# Profiling C++ code with Callgrind

Often times, we have to write code to perform tasks whose complexity vary from mundane, such as retrieving and organizing data, to highly complex, such as simulations CFD simulations comprising the spine of a project. In either case, depending on the complexity of the task and amount of data to be processed, it may happen for the newborn code to leave us staring at an underscore marker blinking gracefully for hours on a command prompt during its execution until the results are ready, leading to project schedule delays and shortages of patience. Two standard and preferred approaches to the problem of time intensive codes are to simplify the algorithm and to make the code more efficient. In order to better select the parts of the code to work on, it is often useful to first find the parts of the code in which more time by profiling the code.

In this post, I will show how to use Callgrind, part of Valgrind, and KCachegrind to profile C/C++ codes on Linux — unfortunately, Valgrind is not available for Windows or Mac, although it can be ran on cluster from which results can be downloaded and visualized on Windows with QCachegrind. The first step is to install Valgrind and KCachegrind by typing the following commands in the terminal of a Debian based distribution, such as Ubuntu (equivalent yum commands area available for Red Hat based distributions):

```\$ sudo apt-get install valgrind
\$ sudo apt-get install kcachegrind
```

Now that the required tools are installed, the next step is to compile your code with GCC/G++ (with a make file, cmake, IDE or by running the compiler directly from the terminal) and then run the following command in a terminal (type ctrl+shift+T to open the terminal):

```\$ valgrind --tool=callgrind path/to/your/compiled/program program_arguments
```

Callgrind will then run your program with some instrumentation added to its execution to measure time expenditures and cache use by each function in your code. Because of the instrumentation, Your code will take considerably longer to run under Callgrind than it typically would, so be sure to run a representative task that is as small as possible when profiling your code. During its execution, Callgrind will output a report similar to the one below on terminal itself:

```==12345== Callgrind, a call-graph generating cache profiler
==12345== Copyright (C) 2002-2015, and GNU GPL'd, by Josef Weidendorfer et al.
==12345== Using Valgrind-3.11.0 and LibVEX; rerun with -h for copyright info
==12345== Command: path/to/your/compiled/program program_arguments
==12345==
==12345== For interactive control, run 'callgrind_control -h'.
IF YOUR CODE OUTPUTS TO THE TERMINAL, THE OUTPUT WILL BE SHOWN HERE.
==12345==
==12345== Events    : Ir
==12345== Collected : 4171789731
==12345==
==12345== I   refs:      4,171,789,731
```

The report above shows that it collected 4 billion events in order to generate the comprehensive report saved in the file callgrind.out.12345 — 12345 is here your process id, shown in the report above. Instead of submerging your soul into a sea of despair by trying to read the output file in a text editor, you should load the file into KCachegrind by typing:

```\$ kcachegrind calgrind.out.12345
```

You should now see a screen like the one below:

The screenshot above shows the profiling results for my code. The left panel shows the functions called by my code sorted by total time spent inside each function. Because functions call each other, callgrind shows two cost metrics as proxies for time spent in each function: Incl., showing the total cost of a function, and self, showing the time spent in each function itself discounting the callees. By clicking on “Self” to order to functions by the cost of the function itself, we sort the functions by the costs of their own codes, as shown below:

Callgrind includes functions that are native to C/C++ in its analysis. If one of them appears in the highest positions of the left panel, it may be the case to try to use a different function or data structure that performs a similar task in a more efficient way. Most of the time, however, our functions are the ones in most of the top positions in the list. In the example above, we can see that a possible first step I can take to improve the time performance of my code is to make function “ContinuityModelROF::shiftStorage” more efficient. A few weeks ago, however, the function “ContinuityModel::continuityStep” was ranked first with over 30% of the cost, followed by a C++ map related function. I then replaced a map inside that function by a pointer vector, resulting in the drop of my function’s cost to less than 5% of the total cost of the code.

In case KCachegrind shows that a given function that is called from multiple places in the code is costly, you may want to know which function is the main culprit behind the costly calls. To do this, click on the function of interest (in this case, “_memcpy_sse2_unalight”) in the left panel, and then click on “Callers” in the right upper panel and on “Call Graph” in the lower right panel. This will show in list and graph forms the calls made to the function by other functions, and the asociated percent costs. Unfortunately, I have only the function “ContinuityModelROF::calculateROF” calling “_memcpy_sse2_unalight,” hence the simple graph, but the graph would be more complex if multiple functions made calls to “_memcpy_sse2_unalight.”

I hope this saves you at least the time spend reading this post!

