Topography

The first step is to build the base digital elevation model. From the DEM, so-called river networks and flow accumulation grids need to be built. For the scale of the work here, the SRTM is sufficient. Further, it was useful to start with the SRTM-based product Hydrosheds, which has initial error-corrections included (http://www.worldwildlife.org/science/projects/freshwater/item1991.html).

Digital Elevation Model, River Networ, and Flow Accumulation

As the VIC hydrological model is being set up at a resolution of 2.5' (~ 4×4.6 km), the DEM was first generalized from 3" to 2.5' as follows (Fig.a):

• The maximum discharge for each 50×50-cell block was determined.
• The minimum and mean elevation for each block was calculated.
• Each large cell was assigned an an elevation value between the minimum and the mean, weighted by the natural log of the discharge.
   

This yields a DEM that tends to respect stream channels better than a DEM derived by other means.
The process for the derivation of the flow direction grid (Fig. b):

• The starting point was the Hydrosheds conditioned flowdirection grid, derived from the SRTM 3" DEM, modified with the aid of mapped rivers.
• Mean annual TRMM rainfall (below) is summed down this network to yield discharge.
•  For each 2.5' block of 2500 small cells, every flow that leaves the block is traced up to 31 steps to determine it's destination.
• Net flows are calculated, and the maximum value is used to set a flow direction on a coarse scale.

The resulting flowdirection grid (Fig.c ) following the rivers much better than a grid derived from a DEM, even the revised DEM.
 

Delineating Drainage Basins

Derivation of slope (Fig. a) can be derived from the DEM, and used for modeling purposes. Next, the river networks (Fig. b) need to be organied by drainage basin. A small portion of Bhutan drains north into the Tsangpo, reaching the Brahmaputra. Most of Bhutan drains south across the Indian plain, reaching the Brahmaputra through a number of rivers. In order to avoid modeling most of the Brahmaputra in order to cover Bhutan, 17 basins were defined (Fig. c). An alternative is to clip the basins as they leave Bhutan. This generates 95 watersheds of 1 to 199 cells, covering the 2225 cells of Bhutan (including every grid cells that touches Bhutan), plus 682 cells that are upstream from Bhutan (Fig. d). This raises the question: what data layers need to extend throughout watersheds that flow into Bhutan. 

 

High-Resolution DEMS: ASTER

For site-specific future studies, where higher spatial resolution is required of a DEM, ASTER-derived DEMs might be of utility  The ASTER scene contains 14 spectral bands, along with a backlooking band used to create the DEM shown here. The highest areas in the north and south of the ASTER tile are clouds. It would be challenging to build a cloud-free mosaic of Bhutan. Close examination of the ASTER-based Global DEM (http://ai-relief.org/auxdetail.html) would be informative. With the recent release of the ASTER DEM, such work could proceed. For example, Fig. (a) shows the DEM derived from SRTM, with one ASTER DEM tile, while (b) shows the quality of the global ASTER DEM for Bhutan. (c) shows the side--by-side quality of SRTM versus ASTER, while (d) and (e0 show the ASTER DEM at 30m and 15m resolution, respectively.

 

 

 

 

Soils and Geology

 Data on geology and soil types and properties are of universal value. Knowledge of soil depths and fertility are of primary importance for determining agriculture importance. Knowledge of soil physical properties (hydraulic conductivity, infiltration capacity) is of primary importance to understanding the hydrology of a region. While no single source provides "adequate" coverage at this time, there are several sources of soil information available, from which products can be derived.

(1) Country level data, extracted from global soil data bases. As a first description, soils for Bhutan were extracted from the Global FAO/UNESCO database (FAO 1995), producing a very crude representation (Fig. 9). For the FAO data to be useful for the hydrology modeling, each soil type ("name") must be converted to a usable physical parameter, soil texture. Texture can then be used to infer the specific physical attributes required by the models. This can be done through the application of several procedures. Soil texture (%clay and %sand) and bulk density (b) cab be derived from the soil type map using the World Inventory of Soil Emission (WISE) potentials pedon data base (Batjes, 1995) with the aid of the Soilprogram (Carter and Scholes, 1999). The model then assigns attributes, based on the texture. This step hasn't been done yet, pending the outcome of (2), below.

(2) Country specific soil profile data. NSSC has an emerging soils database, BHUSOD, which contains detailed information on over 1600 (soon to be nearly 2000) soil auger/pit samples (a). Each sample has a detailed breakdown of multiple soil properties. Mr. Tsheten Dorji converted the database to an ArcGIS-compatible form, and Mr. Hans Noord plotted the station locations. These results are encouraging, showing that there is a fairly wide distribution of samples. Clearly the majority of samples are in the greater Thimphu area, and along the East-West highway, but there are samples from the north and south.
 

If these points could be used to derive a nation-wide and local soil map(s), it would be of great utility for multiple applications. This is doable. The classifications used by USDA/FAO should be rationalized with the system used by the Soil Survey Unit (SSD) of Bhutan. Then, a "soils model" could be derived, whereby soil grids are predicted from knowing the relation of soil properties to geology (b), landcover, slope, and elevation. Key to doing this is that someone who knows these relationships works with the spatial modeler. It is very strongly recommended that a team be committed to this project in Phase II.

 

 Preparation of Soil Data for Hydrology Modeling

Soil physical properties for VIC were obtained from the FAO Soil Program, for bulk density and sand/clay content. From sand and clay content, each 1/12 degree pixel grid cell is assigned to one of the twelve FAO soil textural classes, and soil hydrologic parameters estimated from the USDA soil texture class, following Schaake (2000), including porosity, saturated hydraulic conductivity, field Capacity, and wilting point. Soil depths are taken as 10, 20 and 120 cm as initial guess for the layers one to three respectively (likely to be modified after calibration of simulated to observed flows). Other soil parameters are either computed from those already obtained ; for instance , particle density computed from bulk density and porosity or recommended values from previous studies are used ; for instance soil thermal damping depth for which 4 m is a recommended value.