Hydrology and Flood Risk Management

“Flooding in the ‘flatlands’ … Part II” Flood mitigation measures that modify tributary streamflow hydrographs require careful consideration in low-gradient watershed systems (affectionately, ‘flatlands’ in some circles), where complex flow dynamics are prevalent. A recent study evaluated how modifications in tributary synchronization influence flood behavior.  A calibrated 1D/2D model was used to simulate flooding dynamics along the Vermilion River in Louisiana, considered a good example of a transitional inland coastal low-gradient river system in the Gulf of Mexico coastal plains. The study emphasized the critical role of the interaction between tributaries and large natural storage areas (e.g., swamps and wetlands) common in low-gradient, inland-to-coastal transitional basins. From two representative storm events, one in July 2014 (moderate) and another in August 2016 (extreme), simulations of 54 historical flood events were recreated to characterize synchronization patterns between tributaries and the main river.  A base case flood dynamics scenario and several tributary desynchronization scenarios were developed. Results showed that the alignment between tributary and river peak flows can lead to increases in flood stages and prolonged flooding conditions.  For example, by adjusting tributary peak timing through forward and backward desynchronization, flood risks can be mitigated by widening the temporal gap between tributaries and river peaks. The effectiveness of tributary desynchronization was closely linked to the characteristics of the rainfall storm (e.g., storm duration, intensity, and spatial distribution) implying that timing-based mitigation measures are storm-dependent and must be evaluated in the context of storm variability and its effect on tributary-mainstem interactions. Overall, the study results highlighted the need to account for tributary-river peak timing relationships in flood risk management strategies, particularly in systems with bidirectional flows and limited channel conveyance capacity. See Awaad et al. (2025) in Journal of Flood Risk Management, “Effect of Managing Tributary Flows on Flood Risk in Transitional Low-Gradient River Systems”

𝗙𝗹𝗼𝗼𝗱𝗶𝗻𝗴 𝗜𝘀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗨𝗞 𝗛𝗼𝗺𝗲𝘀 𝗨𝗻𝘀𝗲𝗹𝗹𝗮𝗯𝗹𝗲. 𝗧𝗵𝗮𝘁’𝘀 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝗥𝗶𝘀𝗸. Flooding in the UK is no longer a climate issue. It’s an economic, social and financial risk and one we are still badly mispricing. The UK is now one of the most flood-exposed countries in Europe. Not because of exceptional rainfall, but because of how we manage land, water and development. Last year, the Environment Agency updated its flood models. In some areas, assessed flood risk increased tenfold overnight. That wasn’t climate change accelerating. That was the risk finally being recognised. 𝗪𝗲’𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘀𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀:  • Homes are becoming unsellable or discounted by up to 30%  • Mortgage lenders pulling back as collateral risk rises  • Insurance propped up by the state until 2039  • Businesses that are underinsured or uninsured are draining local economies  • Repeated disruption to transport, utilities and supply chains This is how climate risk quietly becomes financial stability risk. And yet our response hasn’t fundamentally changed. We keep defaulting to downstream engineering walls, barriers and pumps. They’re expensive. They’re slow. They fail when systems are overwhelmed. Flooding isn’t a river problem. It’s a catchment problem. You can’t concrete your way out of a broken hydrological system. Flood risk is created or reduced upstream, across whole landscapes: compacted soils, drained wetlands, straightened rivers, hard surfaces, and developments are assessed individually rather than cumulatively. 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗙𝗹𝗼𝗼𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝘀. Done properly and at scale, it slows and stores water, reduces flood peaks, improves water quality, restores biodiversity, sequesters carbon and improves health more cost-effectively over time than hard defences. This isn’t nature, instead of infrastructure. It’s nature as infrastructure. If flooding is now a systemic risk, the response must be systemic too. Institutional capital, policymakers and corporates all have a role, but capital must move upstream. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆. At scale. At the catchment level. If this still sounds abstract, read this powerful The Guardian account from last week, where flooding has already made homes effectively unsellable: https://lnkd.in/eir8rgMs 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗵𝗼𝘂𝘀𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗵𝗲𝗻 𝗳𝗹𝗼𝗼𝗱 𝗿𝗶𝘀𝗸 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘂𝗻𝗶𝗻𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲? #FloodRisk #NatureAsInfrastructure #ClimateAdaptation #Resilience #UKHousing

#Alhumdulilah, Happy to share that my latest PhD paper, titled "High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework", has been published as open access in the International Journal of Disaster Risk Reduction. In this study, we harnessed state-of-the-art machine learning (ML) models, cloud computing platform (Google Earth Engine) and terabytes of spatial data—including flooding, elevation, drainage, rainfall, Landsat-8 imagery, and socio-economic layers—to create the first national-scale, high-resolution (30m) flood susceptibility maps for Pakistan. This scalable solution provides unprecedented insights into flood risks, addressing gaps in localized, high-resolution assessments that previous regional or coarser studies have overlooked. Paper Link: https://lnkd.in/ejKkv3TE ⚬ Key Results and Implications: ▸ We found that approximately 29% of Pakistan's total area falls under critical flood susceptibility levels, with Sindh and Punjab identified as the most at-risk provinces. ▸ An estimated 95 million people (47% of the population) are exposed to high flood susceptibility, with 74% of Sindh, 56% of Punjab, and 33% of Balochistan residing in these high-risk areas. Our exposure estimates are about 30% larger than prior studies. ▸ Economic hotspots in Sindh and upper Punjab emerge as particularly vulnerable, highlighting the need for proactive disaster preparedness to protect infrastructure and livelihoods. ▸ On a broader scale, this framework offers a transferable approach for global flood risk management, enabling targeted interventions to enhance resilience, reduce impacts from future floods, and support climate change adaptation efforts. This work builds on my ongoing research in GeoAI for flood management. If you're interested, check out our recent paper on a scalable and comparative approach for flood susceptibility prediction: https://lnkd.in/e6sjhMSt. A special thanks to Muhammad SAJJAD (Ph.D.) for his invaluable guidance and collaboration throughout this journey—your expertise made this possible! More exciting PhD updates to come. Plus, the material/app/codes related to this paper will be uploaded to the paper's GitHub repository (https://lnkd.in/eun7RB5j) this week. --------------------------------------------------------- #floodsusceptibility #floodrisk #machinelearning #cloudcomputing #googleearthengine #gee #pakistan #disasterriskreduction #geoai #climatechange #phdresearch