# Map making in Matlab

Greetings,

This weeks post will cover the basics of generating maps in Matlab.  Julie’s recent post showed how to do some of this in Python, but, Matlab is also widely used by the community.  You can get a lot done with Matlab, but in this post we’ll just cover a few of the basics.

We’ll start off by plotting a map of the continental United States, with the states.  We used three  this with three commands: usamap, shaperead, and geoshow.  usamap creates an empty map axes having the Lambert Projection covering the area of the US, or any state or collection of states.  shaperead reads shapefiles (duh) and returns a Matlab geographic data structure, composed of both geographic data and attributes.  This Matlab data structure then interfaces really well with various Matlab functions (duh).  Finally, geoshow plots geographic data, in our case on the map axes we defined.  Here’s some code putting it all together.

```hold on
figure1 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])
tightmap
hold off
```

Note that ‘usastatehi’ is a shapefile containing the US states (duh) that’s distributed with Matlab. The above code generates this figure:

Now, suppose we wanted to plot some data, say a precipitation forecast, on our CONUS map.  Let’s assume our forecast is being made at many points (lat,long).  To interpolate between the points for plotting we’ll use Matlab’s griddata function.  Once we’ve done this, we use the Matlab’s contourm command.  This works exactly like the normal contour function, but the ‘m’ indicates it plots map data.

```xi = min(x):0.5:max(x);
yi = min(y):0.5:max(y);
[XI, YI] = meshgrid(xi,yi);
ZI = griddata(x,y,V,XI,YI);

hold on
figure2 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])

contourm(YI,-1*XI,ZI)
tightmap
hold off
```

Here x, y, and V are vectors of long, lat, and foretasted precipitation respectively.  This code generates the following figure:

Wow!  Louisiana is really getting hammered!  Let’s take a closer look.  We can do this by changing the entry to usamap to indicate we want to consider only Louisiana.  Note, usamap accepts US postal code abbreviations.

```ax = usamap('LA');
```

Making that change results in this figure:

Neat!  We can also look at two states and add annotations.  Suppose, for no reason in particular, you’re interested in the location of Tufts University relative to Cornell.  We can make a map to look at this with the textm and scatterm functions.  As before, the ‘m’ indicates the functions  plot on a map axes.

```hold on
figure4 = figure;
ax = usamap({'MA','NY'});

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])
scatterm(42.4075,-71.1190,100,'k','filled')
textm(42.4075+0.2,-71.1190+0.2,'Tufts','FontSize',30)

scatterm(42.4491,-76.4842,100,'k','filled')
textm(42.4491+0.2,-76.4842+0.2,'Cornell','FontSize',30)
tightmap
hold off
```

This code generates the following figure.

Cool! Now back to forecasts.  NOAA distributes short term Quantitative Precipitation Forecasts (QPFs) for different durations every six hours.  You can download these forecasts in the form of shapefiles from a NOAA server.  Here’s an example of a 24-hour rainfall forecast made at 8:22 AM UTC on April 29.

Wow, that’s a lot of rain!  Can we plot our own version of this map using Matlab!  You bet!  Again we’ll use usamap, shaperead, and geoshow.  The for loop, (0,1) scaling, and log transform are simply to make the color map more visually appealing for the post.  There’s probably a cleaner way to do this, but this got the job done!

```figure5 = figure;
ax = usamap('conus');

set(ax, 'Visible', 'off')
latlim = getm(ax, 'MapLatLimit');
lonlim = getm(ax, 'MapLonLimit');
'UseGeoCoords', true, 'BoundingBox', [lonlim', latlim']);
geoshow(ax, states, 'FaceColor', [0.5 0.5 0.5])
p = colormap(jet);

N = max(size(S));
d = zeros(N,1);
for i = 1:N
d(i) = log(S(i).QPF);
end

y=floor(((d-min(d))/range(d))*63)+1;
col = p(y,:);
for i = 1:N
geoshow(S(i),'FaceColor',col(i,:),'FaceAlpha',0.5)%,'SymbolSpec', faceColors)
end
```

This code generates the following figure:

If you are not plotting in the US, Matlab also has a worldmap command.  This works exactly the same as usamap, but now for the world (duh).  Matlab is distibuted with a shapefile ‘landareas.shp’ which contains all of the land areas in the world (duh).  Generating a global map is then trivial:

```figure6 = figure;

worldmap('World')
geoshow(land, 'FaceColor', [0.15 0.5 0.15])
```

Which generates this figure.

