Upgrade of Sudan FEWS
Final Report
Upgrade of Sudan FEWS
Final Report
dr.ir. M.G.F. Werner
1202849-000
© Deltares, 2010
1202849-000-ZWS-0039, 22 December 2010, final
Contents
List of Abbreviations i
1 Introduction 1 1.1 General 1 1.2 Project background 2 1.3 Project objectives 3 1.4 Scope of work 3 1.5 Purpose of the Draft Final Report 4 1.6 Outline of the Final Report 4
2 Operational Flood Forecasting in Sudan 5 2.1 Current status of forecasting 5 2.2 Developments in forecasting in Sudan 5
3 Development of FEWS Sudan 7 3.1 Introduction 7 3.2 Hydrological and meteorological stations 8 3.2.1 Hydrological stations 9 3.2.2 Meteorological stations 9 3.3 Import Data 10 3.3.1 Observed water levels and flows at hydrological stations 11 3.3.2 Observed rainfall at meteorological gauges (point locations) 12 3.3.3 Satellite Rainfall Estimates (gridded data) 12 3.3.4 Numerical Weather Prediction products (gridded data) 16 3.4 Data processing 17 3.4.1 Catchment averaging 18 3.4.2 Data hierarchy for sources of estimates of observed precipitation 19 3.4.3 Data hierarchy for sources of estimates of future precipitation 19 3.4.4 Data validation rules 19 3.5 Flood warning levels 20 3.6 Available rating curves 20 3.6.1 Recommendation of the use of rating curves 21 3.7 Forecasting models: HEC-HMS and HEC-RAS models 21 3.7.1 Rainfall-Runoff model for the Upper Blue Nile 22 3.7.2 Routing model for the Blue Nile 22 3.8 Forecasting models: linear correlation models 23 3.8.1 Implementation in FEWS Sudan 24 3.9 Forecasting models: Linear Perturbation Models 24 3.10 Forecast reports 25 3.11 Archiving 26
4 Forecasting process 29 4.1 Process 1: Data import 29 4.1.1 Step 1: Preparation of import data 30 4.1.2 Step 2: Copy data to import folder 30 4.1.3 Step 3: Running the FEWS import workflow 31 4.1.4 Step 4: Checking the presence of import data 31
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4.2 Process 2: Data processing 31 4.2.1 Step 1: Data import 31 4.2.2 Step 2: Run Data Processing workflow 32 4.2.3 Step 3: Check presence of data in FEWS database 32 4.3 Process 3: Update run 32 4.3.1 Step 1: Running the Update Run workflow 33 4.4 Process 4: Forecast run 33 4.4.1 Forecasting using HEC-HMS and HEC-RAS models 33 4.4.2 Step 1: Update runs 33 4.4.3 Step 2: Run FEWS workflow 33 4.4.4 Step 3: Check presence of forecast in database 34 4.4.5 Step 4: Proceed to Dissemination process 34 4.4.6 Forecasting using Linear correlation models 35 4.5 Process 5: Dissemination 36 4.5.1 Step 1: Run FEWS workflow 37 4.5.2 Step 2: Check if HTML report have been created 37 4.5.3 Step 3: View and/or disseminate HTML reports 37 4.6 Archiving 37 4.6.1 Archiving observations and forecasts 37 4.6.2 Archiving local database and FEWS configuration 37 4.7 Installation and Training Workshop 37
5 Conclusions 41
6 Recommendations on a roadmap for the developments of forecasting capabilities in Sudan 43 6.1 Improvement of availability of observed data 43 6.2 Development of forecasting models 43 6.3 Forecast organisation, team and procedures 44 6.4 Training and capacity building 44
A Data format for importing data to FEWS Sudan A-1 A.1 Introduction A-1 A.2 Format A-1 A.3 Example A-2
B Guide to flow chart symbols B-1
C References C-1
D HEC-HMS model adapter: consistency test D-1 D.1 Comparison of simulation results from HEC-HMS and Delft-FEWS environments D-1 D.1.1 Initial conditions D-1 D.1.2 Forcing data D-1 D.1.3 Model run D-1 D.1.4 Simulation results: HEC-HMS environment D-2 D.1.5 Comparison of simulation results D-3
E Training Workshop Programme E-1
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List of Abbreviations
Symbol Description ENTRO Eastern Nile Technical Regional Office EMA Ethiopian Meteorological Agency FEWS Flood Early Warning System FAO Food and Agriculture Organization of the United Nations FPEW-I Flood Preparedness and Early Warning Project – Phase 1 HEC Hydraulic Engineering Corps. HMS Hydrological Modelling System MoIWR Ministry of Irrigation and Water Resources NWP Numerical Weather Prediction RAS River Analysis System SMA Sudan Meteorological Agency SRE Satellite Rainfall Estimate
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1 Introduction
1.1 General The Eastern Nile Basin, including the rivers of the Blue Nile, the Atbara and the Baro-Akobo- Sobat, constitutes one of the most important sources of water in the Nile basin. The Blue Nile itself contributes to some 60% of the annual average flow in the Nile basin, and this contribution increases to some 90% during the wet season. This clearly shows that the flows in these basins are highly variable, with the seasonal patterns dominated by monsoonal rains over the Ethiopian Highlands. The region is particularly vulnerable to floods and droughts as a result of this variability. The Blue Nile (known as Abbay in Ethiopia) flows through the Nile Gorge in the reach from Lake Tana (considered as the source of the Blue Nile) down to the Ethiopian-Sudanese border. Downstream of the border the river enters the flatter plains in Sudan, where it joins the White Nile at Khartoum, and then flows as the main Nile into Lake Nasser downstream of the town of Dongola. There are several major towns and cities in these reaches, including Wad Medani, Khartoum and Dongola, as well as scores of smaller communities. Several of these communities are prone to flooding, and widespread damage has been caused by large flood events, including those in 1988, 1998, and 2006. Following the floods of 1988, the Sudanese Ministry of Irrigation and Water Resources (MoIWR) in Khartoum initiated the establishing of an operational flood forecasting system to allow flood warnings to be provided with sufficient lead time. This system was developed with technical assistance from the Netherlands, and became operational in 1992 (Grijsen et al., 1992). Known as FEWS Sudan, this system was operated successfully from 1992 to 1995. Following 1995, there were problems with the obtaining of remotely sensed rainfall data. Since then, alternative sources of rainfall data have been found, and the system has been used in several flood seasons since then, though the quality of the forecasts has diminished. Additionally, there were some difficulties in maintaining a suitably trained team to sustain operation of the system, although currently the system when operated is done so by self- trained staff of the Ministry. The system installed in 1992 was based on the original version of Delft FEWS from 1992 (developed by Delft Hydraulics, now Deltares), which is now quite outdated. Since then the Delft FEWS system has advanced considerably and now uses the latest available technology in integrating data and models in a forecasting environment. The Eastern Nile Technical Regional Office (ENTRO), established as a part of the Eastern Nile Subsidiary Action Program (ENSAP), has initiated the Flood Preparedness and Early Warning (FPEW) project in the Eastern Nile Basin. The aim of this project is to improve flood management, and reduce damage and human suffering due to flooding in the basin. Specifically the objectives of the project are to develop a strong regional institutional basis, and further existing capabilities in the Eastern Nile basin. The advancing of forecasting capabilities in the Eastern Nile is an important part of these objectives. Within this context, ENTRO in conjunction with MoIWR, have the objective to rehabilitate FEWS Sudan, and bring this up to date with the modern standards of the current Delft FEWS forecasting platform. The aim of this project, undertaken by Deltares in cooperation with UNESCO-IHE, is to work with ENTRO and the MoIWR in upgrading FEWS Sudan to the current state of the art in operational forecasting. A key approach to the project is to enhance the sustainability of FEWS Sudan through integrating models developed by consultants locally, and through
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ensuring that staff at the MoIWR is sufficiently trained to use the system. Additionally, the system is required to be flexible, so that when new models and forecasting techniques become available these can be integrated in the forecasting system, in part by staff at the MoIWR. This report is the final report to the project. The report details the findings of the design phase of the system as described in the interim report, and elaborates on how these design principles have been implemented. The report briefly described the models currently integrated in FEWS Sudan, as well as recommendations on the extension of the system once new models become available.
1.2 Project background The original FEWS Sudan was developed between 1990 and 1992, and became operational in 1992. FEWS Sudan comprised of a system that contained three main parts (Grijsen et al., 1992): A Primary Data User Station (PDUS) for receiving and processing of METEOSAT thermal infra-red images on a half-hourly basis. These data were used for the estimation of daily rainfall quantities from cold cloud duration over the catchments of the Blue Nile and Atbara River. A communication system for real-time transmission of observed water levels in the Blue Nile, Atbara river, and the Main Nile in Sudan to the NFC at MoIWR. A flood forecasting system, consisting of set of mathematical models, and temporary data base embedded in a tailor made user interface.
Whilst FEWS Sudan was successful in its time, and at that time represented the state-of-the- art forecasting system, it was built using the technology of its day. One of the main disadvantages of the original FEWS Sudan system was that it was built as a tailor made system that was essentially constructed around the models that it used. Three primary models were used. The rainfall run-off model SAMFIL, which is an extension of the well known Sacramento Soil Moisture Accounting Model concept (SAC-SMA) for use in real time. This model included a Kalman filter approach to correct where possible the CCD estimates. Catchment models were available for the Blue Nile upstream of the station at El Deim (Sudan-Ethiopia border), the Dinder and Rahad catchments, as well as the Ethiopian part of the Atbara catchment. Reservoir models for the reservoirs on the Blue Nile (Roseires & Sennar), on the Atbara (Kashm-el-Girba) and on the White Nile (Jebel Aulia). The reservoir models were quite simple, with no embedded control rules. Specified releases for the reservoirs could be set. Hydrodynamic routing model for the Blue Nile, Atbara and main Nile from Khartoum to Dongola. This routing model was developed using NETFIL, which was an extension of the WENDY model – the then hydrodynamic model in use at Delft Hydraulics. As with the rainfall-runoff model, this included a Kalman filter updating routine to improve the simulations using available data. These models have not been updated since their original development and can be considered out of date. An additional problem is that there is little knowledge of these models available, particularly for the WENDY/NETFIL model, which is no longer in use and is not
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supported. The disadvantage of the tightly integrated approach taken in FEWS Sudan, is that when things changed it was difficult for staff at MoIWR to continue using the model. This happened notably when the CCD & CCC data feed from TAMSAT was discontinued. It was difficult to separate this from the model, and another way of inputting rainfall needed to be found. An additional problem with the system was the ability to maintain sufficient knowledge of the system within MoIWR. Staff originally trained moved to other positions, or other jobs. As all models were developed outside of Sudan, combined with the inflexibility of the system, it was found very difficult to maintain a sufficient knowledge base to operate it with sufficient reliability.
