Zambezi Decision Support System – User's Manual

User’s Manual July 2013

Zambezi Decision Support System – User’s Manual

“A web-based Decision Support System to analyze the impact of climate change and water resources development on runoff in the Zambezi basin.”

Authors: Harald Kling, Martin Preishuber

Zambezi Decision Support System – User’s Manual

Contact: Pöyry Energy GmbH Laaer-Berg-Str. 43 A-1100 Vienna Austria Tel. +43 50 313 - 0 Fax +43 50 313 ext. 165 [email protected] www.poyry.at/en

Zambezi Decision Support System – User’s Manual

Table of contents

1. Introduction ...... 1 2. System requirements ...... 2 3. Quick start guide ...... 3 4. User interface ...... 14 4.1. Map display ...... 15 4.2. Climate Module ...... 21 4.2.1. Main interface ...... 21 4.2.2. Query climate data from map elements ...... 23 4.2.3. Create new climate scenario ...... 23 4.2.4. General climate scenario properties...... 26 4.3. Development Module ...... 28 4.3.1. Main interface ...... 28 4.3.2. Create new development scenario ...... 30 4.3.3. General development scenario properties ...... 30 4.3.4. Computation point properties ...... 31 4.3.5. Add new computation points ...... 34 4.3.6. Remove computation points ...... 38 4.3.7. Sub-basin properties ...... 39 4.4. Run Module ...... 42 4.4.1. Main interface ...... 42 4.4.1. Create and delete Runs ...... 43 4.4.2. Select existing Runs...... 43 4.4.3. Run list...... 44 4.4.4. Edit properties ...... 45 4.4.5. Start Run ...... 46 4.4.6. Query results from map elements ...... 46 4.5. Analysis Tool...... 48 4.5.1. Spatial resolution ...... 48 4.5.2. Time-series selection ...... 49 4.5.3. Display settings ...... 54

Zambezi Decision Support System – User’s Manual

4.5.4. Table view and export of results ...... 57 4.6. Flood Mapping Module ...... 59 5. Model description ...... 62 5.1. Water Balance Model ...... 64 5.2. Water Allocation Model...... 67 5.3. Peak Flow Model ...... 73 5.4. Flood Mapping ...... 79 5.5. Calibration and evaluation ...... 82 6. References ...... 92 7. FAQ – Frequently asked questions...... 94 7.1. General FAQs...... 94 7.2. FAQs editing and map display ...... 95 7.3. FAQs Analysis Tool ...... 97

Zambezi Decision Support System – User’s Manual

List of figures

Figure 1: Main user interface after login...... 3 Figure 2: Modules of DSS...... 4 Figure 3: Climate Module...... 5 Figure 4: Development Module...... 6 Figure 5: Run Module...... 7 Figure 6: Run properties...... 8 Figure 7: Run execution status...... 8 Figure 8: Show run results of sub-basins, either local (single sub-basin) or total (including full upstream catchment)...... 9 Figure 9: Show run results of computation point...... 10 Figure 10: Starting the Analysis Tool from the Analysis Module...... 11 Figure 11: Analysis Tool of DSS...... 12 Figure 12: Time-series selection in Analysis Tool...... 13 Figure 13: Display settings in Analysis Tool...... 13 Figure 14: Basic building blocks of the DSS: “Climate Module”, “Development Module”, “Run Module”, “Analysis Module”, and “Flood Mapping Module”. “Simulation” is performed in the background without user interface. Also the hydraulic database is not visible for the user. .. 14 Figure 15: Options for map display...... 16 Figure 16: Model elements. 27 sub-basins (orange) and computations points (circles): River points (red), Uncontrolled reservoirs (light blue), Controlled reservoirs (dark blue)...... 16 Figure 17: Elevation map for southern Africa...... 17 Figure 18: Mean annual precipitation map for southern Africa. GPCC data for the period 1961-1990...... 17 Figure 19: Hydraulic model domain for flood mapping along the Zambezi River in Mozambique...... 18 Figure 20: Map display with Open StreetMap selected as Base Layer. Example for Zambezi River near Tete, Mozambique...... 19 Figure 21: Map display with Google (Terrain) selected as Base Layer. Example for region downstream of Cahora Bassa reservoir, Mozambique...... 19 Figure 22: Map display with Bing (Road) selected as Base Layer. Example for greater region near Kariba reservoir...... 20 Figure 23: Map display with Bing (Aerial) selected as base layer. Example for region near Caprivi Floodplain and Chobe Swamps...... 20

Zambezi Decision Support System – User’s Manual

Figure 24: Main interface of Climate Module...... 22 Figure 25: Availability of precipitation stations in the GPCC dataset from 1901 to 2009. Upper line: number of stations in the Zambezi basin upstream of Tete. Lower line: number of stations in the Zambezi basin upstream of Victoria Falls...... 22 Figure 26: Querying climate data from sub-basins...... 23 Figure 27: Interface for importing new climate data...... 24 Figure 28: Saving data in CSV-format from Excel with the “Save As” option...... 25 Figure 29: Example for climate data in CSV-format...... 25 Figure 30: Example for climate data in CSV-format for selected sub-basins...... 26 Figure 31: Properties of climate scenarios...... 27 Figure 32: Main interface of Development Module...... 29 Figure 33: General properties of development scenarios...... 30 Figure 34: Accessing computation point properties...... 32 Figure 35: Computation point properties for “Controlled reservoir”. Example for Cahora Bassa under the Historic development scenario...... 33 Figure 36: Complete listing of computation points...... 34 Figure 37: Add new computation point along the river network...... 35 Figure 38: Updating topology (discharge to downstream computation point) in the computation point properties...... 36 Figure 39: Updated computation point network after inserting new computation point...... 37 Figure 40: Deleting a computation point. Links to upstream computation points have to be removed first...... 38 Figure 41: Accessing subbasin parameters for editing...... 40 Figure 42: Sub-basin attributes...... 40 Figure 43: Editing monthly vegetation class parameters...... 41 Figure 44: Main interface of Run Module...... 42 Figure 45: User interface when creating new Run...... 43 Figure 46: Run drop-down list in Run Module...... 44 Figure 47: Run list...... 44 Figure 48: Run properties...... 45 Figure 49: Querying simulation results from computation points in the Run Module...... 47 Figure 50: Selecting a spatial resolution for the Analysis Tool...... 49

Zambezi Decision Support System – User’s Manual

Figure 51: Selection of sub-basin in Analysis Tool...... 50 Figure 52: Selection of sub-basin variables in Analysis Tool...... 51 Figure 53: Selection of computation point variables in Analysis Tool...... 52 Figure 54: List of selected variables in Analysis Tool...... 53 Figure 55: Display of multiple y-axes with different units...... 54 Figure 56: Selection of temporal resolution as display setting in Analysis Tool...... 55 Figure 57: Selection of plot type as display setting in the Analysis Tool...... 55 Figure 58: Example time-series plot of annual values...... 56 Figure 59: Example mean plot of monthly values...... 56 Figure 60: Example duration curve plot of monthly values...... 57 Figure 61: Table view for export of data from the Analysis Tool...... 58 Figure 62: Interface of Flood Mapping Module...... 59 Figure 63: Flood map example near Tete. Inundated areas are shown in transparent blue...... 60 Figure 64: Flood map example for Zambezi delta. Inundated areas are shown in transparent blue. ... 61 Figure 65: Flood map example for potential future hydropower plants Lupata and Chemba. Inundated areas are shown in transparent blue...... 61 Figure 66: General design of DSS...... 62 Figure 67: General concept of the Decision Support System (DSS). IMS...Information Management System. RBM...River Basin Model...... 63 Figure 68: Conceptual structure of River Basin Model consisting of Water Balance Model (left), Water Allocation Model (right) and Peak Flow Model (bottom). Precip: precipitation in mm. ETp: potential evapotranspiration in mm. ETa: actual evapotranspiration in mm. Runoff: runoff-

depth in mm. Qupstream: upstream inflow in m³/s. Qlateral: lateral inflow in m³/s. Preciplake: precipitation on open water body in m³/s (is zero for River points). Evapo: evaporation from open water body in m³/s (is zero for River points). Diversion: withdrawal of water in m³/s.

Discharge: river discharge in m³/s. Qdownstream: routed downstream discharge in m³/s...... 63 Figure 69: Variables of computation points...... 71 Figure 70: Zoning concept for reservoir operation...... 72 Figure 71: Testing of Peak Flow Model with observed discharge data of Kafue River at Mswebi. Black: observed daily discharge. Blue: observed monthly discharge (input to Peak Flow Model). Red: simulated discharge of the Peak Flow Model (triangular hydrograph within months)...... 75 Figure 72: Testing of Peak Flow Model with observed discharge data of Luangwa River at Great East Road Bridge. Black: observed daily discharge. Blue: observed monthly discharge (input to Peak Flow Model). Red: simulated discharge of the Peak Flow Model (triangular hydrograph within months)...... 75

Zambezi Decision Support System – User’s Manual

Figure 73: Average ratio (QCOR) between daily peak flow and mean monthly flow. Analysis based on daily discharge data of numerous gauges in headwater catchments...... 76 Figure 74: Observed daily peak flow versus mean monthly flow at headwater catchments in the western part of the Zambezi basin. Examples for the Kabompo River (left) and upper Kafue River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +20%...... 76 Figure 75: Observed daily peak flow versus mean monthly flow at headwater catchments in the northern and eastern part of the Zambezi basin. Examples for the Luangwa River (left) and Luenha River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +100%...... 77 Figure 76: Observed daily peak flow versus mean monthly flow at headwater catchments in the southern part of the Zambezi basin. Examples for the Gwaai River (left) and the Sanyati River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +300%...... 77 Figure 77: Simulated (red) vs. observed (black) annual daily peak flow of Zambezi River at Victoria Falls. Simulation result with Zambezi DSS (Water Balance Model, Peak Flow Model, Water Allocation Model)...... 78 Figure 78: Simulated (red) vs. observed (black) annual daily peak flow of Zambezi River at Tete. Simulation result with Zambezi DSS (Water Balance Model, Peak Flow Model, Water Allocation Model). Observed data include uncertainties due to imprecise rating curve...... 78 Figure 79: Cross sections of the 1D hydraulic HEC-RAS model of the Zambezi River from Cahora Bassa reservoir until the Zambezi delta. Orange: cross-sections. Blue: river network. Black: Zambezi basin divide. Red: country borders...... 80 Figure 80: Example for cross-section in HEC-RAS model...... 80 Figure 81: Comparison of four different sources of observed data for monthly discharge of Zambezi River near Tete. Daily data were aggregated to monthly values. Ideally, there should not be any differences in the observed data...... 85 Figure 82: Observed data of Zambezi discharge at Senanga (upstream) and Katima Mulilo (downstream). There are no significant tributaries between the two adjacent gauges and discharge data should be quite similar, but they are not due to biased peak flow data...... 85 Figure 83: Simulated (red) and observed (black) monthly hydrographs at key locations along the Zambezi...... 86 Figure 84: Simulated (red) and observed (black) monthly hydrographs of the three main tributaries of the Zambezi...... 87 Figure 85: Simulated (red) and observed (black) seasonality in discharge at key locations in the Zambezi basin. Period 1961-1990...... 88 Figure 86: Simulated (red) and observed (black) monthly flow duration curve at key locations in the Zambezi basin. Period 1961-1990. Observed Shire low flows at Chiromo were caused by blockage of river flows during construction of Kamuzu Barrage in 1965 and other human interventions...... 89

Zambezi Decision Support System – User’s Manual

Figure 87: Simulated (red) and observed (black) annual discharge at key locations in the Zambezi basin...... 90 Figure 88: Simulated and observed water levels in Kariba reservoir (top), Cahora Bassa reservoir (middle) and Lake Malawi (bottom). Observed Cahora Bassa water levels from 1981 to 1998 were affected by altered operations because transmission lines from the HPP were destroyed. Observed Lake Malawi water levels represent min/max levels manually digitized from the report of Beilfuss and dos Santos (2001)...... 91

Zambezi Decision Support System – User’s Manual

List of tables

Table 1: Main characteristics of available development scenarios...... 29 Table 2: Sub-basins of the Water Balance Model. “Area (local)” gives size of sub-basin, “Area (total)” gives size of total catchment (including upstream sub-basins)...... 65 Table 3: Variables of the Water Balance Model that are available in the DSS...... 66 Table 4: Computation points of the Water Allocation Model. Example for the development scenario “Baseline +HPP”. Computation points 1 through 27 are located at the sub-basin outlets of the Water Balance Model (Table 2). The abbreviations “u/s” and “d/s” mean upstream and downstream, respectively. Type 1: River point; Type 2: Uncontrolled reservoir; Type 3: Controlled reservoir. “Area” gives size of total catchment (including all upstream areas)...... 70 Table 5: Variables of the Water Allocation Model...... 71 Table 6: Computation points used for flood mapping...... 81 Table 7: Calibration procedure and performance statistics. CP identifies the location (Table 4) and corresponds to the sub-basins defined in Table 2...... 84

Zambezi Decision Support System – User’s Manual

1. Introduction

Climate change and water resources development may have considerable impacts on future runoff conditions in Mozambique (INGC, 2009). Therefore, tools are required for Mozambican analysts to study the effects of various What-If scenarios. As most Mozambican river basins also extend to upstream areas in neighbouring countries, any analysis has to focus on the full basin scale. Of particular interest is the Zambezi River, where Mozambique is the most downstream country in a basin that is shared between eight countries overall. The Zambezi Decision Support System (DSS) covers the whole Zambezi basin with an area of 1.4 Mio km². The DSS is a state-of-the-art, well calibrated, easy to use analysis tool that enables rapid assessment of impacts of climate change and upstream developments (irrigation, dams) on discharge in the Zambezi River and its main tributaries. Due to its implementation as an open web-based system, the DSS is available to the general public. The DSS has a graphical user interface and combines GIS layers, background maps and model elements, which are linked to a dynamic database. The Water Balance Model of the DSS simulates runoff generation from monthly precipitation and temperature inputs in 27 sub-basins of the Zambezi basin. The Water Allocation Model considers wetlands, reservoir operations and water abstractions and aggregates discharge along the river- network at about 50 locations. The user can interactively add locations of interest and add or modify scenarios including climate change, water withdrawals (irrigation), dam development and reservoir operation rules. Climatic data included in the DSS cover the period 1950-2005 for historic observations and 1960-2100 for data of three climate models, thereby enabling simulations for any time-slice between 1950 and 2100. The DSS includes an analysis tool for visualization of simulation results as time-series, seasonality or distribution (frequencies of high and low flows). Export of results enables post-processing with external software. The latest version (released in July 2013) includes the following new features: x Simulation of energy generation at hydropower plants. x Peak Flow Model to compute daily peak flows within months. x Flood mapping along the Zambezi River in Mozambique. The DSS has minimal system requirements (chapter 2). Novice users should read the quick start guide (chapter 3) and refer to the section with frequently asked questions (chapter 7). Chapter 4 serves as a complete reference for the graphical user interface of the DSS. A short overview about the two hydrological models behind the DSS (Water Balance Model and Water Allocation Model) as well as the Peak Flow Model for flood mapping gives chapter 5.

