Construction of the stage-discharge rating curve and the SSC-turbidity calibration curve in San Antonio Coapa 2009 hydrological season

Similar documents
Measuring discharge. Climatological and hydrological field work

Module 3. Lecture 6: Synthetic unit hydrograph

Water Budget III: Stream Flow P = Q + ET + G + ΔS

Water Budget III: Stream Flow P = Q + ET + G + ΔS

Water Budget III: Stream Flow P = Q + ET + G + ΔS

Water Budget III: Stream Flow P = Q + ET + G + ΔS

Turbidity-controlled suspended sediment sampling

FLOOD FORECASTING MODEL USING EMPIRICAL METHOD FOR A SMALL CATCHMENT AREA

IJSER. within the watershed during a specific period. It is constructed

Pilot Study for Storage Requirements for Low Flow Augmentation

Suspended Sediment Discharges in Streams

ICELANDIC RIVER / WASHOW BAY CREEK INTEGRATED WATERSHED MANAGEMENT PLAN STATE OF THE WATERSHED REPORT CONTRIBUTION SURFACE WATER HYDROLOGY REPORT

Long-term change of stream water quality as a consequence of watershed development and management

OFFICE OF STRUCTURES MANUAL ON HYDROLOGIC AND HYDRAULIC DESIGN CHAPTER 3 POLICY AND PROCEDURES

Hydrostatistics Principles of Application

Simulation of Daily Streamflow

EFFECTIVENESS OF SILT SCREEN IN FRONT OF INDUSTRIAL WATER INTAKE

Module 2, Add on Lesson Turbidity Sensor. Student. 90 minutes

Alternative Approaches to Water Resource System Simulation

Urban Runoff Literature Review

Sediment and nutrient generation rates for Queensland rural catchments an event monitoring program to improve water quality modelling

EDF HYDRO-MONITORING NETWORK

A WEAP Model of the Kinneret Basin

Computer Determination of Flow Through Bridges

Hypothetical Flood Computation for a Stream System

5/25/2017. Overview. Flood Risk Study Components HYDROLOGIC MODEL (HEC-HMS) CALIBRATION FOR FLOOD RISK STUDIES. Hydraulics. Outcome or Impacts

APPENDIX 4 ARROYO MODELING

Effective Discharge Calculation

Lower Mission Creek Watershed Status Survey 2002

Introduction. Keywords: Oil Palm, hydrology, HEC-HMS, HEC-RAS. a * b*

Module 2, Add on Lesson Turbidity Sensor. Teacher. 90 minutes

1 THE USGS MODULAR MODELING SYSTEM MODEL OF THE UPPER COSUMNES RIVER

Distribution Restriction Statement Approved for public release; distribution is unlimited.

Alberta Rainfall Runoff Response

The hydrologic and hydraulic study of the behaviour of the Nyl River floodplain

Cleveland Bay Marine Water Quality (Turbidity and Available Light) Monitoring Plan

Flood forecasting model based on geographical information system

A Finite Difference Method for Analyzing Liquid Flow in Variably Saturated Porous Media

Flood forecasting model based on geographical information system

Hydrologic Year 2015 Turbidity Data Submittal Report

THE RATIONAL METHOD FREQUENTLY USED, OFTEN MISUSED

Jordan River Project Water Use Plan. Monitoring Program Terms of Reference. Addendum 1 to JORMON-01 Lower Jordan River Inflow Monitoring

Review for Chapter 1 Introduction. 1. Sketch the hydrologic cycle and label the location and movement of water.