1.3 Project objectives The overall objective of this project is to upgrade the forecasting capabilities of the MoIWR to the current state-of-the-art in forecasting. Key in this objective is that this upgrade is done such that the resulting system is flexible to change, allowing the system to be extended with relevant models and data once these become available. Additionally the system should incorporate existing models developed by a team of consultants in the Sudan who have been working on establishing rainfall-runoff models and routing models for the Blue Nile basin. Other available models at MoIWR should potentially be integrated.
1.4 Scope of work In accordance with the tender request, the approach taken by the Consultant to deliver the objectives of this project, the work has been divided into three main tasks: 1. Task I: Development of the upgrade of FEWS Sudan, based on the Delft FEWS software, integration of models and data, and installation at the offices of the National Flood Forecasting Centre (NFC) at MoIWR in Khartoum. This task is sub-divided in the following activities; a. Activity I: Scoping Mission and Design of Upgrade of FEWS Sudan b. Activity II: Installation of FEWS Sudan at MoIWR c. Activity III: Integration of rainfall-runoff and hydraulic models d. Activity IV: Integration of outputs of Numerical Weather Prediction models e. Activity V: Development of reports and outputs 2. Task II: Review of available meteorological data and hydrological data, and integration of the data within the forecasting system. a. Activity VI: Review of available hydrological and meteorological data b. Activity VII: Review of remote sensing data 3. Task III: Training and capacity building for the professionals in the NFC in the use and support of the system. a. Activity VIII: Delivery of training for the use of FEWS Sudan b. Activity IX: Documentation and User Manuals c. Activity X: Reporting
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1.5 Purpose of the Draft Final Report This draft final report (dated December 6, 2010) will be discussed with both ENTRO and MoIWR. Comments on the draft will be incorporated before the final version of the report is issued. The report includes the training material that will be used during the week commencing December 12th in training staff from MoIWR. At the time of writing of the draft final report, these materials were not yet complete. Once complete these materials will be added to this document in final form.
1.6 Outline of the Final Report This introduction is followed by a brief description of flood forecasting in Sudan and the role of the forecasting system FEWS Sudan. In chapter 3 a description of models and data that will be used in the forecasting system is given. Chapter 0 describes the forecasting processes that will be incorporated in FEWS Sudan. The report ends with a number of additional observations and conclusion. Recommendations for the further development of FEWS Sudan are provided in this final section.
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2 Operational Flood Forecasting in Sudan
2.1 Current status of forecasting Operational forecasting in Sudan is the responsibility of the Ministry of Irrigation and Water Resources (MoIWR) in Khartoum. Forecasting is carried out by the Nile Forecast Centre during the rainy season (running roughly from June through September) on a daily basis. The current forecasting procedures include collating and analyzing the observed level and flow data, the running of selected models for (reaches of) the Nile, and disseminating information. One of the key recipients of forecast information is the High Flood Commission (HFC) which gathers at the Ministry during the wet season to discuss the available forecast. The HFC is composed by representatives from MoIWR, members from the Sudan Civil Defense, from the Sudanese Meteorological Agency as well as the operators of the dams in the basin or representatives thereof.
In general the daily forecasting process can be divided into the following steps;
• Observed data is gathered primarily through radio communication and faxes with the various gauges and dam sites in the Blue Nile. These data are received through the hydrometric units at the ministry and keyed in to specially formatted spreadsheets. • Correlation-models are used at develop forecasts from gauged levels at upstream locations. These correlation models generally have a lead time of one day, and one or more inputs. • Additional models may be run for in particular the Blue Nile basin – A HEC RAS model is available for the Blue Nile from El Deim to Khartoum. This models allows the user to input daily flow at El Deim, and provides forecast flow and level data at key locations in the Blue Nile. – The Linear Perturbation Model (LPM) may be run. This provides 5 day lead time forecast at some key locations along the river. – The old version of FEWS Sudan may be run. To run this, Cold Cloud Duration estimates are required as an input as well as levels and flows at various points in the river. CCC and CCD values are obtained from the RFE data which is downloaded from an FTP site that contains these files with daily rainfall values. Observed level & flow data are keyed in to the old FEWS and this is run to provide forecast levels and flows at varying lead times at key river locations. • Observed and forecast information is then presented and discussed during the session of the HFC. Based on the discussion and the available data, an official forecast is established and disseminated.
Currently forecasting in the Sudan concentrates on the Nile and its main triburies/branches (Blue, White and Main Nile and Atbara). There is little or no forecasting on other rivers and/or flash flood forecasting, although such capabilities may be developed in the future.
2.2 Developments in forecasting in Sudan The FEWS Sudan that has been in use at MoIWR since 1992 has become partly obsolete. There is a need to upgrade the forecasting capabilities of the MoIWR to the current state-of- the-art in forecasting. Key in this objective is that this upgrade is done such that the resulting system is flexible to change, allowing the system to be extended with relevant models and data once these become available.
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3 Development of FEWS Sudan
3.1 Introduction This chapter describes the steps taken in establishing the upgrade of FEWS Sudan. The approach taken has been to introduce the Delft FEWS operational forecasting platform. This software has been developed specifically for operational forecasting, allowing integration of several sources of data and the running of models in real time, as well as disseminating results through graphs, reports and tables. A key feature of the system is its flexibility, allowing it to be configured to cater for several different data formats, as well as for running a wide variety of forecasting models, provided an adapter has been developed to run that model from the system. A full description of the system, its features, as well as user manuals can be found on http://public.deltares.nl/display/FEWSDOC/Home.
The Delft FEWS system has been set up to establish the core of the forecasting system to be used in the forecasting process described above, with that process being extended with additional data from new forecasting models (HEC-RAS & HEC-HMS) developed in the basin. It is important to note that although the Delft FEWS system has the capabilities of running as a fully automated client-server forecasting system, it is set up for operational use at MoIWR in Stand-Alone form. This has minimum requirements for hardware and network infrastructure, as well as ICT management. The forecasting system developed, referred to as “FEWS Sudan” – includes the following key components, each of which will be described in detail in the following chapters; • Hydrological and meteorological gauging stations • Time series of observed hydrological variables • Time series of observed meteorological variables, both at meteorological stations and gridded data • Forecasts of precipitation over Upper Blue Nile, Kubor, Setit, Dinder and Rahad basins • Stage-discharge relations • Flood warning levels • Forecasting models • Reporting facilities
Within the scope of this project the forecasting models integrated with the system comprise; • a HEC-HMS model of the Upper Blue Nile catchment (upstream of the border at El Deim); • a HEC-RAS model of the Blue Nile between El Deim and Khartoum; and, • linear correlation models between key gauges in the Blue and Main Nile.
These models have all been developed by a team of consultants in the Sudan. The objective of the ministry is to continue working on both rainfall runoff models and routing models for the Nile, and incorporate these as they become available. This will extend the capabilities of FEWS Sudan to provide forecasts at other forecast locations than currently configured. Note that the LPM models have currently not been configured to be part of FEWS Sudan, but some considerations on the possible integration of this model are provided.
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The elements above have been incorporated into FEWS Sudan to form a clear sequence of logical forecast steps. These steps describe the process to be followed for the daily forecast, starting with the importing of data, processing of data, running models on the historical period to propagate the model states, running forecasts, and finally disseminate results. Additionally, regular maintenance tasks and archived will need to be carried out at regular intervals. Figure 3.1 provides an overview of these key steps. A more elaborate description of the steps is provided in the next chapter. Additionally the Appendices include step-by-step user guides that can be used to guide the forecaster through the process.
Model update Model forecast Dissemination of Data import Data processing run run forecasts
Archiving Figure 3.1 Overview of the forecasting processes
3.2 Hydrological and meteorological stations The FEWS Sudan system has a geographically oriented interface. A key feature of that is the display of locations at which (time series) data is available. Primarily these are observed hydrological and meteorological data, but may also include simulated and forecast results from forecast models employed.
To allow differentiation between the different gauge locations, two groups of stations have been established; x Hydrological stations and x Meteorological stations.
During the operation of FEWS Sudan data is be imported into the system at these locations. The locations that are available in FEWS Sudan, and at which data is expected to be imported are listed in the section below.
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3.2.1 Hydrological stations The following hydrological stations have been included in the system configuration:
3.2.2 Meteorological stations The following meteorological stations have been included in the system configuration:
Please note that meteorological gauges are located in the Upper Blue Nile catchment, within Ethiopia. An official identifier as used by the Ethiopian Meteorological Service for these stations is not available, and for that reason they have been allocated an Id.
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Figure 3.2 Overview of hydrological (yellow triangles) and meteorological (green circles) locations included in FEWS Sudan. Please note that some labels may overlap and are therefore not visible. Also note that background maps may differ from those used in the final configuration
Note that in parallel to this project a consultancy is currently being carried out that will recommend improvements to the network of hydrological and meteorological gauges to support flood forecasting in the basin. While the results of this consultancy are not yet available, it is important to note that once new stations identified have been installed and become operational, that the list of stations available within FEWS Sudan can be easily extended to import data from these.
3.3 Import Data Four types of data are imported in FEWS Sudan for use in the forecasting process: 1. Observed water levels and flow rates at hydrological stations 2. Observed precipitation from meteorological gauges 3. Observed precipitation from remote sensing products 4. Forecasted precipitation from Numerical Weather Prediction (NWP) products
All these data are prepared and obtained outside of the FEWS system, manually copied to a defined FEWS import folder, and then imported into the FEWS Sudan database.
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3.3.1 Observed water levels and flows at hydrological stations Observations from hydrological stations include water level and/or flow measurements. These are read manually, and conveyed to the MoIWR by radio or fax. Readings are typically taken at 06:00am daily (Sudan Standard Time). For selected stations data at two-hourly intervals is also available during the flood season. Figure 3.2 provides and overview of the stations at which FEWS Sudan has been configured to import data, with the parameter available at each station indicated.
