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Residence Time. Mean Water Residence Time (aka: turnover time, age of water leaving a system, exit age, mean transit time, travel time, hydraulic age,

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Presentation on theme: "Residence Time. Mean Water Residence Time (aka: turnover time, age of water leaving a system, exit age, mean transit time, travel time, hydraulic age,"— Presentation transcript:

1 Residence Time

2 Mean Water Residence Time (aka: turnover time, age of water leaving a system, exit age, mean transit time, travel time, hydraulic age, flushing time, or kinematic age) – T = V / Q = turnover time or age of water leaving a system – For a 10 L capped bucket with a steady state flow through of 2 L/hr, T = 5 hours Assumes all water is mobile Assumes complete mixing – For watersheds, we dont know V or Q Mean Tracer Residence Time (MRT) considers variations in flow path length and mobile and immobile flow

3 Residence and Geomorphology Geomorphology controls fait of water molecule Soils – Type – Depth Bedrock – Permeability – Fracturing Slope Elevation

4 MRT estimated using Transfer Function Models

5 Transfer Function Models Signal processing technique common in – Electronics – Seismology – Anything with waves – Hydrology

6 Transfer Function Models Brief reminder of transfer function HYDROGRAPH model before returning to

7 Hydrograph Modeling Goal: Simulate the shape of a hydrograph given a known or designed water input (rain or snowmelt) time Precipitation time flow Hydrologic Model

8 Hydrograph Modeling: The input signal Hyetograph can be – A future design event What happens in response to a rainstorm of a hypothetical magnitude and duration – See – A past storm Simulate what happened in the past Can serve as a calibration data set time Precipitation time flow Hydrologic Model

9 Hydrograph Modeling: The Model What do we do with the input signal? – We mathematically manipulate the signal in a way that represents how the watershed actually manipulates the water Q = f(P, landscape properties) time Precipitation time flow Hydrologic Model

10 Hydrograph Modeling What is a model? What is the purpose of a model? Types of Models – Physical – Analog Ohms law analogous to Darcys law – Mathematical Equations to represent hydrologic process

11 Types of Mathematical Models Process representation – Physically Based Derived from equations representing actual physics of process i.e. energy balance snowmelt models – Conceptual Short cuts full physics to capture essential processes – Linear reservoir model – Empirical/Regression i.e temperature index snowmelt model – Stochastic Evaluates historical time series, based on probability Spatial representation – Lumped – Distributed

12 12 Integrated Hydrologic Models Are Used to Understand and Predict (Quantify) the Movement of Water How ? Formalization of hydrologic process equations Lumped Model Distributed Model e.g: Stanford Watershed Modele.g: ModHMS, PIHM, FIHM, InHM Semi-Distributed Model e.g: HSPF, LASCAM p q REW 1 REW 2REW 3 REW 4 REW 5 REW 6 REW 7 Data Requirement: Computational Requirement: SmallLarge Process Representation: ParametricPhysics-Based Predicted States Resolution: CoarserFine

13 Hydrograph Modeling Physically Based, distributed Physics-based equations for each process in each grid cell See dhsvm.pdf Kelleners et al., 2009 Pros and cons?

14 Hydrologic Similarity Models Motivation: How can we retain the theory behind the physically based model while avoiding the computational difficulty? Identify the most important driving features and shortcut the rest.

15 TOPMODEL Beven, K., R. Lamb, P. Quinn, R. Romanowicz and J. Freer, (1995), "TOPMODEL," Chapter 18 in Computer Models of Watershed Hydrology, Edited by V. P. Singh, Water Resources Publications, Highlands Ranch, Colorado, p.627-668. TOPMODEL is not a hydrological modeling package. It is rather a set of conceptual tools that can be used to reproduce the hydrological behaviour of catchments in a distributed or semi-distributed way, in particular the dynamics of surface or subsurface contributing areas.

