About This Course
Water resource management is one of the most pressing challenges of our time, and professionals across government agencies, engineering firms, research institutions, and development organisations increasingly need robust technical skills to understand, model, and respond to hydrological dynamics. From devastating floods to prolonged droughts, the consequences of poor hydrological planning are measured in lives, livelihoods, and infrastructure losses. Hydrological modeling integrated with Geographic Information Systems (GIS) and remote sensing has emerged as the definitive toolkit for professionals tasked with understanding and managing these complex water systems.
This 10-day programme, delivered by TrainingComplex, provides a comprehensive and practical grounding in hydrological modeling using state-of-the-art GIS platforms and remote sensing data sources. Participants will move from foundational hydrological concepts through to advanced spatial analysis, model calibration, and scenario simulation. The course is structured to give equal weight to theory and hands-on application, ensuring that every concept introduced is immediately reinforced through software exercises using real-world datasets.
Throughout the programme, participants will work with industry-standard tools including ArcGIS Pro, QGIS, HEC-HMS, the Soil and Water Assessment Tool (SWAT), and Google Earth Engine. They will learn to process and interpret satellite imagery from sources such as MODIS, Landsat, and Sentinel, and to extract meaningful hydrological parameters such as land use, soil type, Digital Elevation Models (DEMs), and rainfall estimates from these data sources.
By the end of the course, participants will be equipped to independently design and execute hydrological modeling projects, interpret model outputs for decision-making, and communicate findings effectively to both technical and non-technical stakeholders. This programme is directly relevant to water resource management, flood risk assessment, irrigation planning, environmental impact assessment, and climate adaptation planning across Sub-Saharan Africa, the Middle East, and beyond.
Who Should Attend
- Hydrologists and water resources engineers seeking to enhance their spatial modeling skills
- GIS analysts and remote sensing specialists working in environmental or water sectors
- Civil and environmental engineers involved in drainage, flood control, or catchment management
- Urban planners and infrastructure professionals dealing with stormwater and water supply
- Environmental scientists and researchers conducting water-related studies
- Government officials and regulators in water, environment, and land management agencies
- NGO and development sector professionals involved in climate resilience and disaster risk reduction
- Agricultural engineers and irrigation specialists working on water use efficiency
Learning Objectives
- Explain the fundamental principles of hydrology including the water cycle, watershed dynamics, and hydrological processes
- Collect, process, and analyse spatial hydrological data from GIS platforms and remote sensing sources
- Extract and interpret key hydrological parameters from Digital Elevation Models including catchment delineation, flow direction, and stream networks
- Process and classify satellite imagery from Landsat, Sentinel, and MODIS for land use, land cover, and vegetation analysis
- Set up, calibrate, and validate rainfall-runoff models using HEC-HMS
- Apply the SWAT model to simulate streamflow, sediment transport, and nutrient cycling in complex watersheds
- Use Google Earth Engine for large-scale hydrological data processing and time-series analysis
- Conduct flood frequency analysis and produce flood inundation maps
- Integrate climate change scenarios into hydrological models to assess future water availability and flood risk
- Interpret and visualise model outputs using GIS and produce professional hydrological maps and reports
- Communicate hydrological findings effectively to technical teams, policymakers, and community stakeholders
- Apply best practices in data quality management, uncertainty analysis, and model validation
Course Modules
Module 1: Fundamentals of Hydrology and the Water Cycle
- Components and processes of the hydrological cycle
- Precipitation, evapotranspiration, infiltration, and runoff concepts
- Watershed and catchment characteristics and their hydrological significance
- Streamflow generation mechanisms: Hortonian and saturation excess overland flow
- Base flow, interflow, and groundwater contributions to river discharge
- Hydrological data types, measurement methods, and data sources
Module 2: Introduction to GIS for Hydrological Applications
- GIS principles, coordinate systems, and spatial data formats relevant to hydrology
- Navigating ArcGIS Pro and QGIS interfaces for hydrological workflows
- Vector and raster data structures and their use in watershed analysis
- Spatial data acquisition from national and international hydrological databases
- Map projections and their importance in accurate area and distance measurement
- Geoprocessing tools for clipping, merging, reprojecting, and analysing hydrological layers
Module 3: Digital Elevation Models and Terrain Analysis
- Types of DEMs: SRTM, ASTER, TanDEM-X, and LiDAR-derived models
- DEM preprocessing: sink filling, conditioning, and error correction
- Flow direction and flow accumulation computation using D8 and D-infinity algorithms
- Automated watershed and sub-catchment delineation using GIS tools
- Stream network extraction, ordering, and morphometric analysis
- Terrain derivatives: slope, aspect, curvature, and their hydrological interpretation
Module 4: Remote Sensing Principles and Satellite Data Sources
- Electromagnetic spectrum and principles of remote sensing for water applications