Table 3.1 Hydrological measurements to be imported into the FEWS database Id name parameters 800092 Malakal Water level 800221 Aulia U/S Water level 800232 Aulia D/S Flow rate 800251 Mogren Water level 900012 Eddiem Water level (2 hourly during flood season) 900012 Eddiem Flow rate (2 hourly during flood season) 900021 Roseires U/S Water level 900032 Roseires D/S Flow rate 900042 Roseires Water level 900052 Wad Elais Water level 900061 Sennar U/S Water level 900072 Sennar D/S Flow rate 900121 Medani Water level 900131 Kamlin Water level 900152 Khartoum Water level 900162 Gezira MC Water level 900172 Managil MC Water level 901032 Giwasi Water level 902032 Hawata Water level 1000012 Kubur (El Soffi) Water level 1000021 Girba U/S Water level 1000032 Girba D/S Flow rate 1000041 Girba Water level 1000052 Atbara K3 Water level 1000062 Girba MC Water level 1001012 Wad Elheliew Water level 1100012 Tamaniat Water level 1100031 Shendi Water level 1100042 Hassanab Water level 1100051 Atbara Water level 1100092 Dongola Water level 1100121 Merwei Water level 1100131 Debba Water level
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Observed data from these locations are imported into the FEWS database through a Comma Separated Values file, the format of which is described in detail in Appendix A. This file must be prepared outside of the FEWS system and subsequently copied to a specified import directory. Ideally this data file is exported in the correct format from the spreadsheet in which the values received are keyed in.
3.3.2 Observed rainfall at meteorological gauges (point locations) There is currently very limited observed rainfall available. Currently rainfall over the Ethiopian highlands is obtained from the Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/), where the daily report is keyed in to a spreadsheet at the MoIWR. The daily report includes observations from selected meteorological gauges across the Upper Blue Nile catchment, with accumulated precipitation reported over a period of 24 hours. It is assumed that the reporting period is the 24-hour period ending at 06:00am Sudan time. Table 3.2 provides an overview of the rainfall stations considered (these are all in or close to the Blue Nile catchment). It is important to note that the availability of data for these stations is not 100% reliable as it depends on the data being made available on the web site by EMA. As discussed later several sources of observed rainfall are considered, thus providing several backups should these data not be available for reasons outside the control of MoIWR. A more robust communication of rainfall data between EMA and MoIWR is recommended to be established through an agreement between the two organisations, and this manual copying of data from the web-site should be considered a temporary arrangement.
Table 3.2 Meteorological gauging stations for which precipitation measurements will be imported into the FEWS database Id name parameters ETH-M-001 Addis Ababa Precipitation ETH-M-002 Assosa Precipitation ETH-M-003 Axum Precipitation ETH-M-004 Bahir Dar Precipitation ETH-M-005 Combolcha Precipitation ETH-M-006 Gondor Precipitation ETH-M-007 Lalibela Precipitation ETH-M-008 Mekel Precipitation
Rainfall observations from these stations will be imported into the FEWS database via a Comma Separated Values file, which is described in Appendix A. As with the hydrological data, this file will have to be prepared outside of the FEWS system and subsequently copied to a dedicated import folder to be imported into the FEWS database.
3.3.3 Satellite Rainfall Estimates (gridded data) In the original FEWS Sudan, rainfall data over the Ethiopian highlands was obtained primarily through remotely sensed data. The data used was the CCC and CCD images from MeteoSat, which using appropriate regression formulae was used as an estimate of rainfall. This procedure is still currently used for estimating observed rainfall over the catchments. The retrieval mechanism has, however, changed. Currently the process implemented by MoIWR is: • The latest available satellite image is downloaded from the USGS FTP site (ftp://edcftp.cr.usgs.gov/pub/edcuser/fewsips/africa/). • This file is processed using a program obtained by MoIWR in collaboration with FAO Sudan. This processes the satellite images to a CCC and CCD product, and
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subsequently establishes the CCC and CCD values over selected catchments, including the Upper Blue Nile, Rahad, Dinder, Kubur and Setit. • The CCC and CCD values derived per catchment are transformed using a regression equation to derive daily rainfall estimates.
The same approach to deriving daily rainfall estimates is applied in the upgraded FEWS Sudan. The approach has, however, been simplified, where the file that is downloaded through the FTP site can be imported and processed directly with the system itself. Additional to the use of the CCC & CCD-based data for estimating daily rainfall, two sources of Satellite Rainfall Estimates will be incorporated. Three other sources of remote sensing data obtained from the internet were considered to serve as backup data should are described below. With the rainfall estimates originally used, this results in a possible four sources of remotely sensed rainfall in the observed period. 1 Rainfall estimates (RFE) obtained from USGS Famine Early Warning Systems programme1, based on CCC and CCD measurements. These estimates are published as digital Band Interleaved by Line format (.bil) and published on the USGS’s FTP server: ftp://edcftp.cr.usgs.gov/pub/edcuser/fewsips/africa/ , approx. 14 hours after observation. FEWS Sudan has been configured to read the downloaded product without any necessary post-processing outside of the FEWS system, i.e. to directly read the .bil files and apply the algorithm necessary to convert values contained therein to precipitation estimates. This means that the files need only be retrieved from the FTP site and copied to the appropriate import directory. 2 Rainfall Estimates from the Tropical Rainfall Measurement Mission (TRMM2). These are global estimates of rainfall, with accumulations provided at 3 hourly time steps. The resolution of the data is 0.25 degrees. The near real time product is under normal operating conditions made available for download between 7-10 hours after observation. This product can be downloaded from ftp://trmmopen.gsfc.nasa.gov/pub/merged/mergeIRMicro/. FEWS Sudan has been configured to read the downloaded product without any necessary post-processing outside of the FEWS system. Prior to import these data are transformed to a standard NetCDF-CF formatted file, which is then imported into the FEWS database. 3 Rainfall estimates from the NOAA CPC Morphing Technique (CMORPH3). These are again global estimates of rainfall, with accumulations provided at 3 hourly time steps. As with TRMM the resolution of the data considered here is 0.25 degrees (the original CMORPH data has a higher resolution). This product is available in near-real time, with zipped files published to an FTP site about 18 hours after the last observation. This product can be downloaded from ftp://ftp.cpc.ncep.noaa.gov/precip/global_CMORPH/3- hourly_025deg/. FEWS Sudan has been configured to read the downloaded product without any necessary post-processing outside of the FEWS system. Prior to import these data are transformed to a standard NetCDF-CF formatted file, which is then imported into the FEWS database 4 Rainfall Estimates from NOAA NESDIS’s HydroEstimator4. These estimates are supplied in an ASCII format. This is again processed to a standard NetCDF-CF formatted file and imported see. The HydroEstimator data contains an integer value which can be transformed to a rainfall accumulation. Data is available at hourly
1 http://earlywarning.usgs.gov/adds/readme.php?symbol=rf 2 http://trmm.gsfc.nasa.gov/ 3.http://www.cpc.noaa.gov/products/janowiak/cmorph_description.html 4 http://www.star.nesdis.noaa.gov/smcd/emb/ff/HydroEst.php
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resolution at a spatial scale of 0.045o (which is about 4.5 km). The integer values in the original data are transformed into precipitation estimates using a simple equation: R = (value – 2) * .30. HydroEstimator grids contain precipitation estimates accumulated over a one-hour period; this will have to be accumulated to a 24h period. Again these post- processing steps have been configured in FEWS Sudan. This means that the files need only be retrieved from the FTP site and copied to the appropriate import directory.
Figure 3.3 Example of the NESDIS hydro-estimator SRE over Eastern Africa and the Indian sub-continent. Data shown is the image for 24 hour rainfall ending 12:00 UTC on August 20th 2010
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Figure 3.4 Satellite Rainfall Estimates from the four different remotely sensed sources, processed as catchment average precipitation over the Upper Blue Nile Catchment. Note that all data are accumulations over the time step, with the time step of RFE being 24hrs, CMORPH & TRMM being 3 hrs and HydroEstimator being 1 hour.
As discussed in the interim report three sources of satellite rainfall estimates will be considered, being RFE (in continuation of its current usage), TRMM RT and either CMORPH or HydroEstimator.
A comparison of the catchment average rainfall for the months of June, July August (JJA) of 2010 for the upper Blue Nile as derived from the satellite rainfall provided estimates is given in Table 3.3. This shows quite a large variation between the different sources. The table also contains a column of mean rainfall over the catchment based on 32 gauges using data from 1960-2002 (Abtew, 2008). The column shows the climatological mean, as well as the standard deviation (in brackets). Generally it can be seen that the TRMM rainfall is very low. CMORPH provides a better estimate, while HydroEstimator is too high, except for June when the estimate appeared very low. RFE data was only available for July, and this gave an estimate only one standard deviation below the climatological mean. Unfortunately no comparison could be made against observed data over the Ethiopian highlands as this was not available for the period for which RFE data was available.
The data from HydroEstimator was found to be missing in some cases (FTP files unavailable), with the percentage missing for the other sources being much lower. Also the files from HydroEstimator are quite a bit larger (24MB per day). As processing of the HydroEstimator data was found to be slow, combined with unclear improvement over the lower resolution data, it was concluded that HydroEstimator would not be considered further, and the only sources used in FEWS Sudan would be RFE, TRMM and CMORPH. It is recommended, however, to perform a more complete validation of these sources. Biases identified in this validation could then be used to correct the data.
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Table 3.3 Catchment Average Precipitation for the Upper Blue Nile as derived from the different Satellite Rainfall Estimates Climatological Mean RFE TRMM CMORPH HydroEstimator (Abtew, 2002) June 2010 total 197.2 (28.6) - 93.7 181.0 131.8 (mm) July 2010 total 331.9 (42.0) 292.7 193.8 208.8 412.8 (mm) August 2010 total 320.2 (42.7) - 200.9 272.2 516.1 (mm) Missing data 3.2 1.2 1.1 14.9 (%)
To reduce the size of the database, the imported data from both the TRMM and CMORHP have been constrained to cover only the Sudan and Ethiopia. This greatly reduces the amount of data considered;
For both TRMM, CMORPH the domain imported is defined as: • Grid resolution 0.25o • Number of rows: 108 • Number of Columns: 108 • Geographical coordinate top left cell centre: [21.875, 24.978]
For HydroEstimator (not used in real time) the domain imported is defined as: • Grid resolution 0.045o • Number of rows: 600 • Number of Columns: 600 • Geographical coordinate top left cell centre: [21.965, 23.0229]
3.3.4 Numerical Weather Prediction products (gridded data) Forecast rainfall over the Upper Blue Nile, as well as over catchments in Sudan can be obtained from operational Numerical Weather Prediction Models. Currently a regional numerical weather prediction model, ETA is operated on a daily basis by the MoIWR. This model is also operated at the Sudan Meteorological Agency (SMA). The latter also operates an MM5 model of the region.
Results from both the ETA models have been configured to be imported into FEWS Sudan. Ideally the configurations of the two models should be the same, but in practice it was found that there were some minor differences. The details of the model domain for the ETA model operated at MoIWR are: • Grid resolution 0.33o • Number of rows: 67 • Number of Columns: 106 • Geographical coordinate top left cell centre: [15.0, 24.978]
The details of the model domain for the ETA model operated at SMA are: • Grid resolution 0.33o • Number of rows: 118 • Number of Columns: 169 • Geographical coordinate top left cell centre: [4.0, 33.96]
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Please note that current setup of the ETA forecasts produced by SMA are not suitable for use in real-time forecasting, due to the fact that values are not accumulated over the forecast period, but re-set every day at midnight.