FISHER RIVER INTEGRATED WATERSHED MANAGEMENT PLAN STATE OF THE WATERSHED REPORT CONTRIBUTION SURFACE WATER HYDROLOGY REPORT

Is this the right tool for your needs? Lester McKee San Francisco Estuary Institute (SFEI) May 2001

The surface water hydrology of the site has been logically divided into six phases of monitoring, analyses, and investigation as outlined below:

2015 Annual Report on Performance of Iowa CREP Wetlands: Monitoring and Evaluation of Wetland Performance

San Luis Obispo Creek Watershed Hydrologic Model Inputs

Physically-based distributed modelling of river runoff under changing climate conditions

SECTION IV WATERSHED TECHNICAL ANALYSIS

The Texas A&M University and U.S. Bureau of Reclamation Hydrologic Modeling Inventory (HMI) Questionnaire

Evaluation of Simhyd, Sacramento and GR4J rainfall runoff models in two contrasting Great Barrier Reef catchments

Surface Water Sampling

Physically-based distributed modelling of river runoff under changing climate conditions

7.10 ULTRASONIC ANALYZER

Some aspects of the sediment transit on the Mekong river in relation with hydropower development

How to extrapolate rating curve

Improvement of Physical Basis of Conceptual model, LASCAM, with Explicit Inclusion of Within Catchment Heterogeneity of Landscape Attributes

Modelling a Combined Sewage and Stormwater Flood Detention Basin

Analysis of Suspended Sediment Data from Upper Lee River, Nelson

A NEW HYDROLOGIC RESPONSE FUNCTION PHYSICALLY DERIVED FROM DEM AND REMOTE SENSING IMAGE

Hydro Electric Power (Hydel Power)

SEES 503 SUSTAINABLE WATER RESOURCES. Floods. Instructor. Assist. Prof. Dr. Bertuğ Akıntuğ

JOURNAL OF APPLIED SCIENCES RESEARCH

IGHEM 2008 MILANO 3 rd -6 th September International Group for Hydraulic Efficiency Measurements

Hydraulic Engineering and Water Management

Coupled Control of Land Use and Topography on Suspended Sediment Dynamics in an Agriculture- Forest Dominated Watershed, Hokkaido, Japan.

Measurement of Carbon Dioxide Concentration in the Outdoor Environment

Assessing ecological flow conditions for wetlands fed from ungauged stream reaches

CHAPTER 2. Objectives of Groundwater Modelling

Radar-based flood forecasting: Quantifying hydrologic prediction uncertainty

Integrating soakaway infiltration devices in distributed urban drainage models from allotment to neighbourhood scale

Analysis of statistical parameters for rainfall series of Kaneri watershed, Maharashtra and computation of runoff for different return periods

Chapter 7 - Monitoring Groundwater Resources

Monitoring and Data Collection

What is runoff? Runoff. Runoff is often defined as the portion of rainfall, that runs over and under the soil surface toward the stream

Event and Continuous Hydrological Modeling with HEC- HMS: A Review Study

CONSIDERATIONS ON THE DESIGN, ANALYSIS AND PERFORMANCES OF ECCENTRICALLY BRACED COMPOSITE FRAMES UNDER SEISMIC ACTION

Sediment management of hydropower cascade: example of CNR run-of-river developments, French Rhone River, France

Experiences from the use of sensors for assessing water quality in rivers in Finland

LIFE+ PROJECT - SEKRET LIFE12 ENV/IT/ "Sediment ElectroKinetic REmediation Technology for heavy metal pollution removal"

Uncertainty in Hydrologic Modelling for PMF Estimation

Effect of exceptional hydrological events of the Solimões River on the hydrology and physico chemistry of a floodplain lake Part 1: Hydrology

Numerical Model for Assessment of Subsidence due to Dissolution of Salt, Application to Nancy Basin (East of France)

Ensemble flood forecasting based on ensemble precipitation forecasts and distributed hydrological model Hongjun Bao

CE 2031 WATER RESOURCES ENGINEERING L T P C

Measurement of Stream Discharge. Using Weirs and Flumes

Parameter Estimation of Rainfall-Runoff Model Using Hydrograph Section Separation

M. Meering. 1 Note for the authors and editor

3.5 Hydrology & Hydraulics

Comparison of Recharge Estimation Methods Used in Minnesota

EFFECT OF HYDROGRAPHIC DATA QUALITY ON WATER SURFACE PROFILE AND NAVIGATIONAL DREDGING COMPUTATIONS

Discharge Estimation in a Backwater Affected River Junction Using HPG

Comparison of Three Turbidity Instruments DTS-12, YSI, and Hydrolab

Civil and Environmental Research ISSN (Paper) ISSN (Online) Vol.3, No.7, 2013

Introduction to Hydrology (Geog 3511) Fall 2002 Assignment 10: Streamflow Analysis