Matlab also comes with a number of other included that might be of interest.  For instance, shapefiles detailing the locations of major world cities, lakes, and rivers.  We can plot those with the following code:

```figure7 = figure;

worldmap('World')
geoshow(land, 'FaceColor', [0.15 0.5 0.15])
geoshow(lakes, 'FaceColor', 'blue')
geoshow(rivers, 'Color', 'blue')
geoshow(cities, 'Marker', '.', 'Color', 'red')
```

Which generates the figure:

But suppose we’re interested in one country or a group of countries.  worldmap works in the same usamap does.  Also, you can plot continents, for instance Europe.

```worldmap('Europe')
```

Those are the basics, but there are many other capabilities, including 3-D projections. I can cover this in a later post if there is interest.

That’s it for now!

# Using Borg in Parallel and Serial with a Python Wrapper – Part 2

This blog post is Part 2 of a two-part series that will demonstrate how I have coupled a pure Python simulation model with the Borg multi-objective evolutionary algorithm (MOEA). I recommend reading Part 1 of this series before you read Part 2. In Part 1, I explain how to get Borg and provide sample code showing how you can access Borg’s serial and/or parallelized (master-slave) implementations through a Python wrapper (borg.py). In Part 2, I provide details for more advanced simulation-optimization setups that require you pass additional information from the borg wrapper into the simulation model (the “function evaluation”) other than just decision variable values.

In Part 1, the example simulation model I use (PySedSim) is called through a function handle “Simulation_Caller” in the example_sim_opt.py file. Borg needs only this function handle to properly call the simulation model in each “function evaluation”. Borg’s only interaction with the simulation model is to pass the simulation model’s function handle (e.g., “Simulation_Caller”) the decision variables, and nothing else. In many circumstances, this is all you need.

However, as your simulation-optimization setup becomes more complex, in order for your simulation model (i.e., the function evaluation) to execute properly, you may need to pass additional arguments to the simulation model from Borg other than just the decision variables. For example, in my own use of Borg in a simulation-optimization setting, in order to do optimization I first import a variety of assumptions and preferences to set up a Borg-PySedSim run. Some of those assumptions and preferences are helpful to the simulation model (PySedSim) in determining how to make use of the decision variable values Borg feeds it. So, I would like to pass those relevant assumptions and preferences directly into the Borg wrapper (borg.py), so the wrapper can in turn pass them directly into the simulation model along with the decision variable values.

Before I show how to do this, let me provide a more concrete example of how/why I am doing this in my own research. In my current work, decision variable values represent parameters for a reservoir operating policy that is being optimized. The simulation model needs to know how to take the decision variable values and turn them into a useful operating policy that can be simulated. Some of this information gets imported in order to run Borg, so I might as well pass that information directly into the simulation model while I have it on hand, rather than importing it once again in the simulation model.