16 TOPMODEL Surface saturation and soil moisture deficits based on topography – Slope – Specific Catchment Area – Topographic Convergence Partial contributing area concept Saturation from below (Dunne) runoff generation mechanism

17 Saturation in zones of convergent topography

18 TOPMODEL Recognizes that topography is the dominant control on water flow Predicts watershed streamflow by identifying areas that are topographically similar, computing the average subsurface and overland flow for those regions, then adding it all up. It is therefore a quasi-distributed model.

19 Key Assumptions from Beven, Rainfall-Runoff Modeling There is a saturated zone in equilibrium with a steady recharge rate over an upslope contributing area a The water table is almost parallel to the surface such that the effective hydraulic gradient is equal to the local surface slope, tanβ The Transmissivity profile may be described by and exponential function of storage deficit, with a value of To whe the soil is just staurated to the surface (zero deficit

20 Hillslope Element P q total = q sub + q overland We need equations based on topography to calculate q sub (9.6) and q overland (9.5) a β a sat q overland q subsurface c

21 Subsurface Flow in TOPMODEL q sub = Tctanβ – What is the origin of this equation? – What are the assumptions? – How do we obtain tanβ – How do we obtain T? a β a sat q overland q subsurface c

22 Recall that one goal of TOPMODEL is to simplify the data required to run a watershed model. We know that subsurface flow is highly dependent on the vertical distribution of K. We can not easily measure K at depth, but we can measure or estimate K at the surface. We can then incorporate some assumption about how K varies with depth (equation 9.7). From equation 9.7 we can derive an expression for T based on surface K (9.9). Note that z is now the depth to the water table. a β a sat q overland q subsurface c z

23 Transmissivity of Saturated Zone K at any depth Transmissivity of a saturated thickness z-D D a β a sat q overland q subsurface c z

24 Equations Subsurface Surface Assume Subsurface flow = recharge rate Topographic Index Saturation deficit for similar topography regions

25 Saturation Deficit Element as a function of local TI Catchment Average Element as a function of average

26 Hydrologic Modeling Systems Approach A transfer function represents the lumped processes operating in a watershed -Transforms numerical inputs through simplified paramters that lump processes to numerical outputs -Modeled is calibrated to obtain proper parameters -Predictions at outlet only -Read 9.5.1 P t Q t Mathematical Transfer Function

27 Transfer Functions 2 Basic steps to rainfall-runoff transfer functions 1. Estimate losses. W minus losses = effective precipitation (W eff ) (eqns 9-43, 9-44) Determines the volume of streamflow response 2. Distribute W eff in time Gives shape to the hydrograph Recall that Q ef = W eff Q t Base Flow Event flow (W eff )

28 Transfer Functions General Concept W Losses W eff = Q ef Task Draw a line through the hyetograph separating loss and W eff volumes (Figure 9-40) t W ?

29 Loss Methods Methods to estimate effective precipitation – You have already done it one way…how? However, … Q t

30 Loss Methods Physically-based infiltration equations Chapter 6 – Green-ampt, Richards equation, Darcy… Kinematic approximations of infiltration and storage W Uniform: W err (t) = W(t) - constant Exponential: W eff (t) = W 0 e -ct c is unique to each site

31 Examples of Transfer Function Models Rational Method (p443) – q pk =u r C r i eff A d No loss method Duration of rainfall is the time of concentration Flood peak only Used for urban watersheds (see table 9-10) SCS Curve Number – Estimates losses by surface properties – Routes to stream with empirical equations

32 SCS Loss Method SCS curve # (page 445-447) Calculates the VOLUME of effective precipitation based on watershed properties (soils) Assumes that this volume is lost

33 SCS Concepts Precipitation (W) is partitioned into 3 fates – V i = initial abstraction = storage that must be satisfied before event flow can begin – V r = retention = W that falls after initial abstraction is satisfied but that does not contribute to event flow – Q ef = W eff = event flow Method is based on an assumption that there is a relationship between the runoff ratio and the amount of storage that is filled: – V r / V max. = W eff /(W-V i ) where V max is the maximum storage capacity of the watershed If V r = W-V i -W eff,

