Embankment and cut slope monitoring and analysis

Similar documents
Crop Water Requirement. Presented by: Felix Jaria:

CHAPTER FIVE Runoff. Engineering Hydrology (ECIV 4323) Instructors: Dr. Yunes Mogheir Dr. Ramadan Al Khatib. Overland flow interflow

Overview of the Surface Hydrology of Hawai i Watersheds. Ali Fares Associate Professor of Hydrology NREM-CTAHR

Afternoon Lecture Outline. Northern Prairie Hydrology

Water Management: A Complex Balancing Act

Air. Water. Minerals (rocks)

Afternoon Lecture Outline

Application of a Basin Scale Hydrological Model for Characterizing flow and Drought Trend

TheHelper, A User-Friendly Irrigation Scheduling Tool In Florida and Hawaii A. Fares 1, M. Zekri 2 and L.R. Parsons 2. Abstract

Inside of forest (for example) Research Flow

Electric Forward Market Report

July, International SWAT Conference & Workshops

M.L. Kavvas, Z. Q. Chen, M. Anderson, L. Liang, N. Ohara Hydrologic Research Laboratory, Civil and Environmental Engineering, UC Davis

Modeling Your Water Balance

SNAMP water research. Topics covered

Lecture 9A: Drainage Basins

Challenges with Measuring Cover System Performance

Energy Balance and Evapotranspiration Measurement

Hydrologic Modeling with the Distributed-Hydrology- Soils- Vegetation Model (DHSVM)

Lecture 19. Landfill hydrology

Urban Greening and the UHI: Seasonal Trade-offs in Heating and Cooling Energy Consumption in Manchester, UK

M.L. Kavvas, Z. Q. Chen, M. Anderson, L. Liang, N. Ohara Hydrologic Research Laboratory, Civil and Environmental Engineering, UC Davis

Texture Definition: relative proportions of various sizes of individual soil particles USDA classifications Sand: mm Silt:

Water balance of savannah woodlands: a modelling study of the Sudanese gum belt region

Leila Talebi and Robert Pitt. Department of Civil, Construction, and Environmental Engineering, The University of Alabama, P.O. Box , Tuscaloosa

The Confluence Model. Presentation to Modeling and Forecasting Working Group January 21, 2015

Soil Moisture Monitoring By Using BUDGET Model In A Changing Climate (Case Study: Isfahan-Iran)

Air. Water. Minerals (rocks)

Determination of the Optimal Date for Sowing of Wheat in Canal Irrigated Areas using FAO CROPWAT Model

Monitoring soil moisture. For more efficient irrigation

The Impacts of Climate Change on Portland s Water Supply

Calibration of Soil Moisture Measurement Using Pr2 Moisture Meter and Gravimetric-Based Approaches

Crop Water Requirements and Irrigation Scheduling

Climate Change & Urbanization Have Changed River Flows in Ontario

GEOS 4310/5310 Lecture Notes: Unsaturated Flow

12/28/2016. Air. Surface Water. Ground Water. Soil. 1. Calculate agronomic rate. 2. Identify optimal fields. 3. Determine when to apply

Estimation of Irrigation Water Requirement of Maize (Zea-mays) using Pan Evaporation Method in Maiduguri, Northeastern Nigeria

LANDSCAPE WATER CONSERVATION STATEMENT

Irrigating Efficiently: tools, tips & techniques. Steve Castagnoli, OSU Extension Service

EVALUATION OF HYDROLOGIC AND WATER RESOURCES RESPONSE TO METEOROLOGICAL DROUGHT IN THESSALY, GREECE

Sizing Irrigation Fields

Hydrological And Water Quality Modeling For Alternative Scenarios In A Semi-arid Catchment

SEASONAL WATER REQUIREMENTS OF AVOCADO TREES GROWN UNDER SUBTROPICAL CONDITIONS

Hood River Water Conservation Strategy: achieving long-term water resource reliability for agriculture & local fish populations

Simulation and Modelling of Climate Change Effects on River Awara Flow Discharge using WEAP Model

Groundwater, Mining and Sustainability in the Tropics :

Initial Assessment of Climate Change in the Chesapeake Bay Watershed

Change for Western North America. Hydrologic Implications of Climate. and the Columbia River Basin. Dennis P. Lettenmaier. Alan F.

