The document summarizes the analysis of soil erosion potential in the San Marcos Watershed using the RUSLE model in GIS. The RUSLE model calculates average annual soil loss based on 5 factors - rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover (C), and conservation practices (P). Spatial data for each factor was obtained and analyzed in GIS. The results showed higher soil erosion potential in the northwest area due to steeper slopes as indicated by the higher LS values.
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Streamflow simulation using radar-based precipitation applied to the Illinois...Alireza Safari
This paper describes the application of a spatially distributed hydrological model WetSpa (Water and Energy Transfer between Soil, Plants and Atmosphere) using radar-based rainfall data provide by the United States Hydrology Laboratory of NOAA's National Weather Service for a distributed model intercomparison project. The model is applied to the
river basin above Tahlequah hydrometry station with 30-m spatial resolution and one hour time--step for a total simulation period of 6 years. Rainfall inputs are derived from radar. The distributed model parameters are based on an extensive database of watershed characteristics available for the region, including digital maps of DEM, soil type, and land use. The model is calibrated and validated on part of the river flow records. The simulated hydrograph shows a good correspondence with observation (Nash efficiency coeffiecient >80%, indicating that the model is able to simulate the relevant hydrologic processes in the basin accurately.
Matthew Cahalan Georgia Water Resources Conference PresentationMatthew Cahalan
This is the poster I presented at the 2015 Georgia Water Resources Conference. It focuses on my M.S. thesis research that seeks to answer this fundamental question: "why do sinkholes form where they do?". This question was answered using an improved remote sensing sinkhole mapping procedure, integration of many datasets (i.e., hydrologic, anthropogenic, geologic, geomorphologic, and hydrogeologic), and spatial statistics (i.e., ordinary least squares and geographically weighted regression). This poster / my presentation was voted as one of the top 3 posters at the conference.
Soil loss due to erosion is one of the global problems. It affects the crop production and natural vegetation. Mapping soil loss is first step to mitigate the consequences of soil erosion. Universal Soil Loss Equation (USLE) is widely used for the study of soil erosion all over the world.
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Submitted to:
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Presentation of project in the course " Hydro-Geological Risks in Mountain Area (Geological Assessment Part)" for M.Sc. "Civil Engineering for Risk Mitigation" at Politecnico di Milano.
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Submitted to:
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It is increasingly being recognised internationally that integrated catchment management (ICM) is a useful organising framework for tackling the ongoing challenge of balancing sustainable use and development of our natural resource, against achieving environmental goals. The basic principles of ICM (Williams, 2012) are to:
• Take a holistic and integrated approach to the management of land, biodiversity, water and community resources at the water catchment scale;
• Involve communities in planning and managing their landscapes; and
• Find a balance between resource use and resource conservation
ICM is now well established in Australia, New Zealand, and the United States. In Europe the ICM approach has been proposed as being required to achieve effective water and catchment management, and is the approach being promoted by DEFRA for the UK, where it is called the “Catchment Based Approach” (CaBA). The principles and methodologies behind ICM sit well within the context of the Water Framework Directive with its aims and objectives for good water quality, sustainable development and public participation in water resource management. In Ireland it is proposed that the ICM approach will underlie the work and philosophy in developing and implementing future River Basin Management Plans.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Soil loss due to erosion is one of the global problems. It affects the crop production and natural vegetation. Mapping soil loss is first step to mitigate the consequences of soil erosion. Universal Soil Loss Equation (USLE) is widely used for the study of soil erosion all over the world.
Integrated hydro-geological risk for Mallero (Alpine Italy) – part 1: geologyMaryam Izadifar
Presentation of project in the course " Hydro-Geological Risks in Mountain Area (Geological Assessment Part)" for M.Sc. "Civil Engineering for Risk Mitigation" at Politecnico di Milano.
Submitted by:
Maryam Izadifar, Alireza Babaee
Submitted to:
Professor Laura Longoni
Integrated hydro-geological risk for Mallero basin (Alpine Italy) – part 1: g...Alireza Babaee
Presentation of project in the course " Hydro-Geological Risks in Mountain Area (Geological Assessment Part)" for M.Sc. "Civil Engineering for Risk Mitigation" at Politecnico di Milano.
Submitted by:
Maryam Izadifar, Alireza Babaee
Submitted to:
Professor Laura Longoni
This presentation was given as part of the EPA-funded Catchment Science and Management Course focusing on Integrated Catchment Management, held in June 2015. This course was delivered by RPS Consultants. If you have any queries or comments, or wish to use the material in this presentation, please contact catchments@epa.ie
It is increasingly being recognised internationally that integrated catchment management (ICM) is a useful organising framework for tackling the ongoing challenge of balancing sustainable use and development of our natural resource, against achieving environmental goals. The basic principles of ICM (Williams, 2012) are to:
• Take a holistic and integrated approach to the management of land, biodiversity, water and community resources at the water catchment scale;
• Involve communities in planning and managing their landscapes; and
• Find a balance between resource use and resource conservation
ICM is now well established in Australia, New Zealand, and the United States. In Europe the ICM approach has been proposed as being required to achieve effective water and catchment management, and is the approach being promoted by DEFRA for the UK, where it is called the “Catchment Based Approach” (CaBA). The principles and methodologies behind ICM sit well within the context of the Water Framework Directive with its aims and objectives for good water quality, sustainable development and public participation in water resource management. In Ireland it is proposed that the ICM approach will underlie the work and philosophy in developing and implementing future River Basin Management Plans.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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A Strategic Approach: GenAI in EducationPeter Windle
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Biological screening of herbal drugs: Introduction and Need for
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
3. 1. Introduction
Motivation
Morphological Changes
of River Beds.