Following import of the GRIB formatted results from these NWP models into FEWS Sudan, these data are available for visualization (see Figure 4.3). Additionally, a shape file corresponding to the Upper Blue Nile Catchment is used to sample forecast rainfall data for input into the HEC-HMS model in the forecast period. This process is described in the data processing section below.
Note that to be used in FEWS, the outputs of the ETA model should be made available as GRIB files at 6-hourly time steps for the domain indicated above, with rainfall accumulations over the full forecast period. Deviations from this will require reconfiguration of FEWS Sudan to successfully import and display the data.
Additional to integration of the data from the ETA model that is available at MoIWR and at SMA, the WRF and MM5 models are available at the Sudan Meteorological Agency. The original objective was to include both these models to provide rainfall inputs. However, WRF was found not to be operational, and due to operational constraints data from MM5 could not be made available.
Figure 3.5 Example time slice for 12-08-2010 18:00 UTC of the output of the ETA model as displayed in Delft FEWS, showing heavy rainfall forecast over South-Western Sudan, as well as scattered over the Ethiopian Highlands. The forecast base time is 11-08-2010 00:00 UTC
3.4 Data processing Following import of data from the various external sources as described above, the data is processed prior to using these as an input in the hydrological and hydraulic models. The main processing steps are:: 1. Catchment averaging of precipitation estimates from point locations and grids; 2. Applying a hierarchy in meteorological observations to be used as an input to the model; 3. Applying some simple data validation rules.
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3.4.1 Catchment averaging Observations from meteorological gauges in or near the Upper Blue Nile basin are averaged (“simple average”, i.e. using equal weights) to reflect precipitation on the Blue Nile catchment upstream from Eddiem. The meteorological stations listed in Table 3.4 are considered when establishing this average are the same stations that were used in defining the rainfall data during calibration of the hydrological model. Each of the stations is weighted equally in calculating the average. Should data not be available at one or more stations, then the remaining stations are used, although if there are less than four of the seven available then the result is considered unreliable and set to missing.
Table 3.4 List of meteorological stations from EMA used in calculating the catchment average for the Upper Blue Nile id name ETH-M-001 Addis Ababa ETH-M-002 Assosa ETH-M-003 Axum ETH-M-004 Bahir Dar ETH-M-005 Combolcha ETH-M-006 Gondor ETH-M-007 Lalibela ETH-M-008 Mekel
Estimates of catchment precipitation from gridded products, including Satellite Rainfall Estimates and the ETA forecasts are derived by sampling the grid cells within each of the defined catchments. The catchments considered include the Upper Blue Nile, Dinder, Rahad, Kubur and Setit catchments. A shape files is used to define the catchment outline and which grid cells to sample. These shape files are shown in Figure 3.6.
Figure 3.6 Shape files for Blue Nile, Kubor, Setit, Dinder and Rahad catchments
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3.4.2 Data hierarchy for sources of estimates of observed precipitation Following the sampling of the grids and averaging observed rainfall from the gauges, four sources of rainfall data for the historical period are available. A hierarchy has been be defined for the use of these data as an input to the hydrological runoff model over Ethiopia. Through this hierarchy, a merged rainfall product is created from the list below. The source with the highest rank will be used in preference to a source with a lower rank. At least one of the sources should be available prior to making a model run. 1. Averaged rainfall data derived from observed rain gauges in Ethiopia 2. Satellite Rainfall estimates derived using TRMM data 3. Satellite Rainfall estimates derived using USGS FEWS-net data 4. Satellite Rainfall estimates derived using CMORPH.
3.4.3 Data hierarchy for sources of estimates of future precipitation In the forecasting process, only one source of estimates of future precipitation is used as an input to the model. Again a hierarchy has been defined, where the source with the highest rank is used in preference to a source with a lower rank. At least one of the sources should be available when a forecast run is prepared.
Rank Product Produced by 1. ETA MoIWR 2. ETA SMA 3. MM5 SMA 4. WRF SMA
Note that sample MM5 and WRF have not been received by the Consultant and could therefore not be included in the FEWS Sudan configuration.
3.4.4 Data validation rules Some simple validation rules are applied to imported hydrological and meteorological data to avoid spurious data being used in the model forecasts.
Observed precipitation over a 24 hour period is checked that it consists of non-negative values: P 0
Observed water levels will have to fall within a predefined range: Hmin H Hmax (if available; see table below) Observed streamflow rates will have to consist of non-negative values: Q 0
Table 3.5 Acceptable water level ranges for selected stations H Min H Max Name [mAD] [mAD] Dongola 8.8 16.93 Khartoum 9.72 18.14 Malakal 10.16 15.39 Hasanab 10.64 18.55 Hawata 0 8.92 Giwisi 0 15.5 Tamaniat 9.81 18.26 Atbara 9.77 17.64 Deim 6.01 15.03 Kubur 4.4 16.32
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H Min H Max Name [mAD] [mAD] Heliew 3.75 16.98 Mogren 9.96 18.15 Atbara K3 0 16.7 Shendi 9.98 19.01 Merowe 6.03 21.97 Abuhamad 9.88 15.52 Medani 8.8 22.00 Kamlin 8 20.06
Table 3.5 provides the detail of the limits that will be applied to the different gauges. Data that does not conform to the requirements will be flagged as “unreliable” and will not be used in the forecast process.
3.5 Flood warning levels Flood warning levels have been included at key forecast locations to allow for signalling of future or observed threshold exceedence to the forecast operators.
The following warning levels have been included:
Table 3.6 Flood warning levels to be included in FEWS Sudan locationID Name Alert Critical Flooding 900012 Eddiem 10.8 11.8 12.30 900152 Khartoum 15 16 16.5 1100092 Dongola 13.47 14.72 15.22 1000052 Atbara 14.18 15.18 15.78 1100031 Shendi 16.10 17.10 17.60
3.6 Available rating curves For selected locations, rating curves are available. While these are not currently used in the forecast process, FEWS Sudan has been configured to contain a number of rating curves for possible future use.
Table 3.7 Stage-discharge relations to be included in FEWS Sudan, with H in mALD and Q in Mm3/day locationID Name Q-h-relation 2.34 900012 Eddiem QH 6.41952 ( -5.3) 2.496 900152 Khartoum Q = 4.956.(H- 8.999) 5.546 901032 Al Gowis QH 0.0000275 ( -0.0001) 2.62546 902032 Al Hawata QH 0.075839 ( - 0.001) 0.743 800092 Malakal QH 47.946 ( -9.6) 2 1000012 Kubur Q = 3.3237.(H+1.07) -46.943.(H+1.07)+172.66 = 3.33H2 – 39.83H + 126.24 2 1001012 Wad Elheliew Q = 18.105.(H) -395.41.H +2162.4 2.5228 1100092 Dongola QH 2.7915 ( -6.1)
Please note that the rating curve for Kubur has been rewritten as aH2+bH+c; this was necessary to include this rating in the FEWS configuration.
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3.6.1 Recommendation of the use of rating curves Current practice at MoIWR includes using rating curves that are defined as the relation between (i) water levels in metres above local datum [m] and (ii) flow rates in millions of cubic metres per day [Mm3/d]. For use in FEWS, it would be recommended to define rating curves that translate water levels into flow expressed in cubic metres per second [m3/s] and vice versa. There are two reasons for this: x Water level readings are instantaneous values; expressing flow in volume / DAY implies that these flows remain unchanged throughout the day and that the Mm3 in Mm3/d indeed passes that location on that day. This is not necessarily true. x FEWS assumes rating curves to have m3/s as unit; current use of rating curves that express flow in Mm3/d requires an extra step in the “level to flow” routine, namely a unit conversion. If this step can be omitted, the system configuration would be more robust to errors.
The above does NOT mean that flows cannot be presented in Mm3/d. FEWS Sudan is configured to do this automatically, even though its computational core uses SI units only (i.e. m3/s).
3.7 Forecasting models: HEC-HMS and HEC-RAS models
The two primary models used in FEWS Sudan to provide forecast flows and levels in the (Blue) Nile are the HEC-RAS river routing model and the HEC-HMS rainfall runoff model. These are used in the system both in historical mode using observed precipitation (to update internal states) and in forecasting mode using forecast precipitation.
Figure 3.7 shows a schematic approach to the integration of the HEC-RAS and HEC-HMS models within the upgraded FEWS Sudan. This approach is the standard model integration approach used in Delft FEWS. In this case the pre-adapter and post-adapter have been developed specifically by the US Army Corp of Engineers facility in Davis, California for integration with Delft FEWS. This approach has been taken with several other model developers (for a list of models integrated see http://public.deltares.nl/display/FEWSDOC/Models+linked+to+Delft-Fews). This allows ENTRO as well as the Ministry of Irrigation and Water Resources to extend the system with other modelling systems once these become available.
Figure 3.7 Approach to integration of models with Delft FEWS
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3.7.1 Rainfall-Runoff model for the Upper Blue Nile This model has been developed as a HEC-HMS model, with the model set-up and calibration done by the UNESCO Chair in Water Resources in Khartoum, Sudan. This model has been developed to simulate the flow at El Diem, the first station downstream of the Ethiopian- Sudanese border. The model was made available to the Consultant during the Scoping mission.
Some key model characteristics: x The Upper Blue Nile catchment is modelled as a single lumped catchment x Model calibrated using 7 years of rainfall data (for inclusion in FEWS, the first 2 years have been used for determination of a state that may be used as a “cold state”) x Model time step is one day x Single rainfall value entered at each time step. For evaporation average monthly values have been defined in the model. Daily rainfall values were derived by taking a simple average of seven rainfall gauges in or near the catchment (in Ethiopia)
The rainfall runoff model is integrated with FEWS Sudan in its current form, with one minor amendment. The original version which was obtained during the Inception Mission was found to use the Clark Unit Hydrograph method to shape the runoff response of the catchment. When using this feature, HEC-HMS cannot properly restart from a previously saved state, resulting in erratic behaviour. To correct this, the model was amended to use a User Defined Unit Hydrograph, with the ordinates defined based on the ordinates of the Clark Unit Hydrograph in the original model. It is recommended for future model development to ensure that the User Defined hydrograph is applied, rather than the Clark Unit Hydrograph.
Consistency of model runs in the original HEC-HMS environment and the Sudan FEWS environment has been checked; this is described in the Appendices to this report.
3.7.2 Routing model for the Blue Nile This model has also been developed by the UNESCO Chair in Water Resources in Khartoum, Sudan. The model has been developed in HEC-RAS and covers the reach of the Blue Nile between El Diem and Khartoum. The model was made available to the Consultant during the Scoping mission; this model will be integrated with FEWS Sudan in its current form.
