Notes to project: Conflict and cooperation over shared water resources

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1 Notes to project: Conflict and cooperation over shared water resources Jan Ketil Rød Department of geography, NTNU Version: 30/06/2006 Abstract This note outlines various dataset that may be useful for the project Conflict and cooperation over shared water resources. Content Precipitation data... 1 Monthly Climatic Data for the World (TD 3500)... 1 Global Historical Climate Network (GHCN)... 2 Global Monthly Merged Precipitation... 2 Elevation derived database: Hydro1K... 3 Watersheds with topological properties... 4 Outlets with accumulated flow favourable or vulnerable locations?... 5 Global Runoff Data Centre (GRDC)... 6 Freshwater discharge into the oceans... 7 Monthly Discharges and Annual Characteristics... 7 Global River Discharge Database (RivDis v1.0)... 7 Global Terrestrial Network for River Discharge (GTN-R)... 8 Water availability... 8 Global Water Quality Data and Statistics - GEMStat References Precipitation data Although there are numerous records of precipitation locally, there are few with a global coverage. Below I present three data sets: two from NOAA and the third from NASA. The two data sets which are listed below are among the interesting results from a search at the National Climate Data Center / National Oceanic and Atmospheric Administration (NOAA): with the following inputs: rain (as type of data), global (as geographical area) and monthly (as temporal resolution): Monthly Climatic Data for the World (TD 3500) Climatic variables are for larger cities worldwide and include - mean temperature - number of days with precipitation 1 mm or greater - total precipitation - etc Description of variables is found here: Variables date back to 1985 or 1986 (when selected data retrieve for entire region). 1

2 Example of data file found here: (without station names) or (with station names). The data files come without coordinates, but geographical coordinates can be obtained by linking the data file to the station list ( use the WMOID/NUM as identifier. Data are free of charge for all those listed here: Except for metrological organisations, only US organisations are listed. Otherwise it is priced (e.g. $ 170 for Middle East for monthly data from 1986 to 2003). Global Historical Climate Network (GHCN) This data set contains gridded precipitation anomalies calculated from the GHCN V2 monthly precipitation data set. The data set consists of 2592 gridded data points with precipitation anomalies in millimetres for every month from January 1900 to the most recent month. Resolution of the data set is 5 X 5 degree for the entire globe (72 longitude X 36 latitude grid boxes). Global Monthly Merged Precipitation NASA has a project called Global Precipitation Climatology Project (GPCP) which offers monthly globally precipitation data from 1979 to present at a 2.5 x 2.5 degrees spatial resolution. At department of geography we have made the necessary fortran code to extracted the data and save them as excel files. We have structured the excel files so that there are one file for each year with 12 variables for the monthly precipitation numbers as well as variables for latitude and longitude. Some work remains to restructure the Excel files to easely be imported into GIS. Hopefully, these precipitation data can be used to produce distributed hydrological models (see below). Below is a screendump showing the sample points for the monthly precipitation data for Africa and Eurasia. 2

3 Elevation derived database: Hydro1K U.S. Geological Survey's (USGS) EROS Data Center has recently generated GTOPO30 which is a global raster digital elevation model (DEM) with a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). The corresponding drainage area and stream network data set is called Hydro1K Derivatives. In addition to catchment and basin boundaries, the Hydro1K data set provides a set of raster data product derived from GTOPO30 to evaluate or process hydrologic information on a continental scale. The land area over which precipitation falls is called the catchment, and the land area that contributes surface flow of water (runoff) to any point of interest is called a watershed (or catchment area). This can be a few acres in size or thousands of square kilometres. A large watershed can contain many smaller sub-watersheds (Viessman & Lewis 2003: 215). The only difference between a watershed and a sub-watershed is scale. The lines separating the land surface into watersheds are called divides. Since these normally follow ridges and mounds (orthogonally cutting contours) they can be delineated using a digital elevation model (DEM) and standard GIS operations to identify gradient directions. Digital elevation model is the primary data structures used in the delineation of watershed boundaries. Watersheds are included in Hydro1k with several levels of sub-watersheds, classified into a hierarchy of larger to smaller catchment units using the Pfafstetter classification system. In this system, the highest level watersheds are subdivided using a set of rules into 10 sub-watersheds, each of which is similarly subdivided into 10 further subwatersheds, and so on (Maidment 2002). This subdivision is dependent on the resolution of the DEM used to delineate the watersheds and the variability of the land surface may not be 3