Soil Temperature Damping Depth in Boreal Plain Forest Stands and Clear Cuts: Comparison of Measured Depths versus Predicted based upon SWAT Algorithms

Tools for Improving Irrigation Management of Vegetables

Sustainable Landfill Design by Monitoring and Managing Cap Infiltration

Annex 5 - Hydropower Model Vakhsh

Graham Jewitt School of Bioresources Engineering and Environmental Hydrology University of KwaZulu-Natal

AGRAR. Augmenting Groundwater Resources by Artificial Recharge. ICARDA, Aleppo, Syria nd November 2006

Crop Water Requirement Estimation by using CROPWAT Model: A Case Study of Halali Dam Command Area, Vidisha District, Madhya Pradesh, India

I/I Analysis & Water Balance Modelling. Presented by Paul Edwards

Soil moisture measurements

Stormwater and LEED. Vancouver LEED User s Group May 27, Craig Kipkie, M.Sc., P.Eng, LEED AP

Salinity TMDL Development and Modeling in the Otter Creek Watershed. Erik Makus DEQ Hydrologist June 6, 2013

IMPACT OF CLIMATE CHANGE ON WATER AVAILABILITY AND EXTREME FLOWS IN ADDIS ABABA

Monitoring soil moisture helps refine irrigation management

Avocado Irrigation in California. Ben Faber University of California Cooperative Extension Santa Barbara/Ventura Cos.

SAN BERNARD RIVER WATER QUALITY MODEL UPDATE. August 18, 2011

SUGARCANE IRRIGATION SCHEDULING IN PONGOLA USING PRE-DETERMINED CYCLES

Crop water requirements for tomato, common bean and chick pea in Hudeiba, River Nile State, Sudan

Modeling the Middle and Lower Cape Fear River using the Soil and Water Assessment Tool Sam Sarkar Civil Engineer

Administration Division Public Works Department Anchorage: Performance. Value. Results.

Alberta Oil Sands Cover System Field Trial: Development, Construction, and Results Two Years In

ANALYSIS OF RAINFALL DATA TO ESTIMATE RAIN CONTRIBUTION TOWARDS CROP WATER REQUIREMENT USING CROPWAT MODEL

Latest Information from DWR on Prop 84

REGIONAL FORECASTING OF GENERATION FROM SMALL HYDROPOWER PLANTS

Evaluation of the CRITERIA Irrigation Scheme Soil Water Balance Model in Texas Initial Results

Crop Water Use Program for Irrigation

EVALUATING WATER REQUIREMENTS OF DEVELOPING WALNUT ORCHARDS IN THE SACRAMENTO VALLEY

Impact of Point Rainfall Data Uncertainties on SWAT Simulations

IRRIGATION CONTROLLERS

A review of 15 years of oil palm irrigation research in in Southern Thailand

... Flood-Runoff Farming (FRF)

ANNUAL ENERGY PERFORMANCE OF SOLAR THERMAL SYSTEMS IN BRAŞOV, ROMANIA

Use of GIS and remote sensing in identifying recharge zones in an arid catchment: a case study of Roxo River basin, Portugal

Definitions 3/16/2010. GG22A: GEOSPHERE & HYDROSPHERE Hydrology

Factors affecting evaporation 3/16/2010. GG22A: GEOSPHERE & HYDROSPHERE Hydrology. Several factors affect the rate of evaporation from surfaces:

The Climate Impact Report (Updated 25 January 2018) The Immediate Past

From the cornbeltto the north woods; understanding the response of Minnesota. Chris Lenhart Research Assistant Professor BBE Department

Conservation Strategies for Lawn Irrigation During Drought A Colorado Experience

CAEP Study Site: Cannonsville Reservoir Watershed

A soil moisture accounting-procedure with a Richards equation-based soil texturedependent