Zambezi Decision Support System – User’s Manual

2. System requirements

The DSS is a web-based system and requires an internet connection. The DSS runs best with Mozilla Firefox or Google Chrome internet browsers. Some users have reported display problems with Microsoft Internet Explorer. The internet browser has to accept cookies. No local installation of model components is required.

Zambezi Decision Support System – User’s Manual

3. Quick start guide

This quick start guide explains the main steps for running and analyzing a simulation with the DSS. It should take the user only a few minutes.

Step 1: Main interface Figure 1 displays the main interface of the DSS after login. Make yourself familiar with the map display. Click on the blue arrow buttons in the upper left corner for map navigation and on the +/- buttons for zooming. Alternatively, you can navigate with your left mouse button (pan) and scroll button (zoom). Click on the blue plus/minus sign in the upper right corner to open the menu for selection of displayed map layers. Click on map elements to query information. Sub-basins are displayed in orange and computation points are displayed as red (river point), dark blue (reservoirs) and light blue (wetlands, natural lakes) circles. Click on the modules on the left side to switch between the Climate Module, Development Module, Run Module, Analysis Module, Flood Mapping Module, and Help Module (Figure 2). When clicking on map elements, different options become available, depending on what module you opened on the left side.

Figure 1: Main user interface after login.

Zambezi Decision Support System – User’s Manual

Figure 2: Modules of DSS.

Zambezi Decision Support System – User’s Manual

Step 2: Select climate scenario In the Climate Module select a climate scenario from the drop-down list (Figure 3). Several pre- defined climate scenarios are available. The data range (time-period covered) and a description of the selected scenario are displayed. When changing to a different scenario, notice that this is updated in the active scenario summary above the map display (Figure 1).

Figure 3: Climate Module.

Zambezi Decision Support System – User’s Manual

Step 3: Select development scenario In the Development Module select a development scenario from the drop-down list (Figure 4). Several pre-defined development scenarios are available. These scenarios are based on World Bank studies and differ in the diversions of water (withdrawals for irrigation) and number of dams – a short scenario description is given at the bottom of the Development Module (Figure 4).

Figure 4: Development Module.

Zambezi Decision Support System – User’s Manual

Step 4: Run a simulation In the Run Module create a new run by clicking on the button “Create new run” (Figure 5). Click on “Edit properties” to specify the simulation period (Figure 6, opens in new page of the browser). The start and end date of the simulation period must be within the data range of the climate scenario (Figure 3). Do not forget to click “Save” if you made any changes to the simulation period. In the Run Module click on “Start run” for starting the simulation. Wait until the simulation has finished, which usually takes a few seconds (Figure 7).

Figure 5: Run Module.

Zambezi Decision Support System – User’s Manual

Figure 6: Run properties.

Figure 7: Run execution status.

Zambezi Decision Support System – User’s Manual

Step 5: View simulation results To view simulation results open the Run Module (if you are not already there) and click on map elements. To view spatial averages of water balance variables (precipitation, evapotranspiration, runoff, etc.) click on sub-basins (orange). Spatial averages either represent local variables of sub- basins or total variables of full catchment including upstream sub-basins (Figure 8). A click on computation points (Figure 9) shows river discharge and related variables (diversions, reservoir water storage, etc.).

Figure 8: Show run results of sub-basins, either local (single sub-basin) or total (including full upstream catchment).

Zambezi Decision Support System – User’s Manual

Figure 9: Show run results of computation point.

Zambezi Decision Support System – User’s Manual

Step 6: Analyze simulation results The DSS includes an Analysis Tool for display and comparison of simulation results. You can access the Analysis Tool via the Analysis Module. Select the desired spatial resolution (sub-basins, computation points) via the drop-down list and open the Analysis Tool by clicking on the button “Start analysis” (Figure 10). Alternatively, the Analysis Tool is accessed by clicking on map elements in the Climate Module or Run Module (as described above in Step 5). Figure 11 shows the general layout of the Analysis Tool. Display settings are specified in the upper left box and time-series selection is specified in the lower left box. The first step is to add one or more variables to the graph, by selecting from drop-down lists the Run, sub-basin (or computation point), and variable (Figure 12). Under display settings (Figure 13) you can choose monthly or annual temporal resolution and the plot type (including time-series, mean, and duration curve). The pan and zoom buttons enable to navigate in the displayed graph. Alternatively, you can select a section of the time series displayed by drawing a rectangle in the graph with the left mouse button. The button “Show data table” is useful for exporting simulation results.

Figure 10: Starting the Analysis Tool from the Analysis Module.

Zambezi Decision Support System – User’s Manual

Figure 11: Analysis Tool of DSS.

Zambezi Decision Support System – User’s Manual

Figure 12: Time-series selection in Analysis Tool.

Figure 13: Display settings in Analysis Tool.

Zambezi Decision Support System – User’s Manual

4. User interface

The following sections give a detailed description of the user interface of the DSS. Read the Quick start guide (chapter 3) first to get familiar with the basic functionality of the DSS. The user interface is structured in a way such that basic building blocks for simulation (termed “Run”) are grouped into modules. A Run requires a climate scenario (see Climate Module, chapter 4.2), a development scenario (see Development Module, chapter 4.3), and Run properties (see Run Module, chapter 4.4). The actual simulation is performed in the background without user interface. An Analysis Tool enables visualization of results (see chapter 4.5). A Flood Mapping Module uses a hydraulic database to display flood inundation maps along the Zambezi River in Mozambique. Figure 14 outlines these basic building blocks. The focus of the user interface is on the map display, where you can display background maps and model elements (see chapter 4.1). Depending on whether you opened the Climate Module, Development Module or Run Module, different options become available for querying information, editing scenarios or viewing simulation results from the map display.

Figure 14: Basic building blocks of the DSS: “Climate Module”, “Development Module”, “Run Module”, “Analysis Module”, and “Flood Mapping Module”. “Simulation” is performed in the background without user interface. Also the hydraulic database is not visible for the user.

Zambezi Decision Support System – User’s Manual

4.1. Map display

There are several options for map display (Figure 15), which are accessible via the blue plus/minus button in the upper right corner of the map. Several “Overlays” can be selected by the user for display by clicking on the check-boxes (Figure 15):

x Country borders. x Elevation map (Figure 17). To view the legend click on “Map Legend” below the Flood Mapping Module on the left side. x Mean annual precipitation map (Figure 18). The map is based on GPCC data for the period 1961-1990 and visualize the high spatial variability in precipitation. To view the legend click on “Map Legend” below the Flood Mapping Module on the left side. x Sub-basins of the DSS are displayed in orange (Figure 16). These 27 sub-basins are used by the Water Balance Model of the DSS (see chapter 5.1). x Hydraulic model domain for flood mapping (Figure 19). This overlay highlights the area for which flood mapping along the Zambezi River in Mozambique is computed. x Two different river networks are available. The first only covers the major rivers (upstream catchment area greater than 10,000 km²), whereas the second is more detailed (upstream catchment area greater than 1000 km²). The river network has to be displayed if you want to add new computation points (see chapter 4.3). You can click on individual segments of the river network to query information about the upstream catchment area. x Computation points are displayed as circles (Figure 16). They are used by the Water Allocation Model of the DSS (see chapter 5.2). x The computation point network shows the topology between computation points, i.e. upstream/downstream relationship of computation points. The “Base Layer” specifies the map that is displayed in the background. You can choose one of the following maps: x Open StreetMap x Google (Terrain) x Google (Streets) x Google (Hybrid) x Google (Satellite) x Bing (Road) x Bing (Hybrid) x Bing (Aerial) x No Basemap (empty background) Example maps show Figure 20 to Figure 23.

Zambezi Decision Support System – User’s Manual

Figure 15: Options for map display.

Figure 16: Model elements. 27 sub-basins (orange) and computations points (circles): River points (red), Uncontrolled reservoirs (light blue), Controlled reservoirs (dark blue).

Zambezi Decision Support System – User’s Manual

Figure 17: Elevation map for southern Africa.

Figure 18: Mean annual precipitation map for southern Africa. GPCC data for the period 1961-1990.

Zambezi Decision Support System – User’s Manual

Figure 19: Hydraulic model domain for flood mapping along the Zambezi River in Mozambique.

Zambezi Decision Support System – User’s Manual

Figure 20: Map display with Open StreetMap selected as Base Layer. Example for Zambezi River near Tete, Mozambique.

Figure 21: Map display with Google (Terrain) selected as Base Layer. Example for region downstream of Cahora Bassa reservoir, Mozambique.

Zambezi Decision Support System – User’s Manual

Figure 22: Map display with Bing (Road) selected as Base Layer. Example for greater region near Kariba reservoir.

Figure 23: Map display with Bing (Aerial) selected as base layer. Example for region near Caprivi Floodplain and Chobe Swamps.

Zambezi Decision Support System – User’s Manual

4.2. Climate Module

The main purpose of the Climate Module is to select a climate scenario. In addition, you can also create new climate scenarios by importing external data and view the data. These functions are described in the following sections.

4.2.1. Main interface Figure 24 shows the main interface of the Climate Module. Select a climate scenario from the drop- down list. Several pre-defined climate scenarios are available for historic conditions and climate projections with General Circulation Models (GCMs):

x Historic Climate: Historical observations from 1901-2009. Data outside 1950-2005 are not reliable due to low coverage of available stations (see Figure 25). Precipitation data are taken from the GPCC1 dataset and temperature data are taken from the CRU2 dataset. x CNRM: Climate data 1960-2100 of the WATCH project based on the GCM CNRM-CM3 (CNRM institute) for the IPCC emission scenario A2 (high emissions). x ECHAM: Climate data 1960-2100 of the WATCH project based on the GCM ECHAM5/MPIOM (MPI-M institute) for the IPCC emission scenario A2 (high emissions). x IPSL: Climate data 1960-2100 of the WATCH project based on the GCM LMDZ-4 (IPSL institute) for the IPCC emission scenario A2 (high emissions). In the WATCH project (www.eu-watch.org, 2011) daily data of these three GCMs were statistically downscaled with quantile mapping (Piani et al., 2010) to a half degree resolution (approximately 50 x 50 km). Quantile mapping is based on observational data-sets from the period 1960 to 2000. For the Zambezi DSS, additional bias correction was applied with linear scaling using historic observations 1961-1990.

1 GPCC: Global Precipitation Climatology Centre, version 5, published 2011 by Deutscher Wetterdienst, Germany 2 CRU: Climate Research Unit, version TS 3.1, published 2011 by University of East Anglia, United Kingdom

Zambezi Decision Support System – User’s Manual

Figure 24: Main interface of Climate Module.

140

120 ] / [ 100 Upstream Tete s n o i t

a 80 t s

f o

r 60 e b m

u 40

N Upstream Victoria Falls 20

0 1900 1915 1930 1945 1960 1975 1990 2005 Year Figure 25: Availability of precipitation stations in the GPCC dataset from 1901 to 2009. Upper line: number of stations in the Zambezi basin upstream of Tete. Lower line: number of stations in the Zambezi basin upstream of Victoria Falls.

Zambezi Decision Support System – User’s Manual

4.2.2. Query climate data from map elements

Ensure that the overlay “Subbasins” is displayed in the map (Figure 15). Click on a sub-basin and then click the button “Show subbasin input data” (Figure 26). The Analysis Tool (chapter 4.5) will open displaying the precipitation and temperature data for the sub-basin.

Figure 26: Querying climate data from sub-basins.