4 adequately captured at coarse DEM resolutions. For instance, the coast of Norway have not been subdivided into sub-watersheds because the 1 km resolution have been too coarse to delineate the smaller watersheds. Consequently, Hydro1k is not suited for regional watershed studies, but Hydro1k is aimed to be used at continental or global studies. Watersheds with topological properties Topological properties in GIS structured datasets that allows you to identify connectivity (e.g. which line segment follows another) and neighbourhood (which land parcels share boundary). The watershed data set provided with Hydro1K have properties that make it possible, with standard GIS queries, to identify upstream / downstream locations and whether or not the downstream locations will be affected by a drop or increase in water flow. Figure 1 below shows an extraction of the Hydro1K data set for the Euphrat Tigris watershed. The colour patches represent second order sub-watersheds according to the Pfafstetter coding whereas boundaries for lower order watersheds are visible. The Pfafstetter coding divides sub-watersheds into basins and interbasins. Interbasins are sub-watersheds along the main stem whereas basins are sub-watersheds for the tributaries. Further, Pfafstetter coding a coding scheme which makes it very easy to identify upstream and downstream areas since the coding preserves topological properties. The numbers on the map refer to third order sub-watersheds. This is perhaps best explained with an example. If a new dam is constructed in an upstream location one can by standard GIS queries determine whether, irrigation or other water demanding activities nearby will be affected. In Figure 1, the highlighted polygon labelled 293 represents an interbasin at the third level whose outlet co-locate with the Ataturk dam. Will the dam affect the irrigation diversion of a farmer at sub-watersheds 276 or 259? Upstream sub-watersheds have higher numbers than downstream sub-watershed, thus both 4

5 276 and 259 lies downstream. The trailing 6 for the first sub-watershed indicates a tributary off the main stem and above any flows influenced by the dam. Thus, river reaches affected by the dam will have a match of leading digits, 2, and trailing odd digits less than 93. The farmer located in sub-watershed 276 will therefore not be affected by the dam whereas the farmer located at sub-watershed 259 will be affected. Outlets with accumulated flow favourable or vulnerable locations? A watershed generally has no inflows and only one outflow point, referred to as the outlet or drainage point. This Figure (from Maidment 2002) illustrate the location of two outlets in a watershed and a sub-watershed respectively (the red dots). The outlet is the lowest point in the watershed (or sub-watershed) and thus the location within the watershed with the highest accumulated flow of water (all locations in the watershed drain to the outlet). Outlet locations (and other location with high accumulated flow of water) will therefore be particular favourable locations for dams and thereby also vulnerable location for water wars as in July 1966 when Israeli planes struck the diversion works on the Baniyas- Yarmuk canal (Klare 2001: 169). Hydro1K provide us with a data set on flow accumulation which is defined as the number of cells which flow into each downslope cell. Flow accumulation values range from 0 at topographic highs to very large numbers (on the order of millions of square kilometers) at the mouths of large rivers). Flow accumulation can be used to represent the amount of rainfall that would flow from cell to cell, but this approach would ignore water infiltration, evaporation and interception. More realistic therefore would be to use the flow accumulation layer to determine the location of stream channels and to characterize the stream network itself, most effectively done by employing a simple thresholding limit value within a Map Algebra equation such that streams with a certain number of cells flow into them say, 100 cells will be indicative of streams within a network and will be assigned an output value 1 whereas all remaining cells will be assigned a 0 value. Further, one could use the flow accumulation values to categorise the rivers into, for instance, main, median and minor rivers. In commercial GIS packages there are a number of hydrological functions available and for the ArcGIS software package, there is a free available extension called Arc Hydro which could be used for various water management operations. For instance, after inputting a flow accumulation layer and a watershed the Arc Hydro extension will locate the outlets for the given level provided by the watershed. In the Figure below, three levels of outlets are generated and mapped together with armed conflicts centre coordinates. Also shown in the 5

6 map is state boundaries, three-level quantification of streams and the coloured patches are third level sub-watershed. The map illustrate how we could systematically study whether or not the conflicts event tends to be about control over shared water resources. As also can be seen from the map is the fact that several watersheds (even third order subwatersheds) are shared between countries. If a country (or group in power) controls an entire sub-watershed the country (or group) controls the water that drains to this area. At location with scarce resources, irrigation plan or other plans involving an increased upstream water use may lead to tensions and, in worse cases, conflicts. Global Runoff Data Centre (GRDC) GRDC include various regional and global runoff databases with several thousand runoff stations (as the dots on the map zoomed in on the Euphrat- Tigris river system below). 6