In its current form the model is acknowledged to be simple and to not properly reflect all process in the Blue Nile:
x Most notably the dams at Roseires and Sennar have not been included in the model.
x Another issue is the downstream boundary at Khartoum, which is too close to the forecast point at Khartoum for model results to be used in forecasting. For that reason, forecasted water levels at Khartoum will be established using a linear correlation with forecasted water levels at Medani:
HKhartoum, t+n'' t = a × H Medani, t+n t + b + H t where the coefficients a and b and the error
term İt are determined as described in Table 3.9.
x Additionally tributary inflows such as the Rahad and Dinder have not been included in the model, but will be approximated by linear correlations (Delft Hydraulics, 1992): 3 - For the Dinder river at Gwasi: Qt = .868 × Qt-1 + .0088 × Q t,Eddiem - 1.2 m /s
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3 - For the Rahad river at Hawata: Qt = .831 × Qt-1 + .0048 × Q t,Eddiem - 0.8 m /s
Model results are extracted at the following gauged locations:
Table 3.8 Links between gauging stations and HEC-RAS node IDs
Station Station Id HEC-RAS node ID Variables
Eddiem 900012 BLUE NILE EDDEIM-KARTOUM/723.1032 Stage & Flow Roseires D/S 900032 BLUE NILE EDDEIM-KARTOUM/665.7046 Stage & Flow Roseires** 900042 BLUE NILE EDDEIM-KARTOUM/662.4397 Stage & Flow Wad Elais 900052 BLUE NILE EDDEIM-KARTOUM/30.5385 Stage & Flow Sennar D/S** 900072 BLUE NILE EDDEIM-KARTOUM/283.0520 Stage & Flow Medani 900121 BLUE NILE EDDEIM-KARTOUM/203.4645 Stage & Flow Kamlin** 900131 BLUE NILE EDDEIM-KARTOUM/118.3658 Stage & Flow Soba** 900142 BLUE NILE EDDEIM-KARTOUM/43.4172 Stage & Flow Khartoum 900152 BLUE NILE EDDEIM-KARTOUM/30.5385 Flow ** The HEC-RAS label for these locations has been estimated. These can be corrected pending receipt of correct information.
In addition, model results may be extracted at ungauged forecasting locations, provided that these locations are properly described (by a locationID, station name and lat/lon location) and that these locations can be linked to the HEC-RAS nodes.
Sudan FEWS is configured as to show a longitudinal profile of water levels at the Blue Nile between Ed Diem and Khartoum, based on the HEC-RAS model outputs.
3.8 Forecasting models: linear correlation models The current forecast procedures at the MoIWR are largely based on application of correlation models. These provide a relationship between upstream and downstream stations. A lag time of one day is applied (the data used is at the daily time step).
Hdownstream aHb upstream H The general form of the correlations is: tttt' , where
Hdownstream Estimated water level at downstream station at time t+ǻt tt' H upstream Observed water level at upstream station at time t t Correlation coefficients ab, H Error of the correlation applied at time t. t
The following correlations have been implemented for the following sets of upstream and downstream stations:
Table 3.9 Correlations to be included in FEWS Sudan Downstream ID and Name Lag ǻt Upstream ID and Name a b (Days) 900072 D/S Sennar 900121 Medani 1.0112 -397.3 1
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Downstream ID and Name Lag ǻt Upstream ID and Name a b (Days) 900072 D/S Sennar 900131 Kamlin 0.9156 -358.96 1 900121 Medani 900152 Khartoum 0.83 0.66 1 900152 Khartoum 1100031 Shendi 1.255 -3.22 1 1100031 Shendi 1100051 Atbara 0.504 6.7416 1 1100121 Merowe 1100131 Debba 1.3329 -10.259 1 1100131 Debba 1100092 Dongola 0.8508 5.158 1
As the coefficients of the equations are often changed, the approach to changing these will be highlighted during the training so that staff at MoIWR can update these when required.
3.8.1 Implementation in FEWS Sudan In the FEWS configuration, application of the Linear Correlation Models is broken down in three steps:
ˆ downstream* upstream 1 Apply Linear Correlation Model: Httt' aHb
ˆdownstream* downstream 2 Determine error of yesterday's LCM forecast: Httt HH (if no error is available, assume error = 0)
ˆˆdownstream downstream* 3 Add error to Linear Correlation: HHttttt' ' H
3.9 Forecasting models: Linear Perturbation Models Additional to the correlation models and the HEC-HMS and HEC-RAS models, a Linear Perturbation Model (LPM) is used by MoIWR for developing forecast at selected forecast locations. The principle of the LPM approach is that if the input (rainfall) on any day of the year is the climatological mean, then the response in terms of flow at a downstream station should equally be the climatological mean. A perturbation, or departure of the input from the mean is then assumed to be linearly related to the perturbation of the response.
The LPM model has been calibrated at MoIWR for several forecast locations, including; • El Deim to Khartoum; • El Deim and Jebel Aulia to Tamaniat
Currently the LPM model is operated from a specially developed user interface. The lead time provided by the model is generally in the order of 5 days.
Despite this, the status of the model is unclear. During the scoping mission the model was not considered to be used, and this model was also not considered in the ToR for developing the upgrade of FEWS Sudan. As a consequence the model has not been integrated in Delft FEWS within the scope of this study.
However, the structure of the model has been investigated. It would appear that in the way it is currently used the role the LPM model has in the operational forecast process at MoIWR is unclear. In part this could be due to the complexity of the interface through which it is run, and choices that need to be made within. Little information is available as to which forecast from the LPM model are used in the operational forecast process.
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It is recommended that if this model is to be sustained, then it should be integrated with the Delft FEWS user interface that forms FEWS Sudan. In this way the model can be run in tandem with the HEC-RAS & HEC-HMS model chain, and the results compared directly and disseminated as appropriate. Currently the LPM models are run as a stand-alone executable. To be able to run from Delft FEWS, a simple adapter will need to be developed to convert the FEWS formatted input data to a format suitable for the LPM model, run the model, and subsequently read the model results to the FEWS database. This is the same procedure as is used for HEC RAS and HEC HMS, as well as other models used from Delft FEWS.
In summary, to integrate the LPM models within FEWS Sudan the following steps are recommended to be taken; • Establish exactly which LPM models are used in support of the operational forecast at MoIWR. This identification should include the points to which the forecast are made, and the support points used to make these forecasts. • Establish how the results from the LPM model are used in the operational forecast process by the forecasters at MoIWR, and how results should be disseminated to the High Flood Commission or other users. • Develop a model adapter to run the LPM models within a workflow from the Delft FEWS user interface. • Configure the LPM models to run within FEWS Sudan using the adapter developed to provide forecasts at the required locations.
3.10 Forecast reports Forecasts produced within FEWS Sudan are reported in specially designed HTML reports, which may be disseminated externally of the FEWS system. The following three types of reports are produced:
1. Precipitation report for Upper Blue Nile (in Ethiopia), Dinder, Rahad, Kubur and Setit basins. This report contains a table showing both past and forecast precipitation, each accumulated over 24h periods. Please note that future precipitation estimates can only be shown as far into the future as the ETA, MM5 and/or WRF forecasting models show. Table 3.10 Outline of table showing estimates of past and future precipitation over selected basins Day X-7 … Day X-1 Day X Day X+1 … Day X+i (yesterday) (today) (tomorrow) Blue Nile … Dinder Rahad Kubur Setit
2. A report showing graphs with water levels for selected years during the June – September flood season for selected stations (Table 3.11). These figures shall also show flooding thresholds (subject to availability, see Section 3.5). Years to be shown are the current calendar year, the past two calendar years and the year 1988 (subject to availability). These graphs will be similar to that shown in Figure 3.8.
Table 3.11 Stations for which flood season water levels will be shown in graphs locationID Name 900012 Eddeim 900152 Khartoum
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800251 Mogren 1100031 Shendi 1000052 Atbara 1100092 Dongola 800092 Malakal 1000012 Kubur 1001012 Wad Elheilaw
Blue Nile at Khartoum Station 2010
17.5 380.5
17.0 Flooding (16.5) 380.0 16.5 Critical (16) 379.5 16.0 379.0 15.5 Alert (15 ) 378.5
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9.0 373.0 l l l l l l l l l l l g g g g g g g g g g n n n n n n n n n n p p p p p p p p p p u u u u u u u u u u u e e e e e e e e e e u u u u u u u u u u u u u u u u u u u u J J J J J J J J J J J S S S S S S S S S S J J J J J J J J J J ------A A A A A A A A A A ------1 4 7 0 3 6 9 2 5 8 1 ------2 5 8 1 4 7 0 3 6 9 1 4 7 0 3 6 9 2 5 8 3 6 9 2 5 8 1 4 7 0 0 0 0 1 1 1 1 2 2 2 3 0 0 0 1 1 1 2 2 2 2 0 0 0 1 1 1 1 2 2 2 0 0 0 1 1 1 2 2 2 3 Time (days) 2009 2008 2010 2006 mean Figure 3.8 Figure showing flood season water levels, in this case for Khartoum gauging station. Note that, contrary to this figure, the reports in FEWS Sudan will display only the values with respect to the global datum
3. Forecast reports, showing forecasts for selected stations. For these stations, the water levels, rate of rise and discharges are shown, where forecast data is displayed only to the maximum lead times as indicated in Table 4.1. Information shown in the graphs will also be summarised in a table. In addition, this report will indicate available flooding thresholds (subject to availability, see Section 3.5).
3.11 Archiving FEWS Sudan includes basic functions for archiving data: 1. The CSV files that are used to import data into the FEWS database are copied to an “import backup” folder on the workstation that is used to run FEWS Sudan. 2. Forecasts that are produced (Table 4.1) are saved as CSV files in a “forecast backup” folder on the workstation that is used to run FEWS Sudan. 3. Forecasts that are produced (Table 4.1) are stored within the FEWS database for a period of six calendar months. 4. Observed water levels, flow rates at hydrological gauging stations as well as catchment-averaged precipitation will be stored into the FEWS database for an indefinite period.
A manual procedure for backing up the FEWS database will be elaborated on during the training session.
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4 Forecasting process
The approach taken in the setup of FEWS Sudan is that the forecast process is a clear step- by-step process. These steps are carried out consecutively for each daily forecast.
The steps are in brief; 1 Importing of data from external sources 2 Processing of data prior to use in the models 3 Running the Update run, with the objective to update the model states using the most recent observed data, 4 Running the Forecast run using both the HEC models and the correlation models 5 Disseminating forecast results.
These steps are depicted in Figure 4.1. Note that the “forecast run” process has been split into separate processes for (a) using Correlation Models and (b) using HEC-HMS and HEC- RAS models.