4.2.3. Create new climate scenario A quick and easy way of manipulating your climate data for simulation runs is described in chapter 4.4.4. However, you have more flexibility for scenario analysis when importing your own climate data. Click the button “Create new climate scenario” (Figure 24). You will be prompted to enter a name for the new climate scenario. By default, the new climate scenario will have the same data as the last active climate scenario. To import your own data to the new climate scenario click the button “Import subbasin data” (Figure 24). A dialogue will open where you click the button “Upload a file” (Figure 27) and you will be prompted to browse for the file containing the new climate data (see below for format

Zambezi Decision Support System – User’s Manual

specifications). After the import has finished make some double-checks by querying the new climate data from the sub-basins (see chapter 4.2.2). The new data should have replaced the old data. The following format requirements apply for importing climate data from a file:

x The file has to be in CSV-format with the file extension “.csv”. x The delimiters between columns have to be semicolons (“;”). x The first row in the file includes the headers in the following order: year;month;P_1;P_2;P_3;...;P_27;T_1;T_2;T_3;...;T_27 where “year” has to be a four digit numerical value (e.g. “1961” and not “61”), “month” has to be a numerical value 1 to 12, P_1 to P_27 indicates monthly precipitation in [mm] for sub-basin 1 to 27, T_1 to T_27 indicates monthly temperature in [°C] for sub-basin 1 to 27. x The data values have to use the point (“.”) as decimal point. You can create such a data file from Excel by using the “Save As” option and selecting CSV-format (Figure 28). If you open the CSV-file with a text editor it should look similar to the example shown in Figure 29. Instead of importing data for all sub-basins, you can also import data solely for selected sub-basins. The example in Figure 30 shows a data file for importing climate data solely for sub-basin 12 and 15 (precipitation and temperature always both have to be supplied). With this method, you can analyze various climate scenarios for selected sub-basins without changing the climate data for other sub- basins. Ensure that the imported data range (start/end of time-series) corresponds to the data range of the climate data in the other sub-basins.

Figure 27: Interface for importing new climate data.

Zambezi Decision Support System – User’s Manual

Figure 28: Saving data in CSV-format from Excel with the “Save As” option.

Figure 29: Example for climate data in CSV-format.

Zambezi Decision Support System – User’s Manual

Figure 30: Example for climate data in CSV-format for selected sub-basins.

4.2.4. General climate scenario properties In the main interface (Figure 24) click on the button “Edit properties” to view/change properties of the climate scenario. A new page of the internet browser will display the properties in several tabs (Figure 31): x Information tab: Name, acronym and description of the climate scenario can be specified. Date created and date modified is displayed as information for the user. The acronym is used by the Analysis Tool to identify a climate scenario. If two scenarios have the same acronym, the Analysis Tool cannot display both. By default the acronym is identical to the first 5 letters of the Run Name, but you can change this here. x Permission tab: Select check-boxes to define permissions. Un-check “Allow edit” and “Allow delete” to avoid accidental modification/deletion of your scenario. “Is public” enables you to publish the climate scenario, such that also other users can use this scenario (they will see public scenarios in their scenario drop-down list). Do not make overly extensive use of this option, as otherwise the system administrator may block your account. Not all of these properties will be available to you if you do not have permission (e.g. public climate scenarios owned by other users).

Zambezi Decision Support System – User’s Manual

Figure 31: Properties of climate scenarios.

Zambezi Decision Support System – User’s Manual

4.3. Development Module

The Development Module allows you to make detailed specifications regarding the water resources management in the basin. In addition, you can also add computation points to query discharge simulations at any point along the river network or to add new reservoirs.

4.3.1. Main interface Figure 32 shows the main interface of the Development Module. Select a development scenario from the drop-down list. Several pre-defined development scenarios are available (see also Table 1): x Historic Development: Represents historic conditions in the Zambezi basin. Reservoirs start operation after their historic commissioning date. No withdrawals for irrigation are considered (historically these were small and changed over time). The only withdrawal included is for the water supply of Lusaka, Zambia. x Baseline: Represents current conditions in the Zambezi basin. Reservoirs and irrigation withdrawals are always operating (regardless of year of simulation). Withdrawals are specified according to World Bank (2010). x Baseline +HPP: Same as Baseline development scenario for irrigation withdrawals, but with full hydropower plant (HPP) development. New HPPs and extensions: Batoka Gorge, Kariba (ext.), Itezhitezhi (ext.), Kafue Gorge Lower, Cahora Bassa (ext.), Mphanda Nkuwa, Boroma, Lupata, Chemba. x Moderate Development: Future development scenario for the Zambezi basin. Moderate withdrawals for irrigation ("identified irrigation", World Bank, 2010). New HPPs and extensions: Batoka Gorge, Kariba (ext.), Itezhitezhi (ext.), Kafue Gorge Lower, Cahora Bassa (ext.), Mphanda Nkuwa. x High Development: Future development scenario for the Zambezi basin. High withdrawals for irrigation ("high level irrigation", World Bank, 2010). New HPPs and extensions: Batoka Gorge, Kariba (ext.), Itezhitezhi (ext.), Kafue Gorge Lower, Cahora Bassa (ext.), Mphanda Nkuwa. The other buttons allow you to edit general properties, to create new development scenarios, to delete development scenario, and to refresh the display of the computation point network.

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Figure 32: Main interface of Development Module.

Table 1: Main characteristics of available development scenarios. Development scenario: Historic Baseline Baseline +HPP Moderate High Withdrawals(a) [m³/s] 15 118 118 304 945 HPP(b) [MW] Batoka Gorge n/a n/a 1600 1600 1600 Kariba 1470 1470 2340 2340 2340 Itezhitezhi 0 0 120 120 120 Kafue Gorge 990 990 990 990 990 Kafue Gorge Lower n/a n/a 750 750 750 Cahora Bassa 2075 2075 3275 3275 3275 Mphanda Nkuwa n/a n/a 1500 1500 1500 Boroma n/a n/a 400 n/a n/a Lupata n/a n/a 654 n/a n/a Chemba n/a n/a 1040 n/a n/a

(a) Mean annual withdrawals (irrigation) in whole Zambezi basin. (b) Installed capacity at hydropower plants. “n/a” means that the reservoir is not available in this scenario. A value of “0” indicates that reservoir is included, but without hydropower plant.

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4.3.2. Create new development scenario In the main interface (Figure 32) click on the button “Create new development scenario”. Specify a scenario name in the dialog. The scenario will have the same properties as the last active development scenario. In such a way, you can copy scenarios and then make further edits to them.

4.3.3. General development scenario properties In the main interface (Figure 32) click on the button “Edit properties” to view/change general properties of the development scenario. A new page of the internet browser will display the properties in several tabs (Figure 33): x Information tab: Name and description of the development scenario can be specified. Date created and date modified is displayed as information for the user. x Permission tab: Select check-boxes to define permissions. Un-check “Allow edit” and “Allow delete” to avoid accidental modification/deletion of your scenario. “Is public” enables you to publish the development scenario, such that also other users can use this scenario (they will see public scenarios in their scenario drop-down list). Do not make overly extensive use of this option, as otherwise the system administrator may block your account. Not all of these properties will be available to you if you do not have permission (e.g. public development scenarios owned by other users).

Figure 33: General properties of development scenarios.

Zambezi Decision Support System – User’s Manual

4.3.4. Computation point properties In the map display, click on a computation point and then click the button “Edit computation point properties” (Figure 34) - as some computation points are located quite close to each other, you first may want to zoom closer in the map display. A new page of the internet browser will show the properties in several tabs (Figure 35). The following properties are available: x Information tab: “ID”, “Name” and “Description” are just information for the user. The ID is used as label in the map display as well as in drop down lists (e.g. in the Analysis Tool) in conjunction with Name. x Geographical information tab: Coordinates (“Latitude” and “Longitude”) of the computation point may be edited here to change the location. When making changes, click on the button “Refresh computation point network” (Figure 32) to update the map display. “Subbasin” specifies in which sub-basin the computation point is located – this may have to be updated if you change the location (coordinates) of the computation point. Likewise, also “Upstream catchment area” may have to be updated if you change the location (coordinates) of the computation point (you can query the “Upstream catchment” area by clicking on the river network in the map display). x Properties tab: “Type” specifies either “River point”, “Uncontrolled reservoir” or “Controlled reservoir”. “Start year” specifies when the computation point becomes active. This relates to all properties specified under “Monthly values” (e.g. diversions), “Hydropower” as well as to the commissioning date of controlled reservoirs. “Discharge to” specifies the next downstream computation point. Changes may be required here if you add/remove computation points. “Downstream routing” is a model parameter to consider the travel time water needs until reaching the next downstream computation point. x Monthly values tab: The properties here include monthly data for “Environmental flow” and “Diversions” (withdrawals, i.e. net-consumptions). For controlled reservoirs also reservoir operation rules have to be specified, including “Top of flood control zone”, “Guide curve”, and “Top of inactive zone”. These values represent end-of-month reservoir operation water levels. See chapter 5.2 for an explanation how these properties affect reservoir operation and how water is prioritized for allocation to environmental flows, diversions, and reservoir storage. x Rating curve tab: The attributes here include “Volume”, “Area” (surface area of water body), “Elevation” (water level), “Discharge” and “Capacity”. At river points only the attributes Elevation and Discharge are available, representing the stage-discharge relationship. At uncontrolled reservoirs (e.g. wetlands) also Volume and Area are available. For controlled reservoirs, the attribute Capacity gives the total release capacity of the reservoir (i.e. spillway plus turbines) and the attribute Discharge is used as a desired release for reservoir operation (the actual release also depends on e.g. the guide curve, see chapter 5.2 for more information). x Hydropower tab: The attributes here can be used to simulate energy generation at a hydropower plant (simply set the data of the attributes greater than zero). “Turbine

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capacity” gives the maximum discharge that can be used for power generation. “Installed capacity” gives the maximum power that can be generated. “Efficiency” gives the overall efficiency of turbine, generator and transformer. “Tailwater level” gives the water level in the river below the hydropower plant (the water level above the hydropower plant is given by the data in the “Rating curve” tab). “Hydraulic losses” (e.g. in penstock) decrease the hydraulic head for power generation. See chapter 5.2 for more information about hydropower simulation. x Layout settings tab: The properties here affect the map display. “Radius” gives the size of the circle of computation points. The label position can be adjusted with “Label horizontal alignment” and “Label vertical alignment” (giving the corner of the label text box that is adjacent to the computation point). “Font color” gives the label text color as HEX code. The “Save” button is only available if you have permission to edit, which depends on the following: x Development scenario is editable, which is set by “Allow edit” in the “Permission” tab (see Figure 33). x For computation points at sub-basin outlets as well as those computation points used for flood mapping along the Zambezi River in Mozambique (see chapter 5.4), the Save button is not available for the property tabs “Information” and “Geographical Information”. A click on the button “Show computation point attributes” (Figure 32) gives you a complete listing of all computation points (Figure 36).

Figure 34: Accessing computation point properties.

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Figure 35: Computation point properties for “Controlled reservoir”. Example for Cahora Bassa under the Historic development scenario.

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Figure 36: Complete listing of computation points.

4.3.5. Add new computation points You may want to add new computation points for various reasons:

x Querying discharge at new locations of interest. x Specifying diversions at new locations. x Adding new reservoirs. Care is required when adding new computation points, as errors here would result in erroneous simulation results with the DSS. The three steps for adding a new computation point are described below.

Zambezi Decision Support System – User’s Manual

Step 1: Add new computation point along the river network Open the Development Module and choose the development scenario you want to edit (it is recommended to first create a new development scenario). Ensure that the overlays “River network”, “Computation points” and “Computation point network” are shown in the map display (Figure 15). Zoom in to the location of interest and click on the river network (Figure 37) and then click the button “Add new computation point”. A maximum number of 100 computation points is possible. In the unlikely case that you reach this number, you first have to delete old computation points before adding new computation points. It is not recommended to add computation points where the upstream catchment area is smaller than 50,000 km², as there would be a mismatch with the spatial resolution of the input data (spatial averages of precipitation as input for sub-basins vs. high spatial variability of precipitation, see Figure 18 and Figure 16). If you still want to add such a computation point a warning message will be displayed, but you can continue by clicking OK.

Figure 37: Add new computation point along the river network.

Zambezi Decision Support System – User’s Manual

Step 2: Update topology of computation point network The new computation point you just added under step 1 will not be linked to any other computation points. Therefore, you have to update the computation point network accordingly. In the map display, note the computation point ID of the next downstream computation point (displayed as red number next to the circle; or click on the circle to query this information). Then click on the newly added computation point and click the button “Edit computation point properties” (Figure 34). In the “Properties” tab select the appropriate downstream computation point from the drop-down list “Discharge to” (Figure 38) and click the button “Save”. Similarly, you have to update the topology for any computation points that are located upstream of the new computation point. Visually double-check your edits to the computation point network by clicking the button “Refresh computation point network” (Figure 32). An example shows Figure 39 (compare to original network shown in Figure 37).

Figure 38: Updating topology (discharge to downstream computation point) in the computation point properties.

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Figure 39: Updated computation point network after inserting new computation point.

Step 3: Edit computation point properties The new computation point should automatically have correct attribute entries for the “Geographical Information” tab (Subbasin, Latitude, Longitude, Upstream catchment area). Other attributes have to be updated by the user, where appropriate. Enter “ID”, “Name” and “Description” in the Information tab. You can change the “Type” from “River point” (default) to either Uncontrolled reservoir or Controlled reservoir. Continue with entering diversion data, reservoir characteristics, hydropower properties, etc. (see Figure 35).

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4.3.6. Remove computation points

To remove a computation point open the Development Module and display the overlays “Computation point” and “Computation point network” (Figure 15). You cannot delete computation points that are linked to upstream computation points - remove these links first in the property “Discharge to” of the upstream computation point (in the “Discharge to” drop-down list either select the first empty row or select the new downstream computation point). Next click on the computation point and then click the button “Delete computation point” (Figure 40). Computation points at the outlets of sub-basins cannot be removed. The same applies to computation points used for flood mapping along the Zambezi River in Mozambique (see chapter 5.4). Even though it is possible, it is not recommended to remove the computation points representing uncontrolled reservoirs (Barotse Floodplain, Kwando Floodplain, Caprivi Floodplain, Kafue Flats, Lake Niassa) as they have a strong impact on downstream discharge. Further, you cannot remove computation points in development scenarios where you do not have permission for editing (e.g. public development scenarios owned by other users).

Figure 40: Deleting a computation point. Links to upstream computation points have to be removed first.