7 GRDC Data Products: Freshwater discharge into the oceans Long Term Mean Annual Freshwater Surface Water Fluxes into the World Oceans Download not yet available (June 2006). Monthly Discharges and Annual Characteristics Long Term Mean Monthly Discharges and Annual Characteristics of Selected GRDC Stations Global River Discharge Database (RivDis v1.0) Based on the 1986 UNESCO monthly river discharge data collection, Charles J. Vörösmarty, Balázs M. Fekete and B.A. Tucker have compiled spatially-distributed runoff estimates represented as ArcInfo ASCII grids. 7

8 This data product combines observed river discharge information with a climatedriven Water Balance Model in order to develop composite runoff fields which are consistent with observed discharges. Such combined runoff fields preserve the accuracy of the discharge measurements as well as the spatial (0.5 X 0.5 degrees resolution) and temporal distribution (months) of simulated runoff, thereby providing the "best estimate" of terrestrial runoff over large domains. However, no temporal variation beyond the 12 months for The figure shows continent runoff data for Africa (downloaded from Global Terrestrial Network for River Discharge (GTN-R) Water availability The data sets provided in Hydro1K will not be sufficient to estimate water availabilities on any given point. What we ideally would need is results from what is called distributed hydrological models. The good news is that GIS and remote sensing is common tools for such models (see for instance Vieux 2001) and that the results are as grid based layers. The Norwegian Water Resources and Energy Directorate (NVE) do have water availability measures on a 1 km resolution. At 8

9 (from the menu at the right: click on NVE Atlas) You will see the following map: (zoom in on an area and mark årsavrenning punkt from the left menu (the option may not be available if you have not zoomed enough). Activate the i ikon from the toolbar above the map, move the cursor to one of the dot and click. A little table will show below the map the measure is mm annual water available from that point. Vörösmarty and colleagues seems to be working towards such representations on water availabilities although with a courser spatial resolution but a better temporal resolution (months in stead of years as the above figure) (see Vörösmarty et al which much of their NSF application seems to be drawn from). 9

10 Ground truth data on runoff / discharge is of great value but Vörösmarty et al. (2005: 230) seems to be worried on behalf of the availability of such data sets: 'Since 1990 there has been a 90% reduction in routine reporting of African river discharge (an important source of water supply data) to relevant international agencies such as the WMO Global Runoff Data Center'. A solution could be to use more remote sensing data Vörösmarty et al.(2002: 339): '... assembling an unbroken view of the contemporary water cycle can hardly be attained universally using ground-based stations: measurements are taken infrequently and irregularly, and the results are made available (if at all) days, weeks or even years after capture. For remote and inaccessible areas, satellite monitoring may provide the only viable alternative' Global Water Quality Data and Statistics - GEMStat Since its establishment in 1978, UNEP's Global Environmental Monitoring System (GEMS) Water Programme has become the primary source for global environmental water quality data. It is a multi-faceted water science centre oriented towards knowledge development on inland quality issues throughout the world. Major activities include monitoring, assessment and capacity building. The twin goals of the Programme are to improve water quality monitoring and assessment capabilities in participating countries, and to determine the status and trends of regional and global water quality. These goals are implemented through the GEMS/Water data bank, GEMStat, including water quality data and information from more than 100 countries, with over two million entries for surface (lakes, reservoirs, rivers) and groundwater systems. By compiling a global database, GEMS/Water adds value to countrylevel data by contributing to global and regional water quality assessments. The Programme also carries out evaluations on a range of water quality issues and methodologies. However, there are many gaps that need to be filled, especially in terms of geospatial and temporal coverage. The current state of data distribution was featured in the 2005 UNEP Annual Report ( and specific details are reported country-by-country in the 2005 State of the GEMS/Water Global Network and Annual Report ( References Klare, M. T. (2001). Resource Wars: The New Landscape of Global Conflict. New York, Metropolitan Books. 10

11 Maidment, D. R. (2002). Arc Hydro. GIS for Water Resources. Redlands, California, ESRI. Viessman, W. J. and G. L. Lewis (2003). Introduction to Hydrology. Upper Saddle River, Pearson. Vieux, B. E. (2001). Distributed Hydrological Modeling Using GIS. Dordrecht, Kluwer Academic Publishers. Vörösmarty, C. J., E. M. Douglas, P. Green and C. Revenga (2005). Geospatial Indicators of Emerging Water Stress: An Application to Africa. Ambio 34(3):