were dealt with the usage of satellite datasets for the generation of base maps and susceptibility maps, establishing no-fire and post-fire scenarios using susceptibility maps, applying SWAT model for baseline and post-fire scenario for hydrological parameter estimations and finding the effects of fire on water balance parameters. Fire susceptibility maps were generated by considering human, climate and topographic factors. Validation of generated maps was performed using MODIS fire points, which gave an accuracy of around 76% and 81%. These maps which indicate the risk of fire were overlaid with LULC for creating the post-fire scenario. The SWAT model was employed with no-fire and post-fire scenarios by changing the CN values and attributes for estimating water balance parameters. Good correlation and fewer biases were observed between observed and simulated values during calibration and validation phases. The effect of fire on streamflow, infiltration and E.T were studied. The results demonstrated that in recent years, the intensity of the forest fires was increased compared to the previous decade. Around 21.16% increase in burnt area was estimated from the results between the years 2005 and 2017. The increase in the percentage change of runoff from no and postfire scenarios in 2005 is around 1.84% whereas in case of 2017 it is around 32.67%. It can be inferred that around 30.83% increase in the percentage change of streamflow was observed from 2005 to 2017. There were no significant differences observed in hydrological parameters between no-fire and post-fire in 2005 since the percentage of area burnt was less. In 2017, the burnt area for the entire watershed was around 43%, which caused an increase of 25% of annual runoff, making the watershed prone to floods. Estimations were further carried out at sub-basin scale to find out the effect of forest fire on surface and lateral flows. The subbasin-61 under Kudremukh forest region has contributed high stream flows having a burnt area of 86%. These results demand the need for developing and deploying fire mitigation strategies and methods for maintaining healthy water balance in the study area. High precautions and preventive measures should be implemented in Kudremukh National Park of the study area as it is highly prone to fire and is majorly affecting the infiltration thereby causing high runoff in low-level areas. The adopted methodology can be implemented for developing susceptibility maps and finding the effects on water balance in any forested regions across the world for identifying fire risk zones and maintaining sustainable water resource management. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105856. 13
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