Riverine Flood Risk Assessment Using Remote Sensing And Numerical Modeling In South Punjab Floodplains In Pakistan

Flood disasters are among the most common and devastating disasters,posturing human lives and property at risk.In recent years,flood events have grown enormously and brought significant life losses,damages to agriculture crops,and physical infrastructures.In recent years,South Asian countries have suffered an increase in severe floods,especially in Pakistan.In Pakistan,floods are often serious natural disasters,historically originating from the Chenab and Indus rivers.The recent decade witnessed severe flash and riverine flood disasters caused by mainly climate change.In Pakistan,floods occur in the monsoon months of July and September,due to excessive rainfall in the upstream catchment areas of Indus of Chenab.These results flash floods in upper areas and riverine floods in low-lying floodplains.This illustrates that flooding is a severe issue that demands extensive mitigation efforts.Understanding the spatial pattern of flood extent,estimating flood damages,and identifying flood occurrence causes,particularly along the Indus and Chenab plains,is an important first step toward effective flood risk assessment in Pakistan.The flood causes,inundation mapping and monitoring,damages assessment,and flood simulation modeling as essential to ensure long-term sustainable planning for flood risk assessment in Pakistan.This study has adopted GIS,remote sensing technique and numerical HEC-RAS modeling.The HEC-RAS simulation modeling is also useful for simulating flooded areas with depth and velocity in different years of return periods.Thus,the result of modeling would permit the government agencies in Pakistan to identify high flood risk areas and accomplish considerable reductions in flood damages,that will be helpful for decision making regarding flood mitigation along river channels.The combined remote sensing techniques and HEC-RAS model provide a basis for flood risk assessment in floodplains.This study used various methods to delineate and monitor flooded areas,damage assessment and flood simulation modeling in south Punjab floodplains in Pakistan.These methods are explained in chapters 3,4 and 5 and they constitute the main research work of this dissertation,as shown below.Chapter 3: The research study was divided into two sections in chapter 3,focusing on one of the floodplains,of Pakistan: the lower Chenab plain(LCP)and central Indus basin(CIB).In the LCP,nine temporal Landsat-8 Operational Land Imager(OLI)images were used for flood 2014.Furthermore,three Landsat images for pre flooding,during,and post flooding were also utilized for the comprehensive analysis of LULC classification as per damage assessment.The Supervised Maximum Likelihood(ML)classifier was used for LULC classification,and the results were interpreted using change detection analysis.In addition,the analysis permitted us to estimate the inundated and damaged areas and flood duration and recession in LCP.The modified normalized difference water index(MNDWI)was also applied for flood mapping and monitoring during flood-2014.The classified and flood maps produced overall accuracy of about 90%,which shows consistent results.The results revealed that the flood waters remained for almost two months,that further increased the financial cost.Furthermore,the classified results show that agriculture was the most affected sector.The index result shows that about 65% of the area was severely inundated.In the unique agricultural setting,the ccombining approaches have provided insights into flood instances and flood inundation in both built-up and agricultural areas,allowing for a quick check on losses to both built-up and crops and,as a result,offering an estimate of loss that can aid groundbased research,as well as emergency flood disaster management,particularly for relief and response operations.The second part of the study used the central Indus basin(CIB)floodplain.Landsat 7 Enhanced Thematic Mapper(ETM)data for pre and post flood 2010 was combined with the MNDWI index for flood mapping and ML classifier for LULC changes.The Supervised ML classifier was used for LULC classification,and the results were further interpreted using change detection analysis for damage assessment.In addition,the MNDWI water index was also used for pre and post flood monitoring during flood-2010.The inverse distance weightage(IDW)technique was also used for the spatial distribution of rainfall in Pakistan.Also,the tropical rainfall measuring mission(TRMM)based raster rainfall dataset was used to analyse spatial and temporal rainfall patterns and compare IDW results to identify flood generating factors.Detailed field survey data was conducted for instant monetary losses to different socio-economic sectors and flood situations in CIB,Pakistan.The analysis revealed that Indus flood 2010 was occurred in early August due to a heavy monsoon rainfall spell in the last week of July over the north-western parts of Pakistan.The extreme rainfall in the upper Indus region resulted in extra-ordinary flow discharge at various barrages of Indus rivers.As a result,the exceeding flow breached east marginal embankment near Taunsa barrage,which resulted huge flooding in entire CIB.In the post flood instance,the flood mapping result showed that water class has been dramatically increased by 36%,which resulted in a considerable flood inundation in the CIB.The built-up was abridged by 10%.Similarly,the agricultural area was reduced to 20%,which registered a huge damage to all standing crops.The survey finding also revealed that the agricultural sector suffered the most damage and destruction compared to the other sectors.This research study provides mixed-method approaches to the local,regional,and national disaster management authorities to develop an effective flood management plan,including identifying breaching points to reduce potential flood risk,human fatalities,and economic damages in floodplains of Pakistan.Chapter 4: The study used Landsat 8 flood instance images for the year 2014 together with suitable flood inundation processing indices to extract the flood inundation extent in Chenab basin,Pakistan.The Chenab basin is especially vulnerable to frequent riverine floods but has remained understudied.The Normalized Difference Water Index(NDWI),Modified Normalized Difference Water Index(MNDWI),and Water Ratio Index(WRI)were used for the delineation of inundated areas.The supervised classification was also used to detect and compare flooded areas.The analysis enabled the computation of flooded areas,flood duration and flood recession.By comparing water indices,the analysis revealed that the MNDWI index derived images showed better accuracy of above 90%,reflecting the results’ reliability.In contrast,the rapid flood images determined by NDWI and WRI indices showed less accuracy level.The water indicesbased analysis revealed that floodwater covered about 68% of areas,mostly in the north and center parts of the study area.The investigation further revealed that