Renewables. Vacuum Tube Solar Systems. Solar Energy to the Power of

Scientific registration n : 1368 Symposium n : 3 Presentation : poster. HASEGAWA Shuichi

TRACKING THE GAS TEMPERATURE EFFECT IN A DISTRIBUTION SYSTEM

Comparison of Recharge Estimation Methods Used in Minnesota

Climate Change Challenges faced by Agriculture in Punjab

Using capacitance sensors to monitor soil moisture. Interpreting the numbers

Modified Blaney-Criddle for Excel

International Journal of Applied Science and Technology Vol. 2 No. 3; March 2012

Utilization of the SWAT Model and Remote Sensing to Demonstrate the Effects of Shrub Encroachment on a Small Watershed

Surface Soil Moisture Assimilation with SWAT

MODELING PHOSPHORUS LOADING TO THE CANNONSVILLE RESERVOIR USING SWAT

Transcription:

Embankment and cut slope monitoring and analysis Dr Derek Clarke and Dr Joel Smethurst Introduction - to understand the behaviour of clay slopes (natural/engineered/stabilised) - failure and serviceability problems - climate and changing climate - vegetation - design and performance of stabilisation methods - develop models that will predict the behaviour of the slopes - to inform future design and maintenance - to identify critical actions that may have to be taken 1

Introduction - we have a number of instrumented sites used to - monitor slope pore water pressures - understand vegetation effects - climatic conditions - investigate the performance of pile stabilisation structures - we have used these site data to calibrate models of slope behaviour using - simulated soil moisture deficits and surpluses - pore water pressures Introduction Location of field study sites Newbury (cutting) Watford (oak tree) Ironbridge (River Seven valley side) Reading (embankment) Southend (embankment) Hildenborough (pile stabilised embank.) Mill Hill (pile stabilised embankment) Grange Hill (pile stabilised cutting) 2

Slope at Newbury Long term continuous monitoring of soil moisture, climate and pore water pressures in a London Clay cutting Neutron probe soil moisture readings TDR probes to measure moisture content Weather station Piezometers and tensiometers Network Rail study sites Embankments suffering serviceability (shrink/swell) problems Inclinometers (Geo-observations) Magnolia Lane, Southend Piezometers (Geo-observations) Extensometers (Geo-observations) Neutron probe soil moisture readings (Southampton) TDR probes to measure moisture content (Southampton) Poundgreen, Reading Rainfall (Southampton) 3

Discrete piles study sites Discrete pile sites Strain gauges and inclinometer tubes in the piles Hildenborough, Tonbridge, Kent Mill Hill East Northern Line Grange Hill, Central Line Ironbridge, Telford Inclinometer tubes upslope and downslope of pile row Piezometers Weather stations Clay Research Group study site Watford (Aldenham) effects of individual trees Neutron probe soil moisture readings (Southampton) TDR probes to measure moisture content Weather station Surface level monitoring Resistivity measurements Novel treatment methods to reduce clay shrink swell 4

Newbury site Aim: To understand and attempt to model the physical processes that link climate > vegetation vegetation > changes in soil moisture changes in soil moisture > changes in pore pressure/suction Field work Instrumentation of a vegetated London Clay cut slope on the A34 by-pass 4 sensors inserted at 4 locations down the slope Continuous monitoring since 22 Modelling Development & calibration of soil moisture deficit models Slope modelling work using FLAC Newbury site - background London Clay cut slope on the A34 near Newbury, 7.5m high, 16 slope Instrumented section of slope 5

Newbury - instrumentation Instruments are installed in 4 groups TDR s EQT Tensiometers Piezometers Curb of road Tensiometers Piezometers EQT Tensiometers Piezometers B C TDR s EQT Tensiometers Piezometers A Weathered clay D London Clay Newbury site Vegetation changes between summer-winter 9 May 3 9 July 3 1 Sept 3 24 Oct 3 6