4a. Correlation forecast run
4b. HEC- 5. Forecast 1. Data 2. Data 3. Model models dissemi- import processing update run forecast run nation
6. Archiving Figure 4.1 Overall steps in the forecasting process
Each of these steps is detailed in the sections below. This includes both the procedure and the FEWS “workflows”, showing how these steps are configured within the system.
4.1 Process 1: Data import In the Data Import process, observations and forecasts – both from point locations as well as gridded data – are imported into the FEWS database.
The process shows that following import of the data, a check will need to be made by the forecaster if the data is available. Should this not be the case, then missing data should be sought and made available in the import directories in the correct format. The import process can then be re-run. Import data should be as complete as possible before proceeding with the forecast process.
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Prepare import data
Copy data to import folder
Import Import Import USGS Import hydrological meteoro- Rainfall TRMM precip observations logical obs Estimates Estimates
Import Import ETA Import MM5 Import WRF CMORPH rainfall rainfall rainfall no estimates forecasts forecasts forecasts
Imported data
Check presence of data
Data present?
yes
2. Data processing
Figure 4.2 Visual representation of the Data Import process
4.1.1 Step 1: Preparation of import data Data is collected outside of the FEWS system. Observations from hydrological and meteorological stations are converted into Comma Separated Value files, in the format prescribed in the appendix to this report. Gridded data does not need any pre-processing.
4.1.2 Step 2: Copy data to import folder Data should be copied to a specified folder on the computer on which the FEWS software is installed.
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4.1.3 Step 3: Running the FEWS import workflow Within the FEWS software, a “workflow” has been set up that imports all data files (CSV files, grids) to the FEWS data store. The FEWS import workflow includes a number of sub- processes – “modules” - each dedicated to importing a single source/type of data.
4.1.4 Step 4: Checking the presence of import data FEWS allows for a manual, visual check of data. This check will have to be performed after data has been imported. If the data has not been imported, the data import process will have to be repeated.
4.2 Process 2: Data processing Data processing consists of two ‘types’: (1) merging catchment averaged precipitation data and (2) data validation. Within Delft-FEWS, data validation is embedded in import flows. As such, it is a step that is implicitly taken in the data import module that is described in the previous section.
The present section focuses on the merging of catchment averaged precipitation data.
Figure 4.3 Visual representation of the Data Processing procedure
4.2.1 Step 1: Data import This process builds on the imported data from the Data Import process. Recall that, in that process, at least one source of precipitation estimates is imported. Data being imported is a prerequisite to the next steps being carried out.
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4.2.2 Step 2: Run Data Processing workflow In this workflow, all possible sources of precipitation data are transformed into single estimates of precipitation in Blue Nile, Kubor, Setit, Dinder and Rahad basins. For a given basin, this may result in more than one available estimate. Based on a pre-defined hierarchy, the estimate stemming from the highest ranking source will be used. This results in a single value of accumulated precipitation over a period of 24 hours for each of these 5 basins. These values will be stored in the FEWS database.
4.2.3 Step 3: Check presence of data in FEWS database FEWS allows for a manual, visual check of data. This check is to be performed after data has been imported. If the data has not been imported, the data import process will have to be repeated. If data is present, the forecaster may proceed to the next process.
4.3 Process 3: Update run Historical runs – or update runs – are necessary to update internal states of both HEC-HMS rainfall-runoff models and HEC-RAS hydrodynamic models. These internal states will subsequently be used as initial conditions for a forecast run.
2. Data processing
Export Execute Import HEC- states and HEC-HMS HMS model observations update run states
ModuleInstance: HEC-HMS update
Export Execute Import HEC- states and HEC-RAS RAS model observations update run states
ModuleInstance: HEC-RAS update
Workflow: update run
Updated model states
Check presence of updated states no
States present?
yes
4. Model forecast run
Figure 4.4 Visual representation of Update Run process
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4.3.1 Step 1: Running the Update Run workflow The Update Run process can be started after the Data Processing process has finished. The user will run a single workflow in which two modules are executed: a module for updating the HEC-HMS rainfall-runoff model and a module for updating the HEC-RAS routing model. Both modules are similar in structure:
1 The latest available model states and observations are exported from the FEWS database to a folder location from where it can be used by the HEC models. For the HMS model, ‘observations’ pertain to observed precipitation in the Upper Blue Nile basin; for the RAS model, ‘observations’ include simulated and error-corrected water levels and flows at Eddiem. 2. The models are run in historical (“update”) mode. Using observations, the rainfall-runoff and routing models are used to simulate past catchment runoff and routing. The main goal of the historical run is to make sure that the model internal states are updated to reflect antecedent conditions as well as possible. 3. Updated model states are picked up from the folder location where they are saved by the HEC models and imported into the FEWS database.
4.4 Process 4: Forecast run Two forecasting runs will be enabled: 1. Forecast using the chain of HEC-HMS and HEC-RAS models; 2. Forecast using linear correlations.
4.4.1 Forecasting using HEC-HMS and HEC-RAS models Forecasts shall be reported for the following Blue Nile locations and variates, with following lead-times:
Table 4.1 Forecasting locations and variates that will be included in FEWS Sudan locationID Forecasting location Lead time [days] Variate Models used 900012 Eddeim 1, 2, 3 Flow rate HEC-HMS 900042 Old Roseires 1, 2, 3, 4 Water level HEC-HMS, HEC-RAS 900052 Wad el Ais 1, 2, 3, 4 Water level HEC-HMS, HEC-RAS 900061 Sennar u/s 1, 2, 3, 4 Water level HEC-HMS, HEC-RAS 900121 Wad Medani 1, 2, 3, 4 Water level HEC-HMS, HEC-RAS
These lead-times may be extended by using precipitation forecasts, up to the lead-time afforded by the product that is used with time steps of one day.
The forecasting process is visually explained in Figure 4.5. When using the HEC models, the forecast run has to be preceded by an update run. This run will make sure that the internal model states (in the HEC models) are as representative of reality as possible.
4.4.2 Step 1: Update runs The present Forecast Run builds on the Update Run process, which has to be executed prior to the forecast runs.
4.4.3 Step 2: Run FEWS workflow If all preceding steps have been taken, the FEWS database contains the most recent observations; most recently produced precipitation forecasts and updated model states. Using this data, a single workflow containing three modules is run: a module running the
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HEC-HMS model, a module running the HEC-RAS model and a module running the ARMA error correction procedure.
1 The updated model states and precipitation forecasts are exported from the FEWS database to a folder location from where it can be used by the HEC models. For the HMS model, ‘forecasts’ pertain to precipitation forecasts in the Upper Blue Nile basin; for the RAS model, ‘forecasts’ include the flow forecast for Eddiem which is produced by the HEC-HMS model. 2 The models are run in “forecast mode”, where last known model-states are used as initial conditions and forcing consists of most recent observations and/or forecast data. 3 Forecast time series and adjusted model states are picked up from the folder location where they are saved by the HEC models and imported into the FEWS database. 4 Forecasted water levels and flows downstream of Eddiem are error-corrected using a statistical ARMA procedure. This ensures that discrepancies between observed levels and near-future predictions are minimised.
Running the workflow results in new model states and forecast time-series. These are imported into the FEWS local database.
4.4.4 Step 3: Check presence of forecast in database FEWS allows for a manual, visual check of data. This check will have to be performed to determine the presence of forecasted data in the FEWS database.
4.4.5 Step 4: Proceed to Dissemination process If data has correctly been stored in the database, the forecaster may proceed to the next process.
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3. Update run
Export Execute Import states and HEC-HMS forecasted ts forecasts forecast run and states
ModuleInstance: HEC-HMS forecast
Export Execute Import states and HEC-RAS forecasted ts forecasts forecast run and states
ModuleInstance: HEC-RAS forecast
Error correction
Workflow: forecast run States and forecasted timeseries
Check presence of no forecasts
Forecasts present?
yes 5. Forecast dissemi- nation
Figure 4.5 Visual representation of the Forecast Run process using HEC models
4.4.6 Forecasting using Linear correlation models Forecasts based on linear correlations will be produced for the stations listed below; for all stations, forecasts with a lead time of one day shall be produced.
Downstream Station Lead time [days] Variate Medani 1 Water level Kamlin 1 Water level Khartoum 1 Water level Shendi 1 Water level Atbara 1 Water level Debba 1 Water level Dongola 1 Water level
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2. Data Processing
Linear correlations workflow
Forecasted timeseries
no Check presence of forecasts
Forecasts present?
yes 5. Forecast dissemi- nation
Figure 4.6 Visual representation of the Forecast Run process using correlation models
4.5 Process 5: Dissemination After a forecast has been produced, all information for the production of reports is available. Reports are created in HTML-format by the FEWS system; viewing and/or dissemination of reports takes place outside of the FEWS system. Note that the content of the reports has been defined in Section 3.10 of the present report.
4. Forecast run(s)
HTML export workflow
HTML reports
no Check presence of reports
Reports present?
yes View / disseminate reports
Figure 4.7 Visual representation of Dissemination Process
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4.5.1 Step 1: Run FEWS workflow The FEWS workflow gathers necessary data from the FEWS database, converts this into predefined tables and graphs and includes these in predefined reporting templates. The reports are stored in a folder location on the local workstation.
4.5.2 Step 2: Check if HTML report have been created The presence of HTML reports will have to be done after the FEWS workflow has been run. If they are not present in the predefined folder location, the workflow will have to be run again.
4.5.3 Step 3: View and/or disseminate HTML reports Viewing and/or dissemination of HTML reports is done outside of the FEWS system. The reports may be opened by any internet browser. As they consist of a (small) collection of .html and .jpg files, they can easily be disseminated by email, FTP or other.
4.6 Archiving Section 3.11 describes the FEWS system’s archiving capabilities. None of these require the running of a separate process. As “archiving” is listed as a process in the forecasting process (Figure 4.1), here follows a list of how archiving is achieved.
4.6.1 Archiving observations and forecasts Both observations and forecasts are stored in two ways: (1) as CSV files on the workstation, (2) in the FEWS database. Both are done automatically as part of the FEWS workflows that are used in the Data Import and Forecast Run processes.
4.6.2 Archiving local database and FEWS configuration Copies of the FEWS database that contains all the imported data and forecast results should be stored periodically both as backup and as an archive. This is a manual procedure that will be explained during the training session which is planned in Khartoum for December 2010.
4.7 Installation and Training Workshop From December 12th – 16th a training workshop was conducted at the Ministry of Irrigation of Water Resources in Khartoum, Sudan. The focus of the workshop was to train key personnel from MoIWR as well as related agencies on the use of the newly developed FEWS Sudan. Additionally gave an introduction to the configuration of the system and discussion were held on how the new models that are currently being developed by the modelling team at MoIWR. An overview of the participants to the workshop is provided in the table below.