NUTRIENTS AND PARTICLES TERC.UCDAVIS.EDU

PROGRESS WITH MEASURING AND UTILIZING CROP EVAPOTRANSPIRATION (ETc) IN WALNUT

Ministry of Agriculture and Rural Development. Fight against soil erosion and watershed management

Geographic context. Hydropower developments on the upper Rhone River

Transcription:

Construction of the stage-discharge rating curve and the SSC-turbidity calibration curve in San Antonio Coapa 009 hydrological season C. Duvert, N. Gratiot, September 010 1. Introduction Study site The gauging station of San Antonio Coapa (SAC) stands at the outlet of a 90-km catchment that is part of the larger Cointzio basin (Fig. 1). It was equipped and tested during the 008 season; water and suspended sediment fluxes could then be measured all throughout 009. The material consists of a water level floating gauge (Thalimede OTT) and a turbidity sensor (Visolid WTW) connected to a Campbell datalogger. The gauging site was visited on a weekly basis, during which all data were downloaded, and equipments were checked and maintained when necessary. Figure 1: Location of the study site. The blue area corresponds to SAC subcatchment. The Undameo station is situated 5 km downstream, at the outlet of the Cointzio catchment 1

A number of technical difficulties were faced at this monitoring station. The major problem was the instability of the river cross-section because of frequent in-channel sediment deposition occurring all along the rainy season (Fig. ). The site lies within the alluvial plain of the Cointzio catchment and its river section, which is deep-channelled, was fully dredged during winter 007-008 to minimize the flooding potential (Fig. a). As a consequence, the stream constantly deposited material during 008 and 009 floods until getting back to its original equilibrium (Fig. b). a) b) Figure : (a) View of the gauging station of SAC with turbidity sensor (left). (b) View of recent fine sediment deposits left by a flood and water level gauge Given the cross-section area changed during the rainy season, we had to adapt various stagedischarge rating curves in order to obtain some discharge estimates as reliable as possible. Similar difficulties were encountered for the calibration of the turbidity probe. The methodologies used to improve both the stage-discharge rating curve (Section ) and the SSCturbidity calibration curve (Section ) are presented hereafter.. Stage-discharge rating curve.1. Discharge measurements During 009, 8 discharge measurements were obtained using the tracer dilution method (Fig., black squares; Appendix 1). A rating curve of polynomial type was drawn, and the fitting of the curve with all discharge measurements is satisfying (Fig. ). Eight measurements had also been performed during the previous season (Fig., grey diamonds), but a high scattering is visible between the sets of data. This is undoubtedly an expression of the interseasonal unsteadiness of the cross-section area. Those 008 values were evidently not accounted for in the rating curve construction.

6000 Gauging 009 y = 4E-4x + 0.0x + 6.15x Gauging 008 Discharge (l/s) 4000 000 0 0 0 40 60 80 100 10 140 160 180 00 Water level (cm) Figure : Discharge measurements performed in 008 and 009 at San Antonio Coapa. Error bars correspond to ± 10% uncertainties on each measurement At first sight, a total of 8 rating values could be considered low for the construction of a valid stage-discharge law. However, changes in the section did not only occur between 008 and 009, but also regularly all along the year: a scour chain survey carried out in 008 on the river banks of SAC showed that fine sediment deposition could reach up to 10-15 cm weekly. Given these frequent variations of the section, the achievement of more measurements would certainly not have provided a better precision, but rather, would have increased the scattering (each gauging having only an ephemeral validity). Following this idea, the rating displayed in Fig. might be considered as appropriate only for the period encompassing the event during which those measurements were done. The 6 highest values were measured during a flooding event on 1 st July; the relationship found is therefore expected to be relevant only for the beginning of the wet period. Indeed, when applying the same rating to the entire 009 data, discharges appeared to be strongly under-estimated during the second part of the rainy season. A significant and increasing bias was found when the obtained data were compared with discharge values recorded at Santiago Undameo, especially for high values occurring during floods. Thus, some other appropriate ratings had to be elaborated for the rest of the season.

