Presenter/Author Information

F. Kilonzo
Ann Van Griensven
Willy Bauwens

Keywords

lai, yield, swat, remote sensing

Start Date

1-7-2012 12:00 AM

Description

The assessment of the performance of (semi)distributed hydrological models has traditionally depended on parameters monitored at a gauging station usually located at the lowest end of a basin regardless of the size, complexity and spatial- temporal variations. The result of such an approach is that the processes in the basin are lumped by composting the catchment processes over time and space. The Soil and Water Assessment Tool (SWAT) model gives various outputs which are distributed all over the basin by use of the hydrologic response units (HRU). However, due to lack of physical location for the hydrological response unit, and their possible large number in a single watershed or even subbasin, it is physically impossible to monitor the flow, nutrients and sediments at all the outlets of these HRUs. The use of geographic information system (GIS) to overlay datasheets and the availability of gridded remotely sensed data for biomass, evapotranspiration, leaf area index (LAI) and yields in real time makes it possible to perform a dynamic quasi-distributed model validation. The SWAT model is used to test the applicability of remotely sensed variables on a 2905 Km2 basin. The watershed is data challenged, geologically difficult, with dynamic land management practices, elevations from 800m to 3000m above sea level, and drastically changing climatic conditions from semi- arid to humid tundra/montane conditions. The concept of land use soil units (LUSU) created from overlaid soil and land use classes makes it possible to spatially compare outputs. Results indicate that under unfertilized soil conditions, simulated yields for annual agricultural crop types are underestimated due to water and nutrient stresses. Under stress conditions, soil type plays a big role due to the available water retention capacity and hydraulic conductivity parameters. With reduced nutrient stress the type of agricultural crop is the major determinant of the yields in the LUSU. Although remotely sensed Leaf Area Index values are higher than the simulated LAI, it mirrors to a great extent the timing and shape of the simulated LAI, and depicts comparable seasonality characteristics.

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Jul 1st, 12:00 AM

Distributed Validation of Hydrological Model Using Field and Remotely Sensed Data

The assessment of the performance of (semi)distributed hydrological models has traditionally depended on parameters monitored at a gauging station usually located at the lowest end of a basin regardless of the size, complexity and spatial- temporal variations. The result of such an approach is that the processes in the basin are lumped by composting the catchment processes over time and space. The Soil and Water Assessment Tool (SWAT) model gives various outputs which are distributed all over the basin by use of the hydrologic response units (HRU). However, due to lack of physical location for the hydrological response unit, and their possible large number in a single watershed or even subbasin, it is physically impossible to monitor the flow, nutrients and sediments at all the outlets of these HRUs. The use of geographic information system (GIS) to overlay datasheets and the availability of gridded remotely sensed data for biomass, evapotranspiration, leaf area index (LAI) and yields in real time makes it possible to perform a dynamic quasi-distributed model validation. The SWAT model is used to test the applicability of remotely sensed variables on a 2905 Km2 basin. The watershed is data challenged, geologically difficult, with dynamic land management practices, elevations from 800m to 3000m above sea level, and drastically changing climatic conditions from semi- arid to humid tundra/montane conditions. The concept of land use soil units (LUSU) created from overlaid soil and land use classes makes it possible to spatially compare outputs. Results indicate that under unfertilized soil conditions, simulated yields for annual agricultural crop types are underestimated due to water and nutrient stresses. Under stress conditions, soil type plays a big role due to the available water retention capacity and hydraulic conductivity parameters. With reduced nutrient stress the type of agricultural crop is the major determinant of the yields in the LUSU. Although remotely sensed Leaf Area Index values are higher than the simulated LAI, it mirrors to a great extent the timing and shape of the simulated LAI, and depicts comparable seasonality characteristics.