After more than 10 years of experience in hydrology, I still see surface runoff as the most visible and misunderstood part of the hydrological cycle. As rain falls on the land, some evaporates, some infiltrates, and some recharges groundwater. The remaining water flows over the surface as runoff. This simple process controls floods, soil erosion, reservoir inflow, urban drainage, and water quality. Understanding runoff means understanding how a catchment responds to climate, land use, and human activity. How surface runoff forms Runoff is generated when: • Rainfall intensity exceeds infiltration capacity • Soil becomes saturated • Land is sealed by roads and buildings • Slopes accelerate overland flow This is why rainfall alone never tells the full story. Simple ways to estimate runoff: For students, consultants, and early-career hydrologists, these methods still matter: • Runoff coefficient method • Rational method • SCS Curve Number method • Water balance approach • Infiltration index methods (phi and W index) • Unit hydrograph method • Regional empirical equations • Time of concentration-based estimates • Excel-based rainfall runoff calculations Simple does not mean wrong. Many design decisions rely on these methods every day. Widely used hydrological models When scale and complexity increase, models help us organize the hydrological cycle: • HEC-HMS for event-based flood modeling • SWAT for long-term basin-scale runoff and land use studies • MIKE SHE and MIKE 11 for integrated surface and groundwater analysis • VIC and TOPMODEL for regional and terrain-driven runoff processes • IHACRES for data-limited catchments Each model is a tool. None is universal. AI and machine learning in runoff estimation Data-driven methods are now common, especially for forecasting: • Artificial Neural Networks • Random Forest and Decision Trees • Support Vector Machines • Deep learning models such as LSTM They can predict runoff well but often explain little. Physical understanding still matters. A simple rule from experience Start simple. Match the method to your data. Always verify a model against real data. Surface runoff is not just a number. It is the heartbeat of a watershed and the link between climate, land, and society. If you work in water, you work with runoff, whether you realize it or not. #SurfaceRunoff #Hydrology #RainfallRunoff #HydrologicalCycle #WatershedHydrology #HECHMS #SWATModel #HydrologicalModeling #RunoffModeling #FloodModeling #HydrologyAndAI #MachineLearningInHydrology #AIForWater #DataDrivenHydrology #WaterResources #ClimateChangeImpacts #FloodRisk #SustainableWater #WaterSecurity #WaterProfessionals #HydrologyStudents #EnvironmentalEngineering #SWAT #HEC-HMS #AI #Sustainability #Flood #CivilEngineering #ResearchAndPractice #STEM #ScienceCommunication #KnowledgeSharing #LearningEveryday #CFBR

Hydrological & 2D Flood Modeling Workflow 🌊 I recently developed a Hydrological Model in HEC-HMS to simulate rainfall-runoff processes for a given catchment. After calibrating the hydrology, I leveraged HEC-RAS 2D to analyze flood behavior in the same region, ensuring a comprehensive understanding of flow patterns and flood extents. 🔹 Key Steps: ✅ Catchment delineation & hydrological modeling in HEC-HMS ✅ Hydrograph generation for various storm events ✅ Importing results into HEC-RAS 2D for floodplain simulation ✅ Analyzing flow distribution & flood impact This integrated approach provides valuable insights for flood risk assessment and management. 🌍 Would love to hear your thoughts! How do you approach hydrological and flood modeling in your projects? 💬 Video Link: https://lnkd.in/gMwn-nB3 #HECHMS #HECRAS #FloodModeling #Hydrology #WaterResources #GIS #HydraulicModeling

📢 #Publication_Alert! 🌍 I'm pleased to announce the publication of our latest research, "Exploring a #GIS-based Analytic Hierarchy Process (#AHP) for Spatial #Flood_Risk_Assessment in #Egypt: A Case Study of the Damietta Branch," in the #Environmental_Sciences_Europe (Q1) Journal. In this study, we delineate and assess flood risk zones within the Nile districts of Egypt’s Damietta branch by integrating #remote_sensing with GIS and an analytical hierarchy process (#AHP). This approach enabled the development of detailed #flood #susceptibility and #vulnerability #maps using multiple layers of geographic and environmental data. Highlights of the study: 🔹 Flood Susceptibility Zonation (#FSZ): Created using 12 thematic layers, including #elevation, #slope, #NDVI, drainage density, lithology, and more. 🔹 #Flood #Vulnerability #Zonation: Developed using six key parameters, including total population, #LULC, #road_density, and proximity to hospitals. This research provides valuable insights for effective flood risk management, offering data-driven support for policymakers to mitigate flood impacts and promote sustainable land-use planning. 🌊 🔗 https://lnkd.in/e-8eWVyq #FloodRiskAssessment #GIS #RemoteSensing #EnvironmentalResearch #FloodManagement #ScientificResearch #SustainableDevelopment #RiskMitigation