Parallel to the training session, the new operational FEWS Sudan was installed on a dedicated workstation in the forecasting room of the Nile Forecast Centre, a part of the Nile Waters Directorate hosted at MoIWR. All procedures in creating a forecast were implemented, and the forecast of Thursday 16th December successfully established. The agenda of the workshop is provided in the appendix.
During the workshop some comments and suggestions were made. x The workshop was received enthusiastically by the participants, and laid a solid foundation on which the forecasting team at MoIWR could build and extend the forecasting capabilities. x The objective of MoIWR is to integrate new models in FEWS Sudan prior to the 2011 flood season. A clear need for additional training was indicated. While this training need not be delivered to the complete list of participants, those involved in configuring
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the system should take part. MoIWR will work in collaboration with ENTRO and Deltares to explore options to provide such additional training. x Staff at MoIWR will need to work frequently with the system during the coming period to ensure that the knowledge gained is sustained. During this period they may have questions on the use of FEWS Sudan. These questions can be forwarded to Deltares who can provide feedback. However, it was clearly recommended that a more substantial arrangement may be required to provide the required support.
Table 4.2 List of workshop participants
Name Organisation Email Redwan Abdelrahman MoIWR [email protected] Nahid Marioud Amahtab MoIWR [email protected] Hadra Osman Eltib MoIWR [email protected] Rehab Hassan Mohammed MoIWR [email protected] Asma Moheamed Ahmed Civil Defence [email protected] Kamal Al Rahim MoIWR - Abd El Nassir Khidr M. HRS- MoIWR [email protected] Safiya Abdallah MoIWR [email protected] Mohamed Ahmed S/ Elkker SMA - Ahmed Hemadí Civil Defence Rabah Farah SMA [email protected] Badreldin Mamayn SMA [email protected] Maha Eltayb Mohammed HAC-EWC [email protected] Hafsa Abd Elbagi HAC-EWC [email protected] Younis Abdalla Gismalla HRS-MoIWR [email protected] Tagreed Abdelraheem MoIWR [email protected] Hind Massoud MoIWR [email protected] Bashar Kamaleldin Elsiddig UNESCO-CWR [email protected] Yasir Abbas Mohamed MoIWR/UNESCO-IHE [email protected] Widad Mulwakil MoIWR [email protected] Jan Verkade Deltares [email protected] Micha Werner Deltares [email protected]
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Figure 4.8 Participants of the training workshop working hard at one of the exercises
Figure 4.9 FEWS Sudan installed in the Nile Forecasting Centre forecasting room.
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5 Conclusions
In this report the development of FEWS Sudan, as an upgrade to the original system installed at the Ministry of Irrigation and Water Resources in Khartoum, Sudan is described. This upgraded system utilizes the state-of-the-art Delft FEWS operational flood forecasting platform to integrate models and data in the operational forecast process at the Ministry.
The system as it has been configured contains several key steps;
• Import and storage of observed hydrological data from key stations on the Blue Nile, White Nile, main Nile, Rahad and Dinder in Sudan, • Import and storage of observed meteorological data from selected rainfall stations in the Upper Blue Nile catchment in Ethiopia, • Import and processing of satellite rainfall estimates from three different sources, including RFE, TRMM and CMORPH, • Processing of imported precipitation data to provide a merged precipitation input to the running of HEC-HMS rainfall runoff models. • Running of a HEC-HMS model for the Upper Blue Nile catchment using the merged precipitation data, and subsequently routing the observed hydrograph through the Blue Nile using a HEC RAS model of the Blue Nile between El Deim and Khartoum, • Importing and processing of forecast rainfall from the ETA numerical weather prediction model run either at MoIWR, or at the Sudanese Meteorological Agency • Running the HEC-HMS model and HEC-RAS model in the forecast period to predict flows and levels at key stations over the desired forecast period. • Developing of reports for dissemination to users such as the High Flood Commission, and others.
The process through which this forecast is run is described in this document. Additionally a dedicated training has been developed and delivered to those involved in the forecast process at MoIWR.
With the development of FEWS Sudan in its current form, operational forecasting capabilities at MoIWR have been boosted. Although forecasting was done prior to the establishment of this system, this was done in a more fragmented way, and with the introduction of this system this can now be carried out in a more coherent way. However, it is important to realise that the objective of establishing FEWS Sudan in its current form is that the system is flexible to change, and that it can be extended to include models that provide a better simulation of the processes within the Blue Nile, as well as models yet to be developed that cover other reaches of the Nile, as well as other rivers that pose a significant flood hazard. This will allow the system to grow to a comprehensive forecasting system in Sudan.
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6 Recommendations on a roadmap for the developments of forecasting capabilities in Sudan
FEWS Sudan as it has been developed provides a platform to run the currently developed models of the Blue Nile and integrate the available data. However, the current models are recognised to require improvement to properly simulate the processes in the Blue Nile.
To further develop the capabilities of forecasting at MoIWR beyond forecasting locations on the Blue Nile using the current models, several key recommendations are made. These recommendations are grouped in different categories. The objective of these recommendations is that these can be taken forward by MoIWR in establishing a “road-map” for the development of forecasting capabilities in Sudan.
6.1 Improvement of availability of observed data • The process with which observed hydrological data is gathered at MoIWR, including levels, flows and releases from the dams should be consolidated. It is recommended to develop a coherent hydrological database at the Ministry in which the data received from the field is first entered, and preliminary quality control can be applied. The operational forecasting system can link to this database to efficiently import the raw data. Additionally data should be fully quality controlled and persistently stored for later use in for example model calibration, forecast evaluation etc. • Data from other sources in the Sudan, such as observed precipitation data from gauges operated by the Sudan Meteorological Agency should be integrated with FEWS Sudan. This is particularly relevant once the model domain as described below expands to include catchments in Sudan. • Cooperation should be sought with Ethiopia to allow for the exchange of hydrological and meteorological data. It is recommended that rainfall data for gauges in the Upper Blue Nile are made available in real time for forecasting. Additionally long time series of data should be available for model validation on the one hand, and verification and bias identification of satellite rainfall estimates as well as to understand the quality of available NWP forecasts. • It is possible that the points discussed above are addressed in an ongoing consultancy dedicated at data collection and communication for forecasting in the Eastern Nile Basin. The results of this consultancy were not available at the time of writing of this final report, but once these become available then recommendations therein in line with those points made above should be implemented as soon as possible.
6.2 Development of forecasting models • The current models for the Upper Blue Nile (HEC-HMS) and Blue Nile (HEC-RAS) are recognised to be simple models, and are already subject to an improvement programme. It is recommended that once these improved models are made available that the currently integrated models are replaced. • It is recommended that the coverage of rainfall runoff models is extended to include additional catchments in the Blue Nile and Atbara. Primarily these should include the Rahad and Dinder, and subsequently the headwaters of the Atbara. Other important tributaries should be also modelled. It is also recommended to extend the coverage of the HEC-RAS models to include the Atbara, the main Nile from Khartoum to Dongola. Additionally, inclusion of other models such as the LPM model as described in detail above should be considered.
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• It is recommended to make a 3-5 year plan of step-by-step model development for other catchments in Sudan where there is a significant flood hazard. As these models become available, these should be integrated with FEWS Sudan to extend the forecasting service. • Within all models, it is recommended to review the design of the models prior to development to ensure that the models developed are suitable as operational models that can be run in real time. This will allow important choices in the model structure to be made so that the models run efficiently in forecast mode, and also that the results are as accurate as possible and make full use of available data through data assimilation. Model developers should obtain training in establishing real time models, as indicated in the recommended training indicated below.
6.3 Forecast organisation, team and procedures • The organisation of the forecast process should be structured as efficiently as possible. This will allow the forecast to be developed as efficiently as possible and reduce the laborious steps to create that forecast. This will allow more time to analyse the information and thus improve the forecaster’s advice. • A dedicated team should be established that is responsible for the forecast process. This team should contain 2-3 senior experts in models and data who are the custodians of the system. This core team is responsible for the operation and maintenance of the forecast system. During the dry season the focus on system maintenance, improvement and the extending of the system with new modelling and data capabilities. Additionally 3-4 staff should be trained in the operation of the system on a daily basis. • Clear procedures should be developed to guide the forecast process. With FEWS Sudan additional data becomes available at increased lead times. Procedures should be developed to describe how this information is used and disseminated in the warning process. This should describe at what thresholds information is disseminated to which users (e.g. High Flood Commission, Flood Bulletin, Civil Defence, Public etc.). • The forecasting team should have access to an internet connection that can easily download gridded precipitation estimates from the internet. Currently this is not the case. • Ideally, the forecasting team would have access to precipitation forecasts that have been produced at – and quality-controlled by – meteorologists at the Sudanese Meteorological Agency. This requires a dedicated data connection between SMA and MoIWR. Currently such a connection is not available.
6.4 Training and capacity building • It is recommended to develop capacity in the core forecasting team (2-3 senior experts) for the configuration of the Delft FEWS system. This should involve an understanding of how to configure new modelling and data capabilities in the system. • The flood season in Sudan is quite short and it is envisaged the system will only be used for 4-5 months per year. It is recommended that forecasting staff follow and (internal) training (length 2-3 days) just before the start of each flood season to refresh the process in using the system. Additionally this regular training will allow new members of the team to come on board just prior to the flood season. • It is recommended that some basic training in development of models suitable for use in real time is followed. This will allow new models developed either by the forecasting team themselves, or in their supervision, to be suitable for forecasting without further amendment.
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A Data format for importing data to FEWS Sudan
A.1 Introduction This section described the required format for hydrometric data to be transferred from the data centre spreadsheet to FEWS Sudan. It is very important that this format is followed exactly to allow import of the data.
To be sure the process is as simple as possible, an ASCII format has been chosen. This format can be used for all data types.
A.2 Format • Data is to be transferred in as a simple CSV file. • Each file may contain data for one or more stations, for one or more parameters. • The file contains 3 header lines. The texts in bold are fixed items • The file name should include the date/time on which it was exported. This will allow for easily archiving the files.
Line 1: Location Names, Station 1, Station 2, etc… Line 2: Location Ids, Id 1, Id 2, etc.. Line 3: Time, Data Type 1, Data Type 2, etc.
Line 4….Line n contains the data
Column 1: Contains the time of the value (format YYYY-MM-dd HH:mm:ss) Column 2: Contains the values for Station 1, Data Type 1 Column 3: Contains the values for Station 2, Data Type 2 Etc..