.. Inter-comparison with Undameo data Because of the geographical proximity between the gauging stations of Santiago Undameo and SAC (Fig. 1), and because of the geomorphological similarities between those two stations (they are both located in the lowland alluvial plain), we decided to use the discharge data obtained at Santiago Undameo as an indicator for the improvement of discharge estimates at SAC. The discharge values recorded at Undameo were used as markers of the maximum quantity of water that may have flowed through the SAC station. The blue squares and red triangles in Fig. 4 correspond to peak discharges measured in Undameo, to which water levels recorded in SAC during the same event were compared. These points therefore represent some maximum values that should not be overstepped by the rating. The exercise showed that a significant change occurred after the first major flood of the year, corresponding to the third highest of the season in terms of water level, on 1 st July 009 (shifting from blue squares to red triangles). The fitting curve that was established from the discharge measurements (see Section, black curve in Figs. and 4) is only adequate for data recorded until that event. Then, the equation would provide a constant underestimation of discharges. On the basis of the discharge records available at Undameo during the second part of the rainy season (red triangles in Fig. 4), the adjustment of 1 other fitting curve (red line in Fig. 4) appeared to be satisfactory until the end of the season. 1000 Gauging 009 10000 Q Undameo before Q Undameo after Discharge (l/s) 8000 6000 Eq () 4000 Eq (1) 000 0 0 0 40 60 80 100 10 140 160 180 00 0 Water level (cm) Figure 4: Adjustment of the stage-discharge law by means of data from Undameo 4

To adjust this second rating curve with the best accuracy, we carefully analyzed the timing of floods in Undameo, inferring that each subcatchment had a distinct transit time. Some characteristic events were identified, in which various contributions could be individualized in Undameo hydrograph (i.e. double- or triple-peak floods; Fig. 5). During the peak that was expected to correspond to SAC subcatchment contribution only, we considered that there should not be a significant difference between the discharge measured in SAC and the discharge flowing down to the outlet at Undameo. The red law in Fig. 4 was therefore adjusted in order to obtain a good fitting of the hydrographs. Various examples of this technique are given in Fig. 5. Note that a shifting of.5 hours ( transit time between SAC and Undameo) was applied to the discharge values of Undameo in order to obtain a better visual synchronicity of both hydrographs. 6 5 contribution from other subcatchments Q Undameo Q San Antonio 5 (B) Considering a 10% uncertainty on discharge values, Q SAC is not significantly higher than Q Unda Q (m s -1 ) 4 contribution expected to be exclusively from SAC subcatchment Q (m s -1 ) 4 1 (A) 1 Q Undameo Q San Antonio 1/07 1/07 /06 (C) Q Undameo Q San Antonio Q (m s -1 ) 1 Here SAC subcatchment is probably not the only contributor 08/09 Figure 5: Hydrograph adjustment in San Antonio Coapa for various floods We then followed a trial-and-error process, repeating the same analysis for all the multi-peak events of the second part of the season. The obtained law was time after time adjusted to get the best compromise among all events. 5

.. Rating curves Overall, two equations were used to adjust water depth and discharge values. Equations (1) and () are cubic polynomials: Q = 0.4H + 0.18H + 0. 6H (1) Corresponds to the black curve in Fig. 4 Q = 0.5H + 1.14H + 0. 40H () Corresponds to the red curve in Fig. 4 with Q in m /s and H in m. Equation (1) was used from the beginning of the year 009 until the recession phase of the 1 st July event, and Eq. () was used from the end of the 1 st July event up to the end of the year 009. Data acquired through that procedure were then carefully checked in order to point out any anomaly or aberration. We principally verified that there were no floods during which discharge values in SAC were significantly higher than in Undameo..4. Conclusions It is also important to mention that several floods were not recorded in SAC all throughout the rainy season, mostly because of considerable siltation leading to the burying of the water level gauge. The main ungauged events correspond to the following days: 18/07, 0/07 and 14/09/009 (only turbidity peak was recorded during those flooding events). This might explain the relatively lower mean annual specific discharge encountered at SAC than at Undameo (respectively, 1.5 l/s/km and.1 l/s/km ). Overall, the discharge values at SAC estimated by the method presented here are of moderate reliability (uncertainty ± 0%). They were further used for yield and flux calculations, but these estimates should systematically be associated with error bars to prevent misinterpretations. 6