floodwater remained for about seven weeks.This work proposed an approach of comparing the results of these indices to minimize the misclassified water areas.This is in addition to the fact that these different water indices have been recognized as suitable for rapidly mapping flooded areas.Further,these waters indices-based technique works with no time and provide almost instant results.The combination of water indices has also contributed to reliable flood extent mapping in the study area.Moreover,the study identified areas with mixed LULC to better delineate the flooded area using a least misclassified area approach.Although riverine flooding is a frequent phenomenon,this study shows that the optical available satellite dataset and satellite-derived water indices can be suitable for rapid flood mapping and monitoring to articulate emergency flood response activities,mainly for recovery and relief operations in Pakistan.Chapter 5: The study used Hydrologic Engineering Centers River Analysis System(HECRAS)numerical modelling for simulating floods along Chenab River.The goal is to help policymakers and planners to design flood mitigation measures for the Chenab River basin in Punjab Province,which was hit by enormous floods in history,especially in September 2014.This study analyses flood frequency and hydraulic simulation of different water flow in the river basin to simulate flood hazard areas under different return periods.Standard flood frequency analysis was performed by using log-Pearson type III(LP3)distributions to estimate extreme flows with different 5-,10-,50-,100-,200 years return periods.The peak floods output from the frequency analysis was incorporated into the HEC-RAS model to simulate the appropriate flood levels along river reach extending through Trimmu to Panjnad barrages.The simulated and observed stage level hydrograph were also compared and validated.The computed statistical descriptors indicate Nash–Sutcliffe Efficiency(NSE)0.962 and Index of Agreement(d)0.991 at Trimmu station.The percent match and visual interpretation were also applied to compare daily flood extents from 15 th September to 17 th September simulated by HEC-RAS and extracted flood extent from Moderate Resolution Imaging Spectroradiometer(MODIS)provided by United Nations Operational Satellite Applications Programme(UNOSAT).The hydraulic HEC-RAS simulation model of flood 2014 extent matched very well with the MODIS images,thus confirming the ability of the model to simulate floods and produce water surface profile at specified places with reliable accuracy.Using the model in combination with Arc GIS,an investigation of the areas likely to be inundated under different return periods of floods was conducted.The hydraulic simulation analysis of a 50-year flood period showed that about 400% area is likely to be inundated compared to the normal river flow.The flood risk mapping and zoning have also been formulated based on HEC RAS model results.Through maps,different areas with high flood depth and velocity have been recognized and declared vulnerable to flood disasters.In addition,the high risk of human life characterizes the areas with maximum flood depth and maximum flood velocity.Thus,this study also contributed in such a way that moderate to high-risk areas and low to moderate risk areas are identified in the Chenab floodplain.Also,using the approach presented in this study would permit the government agencies in Pakistan to accomplish considerable reductions in flood damages and the development of risk zones which further specified highly vulnerable areas for suitable targeted reduction strategies along Chenab River.The initiatives of this dissertation include:1.The study applied the MNDWI and supervised ML algorithm for detailed flood monitoring and damages assessment for the lower Chenab plain.Landsat imagery combined with a supervised ML algorithm and the MNDWI helps to map flood extents accurately.Even though these two methods are usually used separately in flooded area mapping,their combination can assist in achieving reliable outcomes with complementary properties.Also,in the unique agricultural setting,combining two methods provided insights into flood inundation in built-up and crops areas with different flood instances,which allowed for a quick check on built up and standing crops losses and thus providing loss estimation that can aid ground-based research study.This section has been published in the MDPI Remote sensing Journal(SCI 4.8 IF).2.For the Central Indus basin,the study applied TRMM and gauge station-based rainfall datasets for flood causes.The supervised ML algorithm was utilized for LULC changes and the MNDWI index for flood monitoring using Landsat imagery.The classified and flood inundation images show consistent results.Field survey data was also used for instant flood damage assessment.Combining these mixed methods helped to accurately map flood extents and damage assessment and provide insight into how flood planners can formulate a flood risk management strategy in flood plain areas of Indus.This section has been published in the Natural Hazard’s springer(SCI /3.1 IF);Applied Ecology and Environmental Research(SCI/0.7 IF).3.The NDWI,MNDWI,and WRI adopted for this study show that these methods can serve as a better indicator for delineating inundated areas in diverse LULC areas in the Chenab floodplain.These combined indices have proven to detect and distinguish the flooded regions effectively.As a result,the study provides a novel perspective by introducing a misclassified area approach by comparing water indices and classification and provides a substantial contribution to flood areas mapping and monitoring in floodplains.This section has been published in Natural Hazard’s springer(SCI 3.1 IF);MDPI 5th International ECWS conference 10.3390/ECWS-5-08049.4.The flood numerical modeling was performed to simulate flood 2014 water surface profiles and determine the extent of the flooded area for the Chenab River in Pakistan,under different return periods of floods as per proposed flood risk assessment and management.This study contributed that no such research has been found using simulation modeling in the south Punjab floodplains of Chenab basin.The results of the HEC-RAS model were combined with Arc GIS to prepare flood 2014 risk maps and delineate flood maps for different return periods.The areas that are vulnerable to floods of different periods have been delineated with flood risk mapping.Two flood risk zones were identified ‘low to moderate’ and ‘high to very high’ flood risk zones.Also,flood simulation of 50 years disclosed that about 400% area is likely to be flooded along the Chenab River compared to the normal river flow.Using the approach presented in this study would permit the government agencies in Pakistan to identify high flood risk areas,accomplish considerable reductions in future flood damages,and develop risk reduction strategies in this neglected south Punjab floodplain along the Chenab River.This work is submitted in the MDPI Remote sensing Journal(SCI 4.8 IF)...