34 SCS Concept Assuming V i = 0.2V max (??) V max is determined by a Curve Number

35 Curve Number The SCS classified 8500 soils into four hydrologic groups according to their infiltration characteristics

36 Curve Number Related to Land Use

37 Transfer Function 1. Estimate effective precipitation – SCS method gives us W eff 2. Estimate temporal distribution Base flow Q t Volume of effective Precipitation or event flow -What actually gives shape to the hydrograph?

38 Transfer Function 2. Estimate temporal distribution of effective precipitation – Various methods route water to stream channel Many are based on a time of concentration and many other rules – SCS method Assumes that the runoff hydrograph is a triangle T b =2.67T r Q t On top of base flow T w = duration of effective P T c = time concentration How were these equations developed?

39 Transfer Functions Time of concentration equations attempt to relate residence time of water to watershed properties – The time it takes water to travel from the hydraulically most distant part of the watershed to the outlet – Empically derived, based on watershed properties Once again, consider the assumptions…

40 Transfer Functions 2. Temporal distribution of effective precipitation – Unit Hydrograph – An X (1,2,3,…) hour unit hydrograph is the characteristic response (hydrograph) of a watershed to a unit volume of effective water input applied at a constant rate for x hours. 1 inch of effective rain in 6 hours produces a 6 hour unit hydrograph

41 Unit Hydrograph The event hydrograph that would result from 1 unit (cm, in,…) of effective precipitation (W eff =1) – A watershed has a characteristic response – This characteristic response is the model – Many methods to construct the shape Q ef t 1 1

42 Unit Hydrograph 1.How do we Develop the characteristic response for the duration of interest – the transfer function ? Empirical – page 451 Synthetic – page 453 2.How do we Apply the UH?: For a storm of an appropriate duration, simply multiply the y-axis of the unit hydrograph by the depth of the actual storm (this is based convolution integral theory)

43 Unit Hydrograph Apply: For a storm of an appropriate duration, simply multiply the y-axis of the unit hydrograph by the depth of the actual storm. – See spreadsheet example – Assumes one burst of precipitation during the duration of the storm In this picture, what duration is 2.5 hours Referring to? Where does 2.4 come from?

44 What if storm comes in multiple bursts? Application of the Convolution Integral – Convolves an input time series with a transfer function to produce an output time series U(t- ) = time distributed Unit Hydrograph W eff ( )= effective precipitation =time lag between beginning time series of rainfall excess and the UH

45 Convolution Convolution is a mathematical operation – Addition, subtraction, multiplication, convolution… Whereas addition takes two numbers to make a third number, convolution takes two functions to make a third function x(t) U(t) y(t) x(t) = input function U(t) = system response function τ = dummy variable of integration

46 Convolution Watch these: prrU prrU prrU prrU iXE&feature=related iXE&feature=related

47 Convolution Convolution is a mathematical operation – Addition, subtraction, multiplication, convolution… Whereas addition takes two numbers to make a third number, convolution takes two functions to make a third function x(t) U(t) y(t) x(t) = input function U(t) = system response function τ = dummy variable of integration

48 Unit Hydrograph Convolution integral in discrete form J=n-i+1 For Unit Hydrograph (see pdf notes)

49 Catchment Scale Mean Residence Time: An Example from Wimbachtal, Germany

50 Wimbach Watershed Drainage area = 33.4 km2 Mean annual precipitation = 250 cm Absent of streams in most areas Mean annual runoff (subsurface discharge to the topographic low) = 167 cm Streamflow Gaging Station Precipitation Station Major Spring Discharge Maloszewski et. al. (1992)