Figure 6 shows the 009 hydrographs obtained at Undameo and SAC: 15 Q Undameo Q San Antonio Coapa Discharge (m /s) 10 5 0 01/04 01/07 01/10 01/01 Figure 6: Hydrographs at Undameo and SAC during the 009 hydrological season. SSC-turbidity calibration curve A temporal evolution of the relationship between turbidity and suspended sediment concentration (SSC) was identified during the 009 season in SAC. Such phenomenon is well known and has already been evidenced by a number of authors (e.g. Gippel, 1995; Lewis, 1996; Eder et al., 010). It requires successive calibrations of the measuring device all throughout the season. In our case, various abrupt changes occurred: the first one was recorded around mid-july, and the second one occurred in late August. This might be a consequence of a clogging of the probe after concentrated flood events. Three adjustments were applied all along the rainy season in order to ensure continuity in the calculated SSC time series. The first adjustment corresponds to data covering the period January mid-july 009 (red circles in Fig. 7). The calibration used in Santiago Undameo (catchment outlet, 5 km downstream SAC) was applied on this first period. Indeed, both series of data present a goodquality overlapping (see Fig. 7, red and black circles). As the scattering was lower and the number of values was higher in Undameo, we chose to use this last relationship for the determination of SSC values in SAC, from the beginning of the year until 1th July 009. 7

10 1 Undameo SAC jusqu'au 1/07 SAC entre 1/07 et /08 SAC après /08 MES (g/l) 10 0 10-1 10-10 -1 10 0 10 1 Turbidité (g/l de SiO ) Figure 7: Relation between turbidity and SSC values in Undameo and SAC Furthermore, couples of values measured in SAC and corresponding to the second (intermediary) part of the season (from 1th July till nd August; blue circles in Fig. 7) follow a significantly diverging trend. For similar values of turbidity, SSC values indeed undergo an upward translation. A second adjustment (Ajust. ) was therefore created, to account for this positive bias. This second law is presented in Fig. 8; it takes the shape of a simple linear function. 7 6 5 SSC (g/l) 4 1 0 y = 0.65x + 0.05 0 4 6 8 10 Turbidity (g/l SiO ) Figure 8: Calibration of the values extracted from the intermediary period 8

Ajust. relation is therefore as follows: SSC = 0.65 * Turbidity + 0.05 A verification of this new adjustment was undertaken, by comparing the high frequency SSC data obtained from the two calibrations, to the values measured from water samples. This comparison is displayed in Fig. 9. While the initial calibration would introduce a high under-estimation of suspended sediment fluxes (mean error between measured value and value deduced from the calibration > 400%), the second law provides a better adequacy between measured values and values obtained from the calibration (mean uncertainty 40%). SSC (g l -1 ).5 1.5 1 Mean bias 40% Before adjustment 0.5 SSC (g l -1 ) 0 6 5 4 19/07 6/07 0/08 Mean bias 40% After adjustment 1 0 19/07 6/07 0/08 Figure 9: Comparison of the error related to the calibration selected: the initial adjustment obtained in Undameo is used in the upper graph, and Ajust. is used in the lower graph. The red circles correspond to the values obtained from manual sampling From the end of August 009, the bias between high frequency turbidity data and SSC seemed to increase again (see Fig. 7, green circles). The number of witness SSC-turbidity couples is very low during this period; however, a thorough analysis of the time series confirms that if using Ajust., an under-evaluation of the fluxes recorded during the last floods of the rainy season is very likely to happen (i.e. period September-October). We therefore had to apply a third calibration to the high frequency turbidity records (Ajust. ). 9