Zambezi Decision Support System – User’s Manual

4.3.7. Sub-basin properties Most of the model parameters are not editable (and not visible) for the user, as these parameter values were the result of an extensive calibration with observed discharge data (see chapter 5.5). However, there are a few parameters that may be edited to analyze scenarios of changes in vegetation. In the current set-up of the DSS no differences are considered between different vegetation classes. Due to lack of detailed information and high sensitivity any changes here should only be made by users with considerable experience. Changing vegetation parameters involves two steps, which are described below.

Step 1: Editing vegetation cover percentages Open the Development Module and choose the development scenario you want to edit (it is recommended to first create a new development scenario). Ensure that the overlay “Subbasins” is displayed (Figure 15). Click on a sub-basin and then click the button “Edit subbasin parameters” (Figure 41). A new page will open with the sub-basin attributes. In the “Zones” tab you can change the area percentage for three vegetation classes (Figure 42). The current values correspond to woodland (zone 1), grassland (zone 2) and agriculture (zone 3), which was reclassified from AVHRR satellite data. When making changes to the area percentages this should sum up to 100 percent, otherwise the DSS will re-weight the percentages during model run (to obtain again 100 percent). Note, that changes here only have an effect if you also make changes to the vegetation class parameters under step 2.

Step 2: Editing vegetation class parameters Click on the button “Edit vegetation class parameters” (Figure 42). A new page will open where you can make edits in the “Parameters” tab (Figure 43). The “Evapotranspiration correction factor” is similar to a crop-coefficient to adjust potential evapotranspiration, and the “Interception capacity” is used to simulate interception losses of rainfall (see chapter 5.1 for further information). As currently set up, there are no differences in the parameter values between months or vegetation classes. When making changes this affects all sub-basins, as the vegetation class parameters are not attached to individual sub-basins but instead are a “global” look-up table for all sub-basins.

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Figure 41: Accessing subbasin parameters for editing.

Figure 42: Sub-basin attributes.

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Figure 43: Editing monthly vegetation class parameters.

Zambezi Decision Support System – User’s Manual

4.4. Run Module

The Run Module allows you to simulate runoff conditions under different combinations of climate and development scenarios.

4.4.1. Main interface Figure 44 shows the main interface of the Run Module. You have the following options:

x Select existing Run from the drop-down list. x Start run. x Edit Run properties. x Create new Run. x Delete Run. x Show summary list of all Runs. These options are described in the following sections.

Figure 44: Main interface of Run Module.

Zambezi Decision Support System – User’s Manual

4.4.1. Create and delete Runs

To create a new Run based on the active climate and development scenarios click the button “Create new run” (Figure 44). You will be prompted to enter a name for the new Run (Figure 45). The Run will have default properties, including a simulation period from Jan. 1961 to Dec. 1990. Edit these properties if desired (see chapter 4.4.4). To delete a Run click the button “Delete run” (Figure 44).

Figure 45: User interface when creating new Run.

4.4.2. Select existing Runs Click on the Run drop-down list to show existing Runs (Figure 46). The list only includes those Runs that correspond to the active climate and development scenarios (see active scenario summary at top of screen). The list is empty if there are no existing Runs for the active combination of climate and development scenario. To change the active climate and development scenarios go to the Climate Module or Development Module, respectively. The date and time displayed in the list after the Run name corresponds to the last Run execution. If no date and time is given, then the Run has not been executed, yet.

Zambezi Decision Support System – User’s Manual

Figure 46: Run drop-down list in Run Module.

4.4.3. Run list To get a complete listing of all existing Runs click the button “Show run list” (Figure 44), which will open a table on a new page (Figure 47). This complete list differs from the drop-down list, where only the Runs for the active climate and development scenarios are listed. The attributes in the Run list include: x Name of Run x Acronym of Run x Date created x Last execution x Simulation period x Climate scenario x Development scenario x Owner (user or public) This Run list is especially helpful when you have already created many Runs. You can sort the table by clicking on the headers. To delete Runs from the list click the button “Delete Run” (Figure 47).

Figure 47: Run list.

Zambezi Decision Support System – User’s Manual

4.4.4. Edit properties Click the button “Edit properties” (Figure 44) to open a new page showing the attributes of the Run in several tabs (Figure 48). These properties include:

x Information tab: You can change Name, Acronym and Description of the Run. By the default the Acronym will be identical to the first five letters of Name, but you can change this here. x Simulation period tab: Specify year and month for start and end date of simulation period. The time stamp corresponds to the start of the monthly time-step, therefore day is always equal to 1. x Climate adjustment tab: You can use monthly correction factors to adjust the input data of the active climate scenario in all sub-basins. The “Precipitation adjustment factor” is used multiplicative and the “Temperature adjustment factor” is used additive. This offers a quick and easy option for climate scenario analysis.

Figure 48: Run properties.

Zambezi Decision Support System – User’s Manual

4.4.5. Start Run

Starting a Run is necessary under the following conditions: x New Run was created that has not been executed before. x Changes were made to the development scenario. x Changes were made to the Run properties. Click the button “Start Run” to start a simulation (Figure 44). Execution should only take a few seconds (Figure 7).

4.4.6. Query results from map elements To query simulation results of the active Run you can click on map elements (if the Run execution was completed). To this end, ensure that the overlays “Subbasins” and/or “Computation points” are displayed in the map (Figure 15). A click on sub-basins gives you the option of viewing local (single sub-basin) or total (full upstream catchment) results (Figure 8). The simulation results will be displayed in the Analysis Tool (see chapter 4.5), which opens in a new page. Similarly, a click on computation points (displayed as circles) enables to view simulation results of variables related to computation points (Figure 49).

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Figure 49: Querying simulation results from computation points in the Run Module.

Zambezi Decision Support System – User’s Manual

4.5. Analysis Tool

The Analysis Tool is used for viewing and comparing simulation results. Such comparisons can be between different simulation scenarios (Runs), locations and variables. You can access the Analysis Tool either from the Climate or Run Module by clicking on map elements (see chapters 4.2.2 and 4.4.6) or by opening the Analysis Module and clicking the button “Start analysis”. The Analysis Tool opens in a new page of the browser. For the main features see Figure 11 to Figure 13.

4.5.1. Spatial resolution When accessing the Analysis Tool from the Analysis Module you first have to select a spatial resolution from the drop-down list (Figure 50), with the following options:

x Computation point: Variables related to River points, Uncontrolled reservoirs, and Controlled reservoirs. x Subbasin local input: Local sub-basin input values of precipitation and temperature for climate scenarios. x Subbasin local output: Local sub-basin values of DSS simulation results. x Subbasin total output: Total values (including full upstream catchment) of DSS simulation results. When accessing the Analysis Tool from the Climate or Run Module the spatial resolution is already pre-determined: x Climate Module, click on sub-basin: spatial resolution is “Subbasin local input”. x Run Module, click on sub-basin: spatial resolution is either “Subbasin local output” or “Subbasin total output” (depending on user selection, see Figure 8). x Run Module, click on computation point: spatial resolution is “Computation point”.

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Figure 50: Selecting a spatial resolution for the Analysis Tool.

4.5.2. Time-series selection In the lower left panel of the Analysis Tool you select the time-series to be displayed. You can select any combination of Runs, locations (sub-basins, computation points) and variables to be displayed in the graph. This involves the following steps: x Select a Run from the drop-down list. x Select a location (sub-basin, computation point) from the drop-down list (Figure 51). Clicking on the button “Add chart series” will select a set of default variables (and you can skip the next step). x Select a variable from the drop-down list (Figure 52, Figure 53) and click the button “Add variable”. The available variables depend on the spatial resolution (Figure 50). See chapters 5.1 and 5.2 for a complete list of DSS variables. The number of selected time-series is limited to 20. However, visual comparison of more than about five different time-series may become difficult. Therefore, make use of the check-boxes next to the variable names, to turn time-series on or off in the graph display (Figure 54). The list of selected time-

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series includes information about location (ID-number of sub-basin or computation point), variable name, units, and Run acronym (Figure 54). If you display variables with different units, multiple y-axes will be inserted automatically (Figure 55). Check the graph legend to know which variable belongs to which y-axis. Alternatively, you can hover with the mouse over data points to query information of the time-series/individual value.

Figure 51: Selection of sub-basin in Analysis Tool.

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Figure 52: Selection of sub-basin variables in Analysis Tool.

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Figure 53: Selection of computation point variables in Analysis Tool.

Zambezi Decision Support System – User’s Manual

Figure 54: List of selected variables in Analysis Tool.

Zambezi Decision Support System – User’s Manual

Figure 55: Display of multiple y-axes with different units.

4.5.3. Display settings In the upper left panel of the Analysis Tool you have several options for the graph display. Two temporal resolutions (Monthly or Yearly, see Figure 56) and three plot types (Time-series, Mean, Duration curve, see Figure 57) can be combined for six different graph displays:

x Time-series plot of monthly values. x Time-series plot of annual values (see example in Figure 58). x Mean plot of monthly values (see example in Figure 59). x Mean plot of annual values (only useful for export of results, see chapter 4.5.4). x Duration curve of monthly values (see example in Figure 60). x Duration curve of annual values. Especially for time-series plots of monthly values it is useful to use the pan and zoom options in the display settings panel (refer also to the Quick Start Guide, chapter 3).

Zambezi Decision Support System – User’s Manual

For computing averages (plot type “Mean”, Figure 59) the Analysis Tool always considers the full time-series of the Run. If you want to compute averages for sub-periods you have to edit the simulation period of the Run (and execute the Run again). The duration curve plot shows the full distribution of the data, again for the entire time-series of the Run, showing the percentage of time (x-axis) a given value (y-axis) is exceeded. For example, Figure 60 shows that a discharge of 2000 m³/s is exceeded during 50% of the time at Tete (red duration curve) and only during 20% of the time at Victoria Falls (blue duration curve). When aggregating to annual values (temporal resolution “Yearly”) the Analysis Tool correctly considers that months have different lengths. For example, this affects computation of annual averages of discharge or temperature. You cannot specify graph colours. Instead, the Analysis Tool uses a set of pre-defined colours, where the first selected variable will always be blue, the second red, the third green, etc.

Figure 56: Selection of temporal resolution as display setting in Analysis Tool.

Figure 57: Selection of plot type as display setting in the Analysis Tool.

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Figure 58: Example time-series plot of annual values.

Figure 59: Example mean plot of monthly values.

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Figure 60: Example duration curve plot of monthly values.

4.5.4. Table view and export of results To view the data in tabular format click the button “Show data table” (Figure 13). A new page will open (Figure 61) showing the data for all selected variables of the “Series” panel (lower left). The data are summarized according to the settings for temporal resolution and plot type in the “Settings” panel (upper left). If “Mean” is selected as plot type then the “Date” given in the data table has no meaning. The table view can be used for exporting the data to other software. Below is a step-by-step description for exporting the data to Excel. x Open the table view by clicking the button “Show data table”. x Press Ctrl-A on the keyboard to select all data records (including the headers). x Press Ctrl-C on the keyboard to copy the data. x Open Excel. x Press Ctrl-V on the keyboard to paste the data into Excel.

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Figure 61: Table view for export of data from the Analysis Tool.

Zambezi Decision Support System – User’s Manual

4.6. Flood Mapping Module

The Flood Mapping Module allows you to display flood inundation maps along the Zambezi River in Mozambique. The flood mapping domain is shown in Figure 19 on page 18. The maps are generated from a hydraulic model (see chapter 5.4) and include uncertainties due to imprecise terrain data. Therefore, users should refrain from interpreting the flood maps as exact, deterministic facts. Rather the flood maps give an approximate overview about which areas are likely to be inundated during individual flood events. Figure 62 shows the interface of the Flood Mapping Module.

Figure 62: Interface of Flood Mapping Module.

Displaying flood maps includes the following steps:

Step 1: Select a Run Select an existing Run from the Run drop-down list. If you have not created a Run yet, refer to the Run Module (chapter 4.4).

Step 2: Create flood map Select a date from the drop-down list and click the button “Add map”. This will start the flood mapping, which will take a few seconds. The name of the flood map consists of Run acronym, year and month.

Zambezi Decision Support System – User’s Manual

You can identify dates with interesting floods in the Analysis Tool (chapter 4.5) by displaying the variable “Max_discharge” for computation points (see Table 5 on page 71).

Step 3: View flood map For visual analysis of flood maps you may want to zoom in to see more details of the Zambezi River in Mozambique. You can turn the display of flood maps on or off via the check-boxes next to the name of the flood map (Figure 62). Thereby, you can compare flood maps for different dates and/or Runs. A maximum number of five flood maps can be loaded at the same time. To add an additional flood map, you first have to remove one by clicking on the button “Delete flood map”. The colours of the flood maps are fixed (blue, red, etc.) and cannot be changed.

Figure 63 to Figure 65 show examples for flood maps. Note that the flood maps can also be used to visualize the surface area of reservoirs for simulated water levels (see e.g. Figure 65). However, this option is only available for existing computation points, which are linked to the flood mapping database (see Table 6 in chapter 5.4).

Figure 63: Flood map example near Tete. Inundated areas are shown in transparent blue.

Zambezi Decision Support System – User’s Manual

Figure 64: Flood map example for Zambezi delta. Inundated areas are shown in transparent blue.

Figure 65: Flood map example for potential future hydropower plants Lupata and Chemba. Inundated areas are shown in transparent blue.

Zambezi Decision Support System – User’s Manual

5. Model description

The Decision Support System (DSS) is designed for online access of (multiple) users over the internet (Figure 66). The DSS software is installed on a server and consists of two parts – the River Basin Model (RBM) and the Information Management System (IMS) – as shown in Figure 67. The main components of the River Basin Model are a Water Balance Model (for land-surface modelling) and a Water Allocation Model (for river-network modelling). A Peak-Flow Model is used to transfer data of the Water Balance Model to the Water Allocation Model (Figure 68). A special add-on is a Flood Mapping Model, which is a post-processor used for displaying inundation areas along the Zambezi River in Mozambique. The IMS consists of a database, a graphical user interface and analytical tools. All functions of the DSS are available for the user via the graphical user interface. The following chapters describe the components of the River Basin Model: the Water Balance Model (chapter 5.1), the Water Allocation Model (chapter 5.2), the Peak Flow Model (chapter 5.3) and the Flood Mapping Model (chapter 5.4). In addition, also the calibration procedure and a comparison of observed and simulated discharge are presented in chapter 5.5.