- Key satellite platforms: Landsat, Sentinel-1/2, MODIS, and their hydrological relevance
- Satellite image acquisition, resolution types, and data access portals
- Radiometric and atmospheric correction of satellite imagery
- Spectral indices relevant to hydrology: NDVI, NDWI, MNDWI, and LSWI
- Seasonal and inter-annual change detection using multi-temporal imagery
Module 5: Land Use, Land Cover Classification and Change Analysis
- Supervised and unsupervised image classification techniques in GIS
- Training sample selection and accuracy assessment using confusion matrices
- Mapping impervious surfaces and their role in urban runoff generation
- Forest cover change detection and its effect on catchment hydrology
- Integrating LULC maps into hydrological model parameter estimation
- Post-classification refinement and spatial filtering techniques
Module 6: Satellite-Based Rainfall and Evapotranspiration Estimation
- Principles and products of satellite-based rainfall estimation: CHIRPS, TRMM, GPM, and PERSIANN
- Bias correction and downscaling of satellite rainfall products using ground station data
- Comparative analysis of satellite and gauge rainfall data for model input
- Remote sensing methods for actual and reference evapotranspiration estimation
- SEBAL and SSEBop algorithms for spatial evapotranspiration mapping
- Validating satellite-derived hydrometeorological data for modeling applications
Module 7: Rainfall-Runoff Modeling with HEC-HMS
- HEC-HMS model structure, components, and workflow overview
- Basin model setup: sub-basin delineation, reach definition, and junction configuration
- SCS Curve Number method for runoff volume estimation from GIS-derived parameters
- Unit hydrograph selection and calibration for peak flow estimation
- Meteorology model configuration using observed and satellite rainfall inputs
- Model calibration, validation, and sensitivity analysis using observed streamflow data
Module 8: Watershed Modeling with SWAT
- SWAT model architecture: HRUs, sub-basins, and the channel routing system
- Setting up a SWAT project: DEM, soil, and LULC data integration using ArcSWAT or QSWAT+
- SWAT parameter estimation for surface runoff, lateral flow, and groundwater recharge
- Calibration and uncertainty analysis using SWAT-CUP and SUFI-2 algorithm
- Simulating sediment yield, nutrient loads, and water quality parameters
- Scenario analysis: comparing baseline and land management change impacts on streamflow
Module 9: Google Earth Engine for Large-Scale Hydrological Analysis
- Introduction to Google Earth Engine: cloud computing architecture and JavaScript API
- Accessing and filtering satellite image collections for hydrological time-series analysis
- Computing spectral indices and rainfall accumulations at watershed scale using GEE
- Surface water mapping and inundation extent monitoring using Sentinel-1 SAR data
- Time-series analysis of NDVI and soil moisture indices for drought monitoring
- Exporting GEE results to GIS platforms for further spatial analysis and model integration
Module 10: Flood Frequency Analysis and Inundation Mapping
- Statistical methods for flood frequency analysis: Log-Pearson III, Gumbel, and GEV distributions
- Fitting frequency distributions to annual maximum streamflow series
- Estimating design floods for various return periods: 10, 50, 100, and 500 years
- HEC-RAS model setup for one-dimensional and two-dimensional hydraulic simulation
- Generating flood inundation maps and depth-velocity grids in GIS
- Flood risk mapping: overlaying inundation extents with population, infrastructure, and land use data
Module 11: Climate Change and Hydrological Impact Assessment
- Overview of global climate models, RCP and SSP emission scenarios relevant to hydrology
- Downscaling CMIP6 climate projections using statistical and dynamical methods
- Bias correction of climate model precipitation and temperature outputs for local application
- Propagating climate scenarios through hydrological models to project future streamflow
- Assessing climate change impacts on flood frequency, drought severity, and water availability
- Uncertainty analysis in climate-hydrology modeling chains and communicating uncertainty to decision-makers
Module 12: Hydrological Data Management and Quality Control
- Principles of hydrological data management: collection, storage, and version control
- Identifying and treating errors, outliers, and missing data in rainfall and streamflow records
- Double mass curve analysis and consistency testing of hydrometeorological data
- Building and managing a spatial hydrological database using GIS geodatabases
- Data sharing standards, metadata documentation, and open-data platforms for hydrology
- Automating data processing workflows using Python scripting in QGIS and ArcGIS
Module 13: Reporting, Visualisation, and Stakeholder Communication
- Designing professional hydrological maps using cartographic principles in GIS
- Creating charts, hydrographs, and dashboards for model output presentation
- Structuring a technical hydrological modeling report for regulatory or client submission
- Simplifying complex hydrological findings for non-technical audiences and policymakers
- Developing catchment management briefs and policy notes based on modeling evidence
- Group project presentations: defending modeling assumptions, results, and recommendations
Requirements & Prerequisites
Participants should have a basic working knowledge of GIS concepts and some familiarity with either ArcGIS or QGIS. A foundational understanding of hydrology or water resources engineering is recommended, equivalent to undergraduate-level exposure to the subject. Prior experience with any hydrological modeling software is helpful but not required, as the course begins with fundamentals before advancing to complex applications.
