ASSESSING HILLSLOPE RESPONSE MECHANISMS USING STABLE ISOTOPES C. Freese, SA Lorentz, J van Tol & PAL le Roux 1 Centre for Water Resources Research, University.

Slides:



Advertisements
Similar presentations
Hydrology Rainfall - Runoff Modeling (I)
Advertisements

Spatial classification data are of minimal value to support model representation because the uncertainty associated with parameterization is too high.
PROTECTING AND PRESERVING GROUND WATER with MONITORING SYSTEMS and VULNERABILITY MAPS PAPATHEODOROU Konstantinos, Assoc. Professor 1 EVANGELIDIS Konstantinos,
A Model for Evaluating the Impacts of Spatial and Temporal Land Use Changes on Water Quality at Watershed Scale Jae-Pil Cho and Saied Mostaghimi 07/29/2003.
B1. Quantifying the role of AF in modifying watershed functions Starting from current practice in 'integrated watershed management' with participatory.
4 th International Symposium on Flood Defence Generation of Severe Flood Scenarios by Stochastic Rainfall in Combination with a Rainfall Runoff Model U.
Assessing the impact of a soil surface crust on simulated overland flow at the field scale. N. Chahinian (1, 2), M. Voltz (2), R. Moussa (2), G. Trotoux.
Unit Hydrograph Reading: Applied Hydrology Sections , 7.5, 7.7,
Introduction The agricultural practice of field tillage has dramatic effects on surface hydrologic properties, significantly altering the processes of.
Plot Scale © Oregon State University Isotope Hydrology Shortcourse Prof. Jeff McDonnell Richardson Chair in Watershed Science Dept. of Forest Engineering.
Some of my current research: Modeling sediment delivery on a daily basis for meso-scale catchments: a new tool: LAPSUS-D By: Saskia Keesstra and Arnaud.
بسم الله الرحمن الرحيم قال الله تعالى : “ الله الذي خلق السموات والارض وأنزل من السمآء ماء فأخرج به من الثمرات رزقا لكم وسخر لكم الفلك لتجرى في البحر.
Unit Hydrograph Reading: Sections , 7.5, 7.7,
D.L. Farmer (1), M. Sivapalan (1), and I. Lockley (2) Assessing vegetation influence on water balance in rehabilitation landscapes using simple storage.
Scale Issues in Hydrological Modelling: A Review
Remote Mapping of River Channel Morphology March 9, 2003 Carl J. Legleiter Geography Department University of California Santa Barbara.
Hydrologic Mixing Models Ken Hill Andrew McFadden.
School of Geography FACULTY OF ENVIRONMENT School of Geography FACULTY OF ENVIRONMENT GEOG5060 GIS and Environment Dr Steve Carver
Hydrological Modeling FISH 513 April 10, Overview: What is wrong with simple statistical regressions of hydrologic response on impervious area?
Engineering Hydrology (ECIV 4323)
Integrated Approach for Assessing the Characteristic of Groundwater Recharge in Basin Scale Hsin-Fu Yeh*, Cheng-Haw Lee, Kuo-Chin Hsu Department of Resources.
The Effect of Soil Hydraulic Properties and Deep Seepage Losses on Drainage Flow using DRAINMOD Debjani Deb 26 th April, 2004.
CE 3372 – Lecture 10. Outline  Hydrology Review  Rational Method  Regression Equations  Hydrographs.
Application of seasonal climate forecasts to predict regional scale crop yields in South Africa Trevor Lumsden and Roland Schulze School of Bioresources.
Impact of Climate Change on Flow in the Upper Mississippi River Basin
MODELING HILLSLOPE WATER J. J. van Tol, S.A. Lorentz & P. A. L. Le Roux Hydropedology dialogue Pretoria 2014.
Building risk indicators of surface water contamination by pesticides at the small catchment scale. Taking into account spatial and temporal dimensions.
Dr. R.P.Pandey Scientist F. NIH- Nodal Agency Misconception: A DSS takes decisions ---(No)
Run-Off Characteristics of Streams
Geog 409: Advanced Spatial Analysis & Modelling © J.M. Piwowar1Modelling in Action Hardisty, et al., Computerised Environmental Modelling. Chichester:
U.S. Department of the Interior U.S. Geological Survey Sustainability of Ground-Water Use in the San Pedro River Basin, Cochise County, Arizona James Leenhouts,
6. Conclusions and further work An analysis of storm dew-point temperatures, using all available dew-point estimates was carried out for 10 significant.
Intro to Geomorphology (Geos 450/550) Lecture 5: watershed analyses field trip #3 – Walnut Gulch watersheds estimating flood discharges.
Testing the assumptions in JULES: chalk soils Nataliya Bulygina, Christina Bakopoulou, Adrian Butler, and Neil McIntyre.
Multiscale modelling to assess the impact of regulated rivers in Scotland on the ecology of Atlantic salmon (Salmo salar L.) Study Catchments Tay catchment.
Project: NSF grant EAR “CZO: Transformative Behavior of Water, Energy and Carbon in the Critical Zone: An Observatory to Quantify Linkages among.
Application of a rule-based system for flash flood forecasting taking into account climate change scenarios in the Llobregat basin EGU 2012, Vienna Session.
FUTURE ECO- HYDROLOGY MAPS FOR CITIES Pieter le Roux Khuluma Sizwe Series.
Diagnostics for Model Structure: Improving Hydrological Models using Data from Experimental Basins Hilary McMillan 1 *, Martyn Clark 1, Guillermo Martinez.
CE 424 HYDROLOGY 1 Instructor: Dr. Saleh A. AlHassoun.
Sources of streamflow from hillslopes Baseflow streamflow maintained by groundwater contributions Stormflow Augmented by direct precipitation on saturated.
Application of Neuro-Fuzzy Techniques to Predict Ground Water Vulnerability B. Dixon, Ph.D. University of South Florida St. Petersburg, Florida 33701,
The hydrological cycle of the western United States is expected to be significantly affected by climate change (IPCC-AR4 report). Rising temperature and.
Introduction Conservation of water is essential to successful dryland farming in the Palouse region. The Palouse is under the combined stresses of scarcity.
HYDROLOGICAL PROCESSES IN THE LANDSCAPE
HYDROLOGIC DATA. BACKGROUND Analysis and synthesis of data is required to perform any hydrologic computation. The engineer needs to: Identify and define.
South Platte Decision Support System Colorado Water Conservation Board and Division of Water Resources.
Estimating Groundwater Recharge in Porous Media Aquifers in Texas Bridget Scanlon Kelley Keese Robert Reedy Bureau of Economic Geology Jackson School of.
Preliminary Applications of the HL-RDHM within the Colorado Basin River Forecast Center Ed Clark, Hydrologist Presented July 26 th, 2007 as part of the.
Parameterisation by combination of different levels of process-based model physical complexity John Pomeroy 1, Olga Semenova 2,3, Lyudmila Lebedeva 2,4.
Surface Water Surface runoff - Precipitation or snowmelt which moves across the land surface ultimately channelizing into streams or rivers or discharging.
P B Hunukumbura1 S B Weerakoon1
1 DETERMINATION OF SOURCES AND FLOWPATHS USING ISOTOPIC AND CHEMICAL TRACERS, GREEN LAKES VALLEY, ROCKY MOUNTAINS Fengjing Liu and Mark Williams Department.
Delineation of Ground water Vulnerability to Agricultural Contaminants using Neuro-fuzzy Techniques Barnali Dixon 1, H. D. Scott 2, J. V. Brahana 2, A.
DEVELOPMENT OF A CELL BASED MODEL FOR STREAM FLOW PREDICTION IN UNGAUGED BASINS USING GIS DATA P B Hunukumbura & S B Weerakoon Department of Civil Engineering,
CE 3354 Engineering Hydrology Lecture 2: Surface and Groundwater Hydrologic Systems.
Modeling Efficient and Sustainable Hydroelectric Production at Small Dams through Artificially Induced Streamflow and Application of System Control United.
Sanitary Engineering Lecture 4
WATER RESOURCES DEPARTMENT
Near-surface Geologic Environments
Simulation of stream flow using WetSpa Model
Application of soil erosion models in the Gumara-Maksegnit watershed
in the Neversink River Basin, New York
Aynalem T. Tsegaw and Knut Alfredsen
Hydraulics of Wetlands: Monitoring and Modeling Emily Spargo
EC Workshop on European Water Scenarios Brussels 30 June 2003
Floods and Flood Routing
Motivation Estimation of travel time of water molecules (Transit-Time Distribution) from Rainfall to Outlet needs gauged river location for effective precipitation.
Umweltbundesamt, Austria
Presentation transcript:

ASSESSING HILLSLOPE RESPONSE MECHANISMS USING STABLE ISOTOPES C. Freese, SA Lorentz, J van Tol & PAL le Roux 1 Centre for Water Resources Research, University of KwaZulu-Natal, University of Fort Hare 3 Department of Soil Crop and Climate, University of the Free State, Bloemfontein, *Corresponding author (

Introduction Site specific nature of previous studies makes transfer to ungauged sites difficult due to: 1: Spatial and temporal complexity 2: Current lack of tools Residence time distribution equations generalized descriptors of catchment hydrology Spatially transferrable Potentially low data intensity Develop generalized descriptors of subsurface for use in a catchment scale model δ 18 O isotope data two-step algorithm ( derive Dp and τ) parameterize hillslope sub catchments in the ACRU Intermediate zone model comparative ACRU simulations to assess the ability of Dp and τ

Methodology

Convolution integral relates the output isotope time series to the input isotope time series simulating the probability distribution for a conservative tracer molecules Where: δ(t)=output δO 18 signal t’=integration parameter describing entry time of the tracer into the system t =calendar time δin=input δO 18 signal g(t - t’)=residence time distribution Where: g(t)=response function D p =Dispersion coefficient τ=mean response time. Where: N=number of time steps/samples α i =recharge factor P i =precipitation amount (mm) δ i =precipitation δO 18 value (‰) δ gw =ground water δO 18 value (‰)

Methodology

Results δ in

Results δ(t)

Results

HillslopeSiteDate Dispersion coefficient (D) Mean response time (τ) R2R2 Lower catchmen t 1LC 04February LC 04March LC 08February LC 08March Upper catchmen t 3UC 01February UC 01March UC3/4February UC3/4March

Results (ACRU 2000) R 2 = 0.68

ACRU Intermediate zone model

Results (ACRU Int)

R 2 = 0.71

Conclusions Low D p high τ – event pulse responses of the lower catchment. High D p low τ – sustained drainage of upper catchment. ACRU Int improvement on baseline simulations. – Peak flows (ACRU 2000 & Int) – Low flows (ACRU Int) – Improved simulation of soil water discharge to stream

Proposal Initial field setup/ maintainence – December 2014-February 2015 Improved data sets – Analyse for a range of tracers (EC, silica, N etc.) – Temporal sampling density (tracers) Rainfall Streamflow Soil water Monitor Mooi hillslopes – Hillslopes across different geologies – Identify similar/typical hillslopes

Proposal Further ACRU Int testing – Refine input data set (tracers) – Increase detail of Weatherley simulations (more landsegments) – Define typical hillslopes within certain parts of the Mooi – Parameterise & model Mooi hillslopes Further insight into transferability of Dp and τ – Capability to represent hydrological process across scales – Linked to existing classification systems