Figure 66: General design of DSS.

Zambezi Decision Support System – User’s Manual

Figure 67: General concept of the Decision Support System (DSS). IMS...Information Management System. RBM...River Basin Model.

Water Balance Model Water Allocation Model Land-surface modelling River-network modelling

ETp Precip ETa Preciplake Evapo Diversion

Interception River point Wetland Discharge Reservoir Soil Routing Q Q Surface-flow upstream lateral

separation Qdownstream

Spatial mapping Base-flow Runoff- depth

Peak Flow Model Distribute monthly runoff-depth onto daily time-steps

Figure 68: Conceptual structure of River Basin Model consisting of Water Balance Model (left), Water Allocation Model (right) and Peak Flow Model (bottom). Precip: precipitation in mm. ETp: potential evapotranspiration in mm. ETa: actual evapotranspiration in mm. Runoff-depth: runoff-depth in mm. Qupstream: upstream inflow in m³/s. Qlateral: lateral inflow in m³/s. Preciplake: precipitation on open water body in m³/s (is zero for River points). Evapo: evaporation from open water body in m³/s (is zero for River points). Diversion: withdrawal of water in m³/s. Discharge: river discharge in m³/s. Qdownstream: routed downstream discharge in m³/s.

Zambezi Decision Support System – User’s Manual

5.1. Water Balance Model

The Water Balance Model simulates the natural process of transformation of rainfall into runoff. The key model elements are sub-basins. Monthly time-steps are used and the input data of precipitation and temperature are spatially averaged for 27 sub-basins (Figure 18, Table 2). In each sub-basin three vegetation classes are considered according to the aerial shares of woodland, grassland and agriculture. However, in the current calibration of model parameters no differences are considered between vegetation classes (i.e. vegetation classes have no effect). Figure 68 outlines the general concept of the model. The code was developed based on the water balance model used by Kling et al. (2012), which is similar in its structure as used in earlier studies in the Zambezi basin (e.g. Winsemius et al., 2008). In each sub-basin, the following processes are considered. Potential evapotranspiration is derived from long-term mean monthly potential evapotranspiration computed with Penman-Monteith (CLIMWAT data-set) and an empirical relationship with temperature. Precipitation can be stored and evaporated from the interception storage. The remaining water falling on the ground is either stored in the soil or generates runoff as an exponential function of soil moisture (HBV-type concept). Evapotranspiration from the soil depends on soil moisture and potential evapotranspiration. Generated runoff is split into a fast (surface flow) and slow component representing base flow (simulated as a linear reservoir). In general monthly time-steps are used, but the interception and soil modules internally use discretizations into daily time-steps to account for intra-monthly variability (interception/evaporation of individual rainfall events; inter-dependence of soil moisture, evapotranspiration and runoff generation). Parameters of the Water Balance Model were calibrated by a comparison between simulated and observed discharge at key locations within the Zambezi basin (see chapter 5.5). Further information about the water balance model is available in Kling et al. (2014). Table 3 lists the variables of the Water Balance Model that are available in the DSS for the user. The basic water balance equation (spatially averaged over a sub-basin) is given by: Precip = Evapo + Storage_change + Runoff_depth All variables are in the units of mm. This equation shows how precipitation (“Precip”) is partitioned into evapotranspiration (“Evapo”), changes in soil moisture and deep water storage (“Storage_change”) and runoff-depth (“Runoff_depth”). The variable “Runoff” differs from the variable “Runoff_depth”, as “Runoff” is computed by converting river discharge (units of m³/s) of the Water Allocation Model into the units of mm. Therefore, the variable “Runoff” also includes the impacts of wetlands, reservoirs, diversions and routing (see chapter 5.2). In contrast, the variable “Runoff-depth” is the result of the land-surface modelling and does not include the impacts of wetlands, reservoirs, diversions and routing.

Zambezi Decision Support System – User’s Manual

Table 2: Sub-basins of the Water Balance Model. “Area (local)” gives size of sub-basin, “Area (total)” gives size of total catchment (including upstream sub-basins). [km²] [km²] NB Name Description Area (local) Area (total) 1 Chavuma Mission Zambezi River basin upstream of Chavuma Mission. 79821.3 79821.3 2 Kabompo Kabompo River basin upstream of Watopa Pontoon. 66459.9 66459.9 3 Lukulu Zambezi River sub-basin upstream of Lukulu. 66345.1 212626.3 4 Luanginga Luanginga River basin upstream of Kalabo. 32989.0 32989.0 5 Senanga Zambezi River sub-basin upstream of Senanga. 42938.7 288554.0 6 Katima Mulilo Zambezi River sub-basin upstream of Katima Mulilo. 46328.9 334882.9 7 Kwando Kwando River basin upstream of Kongola. 113501.2 113501.2 8 Victoria Falls Zambezi River sub-basin upstream of Victoria Falls. 71014.6 519398.7 9 Gwaai Gwaai River basin upstream of Kamativi. 39117.8 39117.8 10 Sanyati Sanyati River basin. 45340.5 45340.5 11 Kariba Zambezi River sub-basin upstream of Kariba. 73107.2 676964.2 12 Mswebi Kafue River basin upstream of Mswebi. 51043.4 51043.4 13 Itezhitezhi Kafue River sub-basin upstream of Itezhitezhi. 55526.7 106570.1 14 Kafue Gorge Kafue River sub-basin upstream of Kafue Gorge. 46167.0 152737.1 15 Upper Luangwa Upper Luangwa River basin. 96838.2 96838.2 16 Lower Luangwa Luangwa River sub-basin upstream of Gt E. Road Bridge. 45209.7 142047.9 17 Middle Zambezi Zambezi River sub-basin upstream of border between 33223.2 1004972.4 Mozambique and Zambia/Zimbabwe. 18 Panhane Panhane River basin. 24404.3 24404.3 19 Cahora Bassa Zambezi River sub-basin upstream of Cahora Bassa. 35036.1 1064412.8 20 Luia Luia River basin. 28698.6 28698.6 21 Tete Zambezi River sub-basin upstream of Tete. 10281.2 1103392.6 22 Revubue Revubue River basin. 16262.7 16262.7 23 Luenha Luenha River basin. 53581.2 53581.2 24 Mutarara Zambezi River sub-basin upstream of Mutarara. 26166.7 1199403.2 25 Liwonde Shire River basin upstream of Liwonde. 132277.7 132277.7 26 Chiromo Shire River sub-basin upstream of Chiromo. 19259.1 151536.8 27 Delta Zambezi River sub-basin upstream of delta. 22246.4 1373186.4

Zambezi Decision Support System – User’s Manual

Table 3: Variables of the Water Balance Model that are available in the DSS. Variable Units Type Description Precip mm flux Precipitation. Temp °C state Air temperature. Evapo_pot mm flux Potential evapotranspiration. Evapo mm flux Actual evapotranspiration (interception and soil). Runoff_depth mm flux Runoff-depth. Runoff mm flux Runoff (converted from discharge of Water Allocation Model). Storage_change mm flux Storage change including soil moisture and base flow storage.

Zambezi Decision Support System – User’s Manual

5.2. Water Allocation Model

The Water Allocation Model simulates the aggregation of discharge along the river network as well as the impact of reservoirs, wetlands, lakes, and diversions. Daily time-steps are used as internal computational time-steps and results are aggregated to monthly values. The key model elements are computation points, where three different types are considered: x River points. x Uncontrolled reservoirs, representing wetlands, floodplains or lakes. x Controlled reservoirs, representing large, human-made structures such as e.g. Lake Kariba. Pre-defined sets of computation points are used in the development scenarios (see e.g. Table 4), but new computation points may be added in the DSS by the user. Figure 69 shows a conceptual representation of the fluxes (units of m³/s) into and out of computation points. State variables characterizing the water body of computation points include water storage, surface area of water body, and water level. Table 5 lists all variables of the Water Allocation Model. The balance in a computation point is given by: Inflow_upstream + Inflow_local + Precip = Evapo + Diversion + Discharge + 'Water_storage All variables are in the units of m³/s. The variable “Inflow_local” is obtained from the Water Balance Model (chapter 5.1). Routed discharge is an input to the next downstream computation point, where for routing a simple time-lag is considered (see below). Computations points representing River points do not have a water body. Therefore, River points do not have any state variables and precipitation and evaporation are zero. A key characteristic of controlled and uncontrolled reservoirs is the relationship of volume-area- elevation, where volume is the key accounting unit (hm³) for the mass balance computations of the model. Evaporation is computed from the potential evapotranspiration increased by 5% (according to FAO 56, Allen et al., 1998) and the surface area of the water body. Wetlands are modelled as reservoirs with a fixed storage-discharge relationship. Releases from reservoirs are modelled according to reservoir operations with guide curves. Such guide curves enable to simulate a seasonal drawdown of reservoirs, which mimics the actual operations of e.g. Kariba and Cahora Bassa for flood control. Additional attributes are environmental flow requirements as a function of month and a desired release as a function of water level (attribute “Discharge” at rating curve of controlled reservoir). Diversions of water (consumptive use, e.g. for irrigation) can be withdrawn at all three types of computation points. In the case of river points and uncontrolled reservoirs (wetlands) the water is taken out of the river, but only if the (optional) required downstream flow (environmental flows) is maintained. In the case of controlled reservoirs the water is taken out of the reservoir storage. The following prioritization of water is used by Controlled reservoirs to determine the reservoir storage and releases (1 has highest priority and 4 has lowest priority):

Zambezi Decision Support System – User’s Manual

1. Environmental flow as a function of month 2. Diversions (e.g. for irrigation) as a function of month 3. Desired release (e.g. for hydropower) as a function of storage 4. Guide curve operation (e.g. for flood control) as a function of month The attributes above are part of the computation point properties (see chapter 4.3.4) and can be specified by the user. The model uses a zoning concept for simulation of reservoir operation, with an inactive zone, a conservation zone and a flood control zone (Figure 70):

x The inactive zone defines the “dead” storage of the reservoir with no outflow or diversions from the reservoir. The water level may drop in the inactive zone due to low inflows and high evaporation. x The conservation zone is located above the inactive zone. The top of the conservation zone is defined by the guide curve. As long as the water level is within the conservation zone, the reservoir is in normal operation mode, i.e. releases are equal to the operation rules. Lower water levels than the guide curve are possible e.g. due to releases with high priorities (see above, priorities 1 to 3). x If the water level rises above the guide curve into the flood control zone, then operation rules for flood buffering come into effect. To buffer flood peaks, the target release is a moving average of the inflow over the past couple of weeks. In addition, to avoid rapid rise of the water level during floods, 10% of the surplus storage above the guide curve is also released each day. If during large floods the water level reaches the top of the flood control zone, then there is no further buffering of floods and the release will be at full capacity (the water level may still rise if the spillway capacity is smaller than the inflow). Overall, the model is able to mimic the most important reservoir operation characteristics, as e.g. also used by the well-known HEC-ResSim model. The outflow of a computation point is routed to the next downstream computation point with a simple lag-method. This routing is only considered at computation points at the outlets of sub-basins and between computation points along the Zambezi River in Mozambique. In general, a value of three days is used for routing at sub-basin outlets, with the exception of computation points located directly upstream of large wetlands (Barotse Floodplain, Chobe Swamps, Kafue Flats), where a value of 10 days is used for routing. This simple routing method results in approximately 5 weeks travel- time for floods from the Zambezi headwaters to Mozambique. Note, that also the retention in uncontrolled and controlled reservoirs causes an additional attenuation for floods travelling downstream. For routing between computation points along the Zambezi River in Mozambique, the lag-time is between 0 and 2.5 days.

Zambezi Decision Support System – User’s Manual

Energy generation at hydropower plants can be simulated at all three types of computation points. For River points and Uncontrolled reservoirs this represents run-of-river hydropower plants, whereas for Controlled reservoirs this represents hydropower plants with storage schemes. The following equation is used for computing power: P = Q * eta * (WL – TWL – HL) * 9.81 / 1000 where P is Power in [MW], Q is Discharge in [m³/s], eta is efficiency (turbine, generator, transformer) in [/], WL is Water level in [m], TWL is Tailwater level in [m], and HL is Hydraulic losses in [m]. In the above equation Q is limited to the “Turbine capacity” and P is limited to the “Installed capacity”.

Zambezi Decision Support System – User’s Manual

Table 4: Computation points of the Water Allocation Model. Example for the development scenario “Baseline +HPP”. Computation points 1 through 27 are located at the sub-basin outlets of the Water Balance Model (Table 2). The abbreviations “u/s” and “d/s” mean upstream and downstream, respectively. Type 1: River point; Type 2: Uncontrolled reservoir; Type 3: Controlled reservoir. “Area” gives size of total catchment (including all upstream areas).