51 Geology of Wimbach Fractured Triassic Limestone and Karstic Triassic Dolomite 300 meter thick Pleistocene glacial deposits with Holocene alluvial fans above Many springs discharge at the base of the Limestone unit Maloszewski, Rauert, Trimborn, Herrmann, Rau (1992) 3 aquifer types – Porous, Karstic, Fractured

52 18 O in Precipitation and Springflow Seasonal variation of 18 O in precipitation and springflow Variation becomes progressively more muted as residence time increases These variations generally fit a model that incorporates assumptions about subsurface water flow

53 Modeling Approach Lumped-parameter models (black-box models): – Origanilly adopted from linear systems and signal processing theory and involves a convolution or filtering – System is treated as a whole & flow pattern is assumed constant over the modeling period (can have many system too) Filter/ Transfer Function Watershed/Aquifer Processes Weight Normalized Time 0 1

54 Modeling by Convolution A convolution is an integral which expresses the amount of overlap of one function g as it is shifted over another function C in. It therefore "blends" one function with another where C(t) = output signature C in (t) = input signature t = exit time from system = integration variable that describes the entry time into the system g(t- ) = travel time probability distribution for tracer molecules in the system Its a frequency filter, i.e., it attenuates specific frequencies of the input to produce the result

55 Convolution Illustration C in ( ) g( ) = e -a Folding g(- ) e -(-a Displacement g(t- ) e -a(t- t Multiplication C in ( )g(t- ) t Integration C(t) t t Shaded area 1 2 3 4 Step

56 Transfer Functions - Piston Flow (PFM) Assumes all flow paths have same residence time – All water moves with advection (no dispersion or diffusion) Represented by a delta function – This means the output signal at a given time is equal to the input concentration at the mean residence time T earlier. PFM Maloszewski and Zuber

57 Transfer Functions - Exponential (EM) Assumes contribution from all flow paths lengths and heavy weighting of young portion. Similar to the concept of a well-mixed system in a linear reservoir model Maloszewski and Zuber EM EPM DM

58 Exponential-piston Flow (EPM) Combination of exponential and piston flow to allow for a delay of shortest flow paths This model is somewhat more realistic than the exponential model because it allows for the existence of a delay DM Maloszewski and Zuber

59 Dispersion (DM) Assumes that flow paths are effected by hydrodynamic dispersion or geomorphological dispersion – Geomorphological dispersion is a measure of the dispersion of a disturbance by the drainage network structure (White et al. 2004) DM Maloszewski and Zuber

60 Input Function We must represent precipitation tracer flux to what actually goes into the soil and groundwater – Weighting functions are used to amount-weight the tracer values according recharge: mass balance where P i = the monthly depth of precipitation N = number of months with observations = summer/winter infiltration coefficient C out = mean output 18O composition (mean infiltration composition)

61 Infiltration Coefficient was calculated using 18O data from precipitation and springflow following Grabczak et al., 1984 Application of this equation yielded an value of 0.2, which means that winter infiltration exceeds summer infiltration by five times Grabczak, J., Maloszewski, P., Rozanski, K. ans Zuber, A., 1984. Estimation of the tritium input function with the aid of stable isotopes. Catena, 11: 105-114 where C out (1988-1990) = -12.82 o / oo (spring water) Mean Weighted Precipitation (1978-1990) = -8.90 o / oo and -13.30 o / oo, for summer and winter, respectively

62 Input Function Convolution using FLOWPC

63 Application of FLOWPC to estimate MRT for the Wimbach Spring Maloszewski, P., and Zuber, A., 1996. Lumped parameter models for interpretation of environmental tracer data. Manual on Mathematical Models in Isotope Hydrogeology, IAEA:9-58

64 Convolution Summation in EXcel Work in progress Your Task: – Evaluate my spreadsheet. Figure out if Im doing it right – Get FlowPC to work Reproduce Wimbachtal results – Run FlowPC or Excel for Dry Creek.

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