- Settlement,
- Entrainment.
Sediments
by Soil Erosion
Decrease of Water
Quality in River Systems.
- Water depth decrease,
- Turbidity increase,
- Nutrient increase.
By Thavasi R
Assessment of the Potential of Soil
Erosion Using GIS and RUSLE model.
Objective
4. 1. Introduction
RUSLE(Revised Universal Soil Loss Equation)
By Thavasi R
* The Universal Soil Loss Equation (USLE) was first developed in the 1960s by Wischmeier and Smith.
* It was later revised in 1997 in an effort to better estimate the values of the various parameters
in the USLE.
A = R x K x LS x C x P
A: average annual potential soil loss
(tons/acre/year),
R: rainfall-runoff erosivity factor,
K: soil erodibility factor,
LS: slope length and degree factor,
C: land-cover management factor,
P: conservation practice factor.
5. 1. Introduction
Methodology
By Thavasi R
Study Area
- Data sources: USDA, USGS, and NRCS.
- Coordinate system(Projection).
▪ North America Albers Equal Area Conic.
* Datum: D North American 1983.
- Data form and cell resolution.
▪ Raster data with a cell size of 30m x 30m.
- San Marcos Watershed.
(Subbasin in central Texas)
6. 2. Analysis
R factor(Rainfall erosivity parameter)
By Thavasi R
- Accounts for the energy and runoff of rainfall.
- An empirical equation to determine R factor.
(by Kurt cooper, 2011)
▪ P is mean annual precipitation(inches).
- Data: 1961~1990 precipitation (USDA/NRCS).
- GIS function:
Raster calculator.
- Raster data of
R factor: 140~160.
(uniform precipitation)
7. 2. Analysis
K factor(Soil erodibility parameter)
By Thavasi R
- Accounts for soil texture, structure, organic matter,
and even permeability.
- Data : GIS shapefiles with tabular data including the
K factor for each soil type from NRCS.
- GIS function:
Feature to raster.
- Raster data of K
factor: higher value
in south east area.
(flat topography and
alluvial accumulation)
8. 2. Analysis
LS factor(Slope length parameter)
By Thavasi R
- Represents the effects of slope length (L) and
slope steepness (S) on the erosion in the basin.
- USPED(Unit Stream Power Erosion and Deposition,
Jim Pelton et al, 2012) method: Two equations for L and S
▪ L is the slope length factor at some point on
the landscape,
▪ λA is the area of upland flow,
▪ m is an adjustable value depending on the
soil’s susceptibility to erosion,
▪ 22.1(m) is the unit plot length.
▪ θ is the slope in degrees,
▪ 0.09 is the slope gradient constant,
▪ n is an adjustable value depending on the soil’s
susceptibility to erosion
Here, m=0.4 and n=1.4, which is typical of farm and rangeland with
low susceptibility .
9. 2. Analysis
LS factor(Slope length parameter)
By Thavasi R
- Calculation of LS.
▪ Step1: calculate flow direction from DEM(ArcGIS server),
▪ Step2: calculate flow accumulation from flow direction,
▪ Step3: calculate slope in degree from DEM,
▪ Step4: raster calculation of LS using USPED equations.
- Raster data of LS
factor: higher value
in north west area.
(steep slope)
10. 2. Analysis
C factor(Land-cover management parameter)
By Thavasi R
- A ratio comparing the soil loss from a specific type of
vegetation cover.
- Data: land-cover data of shapefile from USGS.
- C value table for land cover specification (Haan et al, 1994)
11. 2. Analysis
C factor(Land-cover management parameter)
By Thavasi R
- GIS function: Feature to raster.
- Raster data of C factor: Not much variation over the
target area
<Land cover(shapefile)> <C value(raster data)>
12. 2. Analysis
P factor(Conservation practice parameter)
By Thavasi R
- Represents the ratio of soil loss by a support
practice to that of straight-row farming up and down
the slope.
- For this project the ratio is kept at 1, indicating
straight-row farming.
13. 2. Analysis
Soil Erosion
By Thavasi R
- Average annual soil erosion (tons/acre/year)
= R x K x LS x C x P.
- GIS function : Raster calculator.
- Soil erosion:
higher value in north
west area.