ID Name Description Type Area [km²] 1 Chavuma Mission Zambezi River at Chavuma Mission. 1 79,821 2 Kabompo River Kabompo River at Watopa Pontoon. 1 66,460 3 Lukulu Zambezi River at Lukulu. 1 212,626 4 Luanginga River Luanginga River at Kalabo. 1 32,989 5 Senanga Zambezi River at Senanga. 1 288,554 05a Barotse Floodplain Barotse Floodplain of Zambezi River. 2 288,554 6 Katima Mulilo Zambezi River at Katima Mulilo. 1 334,883 7 Kwando River Kwando River at Kongola. 1 113,501 07a Kwando Floodplain Kwando Floodplain of Kwando River. 2 113,501 8 Victoria Falls Zambezi River at Victoria Falls. 1 519,399 08a Caprivi Floodplain Caprivi Floodplain / Chobe Swamps of Zambezi / Chobe River. 2 515,775 9 Gwaai River Gwaai River at Kamativi. 1 39,118 10 Sanyati River Sanyati River near mouth. 1 45,341 10a Sanyati reservoirs Cumulative reservoirs in Sanyati basin. 3 11,261 11 Kariba Zambezi River at Kariba. 3 676,964 11a Batoka Gorge Planned HPP Batoka Gorge. 3 520,875 12 Mswebi Kafue River at Mswebi. 1 51,043 13 Itezhitezhi Kafue River at Itezhitezhi. 3 106,570 14 Kafue Gorge Kafue River at Kafue Gorge. 3 152,737 14a Kafue Flats Kafue Flats of Kafue River. 2 151,531 15 Upper Luangwa Luangwa River upstream confluence Lunsemfwa River. 1 96,838 16 Luangwa River Luangwa River at Great East Road Bridge. 1 142,048 17 Zambezi entering Mozambique Zambezi River entering Mozambique at border with Zambia and Zimbabwe. 1 1,004,972 17a Kafue Gorge Lower Planned HPP Kafue Gorge Lower. 3 153,570 18 Panhane River Panhane River near mouth. 1 24,404 18a Panhane reservoirs Cumulative reservoirs in Panhane basin. 3 5,896 19 Cahora Bassa Zambezi River at Cahora Bassa. 3 1,064,413 20 Luia River Luia River near mouth. 1 28,699 21 Tete Zambezi River at Tete. 1 1,103,393 21a Zambezi u/s Luia River Zambezi upstream confluence of Luia River. 1 1,064,966 21b Zambezi d/s Luia River Zambezi downstream confluence of Luia River. 1 1,094,067 21c Mphanda Nkuwa Zambezi River at Mphanda Nkuwa. 3 1,096,114 21d Boroma Zambezi River at Boroma. 3 1,101,244 22 Revubue River Revubue River at Chingoze. 1 16,263 23 Luenha River Luenha River downstream confluence Mazoe River. 1 53,581 24 Mutarara Zambezi River at Mutarara (Ponte Dona Ana). 1 1,199,403 24a Zambezi d/s Revubue River Zambezi downstream confluence of Revubue River. 1 1,119,705 24b Zambezi u/s Luenha River Zambezi upstream confluence of Luenha River. 1 1,120,857 24c Zambezi d/s Luenha River Zambezi downstream confluence of Luenha River. 1 1,175,436 24d Lupata Zambezi River at Lupata. 3 1,177,849 24e Chemba Zambezi River at Chemba. 3 1,195,096 25 Liwonde Shire River at Liwonde. 1 132,278 25a Lake Niassa (Lake Malawi) Lake Niassa (Lake Malawi) in Shire River basin. 2 128,220 26 Chiromo Shire River at Chiromo. 1 151,537 27 Zambezi Delta Zambezi River at mouth. 1 1,372,935 27a Zambezi u/s Shire River Zambezi upstream confluence of Shire River. 1 1,201,603 27b Zambezi d/s Shire River Zambezi River downstream confluence of Shire River. 1 1,362,018 27c Caia Zambezi River at Caia. 1 1,362,211 27d Zambezi start delta Zambezi at start of delta. 1 1,371,865 27e Marromeu Zambezi River at Marromeu. 1 1,372,558

Zambezi Decision Support System – User’s Manual

Precip Evapo

Routed Upstream Storage Discharge Discharge Inflow Area Water level

Local Diversion Inflow

Figure 69: Variables of computation points.

Table 5: Variables of the Water Allocation Model. Variable Units Type Description Inflow_upstream m³/s flux Inflow from upstream computation points. Inflow_local m³/s flux Inflow from intermediate sub-catchment. Precip m³/s flux Precipitation on water body. Evapo m³/s flux Evaporation from water body. Diversion m³/s flux Subtraction of water (net-consumption). Discharge m³/s flux River discharge (outflow). (Discharge_routed) m³/s flux River discharge routed downstream (not available for user in DSS). Water_storage hm³ state Volume of water stored in water body (end-of-month value). Water_area km² state Surface area of water body (end-of-month value). Water_level m state Elevation of water level (end-of-month value). Max_discharge m³/s output Maximum daily discharge within the monthly time-step. Max_water_level m output Maximum daily water level within the monthly time-step. Power MW output Power generated at hydropower plant (mean over month).

Zambezi Decision Support System – User’s Manual

Figure 70: Zoning concept for reservoir operation.

Zambezi Decision Support System – User’s Manual

5.3. Peak Flow Model

For the purpose of flood mapping (see chapter 5.4) it is necessary to provide daily peak flows, which may be considerably higher than the monthly means. However, the Zambezi DSS runs in monthly time-steps. This relates to the temporal resolution of the input and output data, whereas the internal discretization in the Water Allocation Model is daily time-steps before aggregating the results back to monthly values. A Peak Flow Model is used to transfer runoff data from the Water Balance Model to the Water Allocation Model. The Peak Flow Model distributes monthly runoff (units in mm) generated by the Water Balance Model onto daily time-steps. Thereby, a plausible intra-monthly variability is generated for simulation of discharge (units of m³/s) in daily time-steps in the Water Allocation Model. Thus, maximum daily discharge within each month is readily available for flood mapping. An advantage of the method is that the impacts of reservoir operations (e.g. flood buffering), routing (travel time of floods downstream) and the superposition of flood waves from different tributaries of the Zambezi River are correctly accounted for. The following method is used by the Peak Flow Model for distributing monthly runoff onto daily time-steps. In a first step, the monthly runoff is distributed onto a three-point hydrograph (triangular hydrograph), with the following equations:

Qstart,t = (Qm,t + Qm,t-1) / 2

Qend,t = (Qm,t + Qm,t+1) / 2

Qmid,t = 2*Qm,t – (Qstart,t + Qend,t) / 2 where Qstart is the runoff at the start of the month in mm/d, Qend is the runoff at the end of the month in mm/d, Qmid is the runoff in the middle of the month in mm/d, Qm is the mean monthly runoff in mm/d, and the index t identifies the month. These equations preserve the mass balance over the month.

In a second step, months with flood peaks are identified as those months where Qmid is greater than both Qstart and Qend. For these months the flood peak is computed as follows:

Qpeak,t = Qm,t * QCOR where Qpeak is the peak runoff in the middle of the month in mm/d, and QCOR is a dimensionless empirical factor greater than 1.0. Qmid is replaced by Qpeak in the triangular hydrograph. Constant flows of Qstart and Qend are assumed at the start and end of the month, where the duration of constant flows is computed such that the mass balance over the month is preserved. The first step described above yields a good approximation of rising and falling limbs of the seasonal hydrograph (important e.g. for the Kafue River, see Figure 71), whereas the second step ensures that also sharp flood peaks are matched reasonably well (important e.g. for the Luangwa River, see Figure 72).

Zambezi Decision Support System – User’s Manual

Thus, the Flood Peak Model only requires one parameter (QCOR), which gives the ratio of daily peak flow to mean monthly flow. QCOR is a parameter that has to be provided for each of the 27 sub- basins of the Water Balance Model. QCOR was estimated based on an analysis with observed daily discharge data in headwater catchments (which are not impacted by reservoirs and floodplains). Figure 73 shows the result of the regional analysis of daily observed discharge data. In the western part of the Zambezi basin (Upper Zambezi River, Kabompo River, Luanginga River, Kwando River, Kafue River, etc.), there are no sharp flood peaks and the daily peak flow is on average only +20% larger than the mean monthly flow. In the northern and eastern part of the Zambezi basin (Luangwa River, Revubue River, Luenha River, Shire tributaries, etc.) daily peak flows are on average twice as large as the mean monthly flow. Flood peaks are most extreme in the southern part of the Zambezi basin (Gwaai River, Sanyati River), where daily peak flow is on average +300% larger than the mean monthly flow. From this analysis it is clear that it makes a significant difference for the daily peak flow if a Zambezi flood entering Mozambique originated from the western part (e.g. Kafue River) or from the northern part (e.g. Luangwa River), even if the monthly mean flows are similar. These ratios of +20% in the western part, +100% in the northern and eastern part, and +300% in the southern part are also used as the values for the parameter QCOR in the 27 sub-basins of the Zambezi DSS. The simulation results of the Peak Flow Model can be evaluated with observed daily discharge data. The simulated and observed flood peaks show a good concordance, both for the Upper Zambezi (Figure 77) as well as downstream sections of the Zambezi River (Figure 78). Note that for the downstream sections the strong impact of floodplains and reservoir operations is already included in the simulation results (via the Water Allocation Model).

Zambezi Decision Support System – User’s Manual

600 Qobs_daily Qobs_monthly Qsim_triangular 500

400 ] s / ³ 300 m [

200

100

0 01.10.1963 30.09.1964 30.09.1965 30.09.1966 01.10.1967 30.09.1968 Figure 71: Testing of Peak Flow Model with observed discharge data of Kafue River at Mswebi. Black: observed daily discharge. Blue: observed monthly discharge (input to Peak Flow Model). Red: simulated discharge of the Peak Flow Model (triangular hydrograph within months).

4000 Qobs_daily Qobs_monthly 3500 Qsim_triangular

3000

2500 ] s / ³ 2000 m [ 1500

1000

500

0 01.10.1968 01.10.1969 01.10.1970 01.10.1971 01.10.1972 01.10.1973 Figure 72: Testing of Peak Flow Model with observed discharge data of Luangwa River at Great East Road Bridge. Black: observed daily discharge. Blue: observed monthly discharge (input to Peak Flow Model). Red: simulated discharge of the Peak Flow Model (triangular hydrograph within months).

Zambezi Decision Support System – User’s Manual

Figure 73: Average ratio (QCOR) between daily peak flow and mean monthly flow. Analysis based on daily discharge data of numerous gauges in headwater catchments.

gauge Watopa Pontoon gauge Ndubeni river Kabompo river Kafue source ZRA source GRDC length of record [y] 34.4 length of record [y] 18.0

1400 900 800 1200 700

] 1000 ] s s

/ / 600 ³ ³ m m [ [ 800 500 w w o o l l

f f 400 600 k k

a observed peak flow a observed peak flow e e 300 p 400 p estimated peak flow 200 estimated peak flow 200 1:1 line 100 1:1 line 0 0 0 200 400 600 800 1000 1200 1400 0 200 400 600 800 1000 mean monthly discharge [m³/s] mean monthly discharge [m³/s]

Figure 74: Observed daily peak flow versus mean monthly flow at headwater catchments in the western part of the Zambezi basin. Examples for the Kabompo River (left) and upper Kafue River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +20%.

Zambezi Decision Support System – User’s Manual

gauge Luangwa Road Bridge gauge Luenha 1 (bias removed) river Luangwa river Luenha source SA FRIEND source DNA length of record [y] 16.2 length of record [y] 12.8

8000 5000 7000 4500 4000 6000 ] ]

s s 3500 / / ³ 5000 ³ m m 3000 [ [

w 4000 w 2500 o o l l f f

k k 2000 a 3000 observed peak flow a observed peak flow e e

p p 1500 2000 estimated peak flow estimated peak flow 1000 1:1 line 1:1 line 1000 500 0 0 0 2000 4000 6000 8000 0 1000 2000 3000 4000 5000 mean monthly discharge [m³/s] mean monthly discharge [m³/s]

Figure 75: Observed daily peak flow versus mean monthly flow at headwater catchments in the northern and eastern part of the Zambezi basin. Examples for the Luangwa River (left) and Luenha River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +100%.

gauge Deka Road Bridge gauge Binga Road Bridge river Gwayi river Sanyati source ZRA 2013 source ZRA length of record [y] 10.0 length of record [y] 13.2

2000 9000 1800 8000 1600 7000 ] ] s

1400 s / / 6000 ³ ³ m

1200 m [ [

5000 w 1000 w o o l l f f 4000

k 800 k a observed peak flow a observed peak flow e e 3000 p 600 p estimated peak flow estimated peak flow 400 2000 1:1 line 200 1000 1:1 line 0 0 0 500 1000 1500 2000 0 2000 4000 6000 8000 10000 mean monthly discharge [m³/s] mean monthly discharge [m³/s]

Figure 76: Observed daily peak flow versus mean monthly flow at headwater catchments in the southern part of the Zambezi basin. Examples for the Gwaai River (left) and the Sanyati River (right). The estimated peak flow (red dashed line) uses a value for QCOR of +300%.

Zambezi Decision Support System – User’s Manual

10000 max Qobs 9000 max Qsim ] s

/ 8000 ³ m [

7000 w o l

f 6000

k a

e 5000 p

y l i 4000 a d

a 3000 u n

n 2000 a 1000 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Figure 77: Simulated (red) vs. observed (black) annual daily peak flow of Zambezi River at Victoria Falls. Simulation result with Zambezi DSS (Water Balance Model, Peak Flow Model, Water Allocation Model).

16000 max Qobs max Qsim 14000 ] s / ³ 12000 m [

o 10000 l f

k a

e 8000 p

y l i

a 6000 d

l a

u 4000 n n a 2000

0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Figure 78: Simulated (red) vs. observed (black) annual daily peak flow of Zambezi River at Tete. Simulation result with Zambezi DSS (Water Balance Model, Peak Flow Model, Water Allocation Model). Observed data include uncertainties due to imprecise rating curve.