The parameter identifies the type of data:
Identifier Description Unit H Observed level – with reference to the station zero m HMSL Observed level – with reference to mean seal level m Q Flow m3/s P Rainfall mm T Temperature (Dry Bulb) Deg C
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A.3 Example
Filename: fews_csv_import.20100811220035
Table A.1 Sample CSV file used to import observations Location Names,Eddiem,Khartoum Location Ids,900012,900152 Time,H,H 2010-07-25 00:00:00,12.1,14.12 2010-07-26 00:00:00,12.55,14.79 2010-07-27 00:00:00,12.2,15.25 2010-07-28 00:00:00,11.58,15.48 2010-07-29 00:00:00,12.08,15.64 2010-07-29 02:00:00,12.12,-999 2010-07-29 04:00:00,12.06,-999 2010-07-29 06:00:00,12.83,-999 2010-07-30 00:00:00,11.78,-999 2010-07-31 00:00:00,11.58,15.66 2010-08-01 00:00:00,11.49,15.74 2010-08-02 00:00:00,11.22,15.77 2010-08-03 00:00:00,-999,-999 2010-08-04 00:00:00,12.13,15.52 2010-08-05 00:00:00,11.99,15.48 2010-08-06 00:00:00,11.77,15.75 2010-08-07 00:00:00,11.99,15.95 2010-08-08 00:00:00,11.97,16 2010-08-09 00:00:00,11.62,16 2010-08-10 00:00:00,11.47,16.1 2010-08-11 00:00:00,11.95,16.1
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B Guide to flow chart symbols
The following symbols are used in the flow charts; in below graph, their meaning is listed within the actual symbol.
Predefined Manual Process Stored data Decision process operation
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C References
Abtew, W., A.M. Melesse and T. Dessalegne (2008) Characteristics of Monthly and Annual Rainfall of the Upper Blue Nile River Basin, Workshop on Hydrology and Ecology of the Nile River Basin under Extreme Conditions, June 16-19, 2008, Addis Ababa, Ethiopia
Delft Hydraulics (1992) Sudan Flood Early Warning System, Delft Hydraulics report Q1012_1, Delft, The Netherlands
Grijsen, J., Snoeker, X., Vermeulen, C., El Amin Moh. Nur, M., and Mohamed, Y. (1992). An information system for flood early warning. In Floods and Flood Managament Saul, A. (Ed.) 263–289, Kluwer Academic Publishing
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D HEC-HMS model adapter: consistency test
The consultant was provided with a working copy of a HEC-HMS rainfall-runoff model for the Upper Blue Nile basin. This model consists of a single catchment with a single point of outflow; the latter corresponds with EdDeim gauging station. This schematisation remained unchanged.
The HEC-HMS model files were used to create two runs: one historical (update) run (1990 and 1991) and one forecast run (1992-1996 inclusive). The historical (update) run served to create a .state file reflecting the hydrological conditions in the basin at the end of the historical (update) run period. This state is subsequently used within the FEWS system as a “cold state”.
While the schematisation has not been changed, some cosmetic changes have been made, namely concerning the names of the basin and gauges. These changes included: x renaming the project files from BNile to UpperBlueNile x renaming the basin from Deim to UpperBlueNile-basin x renaming the point of outflow from Junction-1 to EdDeim_station x renaming the precipitation inputs from Gage 1 to Precip_Gage1 x renaming the flow inputs from Gage 1 to Flow_Gage1
D.1 Comparison of simulation results from HEC-HMS and Delft-FEWS environments A comparison of simulation results was carried out to make sure that the HEC-HMS model for the Upper Blue Nile catchment was implemented in FEWS Sudan correctly.
Please note that the comparison was made between model simulation results and NOT between simulated values and observed values!
D.1.1 Initial conditions Within the HEC-HMS environment, a simulation was run for the calendar years 1990 and 1991, based on the data within the .dss file that was supplied to the Consultant during the inception mission. At the end of the simulation run, model states were saved. This state file was used for the two comparison runs.
D.1.2 Forcing data The Upper Blue Nile model is forced with precipitation data and evaporation data. Precipitation data is stored in a .dss file; evaporation data is stored in a .met file. A transformation was necessary to ensure that models were forced with identical precipitation data. Note that precipitation values in the DSS file have been rounded off to two decimals; in FEWS it is not. This causes (very!) small differences in resulting simulated outflows.
D.1.3 Model run Comparison was done for the 5-year period starting January 1, 1992 and ending December 31, 1996. Below figures show how the runs are started from the respective environments.
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Figure D.1 Simulation run settings within the HEC-HMS environment
Figure D.2 Simulation run settings within the FEWS Sudan environment
D.1.4 Simulation results: HEC-HMS environment Simulated hydrographs from the HEC-HMS and FEWS Sudan environments are shown in Figure D.3and Figure D.4 respectively.
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Junction "EdDeim_station" Results f or Run "Run2_f orecast" 12,000
10,000
8,000 ) S /
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w o l F
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0 1992 1993 1994 1995 1996 1992 1993 1994 1995 1996 Run:Run2_f orecast Element :EDDEIM_STATION Result:Outf low Figure D.3 Simulated hydrographs from the HEC-HMS environment
Figure D.4 Simulated hydrographs from the FEWS Sudan environment
D.1.5 Comparison of simulation results Simulation results from the HEC-HMS and FEWS Sudan environments were copied to Excel for comparison. Three graphs were produced: 1. A graph showing time series of both simulation results (Figure D.5); 2. A graph plotting the simulated values of the two environments vis-à-vis one another (an XY-plot) (Figure D.6); 3. A graph plotting the time series of the difference between the two simulation runs (Figure D.7).
The time series plot (Figure D.5) of the two simulation runs shows that values are plotted on top of one another, i.e. that values are –near- identical. This is confirmed by the XY-plot (Figure D.6), which shows that all values are on the diagonal 1:1 line. Close observation shows that there are some differences (Figure D.7), but that these are all well below one- tenth of a cubic metre/second and may be assumed to be negligible. The differences may be attributed to round off (to 2 decimals) of precipitation values within the HEC-HMS environment.
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It is concluded that running the Upper Blue Nile model within HEC-HMS and FEWS Sudan environments yields identical results.
FEWS v. HEC-HMS environments
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Figure D.6 XY-plot of the simulated values from the two runs
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FEWS v HEC-HMS environments
0.06
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E Training Workshop Programme
Introduction This memo provides an overview of the proposed programme for the training workshop for the updated version of FEWS Sudan. This training workshop is given at the Ministry of Irrigation and Water Resources in Khartoum. The objective of this workshop is to provide a hands-on introduction to those working with the forecasting system on a day-to-day basis, as well as those working closely with the models and data included in the system.
The training workshop will be held from Sunday December 12th 2010 until Thursday December 16th 2010. The programme is largely divided into two sections. In the first participants will be given an introduction to the system, and how this is used accomplish the day-to-day forecasting tasks. In the second the participants will be given an introduction to the configuration of the system, how small changes can be made, as well as an introduction to how the system can be extended with additional forecasting models once these become available.
It is expected that all participants to the course will have;
• A good understanding of forecasting, and the responsibilities of MoIWR for delivering forecasts in the Nile Basin. • A good understanding of models and data • A good technical knowledge and experience in using computers
Programme
Day 1: 12 December 2010
09:00 Welcome & opening (presentation)
09:30 Introduction to the role of flood forecasting within MoIWR (presentation)
10:00 Introduction to FEWS Sudan & system demonstration x Concepts of the system, initial configuration and current developments x Data imported into the system x Types of forecasts delivered by the system x Forecast process (presentation)
12:00 Lunch
13:00 Exploring FEWS Sudan x Installing the system x Explaining the main displays (brief introductions & exercises)
14:30 Tea & Coffee break
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15:00 Importing observed meteorological and hydrological data x Importing level & discharge data from hydrological gauges x Importing rainfall data from meteorological gauges x Data quality control and editing (brief introductions & exercises)
16:30 Closure
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Day 2: 13 December 2010
08:00 Importing & processing observed and forecast meteorological & hydrological data x Importing satellite rainfall data (RFE, TRMM & HydroEstimator) x Importing rainfall forecasts (MoIWR, SMA) x Concepts for processing data x Processing hydrological data x Sampling gridded data x Merging precipitation (brief introductions & exercises)
10:00 Tea & Coffee break
10:30 Importing & processing (continued…)
12:00 Lunch
13:00 Introducing models used in the system x HEC-HMS model of the Upper Blue Nile x HEC-RAS model of the Blue Nile x Correlation Models x Principle of the update run x Principle of the forecast run (presentation)
14:30 Tea & Coffee break
15:00 Forecasting with FEWS Sudan x Running the daily update run x Running Forecasts x Running correlations x Viewing results (presentation & exercises)
16:30 Closure
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Day 3: 14 December 2010
08:00 Publishing forecast results x Forecast Products from FEWS Sudan (presentation & exercises)
10:00 Tea & Coffee break
10:30 Maintenance tasks for FEWS Sudan x Daily maintenance checks x Periodic checks and archiving data x Making backups of the system (presentation & exercises)
12:00 Lunch
13:00 Using FEWS Sudan in an event x Initialising the system at the start of the flood season x Running daily forecasts and assessing results (brief introduction & exercises)
14:30 Tea & Coffee break
15:00 Using FEWS Sudan in an event (continued …) (exercises)
16:30 Closure
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Day 4: 15 December 2010
08:00 Introduction to FEWS configuration (1) x Principles of FEWS configuration x Static information – locations & maps x Threshold levels & validation limits x Time series in Delft FEWS (presentation)
10:00 Tea & Coffee break
10:30 Introduction to FEWS configuration (1) (continued) x Principles of FEWS configuration x Static information – locations & maps x Threshold levels & validation limits x Time series in Delft FEWS (presentation & exercises)
12:00 Lunch
13:00 Amending the FEWS configuration (1) x Editing location details x Changing threshold levels (brief introduction & exercises)
14:30 Tea & Coffee break
15:00 Amending the FEWS configuration (1) x Changing correlations x Changing rating curves (brief introduction & exercises)
16:30 Closure
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Day 5: 16 December 2010
08:00 Using FEWS Sudan in the forecasting environment x Organisation of the forecasting process in the forecasting room (exercise)
10:00 Tea & Coffee break
10:30 Using FEWS Sudan in the forecasting environment (continued) x Organisation of the forecasting process in the forecasting room (exercise)
12:00 Lunch
13:00 Introduction to FEWS configuration (2) x Principle of integrating models in Delft FEWS x Investigation of running HEC-HMS & HEC-RAS models x Discussion on how Delft FEWS can be extended x Discussion on integration of other models (LPM) (presentation & discussion)
14:30 Tea & Coffee break
15:00 Open session x Further development of FEWS Sudan x Queries raised by participants (discussion)
16:30 Closure
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