Escenario Experimentación con humanos

For setting up SWAT-MAROC the following physical and empirical soil-related parameters are determined. Optional parameters considering chemical and biological properties as well as erodibility have not been determined.

 Available Soil Water Capacity: SOL_AWC [%]

This parameter can also be referred to as plant available water. While wilting point and field capacity are calculated by SWAT, available soil water capacity is a user input. Soil water saturation, and hence the related processes of evaporation and percolation, are strongly influenced by this factor.

 Clay, Silt, Sand and Rock: SOL_CLAY, SOL_SILT, SOL_SAND [%]

The volumetric contents of the different particle classes are used to calculate the wilting point.

 Soil Depth: SOL_Z [mm]

Soil depth governs the size of the soil storage for water, thereby it strongly influences evaporation, plant growth and direct groundwater recharge (percolation).

 Soil Hydrological Group: HYDGRP[A-D]

The NRCS classifies soils in four Soil Hydrological Groups, based on their infiltration capacities. This classification is needed, as the SCS Curve Number infiltration routine is used.

 Saturated Hydraulic Conductivity: SOL_K [mm/h]

This parameter is used for the determination of the conceptual hydrological soil groups. Further uses are the calculation of percolation and interflow.

 Bulk Density: SOL_BD [g/cm³]

Bulk density mainly affects the temperature distribution within the soil and therefore evaporation rates.

 Albedo: SOL_ALB [%]

Albedo affects the energy balance of the soil, as incoming short-wave radiation is partly reflected.

 Depth Distribution of Evaporative Demand: ESCO [-]

This conceptual compensation factor adapts the depth distribution that is used to meet the evaporative demand from the soil.

Available soil data in the research area is limited to agricultural areas, where detailed investigations have been carried out but results are not representative for the whole catchment (e.g. Brancic 1968; Radanovic 1968). The soil map of Morocco (Cavallar 1950) is on the one hand rather coarse (1:1,500,000), on the other hand soil units are not attributed with the properties required to set up a hydrological model. Global datasets are available as well, but their resolution is too coarse and local conditions are not considered (e.g. the Harmonized World Soil database; FAO 2009).

Within the IMPETUS project a soil database has been established which includes data from 211 soil profiles within the Drâa Catchment (Klose 2008a). For each of these soil profiles skeleton content, texture, carbonate, organic carbon, nitrogen content and pH have been determined. In order to derive soil hydraulic properties which could not be measured in situ due to high skeleton contents, pedotransfer functions were applied including a correction for skeleton content (Rawls et al. 1982).

To derive continuous maps of soil properties Klose (2008a) used the CORPT approach. The CORPT approach is based on analyzing the relationships between soil properties and the five factors of soil formation Climate, Organisms, Relief, Parent material and Time (Jenny 1941).

Except time each of these factors is represented by adequate datasets as shown in Table 5-5.

Multiple linear regressions including dummy variables accounting for nominal parameters are used to evaluate the relationship of soil parameters and soil formation factors statistically. The regression equations were used to generate maps of soil properties. An idealized soil profile consisting of two layers had to be assumed in order to carry out the regionalization.

Since the results are continuous maps for every parameter and SWAT-MAROC works on discrete soil types, a classification is required. Two parameters have been determined to base the classification on: Available water capacity of the profile (AWC, [mm]) and saturated hydraulic conductivity (Ks, [mm/h]) for each layer. The former is an integral measure of the

parameters soil depth and porosity which in combination most prominently influence evaporation; the latter determines the runoff composition, as the Curve-Number is dependent on saturated hydraulic conductivity and interflow is simulated only when a sudden decrease in saturated conductivity occurs between two soil layers. Consequently these parameters are among the most sensitive in SWAT simulations (see section 5.2).

Table 5-5: Input data for the CORPT soil analysis (Klose 2008a)

Factor Dataset Source

Climate temperature and precipitation (Schulz 2007)

Organisms dominant vegetation type (Finckh 2008)

Relief

various terrain analyses using the SRTM digital elevation

model

(Global Land Cover Facility 2006)

Parent material Geological map 1:500,000 (ME 1959; Klose 2008b)

Time No data available

Quartiles of AWC and Ks for the upper soil layer have been determined; whereas subsoil Ks has been split in only two classes (see Figure 5-12). The resulting 32 soil classes cover similar areas (0.92 – 7.62% of the catchment), hence distortion during model discretization is less likely to occur than by using a more detailed map.

Figure 5-12: Soil classification for SWAT-MAROC and naming scheme

All required parameters have been taken from the soil property maps (see Appendix 4). Since the soil classification is based on the parameters that are required to determine the hydrological soil groups, the determination of these classes can be considered rather certain.

The Hydrological Soil Groups have been determined according to the USDA Soil Survey Guidelines (Neitsch et al. 2004). Class A soils are characterized by a high infiltration potential (see Table 5-6), whereas class D soil are characterized by a high runoff potential. Other thresholds considering cemented horizons or depth to an impermeable bedrock have not been considered, due to a lack of data. The catchment is dominated by class D soils.

Table 5-6: Thresholds for Hydrological Soil Group classification (Neitsch et al. 2004)

Hydrological Soil Group

A B C D

Saturated hydraulic conductivity of the most

restrictive soil layer to a depth of 100 cm [mm/h] >254.0 84.9 - 254.0 8.4 - 84.0 <8.4

The Bulk density has been estimated according to the sand content as proposed by Kemp et al. (1997):

Albedo of moist soil has been set to 20% according to Garratt (1993), who compiled albedo measurements for different climate zones. Albedo for desert soils ranged from 10-20%.

The global parameter ESCO modifies the depth distribution to meet the evaporative demand from the soil; this conceptual dimensionless parameter is adapted during calibration, as it cannot be determined from measured data.

The regionalization of soil data, i. e. the construction of a large scale soil map, based on 211 soil profiles is highly uncertain. In this study this is especially true for soil depth, as a generally idealized two layer profile had to be assumed. Therefore, the depth representation of the created soil map only weakly represents the measured depths (r²=.47). A more detailed statistical evaluation of the regionalization quality is provided by Klose (2008a). Furthermore the determination of the soils lower boundary, separating soil from unprocessed parent material is difficult, especially in arid regions, where already the soil itself is virtually devoid of any signs of biological activity (Klose 2009). In other studies the lower boundary is often set arbitrarily (e.g. 2 m; NRCS 1999). Therefore the soil depth is one of the most prominent factors examined in the uncertainty analysis.

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