Training Methodology
- Instructor-led conceptual sessions introducing each hydrological or remote sensing principle with real-world case studies drawn from African and Middle Eastern river basins
- Hands-on computer laboratory exercises using ArcGIS Pro, QGIS, HEC-HMS, SWAT, and Google Earth Engine with pre-loaded real datasets
- Step-by-step model-building workshops where participants construct, calibrate, and validate their own hydrological models from raw data
- Satellite image processing practicals involving land use classification, change detection, and vegetation index computation using Sentinel and Landsat imagery
- Group-based watershed analysis projects that simulate real consulting or government planning assignments
- Flood scenario simulation exercises requiring participants to generate and interpret flood inundation maps under different return period events
- Climate scenario integration workshops applying CMIP6 downscaled data to hydrological models and interpreting output uncertainty
- Peer review and critique sessions where participants present model results and receive structured feedback from instructors and peers
- Technical report writing exercises producing professional hydrological assessment documents suitable for submission to clients or regulatory bodies
Organisational Benefits
- Enhanced in-house capacity to conduct hydrological assessments without relying entirely on external consultants
- Improved flood risk management and early warning system design based on scientifically sound modeling
- Better-informed infrastructure planning for roads, bridges, drainage systems, and water supply schemes
- Stronger evidence base for environmental impact assessments and regulatory compliance submissions
- More effective integration of climate change projections into water resource planning and adaptation strategies
- Improved irrigation and agricultural water management decisions supported by spatial data analysis
- Greater organisational credibility and competitive advantage in tendering for water-related projects
- Strengthened ability to engage with international donors and development partners on water security programmes
Where Is Hydrological Modeling with GIS and Remote Sensing Training Offered?
This course runs in-person across: Pretoria, Nairobi, Kigali, Dubai, Addis Ababa, Zanzibar, Accra, Naivasha, Abuja, Nakuru, Kisumu, Arusha, Lagos, Mombasa, Cape Town, Riyadh, Dar es Salaam, Kampala, Johannesburg, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
28 Sept 2026 – 9 Oct 2026 |
KES 160,000 |
| Pretoria, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 6,600 |
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Kigali, Rwanda |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 3,800 |
| Dubai, United Arab Emirates |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 8,200 |
| Live Virtual |
Online |
17 Oct 2026 – 6 Dec 2026 |
KES 160,000 |
| Addis Ababa, Ethiopia |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,800 |
| Zanzibar, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,000 |
| Accra, Ghana |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 7,600 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
KES 230,000 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,600 |
| Nakuru, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Arusha, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,200 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
+235 more dates — view live schedule →
Frequently Asked Questions
Do I need prior GIS or hydrological modeling experience to attend this course?
A basic familiarity with GIS concepts and foundational knowledge of hydrology is recommended. The course starts from first principles in both domains before advancing to complex modeling, so participants with undergraduate-level exposure will be well-positioned to benefit fully.
What software will be used during the course?
Participants will work with ArcGIS Pro, QGIS, HEC-HMS, SWAT (via ArcSWAT and QSWAT+), HEC-RAS, and Google Earth Engine throughout the programme. TrainingComplex provides access to all required software and datasets during the training, and guidance is given on accessing free tools post-training.
Will I receive a certificate upon completion?
Yes. Participants who attend and complete the full 10-day programme will receive a Certificate of Completion from TrainingComplex, which can be used for professional development portfolios, CPD records, and employer recognition purposes.
Are real datasets used in the practical exercises?
Yes. All hands-on exercises use real-world datasets drawn from actual river basins in Africa and the Middle East, including rainfall records, DEMs, satellite imagery, and streamflow data. This ensures the skills learned are directly transferable to participants' own professional contexts.
Can this training be delivered in-house for our organisation?
Yes. TrainingComplex offers this programme as a customised in-house training solution for government agencies, engineering consultancies, research institutions, and NGOs. The content and datasets can be tailored to reflect the specific catchments, tools, and regulatory context relevant to your organisation.