Newbury measurement of volumetric moisture content TDR ThetaProbes Used to monitor soil moisture content Inserted at 3 cm intervals in top 2 metres of the soil profile Small response zone Continuous data-logged readings Neutron Probe Lowered down access tubes during visits to site Measures moisture content of football size zone of soil Newbury measurement of suction Vibrating wire piezometers Flushable water filled tensiometers in top 1.5m of the soil profile Indirect estimates of suction near the soil surface made using Equitensiometers. These Consists of a TDR probe embedded in a ceramic with known SWCC. Readings up to 15Pka possible but calibration is difficult. 7

Newbury climate and hydrological monitoring Two climate stations used to measure - Wind speed and direction - Temperature and humidity Runoff collection area - Solar radiation Above data used to estimate potential evapotranspiration Using the Penman Montieth equation - Rainfall Cut-off trench to measure runoff and interflow Newbury measured moisture contents in weathered material 23-26.6.55 Volumetric soil moisture content.5.45.4.35.3.25.2.15.1 A.3m A.45m A.6m A.9m A 1.5m Mar-3 Apr-3 May-3 Jun-3 Jul-3 Aug-3 Sep-3 Oct-3 Nov-3 Dec-3 Jan-4 Feb-4 Mar-4 Apr-4 May-4 Jun-4 Jul-4 Aug-4 Sep-4 Oct-4 Nov-4 Dec-4 Jan-5 Feb-5 Mar-5 Apr-5 May-5 Jun-5 Jul-5 Aug-5 Sep-5 Oct-5 Nov-5 Dec-5 Jan-6 Feb-6 Mar-6 Apr-6 May-6 Jun-6 Jul-6 8

Newbury changes in volumetric moisture content in weathered clay Volumetric water content % 2 3 4 5 6.5 End of summer Winter Seasonal change in moisture content over top 1.25m depth of soil 1 Depth from ground surface (m) 1.5 2 2.5 3 12 Aug 3 14 Aug 3 22 Aug 3 3 Sept 3 3 Sept 3 17 Oct 3 4 Nov 3 3.5 28 Nov 3 15 Dec 3 Neutron probe site A : 23 4 Newbury changes in volumetric moisture content in cutting Volumetric moisture content % 2 3 4 5 6.5 Seasonal change in moisture content over top.8 m depth of soil Depth from ground surface (m) 1 1.5 2 2.5 3 3.5 22 Aug 3 3 Sept 3 3 Sept 3 17 Oct 3 4 Nov 3 15 Dec 3 Neutron probe site C : 23 9

Newbury soil water balance Simple 1-D soil moisture balance model Rainfall and ET Soil moisture deficit (SMD) = amount of water required to recharge the profile Runoff (Runoff only occurs at field capacity) Assume that the major moisture changes occur only in the rooting zone At Newbury =.8m to 1.2m depth Draw up of water from below the major rooting zone Newbury estimated potential evapotranspiration (Penman Montieth) 5. 4.5 4. 3.5 Daily ETo mm/d A34 solarimeter Monthly ETo mm/d XLS solarimeter Monthly ETo mm/d Soton 1963-9 3. 2.5 2. 1.5 1..5. 1-Jan-3 31-Jan-3 3-Mar-3 2-Apr-3 3-May-3 2-Jun-3 3-Jul-3 2-Aug-3 2-Sep-3 2-Oct-3 2-Nov-3 2-Dec-3 2-Jan-4 ETo mm/day 1

Newbury soil water balance Infiltration and ET Total depth of available water in the rooting zone = 144 mm RAW = 58mm TAW = 144mm Plants can readily access the initial ~4% of the water in profile Actual ET = full Potential ET The plants become stressed and find it harder to remove the remaining water: Actual ET = Potential ET x f(smd) Draw up of water from below the major rooting zone Max. draw up from measured summer suction gradient at Newbury =.4 mm/day = negligible Newbury estimated soil moisture deficit 23 4 Dailyrainfal (m/day) 35 3 25 2 15 1 5 Rainfall 2 4 6 8 1 AE < PE when SMD > 58mm Modelled Soil Moisture Deficit 12 14 16 1-Jan 31-Jan 2-Mar 1-Apr 2-May 1-Jun 2-Jul 1-Aug 1-Sep 1-Oct 1-Nov 1-Dec 1-Jan SMD (mm) Readily available water (RAW) = 58 mm Actual ET < Potential ET Total available water (TAW) = 144 mm 11