Rather than relying on a linear function elaborated from the (low) number of SSC-turbidity values, this last adjustment was realized by simply multiplying three times the gradient of Ajust.. Indeed, the study of events exhibiting similar discharge records in both Undameo and SAC proves that the underestimation is threefold for this period. An example of one of these events is presented in Fig. 10. 1 10 SSC Undameo Q Undameo Q San Antonio SSC San Antonio with Ajust. SSC San Antonio with Ajust. Q (m/s) - SSC (g/l) 8 6 4 0 07/09 Figure 10: Adjustment achieved for the last part of the rainy season (Ajust. ) and comparison with Ajust. for the 7th September 009 flood event It appears that the SSC peak obtained from Ajust. (blue line) is analogous to the one recorded in Undameo a few hours later (red line), and this for a flood with very similar discharge pattern (both in terms of Q max and V tot ). Eventually, this method ensures a better adequacy with the few measured data (obtained from manual sampling). Ajust. relation is therefore as follows: SSC = 1.95 * Turbidity + 0.05 Overall, the results obtained after using these three calibration laws are satisfying. The annual flux recorded in SAC amounts to 1 500 Mg (in comparison, the flux recorded at the outlet of the catchment over the same period is 8 000 Mg). However, it is worth mentioning that a total of 1 events were not measured throughout the season. Those 1 events can be compared to the events for which discharge and sediment flux have been properly measured (i.e. 71% of events occurring in 009 were measured). A significant underestimation of the 009 annual export from SAC is therefore to be expected (a value of around 0 000 Mg would rather be expected). 10

Appendix 1 Discharge measurements obtained at SAC by means of the dilution gauging method Distance from injection (m) : 0 NaCl mass (g) : 450 Sum C(t) 1.78 Staff : Julien Némery, Clément Duvert Discharge (l/s) 5.0 Water level (cm) : 7 10 00 90 80 70 60 50 09/1/009 11:4 09/1/009 11:6 09/1/009 11:9 09/1/009 11:4 09/1/009 11:45 Distance from injection (m) : 55 NaCl mass (g) : 996 Sum C(t) 1.599 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 6.81 Water level (cm) : 4 0 10 90 70 50 1/10/009 1:46 1/10/009 1:48 1/10/009 1:49 1/10/009 1:50 1/10/009 1:5 1/10/009 1:5 1/10/009 1:55 11

Distance from injection (m) : 100 NaCl mass (g) : 000 Sum C(t) 1.066 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 1876.18 Water level (cm) : 1 185 180 175 170 165 160 155 150 0:41 0:4 0:44 0:45 0:47 0:48 Distance from injection (m) : 100 NaCl mass (g) : 000 Sum C(t) 0.85 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 45.67 Water level (cm) : 14 155 150 145 140 15 10 01:40 01:4 01:4 01:45 01:46 01:48 1

Distance from injection (m) : 100 NaCl mass (g) : 115 Sum C(t) 1.5 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 54.6 Water level (cm) : 148 180 175 170 165 160 155 00:4 00:44 00:46 00:47 00:48 00:50 Distance from injection (m) : 100 NaCl mass (g) : 090 Sum C(t) 1.18 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 714.60 Water level (cm) : 155 196 19 188 184 180 176 00:07 00:08 00:10 00:11 00:1 00:14 00:15 1

Distance from injection (m) : 100 NaCl mass (g) : 000 Sum C(t) 0.69 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 46.5 Water level (cm) : 178 1 18 14 10 116 11 1:48 1:50 1:51 1:5 1:54 1:56 1:57 Distance from injection (m) : 100 NaCl mass (g) : 000 Sum C(t) 0.51 Staff : Nicolas Gratiot, Clément Duvert Discharge (l/s) 895.46 Water level (cm) : 180 116 114 11 110 108 106 104 1: 1:4 1:6 1:7 1:8 1:40 14