Distributed modelling Today’s topics Distributed modelling 08:45 – 09:30 Distributed catchment modelling 09:45 – 10:30 Choices in degree of distribution and data ? Means that I should ask you a question July 21, 2018

Spatial representation of river basin models Trade-off between data availability and model complexity E P P E P, E P, E E P P, E P, E Lumped Semi-distributed Distributed Water Resources Modelling – UNESCO IHE July 21, 2018

Example: the Rhine upstream Lobith Lumped Semi-distributed Lobith July 21, 2018

Why distributed modelling? Scientific reason Known variability in hydrological processes Distributed data available, so why not use it? Engineering reasons Evaluate water balance in specific sub-domains We have a certain question that can only be answered with a distributed model … July 21, 2018 ?

Lumped Advantages Fast Easy to implement Disadvantages only the outlet (discharge) behaviour or lumped over whole catchment No internal info  no coupling with e.g. soil erosion or vegetation models Loss of information on spatial distribution July 21, 2018

Distributed Advantages Include distributed data sources (e.g. rainfall) Include distributed scenarios (e.g. land use change) Interpret distributed results (e.g. local erosion potential, vegetation changes, include local pollution sources) Disadvantages Slow Equifinality (i.e. distribution of parameters often causes confusion and not necessarily a better model) July 21, 2018 ?

A distributed catchment model Consists of: 1. A (conceptual or physical) model for the water balance (input: rainfall, potential evaporation; output: evaporation, river discharge, and any other flux or store that a user may want to incorporate in the model. 2. Some form of river routing July 21, 2018 ?

1. Conceptual water balance model Water balance computation Rainfall Evaporation River discharge Storage dynamics grid cell Runoff (according to flow direction) July 21, 2018

1. Conceptual WB model 1-α α Flux State Perception Model structure Interception Unsaturated zone Groundwater Rainfall Radiation, humidity /etc. Base flow (Sub)surface flow Transpiration 1-α α Flux State Percolation Perception Model structure River discharge July 21, 2018

2. Routing Each cell has: Input (e.g. rainfall) Evaporation River discharge Storage dynamics Each cell has: Input (e.g. rainfall) State variables (e.g. soil moisture, groundwater level) Output (e.g. evaporation, runoff) Even groundwater outflow goes straight to the river What to do with the runoff? July 21, 2018

2. Routing Runoff moves horizontally over the drainage network Transport in a cell without storage consideration is: where n : location of upstream cell i : location cell under computation n = 0: the location of a water divide July 21, 2018

Example: Kabompo basin, Zambia July 21, 2018

2. Routing Taking into account that there is channel storage Kinematic wave approximations Q1 A Δx q P Q2 July 21, 2018

Software environment Usually works with a GIS oriented environment Often raster data but triangular shapes are also possible Model consists of an excel-like script that combines dynamic input maps and parameter maps with arithmetic operators July 21, 2018

Steps to the setup of a distributed model, catchment delineation Step 1: obtain a digital elevation model (DEM) of area Step 2: derive flow directions per cell Step 3: Select outlet (and locations of intermediate flow stations) Step 4: derive (sub)catchment boundaries (e.g. based on strahler stream orders) Step 5: distribute parameters (?)

Strahler order July 21, 2018

Example: Kabompo, strahler threshold ??? Catchment boundary July 21, 2018

Lumwana river (4th order stream) July 21, 2018

Catchment boundary July 21, 2018

Kabompo river (5th order stream) July 21, 2018

Catchment boundary July 21, 2018

Kabompo river (8th order stream) July 21, 2018

Hydrotopes Areas (classes) of assumed similar hydrological behaviour Remotely sensing data is interesting Expert judgment or field examinations are required July 21, 2018

Hydrotopes, Example: Volta basin slopes plateaus wetlands July 21, 2018

Hydrotopes LandSAT imagery Use thresholds to derive the classes Normalized Difference Vegetation Index (NDVI) Logical expression: If NDVI > 0.05, wetland July 21, 2018

Hydrotopes DEM  slopes Use thresholds to derive the classes Slope of topography Logical expression: If slope > 0.04, hill July 21, 2018

Hydrotopes Classification 1. Plains (DEM) 2. Wetlands (NDVI) 3. Rivers (runoff) 4. Slopes (DEM) July 21, 2018

Hydrotopes only on elevation: Height-Above-Nearest-Drainage (HAND) height almost equal to stream height large height difference with stream

FLEX TOPO: topography driven distributed modelling