(Effects of LS rather
than R, K, and C)
14. 3. Conclusion
Caution is needed when interpreting the
results considering the assumptions made to
create each variable and errors of an
empirical equation for the RUSLE model.
By Thavasi R
The RUSLE model combined with GIS is
effective to estimate the potential of soil
erosion for the target watershed.
- From basic overlays of the 5 variables and
the raster calculator, the model was
accurately depicted.
15. Acknowledgements
By Thavasi R
Thanks to the USGS, the USDA, and NRCS
for making data accessible to the public.
Thanks to David R. Maidment, David G.
Tarboton, Anthony Castronova, and Larry
Band for the passionate lectures.
Thanks to TA(Gonzalo Espinoza Davalos).
16. References
By Thavasi R
USDA-NRCS <www.soils.usda.gov>
USGS <http://seamless.usgs.gov>
Haan, C.T., Barfield, B.J. and Hayes J.C. (1994). Design
Hydrology and Sedimentology for Small Catchments.
Academic Press, Inc, California.
Evaluation of the relationships between the RUSLE R-
factor and mean annual precipitation, Kurt Cooper, 2011
<http://www.engr.colostate.edu/~pierre/ce_old/Projects/linkfiles/Cooper
%20R-factor-Final.pdf>
Calculating Slope Length Factor (LS) in the Revised
Universal Soil Loss Equation (RUSLE), Jim Pelton, Eli
Frazier, and Erin Pickilingis, 2012
<http://gis4geomorphology.com/wp-content/uploads/2014/05/LS-Factor
-in-RUSLE-with-ArcGIS-10.x_Pelton_Frazier_Pikcilingis_2014.docx>
It is a pleasure to show you the results of my Term Project. I estimated the soil erosion potential using GIS and RUSLE model.
The contents are introduction, analysis, and conclusion.
In introduction, I will talk about the motivation, objective, explanation of RUSLE model, methodology, and study area.
In analysis, I will show you the estimation of each parameter for RUSLE model using GIS.
Sediments by soil erosion have been an important factor that affects the morphological changes of river beds and water quality in river systems.
So, for the best management of sediment particles, it is essential to estimate the potential of soil erosion accurately.
To estimate the potential of soil erosion, I adopted RUSLE model.
The RUSLE model uses the simple equation as you see. Average annual soil loss can be calculated by multiplying 5 parameters.
R is the rainfall-runoff erosivity factor, K is the soil erodibility factor, LS is the slope length and degree factor, C is the landcover management factor, and P is the conservation practice factor.
I will explain these parameters more precisely in analysis section.
All data came from USDA, USGS, and NRCS.
And I used a constant coordinate system for all maps.
All of the shapefile form data are converted into raster data with a cell size of 30m.
Study area is San Marcos watershed which is familiar with me through the GIS class.
Although I am in an international section, my target area is San Marcos watershed.
Now, I will talk about the estimation process of each parameter.
R factor accounts for the energy and runoff of rainfall. And, I used empirical equation to calculate R factor I used precipitation data from 1961 to 1990 offered by USDA and NRCS.
Using the raster calculator in GIS, mean annual precipitation is converted into raster data of R factor.
As you see, there is not so much variation in R factor due to the uniform precipitation over the basin area.
K factor accounts for soil texture, structure, organic matter, and even permeability.
K factor is provided by NRCS with GIS shapefiles.
Using the feature to raster function in GIS, shapefile of soil type is converted into raster data of K factor.
K factor is higher in south east area.
I think that the flat topography and alluvial accumulation in south east area caused higher K value in that area.
LS factor represents the effects of slope length and slope steepness on the erosion in the basin.
I adopted USPED method to calculate LS.
USPED method suggests two equations for L and S as you see.
Using these equations in GIS raster calculator, LS factor is calculated.
This is the detail steps for LS calculation.
At Step 1, flow direction is calculated from DEM.
At Step 2, from the flow direction, flow accumulation is calculated.
At Step 3, Slope is calculated in degree form DEM.
At Step 4, Using USPED equations with flow accumulation and slope data, LS is calculated.
LS factor is higher in north west area. I think it is the effect of steep slope in that area.
C factor is a ratio comparing the soil loss from a specific type of vegetation cover.
Landcover data of shapefile came from USGS. And C values for specific landcovers are provided by Haan in 1994.
Using GIS feature to raster function, the shapefile of landcover is converted into a raster data of C factor.
There is not much variation in C factor over the target area except some points in the middle of the area.
P factor represents the ratio of soil loss by a support practice to that of straight-row farming up and down the slope.
For this project, the ratio is kept at 1, indicating straight-row farming.
The average annual soil loss is calculated by multiplying 5 parameters using the GIS raster calculator.
The amount of soil erosion is high in the north west area because of the effect of steeper slope which is shown in the LS factor.
The RUSLE model combined with GIS is effective to estimate the potential of soil erosion for the target watershed.
Caution is needed when interpreting the results considering the assumptions made to create the variables and errors of an empirical equation for the RUSLE model.
Thanks to professors and TA for the passionate lectures and helps.