Newbury estimated SMD and measured drying 2 4 Link climate and vegetation with changes in soil moisture 6 8 1 12 14 16 1-Jan-3 31-Jan-3 2-Mar-3 2-Apr-3 2-May-3 2-Jun-3 2-Jul-3 1-Aug-3 SMD (mm) 1-Sep-3 1-Oct-3 1-Nov-3 1-Dec-3 31-Dec-3 SMD calculated from climate parameters Group A neutron probe readings Group C neutron probe readings Group A TDR data Newbury FLAC model Vegetation and climate applied through a series of flow boundaries built into surface rooting zone 7.2m 3.m 4.m 26.3m 24m clay weathered clay top soil silty clay bands 12

Newbury - FLAC pore water pressures 4 2 Pore water pressure (kpa) 2 1-1 -2-3 -4-5 -6 2.5 m 2. m 1.5 m 1. m 1. m depth Onset of plant stress 1.5 m depth 2. m depth 2.5 m depth -2-7 1-Jan-3 31-Jan-3 2-Mar-3 2-Apr-3 2-May-3 2-Jun-3 2-Jul-3 1-Aug-3 1-Sep-3 1-Oct-3 1-Nov-3 1-Dec-3 31-Dec-3 Pore pressure (kpa) -4-6 -8.3m.6m.9m 1.5m 3.m -1-12 FLAC pore water pressures at C location, 23-14 12 Dec 12 Jan 12 Feb 12 Mar 12 Apr 12 May 12 Jun 12 Jul 12 Aug 12 Sep 12 Oct 12 Nov 12 Dec 22-23 Modelling London Clay slopes (grass cover) with historical climate data We have shown that the measured soil moisture changes match the modelled Soil Moisture Deficit SMD is dependent on climate and vegetation Historical climate data is available for Southern England at various sites going back to 1885 (Oxford), 1895 (Southampton). The climate data sets contain rainfall and data to estimate Potential Evapotranspiration. These were used to run the soil moisture deficit models from 1885-199 Additionally we have used the BETWIXT future climate data sets to simulate SMD s from 21-21 13

Simulated daily soil moisture deficits 1895-21 (London) SMD (mm) 189 194 199 24 29 2 4 6 8 1 12 14 16 18 2 Grass cover, TAW = 18 mm, RAW = 9 mm Betwixt medium high scenario, London Heathrow Long term modelling of maximum summer Soil Moisture Deficit 25 Maximum Soil Moisture Deficit (mm) 2 15 1 5 188 19 192 194 196 198 2 22 24 26 28 21 212 Grass cover, TAW = 18 mm, RAW = 9 mm Betwixt medium high scenario, London Heathrow 14

Increased stress on vegetation : Number of days SMD > RAW 45 4 35 3 Days/Year 25 2 15 1 5 188 19 192 194 196 198 2 22 24 26 28 21 212 Long term modelling Excess rainfall that becomes Runoff 3 25 2 Runoff (mm/year) 15 1 5 188 19 192 194 196 198 2 22 24 26 28 21 212 15

Modelling London Clay behaviour Pattern shows - increase in maximum Soil Moisture Deficits in summer - increase in summer drought - higher frequency of dry winters when soil does not re-wet Implications - greater clay shrinkage for longer periods in summer - shrink-swell cycles will increase in magnitude - vegetation is likely to die off or change to other (semi arid) plants - vegetation cover may decrease (exposure to surface erosion in winter) - winter runoff reduces (however storminess is not accounted for) 16