Zambezi Decision Support System – User’s Manual

5.4. Flood Mapping

The Flood Mapping is based on a hydraulic HEC-RAS model for the Zambezi River from Cahora Bassa until the Zambezi delta (Petersen 2012). The 1D hydraulic model consists of approximately 1200 cross-sections, i.e. on average roughly every 500 m there is a cross-section along the river channel (Figure 79). For a given discharge as input, the hydraulic model simulates the river water level in each cross-section for stationary hydraulic conditions. For the delta region a 2D hydraulic model (Adaptive Hydraulics AdH) is applied. The hydraulic model was calibrated with historic flood events using observed discharge and observed spatial extents of flood inundations. The calibration mainly focused on hydraulic roughness parameters (Manning’s n). Due to lack of available cross-section data in the main channel, assumptions had to be made about the geometry of cross-sections, i.e. the depth of the river is not precisely known (see example cross-section geometry in Figure 80). Another source of uncertainty is imprecise terrain data in the left bank and right bank of the Zambezi River. The terrain data are based on the digital elevation model of the Shuttle Radar Topography Mission (SRTM). The hydraulic simulations results for a range of discharge from 10 m³/s to 50,000 m³/s are stored in a database, i.e. for each cross-section the relationship between discharge and river water level is readily available. This database is used by the Zambezi DSS for the flood mapping with the method outlined below. The discharge input to the flood mapping is the variable “Max_discharge” of the Water Allocation Model (see Table 5 on page 71). This variable is provided at 18 computation points along the Zambezi River (Table 6). These computation points are fixed and cannot be changed by the user. Note that at important tributaries (Luia River, Revubue River, Luenha River, Shire River) there is always a computation point located directly upstream and downstream of the tributary. Between computation points the discharge is linearly interpolated to determine the discharge in each cross- section. The pre-computed database is used as a look-up table to obtain the river water level from the discharge data. The water level is intersected with a digital elevation model (SRTM) to create the flood map. As the hydraulic model considers the Zambezi River under the current, natural conditions, the impacts of possible future reservoirs have to be accounted for separately. Therefore, the flood map is adjusted for areas with reservoir water bodies, where the water level of the reservoir (simulated by the Water Allocation Model with the variable Max_water_level, see Table 5 on page 71) is used instead of the river water level for creating the flood map.

Zambezi Decision Support System – User’s Manual

Figure 79: Cross sections of the 1D hydraulic HEC-RAS model of the Zambezi River from Cahora Bassa reservoir until the Zambezi delta. Orange: cross-sections. Blue: river network. Black: Zambezi basin divide. Red: country borders.

Figure 80: Example for cross-section in HEC-RAS model.

Zambezi Decision Support System – User’s Manual

Table 6: Computation points used for flood mapping. ID Name Description 19 Cahora Bassa Zambezi River at Cahora Bassa. 21 Tete Zambezi River at Tete. 21a Zambezi u/s Luia River Zambezi upstream confluence of Luia River. 21b Zambezi d/s Luia River Zambezi downstream confluence of Luia River. 21c Mphanda Nkuwa Zambezi River at Mphanda Nkuwa. 21d Boroma Zambezi River at Boroma. 24 Mutarara Zambezi River at Mutarara (Ponte Dona Ana). 24a Zambezi d/s Revubue River Zambezi downstream confluence of Revubue River. 24b Zambezi u/s Luenha River Zambezi upstream confluence of Luenha River. 24c Zambezi d/s Luenha River Zambezi downstream confluence of Luenha River. 24d Lupata Zambezi River at Lupata. 24e Chemba Zambezi River at Chemba. 27 Zambezi Delta Zambezi River at mouth. 27a Zambezi u/s Shire River Zambezi upstream confluence of Shire River. 27b Zambezi d/s Shire River Zambezi River downstream confluence of Shire River. 27c Caia Zambezi River at Caia. 27d Zambezi start delta Zambezi at start of delta. 27e Marromeu Zambezi River at Marromeu.

Zambezi Decision Support System – User’s Manual

5.5. Calibration and evaluation

Observed discharge data measured at gauges were used for calibration of model parameters. The objective was to obtain a good agreement between simulated and observed discharge for the period 1961-1990. A challenge was that observed discharge data of some gauges appear to be affected by severe biases (e.g. Chavuma Mission discharge data). Such data were excluded from the calibration. In addition to the discharge data, also observed water level data of reservoirs were used for comparison with simulated water levels. The following methods were used during parameter calibration: x Multi-dimensional sensitivity tests. x Shuffled Complex Evolution (SCE) optimization (Duan et al., 1992). x Multi-objective Dynamically Dimensioned Search (moDDS) optimization (extension of DDS, Tolson and Shoemaker, 2007) x Manual parameter calibration. In addition to a visual comparison of simulated and observed discharge and water levels, model performance for discharge simulation at gauges was measured with the modified KGE statistic (Gupta et al., 2009; Kling et al., 2012) and its three dimension-less sub-components correlation, bias ratio, and variability ratio:

=1 ( 1) +( 1) +( 1) ᇱ ଶ ଶ ଶ ܭܩܧ= െඥ ݎെ ߚെ ߛെ ߤ௦ ߚ CV௢ =ߤ = CV ୱ ɐୱȀɊୱ ɀ where ୭ ɐ୭ȀɊ୭ KGE’ is the modified KGE-statistic [/] r is the correlation between simulated and observed discharge time-series [/] E is the bias ratio [/] J is the variability ratio [/] P is mean discharge [m³/s] CV is the coefficient of variation [/] V is the standard deviation [m³/s] s is the subscript denoting simulated values o is the subscript denoting observed values Optimization on the KGE-statistic ensures that a balanced solution for correlation (i.e. temporal dynamics), bias (i.e. mean volume of flow), and variability (i.e. distribution of flows, flow duration curve) is obtained. This would not be ensured if optimizing on e.g. the well-known Nash-Sutcliffe efficiency (NSE, Nash and Sutcliffe, 1970) or the related Mean Squared Error (see Gupta et al., 2009).

Zambezi Decision Support System – User’s Manual

The calibration mainly focused on four parameters of the Water Balance Model and the storage- release function of the wetlands (Uncontrolled reservoirs) of the Water Allocation Model. The four calibrated parameters of the Water Balance Model are:

x Soil storage capacity x Exponent for computing runoff generation (HBV-method) x Base-flow-index, i.e. fraction of generated runoff that percolates to the base flow storage x Base flow recession coefficient (linear reservoir coefficient) During calibration it proved to be sufficient to use the same base flow recession coefficient in all sub- basins, whereas for the other three parameters different (calibrated) values are used in individual sub-basins, thereby implicitly accounting for the complex interplay between climate, vegetation, soils, geology, aquifers and runoff. The calibration period was defined as 1961 to 1990, with a 1-year spin-up period (for state variables of the model). The period 1961-1990 has the best quality in precipitation station availability as well as number of gauges with observed discharge. In the middle of this period in the 1970s Itezhitezhi and Cahora Bassa reservoirs were built. Therefore, the observed discharge datasets are not homogeneous. Furthermore, frequent changes in operation rules at the large reservoirs (Kariba, Cahora Bassa) complicate evaluation of the discharge simulations in the downstream sections of the Zambezi River. In addition, the relationship between water level and outflow from Lake Malawi is affected by changes in the outlet level (e.g. due to sedimentation) and human interventions (e.g. Kamuzu Barrage just downstream of the lake outlet was constructed in 1965 to control Shire River flows; Shela, 2000). Above all, the quality of the observed discharge data is at some gauges questionable, as there are implausible differences when comparing observed discharge time-series (see Figure 81 and Figure 82). A main cause of uncertainty may be an imprecise (or outdated) stage- discharge relationship, which comes into effect when transforming the water level data (which is actually the data measured at the gauge) to discharge. Table 7 summarizes the calibration procedure and performance statistics, with the caveat that observed discharge data are unreliable at several gauges. In general the model performs well, with performance statistics similar or higher than in previous Zambezi studies (e.g. Harrison and Whittington, 2002; Winsemius et al., 2006; Beck, 2010; Yamba et al., 2011). Figure 83 and Figure 84 show simulated and observed monthly hydrographs at six key locations in the Zambezi basin. The observed temporal dynamics of discharge are reproduced well in the simulation. Further comparisons with observed discharge data are presented for seasonality in discharge (Figure 85), monthly flow duration (i.e. distribution of flows, Figure 86), and annual variations in discharge (Figure 87). Simulated water levels in Kariba and Cahora Bassa reservoirs as well as Lake Malawi exhibit similar fluctuations (Figure 88). However, from this figure it is also clear that actual operation rules changed over time – as exemplified by Cahora Bassa where drawdowns were smaller during the 2000s than during the 1970s after the first filling of the reservoir (transmission lines from Cahora Bassa were destroyed during the turmoil in Mozambique during the 1980s; Beilfuss and dos Santos, 2001). Such changes over time in the operation rules are not considered by the DSS, where instead operation rules are fixed.

Zambezi Decision Support System – User’s Manual

Table 7: Calibration procedure and performance statistics. CP identifies the location (Table 4) and corresponds to the sub-basins defined in Table 2. CP period method comment r [/] E [/] J [/] KGE’ [/] NSE [/] 1 1961-1990 with 3 Qobs not plausible 0.87 0.55 0.66 0.42 0.39 2 1961-1990 SCE 0.94 1.00 0.99 0.94 0.89 3 1961-1990 moDDS with 8 0.94 0.98 1.07 0.90 0.87 4 1961-1990 SCE 0.87 1.00 0.99 0.87 0.75 5 1961-1990 with 8 Qobs not plausible 0.94 1.08 1.41 0.58 0.53 6 1961-1990 with 8 Qobs not plausible 0.93 0.84 0.93 0.82 0.82 7 1961-1990 SCE 0.62 1.07 0.94 0.61 0.20 8 1961-1990 moDDS with 3 0.93 1.00 0.96 0.92 0.87 9 1961-1990 SCE 0.88 1.00 1.00 0.88 0.76 10 1961-2009 SCE 0.88 1.00 0.99 0.88 0.76 11 1961-1990 manual (a) Kariba operations 0.53 0.96 0.75 0.47 0.24 12 1961-1990 SCE 0.93 1.00 0.99 0.93 0.87 13 1961-1976 SCE 0.95 1.00 0.98 0.95 0.90 14 1961-1976 SCE 0.91 1.06 0.84 0.81 0.83 15 1961-1990 with 16 n/a n/a n/a n/a n/a 16 1961-1990 SCE 0.93 1.00 1.00 0.93 0.87 17 1961-1990 manual (a) 0.93 0.92 0.77 0.74 0.80 18 1961-2009 with 10 n/a n/a n/a n/a n/a 19 1961-1990 manual (a) Cahora Bassa operations 0.41 0.96 0.87 0.39 -0.02 20 1961-1990 with 22 n/a n/a n/a n/a n/a 21 1961-1990 manual (a) 0.73 1.09 0.94 0.71 0.44 22 1961-1990 SCE 0.93 1.00 1.00 0.92 0.85 23 1961-1990 SCE 0.96 1.00 1.00 0.96 0.91 24 1961-1990 manual (a) n/a n/a n/a n/a n/a 25 1961-1990 manual (b) Lake Malawi operations 0.40 1.00 0.81 0.37 -0.01 26 1961-1990 manual (b) 0.65 1.05 1.01 0.65 0.24 27 1961-1990 manual (a) 0.73 1.06 0.84 0.68 0.50 manual (a): manual calibration with same parameter values for sub-basin (CP) 11, 17, 19, 21, 24, 27 manual (b): manual calibration with same parameter values for sub-basin (CP) 25, 26 changing operations at Kariba, Cahora Bassa and Lake Malawi affected observed discharge data

Zambezi Decision Support System – User’s Manual

9000 8000

7000

6000 DNA Matundo ]

s 5000

/ DNA Tete daily ³ m

[ 4000 DNA Tete monthly 3000 Mepanda Uncua study 2000 Tete daily 1000 0 01.01.1964 01.01.1968 01.01.1972 01.01.1976

Figure 81: Comparison of four different sources of observed data for monthly discharge of Zambezi River near Tete. Daily data were aggregated to monthly values. Ideally, there should not be any differences in the observed data.

9000 observed Zambezi discharge at Senanga and Katima Mulilo Senanga 8000 Katima Mulilo 7000 6000 ]

s 5000 / ³ m

[ 4000 3000 2000 1000 0 01.01.1965 01.01.1969 01.01.1973 01.01.1977 Figure 82: Observed data of Zambezi discharge at Senanga (upstream) and Katima Mulilo (downstream). There are no significant tributaries between the two adjacent gauges and discharge data should be quite similar, but they are not due to biased peak flow data.

Zambezi Decision Support System – User’s Manual

6000 Zambezi at Lukulu QOBS_03 QSIM_03 5000

4000 ] s /

³ 3000 m [ 2000

1000

0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992

12000 Zambezi at Victoria Falls QOBS_08 QSIM_08 10000

8000 ] s / ³ 6000 m [ 4000

2000

0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992

20000 Zambezi at Tete QOBS_21 18000 QSIM_21 16000 14000

] 12000 s / ³ 10000 m [ 8000 6000 4000 2000 0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992 Figure 83: Simulated (red) and observed (black) monthly hydrographs at key locations along the Zambezi.

Zambezi Decision Support System – User’s Manual

3000 Kafue at Itezhitezhi QOBS_13 QSIM_13 2500

2000 ] s /

³ 1500 m [ 1000

500

0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992

4000 Luangwa at Gt. East Road QOBS_16 3500 QSIM_16 3000 2500 ] s / ³ 2000 m [ 1500 1000 500 0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992

2500 Shire at Chiromo QOBS_26 QSIM_26 2000

] 1500 s / ³ m [ 1000

500

0 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988 01.01.1992 Figure 84: Simulated (red) and observed (black) monthly hydrographs of the three main tributaries of the Zambezi.

