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Erkan Istanbulluoglu

University of Washington | Associate Professor

Subject Areas: Hydrology

 Recent activity

ABSTRACT:

Presentations created by participants of the GSA 2017 meeting short course, Landlab Earth Surface Modeling Toolkit: Building and Applying Models of Coupled Earth Surface Processes.

Participants selected a tutorial group to join in the second part of the course. Throughout the afternoon, groups explored the topic they chose with a Landlab developer. At the end of the day groups shared what they did with Landlab using these presentations.

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ABSTRACT:

This model driver couples ecohydrologic processes of solar radiation, probabilistic soil moisture, annual net primary productivity, detachment limited fluvial erosion and hillslope diffusion mediated by biomass.

Literature: The radiation model and radiation ratios are based on methods in Bras, R. L.: Hydrology: an introduction to hydrologic science, Addison Wesley Publishing Company, Boston, Mass., USA, 643 pp., 1990.

Soil mositure model is based on: Porporato et al 2004: Porporato, A., Daly, E., & Rodriguez‐Iturbe, I. (2004). Soil water balance and ecosystem response to climate change. The American Naturalist, 164(5), 625-632.

Biomass is considerd to reflect the highest grass/shrubland biomass [gr/m2] in a year (Annual Primary Productivity) and calculated as a function of mean annual actual ET scaled by plant water stress. ANPP equation used is from: Webb WL, Szarek SR, Lauenroth WK, Kinerson RS. 1978. Primary productivity and water use in native forest, grassland, and desert ecosystems. Ecology 59(6): 1239–1247. DOI: 10.2307/1938237

This notebook is developed by Erkan Istanbulluoglu (UW), Sai Nudurupati (UW), Greg Tucker (CU)

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ABSTRACT:

Geospatial tools and visualization is needed to develop a data and model integration pipeline for assessing landslide hazards.  This project is one component of multi-hazard (earthquake, flood, tsunami) assessment in watersheds spanning mountain peaks to coastal shores.  A common challenge in interpreting and validating distributed models is in comparing these data to direct observations on the ground. Modeling data of landslides for regional planning intentionally cover large regions and many landslides, incorporating different temporal and spatial sampling frequency and stochastic processes than observations derived from landslide inventories developed in the field. This kind of analysis requires geospatial tools to enable visualization, assessment of spatial statistics and extrapolation of spatial data linked to hierarchical relationships, such as downstream hydrologic impacts.  
Landslide geohazards can be identified through numerous methods, which are generally grouped into quantitative (e.g., Hammond et al. 1992; Wu and Sidle 1995) and qualitative (e.g., Gupta and Joshi 1990; Carrara et al. 1991; Lee et al. 2007) approaches. Mechanistic process-based models based on limited equilibrium analysis can quantify the roles of topography, soils, vegetation, and hydrology (when coupled with a hydrologic model) in landsliding in quantitative forms (Montgomery and Dietrich 1994; Miller 1995; Pack et al. 1998).  Processed-based models are good for predicting the initiation of landslides even where landslide inventories are lacking, but missed predictions likely stem from parameter uncertainty and unrepresented processes in model structure implicitly captured in qualitative approaches. A common qualitative approach develops landslide susceptibility based on experts rating multiple landscape attributes.  These approaches provide general indices rather than quantified probabilities of relative landslide susceptibility applicable to the study location and cannot represent causal factors or triggering conditions that change in time (van Western et al. 2006). Both approaches rarely provide a probabilistic hazard in space and time, requisite for landslide risk assessments beneficial for planning and decision making (Smith 2013).
This project will start the groundwork to integrate numerical modeling developed by University of Washington  with qualitative assessments of landslide susceptibility performed by Washington Department of Natural Resources.

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ABSTRACT:

In this tutorial we illustrate reading watershed DEM into Landlab, conducting several basic watershed analysis necessary for most hydrologic model applications, and flood routing using the de Almedia (2012) algorithm on the watershed processed.

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ABSTRACT:

This Landlab driver illustrates the use of Landlab ecohydrology components to model semi-arid ecohydrological dynamics driven by a storm pulse and solar radiation. Components (names given in parenthesis) we will use are:
* Solar radiation (Radiation)
* Potential Evapotranspiration (PotentialEvapotranspiration)
* Soil Moisture (SoilMoisture)
* Vegetation (Vegetation)
A digital elevation model (DEM) of a headwater region in central New Mexico (latitude 34N) will be used as input.
Components will be introduced step by step. First, we will start with mapping solar radiation and potential evapotranspiration (PET). Note that some of the commands used are only to provide information about the in/outputs of components and can be deleted or not run. We will then run soil moisture and vegetation modules and show how to write outputs in a file.