Newbury measured pore water pressures near the surface (kpa) Suction (kpa) -2 2 4 6 8 1 12 14 16 18 2 22 24 26 28 3 32 34 36 38 4 42 44 Mar-3 Apr-3 May-3 Jun-3 Jul-3 Aug-3 Sep-3 Oct-3 Nov-3 Dec-3 Jan-4 Feb-4 Mar-4 Apr-4 May-4 Jun-4 Jul-4 Aug-4 Sep-4 Oct-4 Nov-4 Dec-4 Jan-5 Feb-5 Mar-5 Apr-5 May-5 Jun-5 Jul-5 Aug-5 Sep-5 Oct-5 Nov-5 Shallow tensiometers site C&D: 22-26 Dec-5 Jan-6 C.3 C.6 D.3 D.6 EQT C.3 Feb-6 Mar-6 Apr-6 May-6 Jun-6 Jul-6 Newbury measured in pore water pressures (kpa) 1.m 2.5m 2 1-1 Nov-2 Jan-3 Mar-3 May-3 Jul-3 Sep-3 Nov-3 Jan-4 Mar-4 May-4 Jul-4 Sep-4 Nov-4 Jan-5 Mar-5 May-5 Jul-5 Sep-5 Nov-5 Jan-6 Mar-6 May-6 Jul-6 Pressure (kpa) -2-3 -4-5 -6 C 1. C 1.5 C 2. C 2.5-7 Day Piezometers site C: 22-26 17

Newbury envelope of pore water pressure changes Equitensiometer 44 kpa suction @.3 m depth Pore pressure (kpa) -1-9 -8-7 -6-5 -4-3 -2-1 1 2 3 4. September 23.5 1. Approx. maximum pore water pressure envelope Limit of measurement for tensiometers 1.5 Depth (m) Group A Group B Group C 2. 2.5 3. January 23 Group D Approx. minimum pore water pressure envelope 3.5 4. 4.5 Newbury site London Clay properties Soil Water Characteristic Curve (SWCC) Oven dry 8 7 LC drying LC wetting 2 18 16 LC drying LC wetting Taken from Croney (1977) 6 14 Wilt point 15 kpa Soil Suction pf 5 4 3 1, kpa 1, kpa 1 kpa Soil Suction (kpa) 12 1 8 2 1 kpa 6 4 1 1 kpa 2 1 2 3 4 Gravimetric Moisture content % 1 2 3 4 Gravimetric moisture content % Permeability: Triaxial ~ 1x 1-1 m/s; field (bail tests) ~ 1x 1-8 m/s 18

2 18 16 14 12 1 8 6 4 2 LC drying LC wetting 1 2 3 4 Gravimetric moisture content % Pore pressure (kpa) -1-9 -8-7 -6-5 -4-3 -2-1 1 2 3 4. Approx. maximum Equitensiometer September.5 pore water pressure 44 kpa sucti on 23 envelope @.3 m depth 1. Limit of measurement for tensiometers 1.5 2. Group A Group B 2.5 January Group C 23 3. Group D 3.5 Approx. minim um pore water pressure envelope 4. 4.5 Newbury - moisture contents Soil Suction (kpa) SWCC.5 1 Volumetric moisture content % 25 3 35 4 45 5 55 Link measured pore water pressures with measured moisture contents Envelope of pwp s Depth (m) Depth from ground surface (m) 1.5 2 2.5 3 3.5 22 Aug 3 3 Sept 3 3 Sept 3 17 Oct 3 4 Nov 3 15 Dec 3 Neutron probe measurements 4 4.5 Upper bound from measurements of suction Low er bound from measurements of suction Newbury developing models to predict soil moisture deficit and pore water pressures The long run of observed data has provided information on soil moisture and pore water pressure - in wintertime - in summer time and shows behaviour in - a very wet winter (22-3) - a typical damp summer (24) - a typical dry summers (25) - an exceptionally dry summer (23) We have developed soil moisture deficit models (linked to climate) and we can use historical climate data sets to estimate the likely behaviour of the site over many years 19