Zambezi Decision Support System – User’s Manual

2500 Zambezi at Lukulu 1000 Kafue at Itezhitezhi QOBS_03 QOBS_13 2000 QSIM_03 800 QSIM_13

1500 600 ] ] s s / / ³ ³ m m [ 1000 [ 400

500 200

0 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 month month

4000 Zambezi at Victoria Falls 2000 Luangwa at Gt. East Road QOBS_08 QOBS_16 QSIM_08 QSIM_16 3000 1500 ] ] s s / /

³ 2000 ³ 1000 m m [ [

1000 500

0 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 month month

5000 Zambezi at Tete 1000 Shire at Chiromo QOBS_21 QOBS_26 4000 QSIM_21 800 QSIM_26

3000 600 ] ] s s / / ³ ³ m m [ 2000 [ 400

1000 200

0 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 month month

Figure 85: Simulated (red) and observed (black) seasonality in discharge at key locations in the Zambezi basin. Period 1961-1990.

Zambezi Decision Support System – User’s Manual

4500 Zambezi at Lukulu 2500 Kafue at Itezhitezhi QOBS_03 QOBS_13 4000 QSIM_03 QSIM_13 3500 2000 3000 1500 ] ]

s 2500 s / / ³ ³ m m

[ 2000 [ 1000 1500

1000 500 500 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 pct. [/] pct. [/]

8000 Zambezi at Victoria Falls 4000 Luangwa at Gt. East Road QOBS_08 QOBS_16 7000 3500 QSIM_08 QSIM_16 6000 3000

5000 2500 ] ] s s / /

³ 4000 ³ 2000 m m [ [ 3000 1500

2000 1000

1000 500

0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 pct. [/] pct. [/]

12000 Zambezi at Tete 1800 Shire at Chiromo QOBS_21 QOBS_26 1600 10000 QSIM_21 QSIM_26 1400 8000 1200 ] ]

s s 1000 / / ³ 6000 ³ m m

[ [ 800 4000 600 400 2000 200 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 pct. [/] pct. [/] Figure 86: Simulated (red) and observed (black) monthly flow duration curve at key locations in the Zambezi basin. Period 1961-1990. Observed Shire low flows at Chiromo were caused by blockage of river flows during construction of Kamuzu Barrage in 1965 and other human interventions.

Zambezi Decision Support System – User’s Manual

1600 Zambezi at Lukulu QOBS_03 800 Kafue at Itezhitezhi QOBS_13 1400 QSIM_03 700 QSIM_13 1200 600 1000 500 ] ] s s / /

³ 800 ³ 400 m m [ [ 600 300 400 200 200 100 0 0 1960 1965 1970 1975 1980 1985 1990 1960 1965 1970 1975 1980 1985 1990 year year

2500 Zambezi at Victoria Falls QOBS_08 1200 Luangwa at Gt. East Road QOBS_16 QSIM_08 QSIM_16 1000 2000

800 1500 ] ] s s / /

³ ³ 600 m m [ 1000 [ 400

500 200

0 0 1960 1965 1970 1975 1980 1985 1990 1960 1965 1970 1975 1980 1985 1990 year year

7000 Zambezi at Tete QOBS_21 1000 Shire at Chiromo QOBS_26 QSIM_21 900 QSIM_26 6000 800 5000 700 600 ] 4000 ] s s / /

³ ³ 500 m m [ 3000 [ 400 2000 300 200 1000 100 0 0 1960 1965 1970 1975 1980 1985 1990 1960 1965 1970 1975 1980 1985 1990 year year

Figure 87: Simulated (red) and observed (black) annual discharge at key locations in the Zambezi basin.

Zambezi Decision Support System – User’s Manual

490 Kariba water levels

485 ]

m 480 [ WL sim 475 WL obs

470 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988

330 Cahora Bassa water levels

325 ]

m 320 [ WL sim 315 WL obs 310 01.01.1975 01.01.1979 01.01.1983 01.01.1987 01.01.1991 01.01.1995 01.01.1999 01.01.2003 01.01.2007

480 Lake Malawi water levels WL sim 478 WL obs 476 ] m [ 474

472

470 01.01.1960 01.01.1964 01.01.1968 01.01.1972 01.01.1976 01.01.1980 01.01.1984 01.01.1988

Figure 88: Simulated and observed water levels in Kariba reservoir (top), Cahora Bassa reservoir (middle) and Lake Malawi (bottom). Observed Cahora Bassa water levels from 1981 to 1998 were affected by altered operations because transmission lines from the HPP were destroyed. Observed Lake Malawi water levels represent min/max levels manually digitized from the report of Beilfuss and dos Santos (2001).

Zambezi Decision Support System – User’s Manual

6. References

Allen, R., Pereira, L., Raes, D., 1998. Crop evapotranspiration - Guidelines for computing crop water requirements – FAO irrigation and drainage paper 56. FAO Beck L. 2010. Transboundary water allocation in the Zambezi River basin. Dissertation ETH Zurich, 209 pp. Beilfuss R, dos Santos D. 2001. Patterns of hydrological change in the Zambezi Delta, Mozambique. Working paper #2, Program for the sustainable management of Cahora Bassa Dam and the Lower Zambezi Valley, 159 pp. Duan, Q., Sorooshian, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resour. Res. 28 (4), 1015–1031. Gupta, H.V., Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80– 91. Harrison, G.P., Whittington, H., 2002. Susceptibility of the Batoka Gorge hydroelectric scheme to climate change. Journal of Hydrology 264, 230-241. INGC. 2009. Main report: INGC Climate Change Report: Study on the Impact of Climate Change on Disaster Risk in Mozambique. [Asante, K., Brundrit, G., Epstein, P., Fernandes, A., Marques, M.R., Mavume, A , Metzger, M., Patt, A., Queface, A., Sanchez del Valle, R., Tadross, M., Brito, R. (eds.)]. INGC, Mozambique, 338 pp. Kling, H., Fuchs, M., Paulin, M., 2012. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology 424-425, 264-277. Kling, H., Stanzel, P., Preishuber, M. 2014. Impact modelling of water resources development and climate scenarios on Zambezi River discharge. Journal of Hydrology: Regional Studies (in press) Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models: Part 1 – A discussion of principles. J. Hydrol. 10, 282–290. Petersen, G. 2012. Responding to climate change in Mozambique, Phase II – Component Water. Final report, National Institute of Disaster Management (INGC), Maputo, Mozambique. Piani, C., Haerter, J.O., Coppola, E., 2010. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol. 99, 187–192. Shela ON. 2000. Naturalisation of Lake Malawi levels and Shire river flows. 1st WARFSA/WaterNet Symposium, Maputo 1-2 November 2000, 12 pp Tolson, B.A., Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research 43, 16.

Zambezi Decision Support System – User’s Manual

Winsemius HC, Savenije HHG, Gerrits AMJ, Zapreeva EA, Klees R. 2006. Comparison of two model approaches in the Zambezi River basin with regard to model reliability and identifiability. HESS 10: 339-352 Winsemius HC, Savenije HHG, Bastiaanssen WGM. 2008. Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins? HESS 12: 1403-1413 World Bank. 2010. The Zambezi River Basin – A multi-sector investment opportunity analysis. Volumes 1-4 Yamba FD, Walimwipi H, Jain S, Zhou P, Cuamba B, Mzezewa C. 2011. Climate change/variability implications on hydroelectricity generation in the Zambezi River Basin. Mitig Adapt Strateg Glob Change 16: 617-628

Zambezi Decision Support System – User’s Manual

7. FAQ – Frequently asked questions

Below is a list of frequently asked questions (Q) and answers (A). The questions are grouped into general questions, questions related to editing and map display, and questions related to the Analysis Tool.

7.1. General FAQs

Q: How do I save my scenarios before logout? A: All scenarios are stored automatically. When you log out no data are lost, i.e. all your scenarios will also be available after your next login.

Q: How do I change my password of my DSS user account? A: Click on your user name at the top right corner of the screen. A new window will open. In the “Password” tab you can change your password.

Q: I forgot my password, how do I get a new one? A: Send an e-mail to the DSS system administrator (e-mail is given at login page).

Q: How long am I logged in? A: After three hours of inactivity you are automatically logged out. You have to log in to the Zambezi DSS again to continue with your work.

Zambezi Decision Support System – User’s Manual

7.2. FAQs editing and map display

Q: I click on map elements, but no options for editing properties or viewing simulation results are offered. Where do I find these options? A: Make sure that you have logged in to your account and that you opened the correct module, which is displayed on the left side. For example, if you want to edit computation point properties you have to open the Development Module, whereas if you want to query simulation results from map elements you have to open the Run Module.

Q: In the Climate module, I click on computation points and I am told “No action available”. How can I query climate data? A: Climate data are attached to sub-basins, not computation points. Therefore, you have to click on sub-basins to query climate data.

Q: What is the difference between the scenarios “Historic development” and “Baseline”? A: The development scenario “Baseline” represents current conditions in the Zambezi basins, including all existing reservoirs and diversions. In contrast, under the scenario “Historic development” no diversions are considered (historically these were small and changed over time) and in the simulation, reservoirs only come into effect after they were built. For example Cahora Bassa started operation in 1975, marking an abrupt change in downstream discharge conditions. It is recommended to use the scenario “Baseline” as a reference for comparison with future climate/development scenarios.

Q: I added a new computation point on the river-network, but this has strange effects on the simulation. What is the reason? A: If the new computation point represents an uncontrolled or controlled reservoir make sure that you specified sensible properties, especially for the Rating curve (e.g. for controlled reservoirs ensure that the capacity is greater than zero, otherwise there will be no outflow from the reservoir). Also ensure that you have updated the computation point network. The new computation point has to be connected to the next downstream computation point. Further, any upstream computation points have to be connected to the new computation point. See chapter 4.3.5 for further information.

Q: I made changes to a development scenario. Do these edits affect existing Runs? A: Existing Runs are not affected unless you re-run them (go to Run Module and click on “Start run” button).

Zambezi Decision Support System – User’s Manual

Q: How many computation points can I add? A: The maximum possible number of computation points is 100. If you add more computation points the execution of Run will not succeed.

Q: Can I edit two development scenarios at once? A: No, you should avoid doing this. Even if you have multiple browser windows open, the DSS will solely consider one active development scenario (for your user account). Editing different development scenarios in multiple browser windows at the same time may therefore yield unexpected results.

Q: In the Flood Mapping module it says that I am not allowed to use the Flood Inundation Map. How do I get permission? A: The Flood Mapping feature is only made available to a selected group of users. Contact the DSS system administrator (e-mail available at the login page) for more information.

Q: Why can the flood map extend beyond the overlay “Hydraulic model domain for flood mapping”? A: This can be the case when you have inserted a reservoir. In this case the flood map shows the surface area of the reservoir water body, which may extend beyond the hydraulic model domain.

Zambezi Decision Support System – User’s Manual

7.3. FAQs Analysis Tool

Q: I created a new run, but cannot find the simulation results in the Analysis Tool. Where do I find them? A: Make sure that you have executed the run by clicking on the button “Start run” in the Run Module. Also ensure that the run finished correctly by checking the status bar at the bottom of the screen.

Q: I want to compare two runs in the Analysis Tool but get the message “Series has already been added to chart.” What is the reason? A: The Analysis Tool cannot compare two runs that have the same acronym. Go to the Run Module and click on “Edit properties”. In the “Information” tab you can specify a different acronym. By default, the acronym is identical to the first five letters of the run name.

Q: Can I analyze sub-periods in the Analysis Tool? A: You can zoom in on the time axis in the graph display, but means and duration curve will always be computed from the full simulation period. Edit the simulation period (in the Run Module) and re- start the Run if you want to focus the analysis on a specific time-period.

Q: What is the difference in climate data (precipitation, temperature) between “sub-basin local input” vs. “sub-basin local output”? A: These two types of variables will be identical, unless you use the climate adjustment factors in the run properties. In such a case, the “sub-basin local input” represents the original climate data and “sub-basin local output” represents the adjusted climate data.

Q: Why is precipitation and evaporation given in the units of m³/s and why are they zero for most computation points? A: Precipitation and evaporation are given in the units of m³/s when analyzing computation points, whereas for sub-basins these variables are given in the units of mm. For computation points these variables correspond to fluxes to/from the (changing) surface area of water bodies (reservoirs, lakes, wetlands). Therefore, these fluxes are zero at river points, where no surface area of the water body is considered in the model. See chapter 5.2 for further information.

Zambezi Decision Support System – User’s Manual

Q: What is the difference between the variables “Runoff” and “Runoff-depth”? A: “Runoff-depth” is computed from the basic water balance equation and considers spatial (sub- basin) averages of precipitation, evapotranspiration and storage change (soil moisture, etc.). It does not include impacts along the river network, such as wetlands, reservoirs, and withdrawals. “Runoff” (units of mm) includes all those impacts and is converted from discharge (units of m³/s) by considering the catchment area and length of time-step. See chapter 5.2 for further information.

Q: What is the difference between the variables “Discharge” and “Max_discharge”? A: “Discharge” (units of m³/s) gives the mean flow over a month, whereas “Max_discharge” (units of m³/s) gives the maximum daily discharge of the internal computational time-steps within a month. See chapter 5.3 for further information.

Q: Why is the variable “Runoff” negative in some sub-basins? A: This variable may become negative when analyzing the spatial resolution “Subbasin local output”. It is computed as the difference between outflows and all upstream inflows. Negative values indicate that water losses are greater than local inflows, which may be the case due to withdrawals, evaporation from water bodies and storage in reservoirs/wetlands.

Q: I want to export data with the “Show data table” option, but the table lists some “nodata” values. What is the reason? A: The table includes “nodata” values if you compare simulation results of different Runs with different simulation periods. For example, if your first Run starts in Jan. 1961 and the second Run starts in Jan. 1971, then there will be “nodata” values during the first 10 years of the second Run.