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Generic Generic
Landlab Ecohydrologic Mapping Tutorial
Created: July 21, 2016, 1:34 a.m.
Authors: Erkan Istanbulluoglu · Sai S. Nudurupati

ABSTRACT:

This Landlab driver illustrates the use of Landlab ecohydrology components to model semi-arid ecohydrological dynamics driven by a storm pulse and solar radiation. Components (names given in parenthesis) we will use are:
* Solar radiation (Radiation)
* Potential Evapotranspiration (PotentialEvapotranspiration)
* Soil Moisture (SoilMoisture)
* Vegetation (Vegetation)
A digital elevation model (DEM) of a headwater region in central New Mexico (latitude 34N) will be used as input.
Components will be introduced step by step. First, we will start with mapping solar radiation and potential evapotranspiration (PET). Note that some of the commands used are only to provide information about the in/outputs of components and can be deleted or not run. We will then run soil moisture and vegetation modules and show how to write outputs in a file.

···
Generic Generic

ABSTRACT:

In this tutorial we illustrate reading watershed DEM into Landlab, conducting several basic watershed analysis necessary for most hydrologic model applications, and flood routing using the de Almedia (2012) algorithm on the watershed processed.

···
Collection Resource Collection Resource

ABSTRACT:

Geospatial tools and visualization is needed to develop a data and model integration pipeline for assessing landslide hazards.  This project is one component of multi-hazard (earthquake, flood, tsunami) assessment in watersheds spanning mountain peaks to coastal shores.  A common challenge in interpreting and validating distributed models is in comparing these data to direct observations on the ground. Modeling data of landslides for regional planning intentionally cover large regions and many landslides, incorporating different temporal and spatial sampling frequency and stochastic processes than observations derived from landslide inventories developed in the field. This kind of analysis requires geospatial tools to enable visualization, assessment of spatial statistics and extrapolation of spatial data linked to hierarchical relationships, such as downstream hydrologic impacts.  
Landslide geohazards can be identified through numerous methods, which are generally grouped into quantitative (e.g., Hammond et al. 1992; Wu and Sidle 1995) and qualitative (e.g., Gupta and Joshi 1990; Carrara et al. 1991; Lee et al. 2007) approaches. Mechanistic process-based models based on limited equilibrium analysis can quantify the roles of topography, soils, vegetation, and hydrology (when coupled with a hydrologic model) in landsliding in quantitative forms (Montgomery and Dietrich 1994; Miller 1995; Pack et al. 1998).  Processed-based models are good for predicting the initiation of landslides even where landslide inventories are lacking, but missed predictions likely stem from parameter uncertainty and unrepresented processes in model structure implicitly captured in qualitative approaches. A common qualitative approach develops landslide susceptibility based on experts rating multiple landscape attributes.  These approaches provide general indices rather than quantified probabilities of relative landslide susceptibility applicable to the study location and cannot represent causal factors or triggering conditions that change in time (van Western et al. 2006). Both approaches rarely provide a probabilistic hazard in space and time, requisite for landslide risk assessments beneficial for planning and decision making (Smith 2013).
This project will start the groundwork to integrate numerical modeling developed by University of Washington  with qualitative assessments of landslide susceptibility performed by Washington Department of Natural Resources.

···
Generic Generic

ABSTRACT:

This model driver couples ecohydrologic processes of solar radiation, probabilistic soil moisture, annual net primary productivity, detachment limited fluvial erosion and hillslope diffusion mediated by biomass.

Literature: The radiation model and radiation ratios are based on methods in Bras, R. L.: Hydrology: an introduction to hydrologic science, Addison Wesley Publishing Company, Boston, Mass., USA, 643 pp., 1990.

Soil mositure model is based on: Porporato et al 2004: Porporato, A., Daly, E., & Rodriguez‐Iturbe, I. (2004). Soil water balance and ecosystem response to climate change. The American Naturalist, 164(5), 625-632.

Biomass is considerd to reflect the highest grass/shrubland biomass [gr/m2] in a year (Annual Primary Productivity) and calculated as a function of mean annual actual ET scaled by plant water stress. ANPP equation used is from: Webb WL, Szarek SR, Lauenroth WK, Kinerson RS. 1978. Primary productivity and water use in native forest, grassland, and desert ecosystems. Ecology 59(6): 1239–1247. DOI: 10.2307/1938237

This notebook is developed by Erkan Istanbulluoglu (UW), Sai Nudurupati (UW), Greg Tucker (CU)

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Generic Generic
End-of-course tutorial presentations
Created: Nov. 1, 2017, 4:14 p.m.
Authors: Nathan Lyons

ABSTRACT:

Presentations created by participants of the GSA 2017 meeting short course, Landlab Earth Surface Modeling Toolkit: Building and Applying Models of Coupled Earth Surface Processes.

Participants selected a tutorial group to join in the second part of the course. Throughout the afternoon, groups explored the topic they chose with a Landlab developer. At the end of the day groups shared what they did with Landlab using these presentations.

···