To do what I describe above, we just need to modify the two functions in the example_sim_opt.py module so that a new argument “additional_inputs” is passed from borg to the simulation handle.  Using my python code from blog post 1, I provide code below that is modified in the Simulation_Caller() function on lines 5, 21, 22 and 27; and in the Optimization() function on lines 55, 56 and 70. After that code, I then indicate how I modify the borg.py wrapper so it can accept this information.

```import numpy as np
import pysedsim # This is your simulation model
import platform  # helps identify directory locations on different types of OS

'''
Purpose: Borg calls this function to run the simulation model and return multi-objective performance.

Note: You could also just put your simulation/function evaluation code here.

Args:
vars: A list of decision variable values from Borg
additional_inputs: A list of python data structures you want to pass from Borg into the simulation model.
Returns:
performance: policy's simulated objective values. A list of objective values, one value each of the objectives.
'''

borg_vars = vars  # Decision variable values from Borg

# Unpack lists of additional inputs from Borg (example assumes additional inputs is a python list with two items)

# Reformat decision variable values as necessary (.e.g., cast borg output parameters as array for use in simulation)
op_policy_params = np.asarray(borg_vars)
# Call/run simulation model with decision vars and additional relevant inputs, return multi-objective performance:
return performance

def Optimization():

'''

Purpose: Call this method from command line to initiate simulation-optimization experiment

Returns:
--pareto approximate set file (.set) for each random seed
--Borg runtime file (.runtime) for each random seed

'''

import borg as bg  # Import borg wrapper

parallel = 1  # 1= master-slave (parallel), 0=serial

# The following are just examples of relevant MOEA specifications. Select your own values.
nSeeds = 25  # Number of random seeds (Borg MOEA)
num_dec_vars = 10  # Number of decision variables
n_objs = 6  # Number of objectives
n_constrs = 0  # Number of constraints
num_func_evals = 30000  # Number of total simulations to run per random seed. Each simulation may be a monte carlo.
runtime_freq = 1000  # Interval at which to print runtime details for each random seed
decision_var_range = [[0, 1], [4, 6], [-1,4], [1,2], [0,1], [0,1], [0,1], [0,1], [0,1], [0,1]]
epsilon_list = [50000, 1000, 0.025, 10, 13, 4]  # Borg epsilon values for each objective
borg_dict_1 = {'simulation_preferences_1': [1,2]}  # reflects data you want Borg to pass to simulation model
borg_dict_2 = {'simulation_preferences_2': [3,4]}  # reflects data you want Borg to pass to simulation model

# Where to save seed and runtime files
main_output_file_dir = 'E:\output_directory'  # Specify location of output files for different seeds
os_fold = Op_Sys_Folder_Operator()  # Folder operator for operating system
output_location = main_output_file_dir + os_fold + 'sets'

# If using master-slave, start MPI. Only do once.
if parallel == 1:
bg.Configuration.startMPI()  # start parallelization with MPI

# Loop through seeds, calling borg.solve (serial) or borg.solveMPI (parallel) each time
for j in range(nSeeds):
# Instantiate borg class, then set bounds, epsilon values, and file output locations
borg = bg.Borg(num_dec_vars, n_objs, n_constrs, Simulation_Caller, add_sim_inputs = [borg_dict_1, borg_dict_2])
borg.setBounds(*decision_var_range)  # Set decision variable bounds
borg.setEpsilons(*epsilon_list)  # Set epsilon values
# Runtime file path for each seed:
runtime_filename = main_output_file_dir + os_fold + 'runtime_file_seed_' + str(j+1) + '.runtime'
if parallel == 1:
# Run parallel Borg
result = borg.solveMPI(maxEvaluations='num_func_evals', runtime=runtime_filename, frequency=runtime_freq)

if parallel == 0:
# Run serial Borg
result = borg.solve({"maxEvaluations": num_func_evals, "runtimeformat": 'borg', "frequency": runtime_freq,
"runtimefile": runtime_filename})

if result:
# This particular seed is now finished being run in parallel. The result will only be returned from
# one node in case running Master-Slave Borg.
result.display()

# Create/write objective values and decision variable values to files in folder "sets", 1 file per seed.
f = open(output_location + os_fold + 'Borg_DPS_PySedSim' + str(j+1) + '.set', 'w')
f.write('#Borg Optimization Results\n')
f.write('#First ' + str(num_dec_vars) + ' are the decision variables, ' + 'last ' + str(n_objs) +
' are the ' + 'objective values\n')
for solution in result:
line = ''
for i in range(len(solution.getVariables())):
line = line + (str(solution.getVariables()[i])) + ' '

for i in range(len(solution.getObjectives())):
line = line + (str(solution.getObjectives()[i])) + ' '

f.write(line[0:-1]+'\n')
f.write("#")
f.close()

# Create/write only objective values to files in folder "sets", 1 file per seed. Purpose is so that
# the file can be processed in MOEAFramework, where performance metrics may be evaluated across seeds.
f2 = open(output_location + os_fold + 'Borg_DPS_PySedSim_no_vars' + str(j+1) + '.set', 'w')
for solution in result:
line = ''
for i in range(len(solution.getObjectives())):
line = line + (str(solution.getObjectives()[i])) + ' '

f2.write(line[0:-1]+'\n')
f2.write("#")
f2.close()

print("Seed %s complete") %j

if parallel == 1:
bg.Configuration.stopMPI()  # stop parallel function evaluation process

def Op_Sys_Folder_Operator():
'''
Function to determine whether operating system is (1) Windows, or (2) Linux

Returns folder operator for use in specifying directories (file locations) for reading/writing data pre- and
post-simulation.
'''

if platform.system() == 'Windows':
os_fold_op = '\\'
elif platform.system() == 'Linux':
os_fold_op = '/'
else:
os_fold_op = '/'  # Assume unix OS if it can't be identified

return os_fold_op
```

Next, you will need to acquire the Borg wrapper using the instructions I specified in my previous blog post. You will need to make only two modifications: (1) modify the Borg class in borg.py so it accepts the inputs you want to pass to the simulation; and (2) some additional internal accounting in borg.py to ensure those inputs are passed to the borg.py methods that deal with your function handle. I will address these two in order.

First, modify the Borg class in borg.py so it now accepts an additional input (I only show some of the borg.py code here, just to indicate where changes are being made):

```
class Borg:
def __init__(self, numberOfVariables, numberOfObjectives, numberOfConstraints, function, epsilons = None, bounds = None, directions = None, add_sim_inputs=None):

# add_sim_inputs is the new input you will pass to borg

```

Then, modify the portion of the borg.py wrapper where self.function is called, so it can accommodate any simulation inputs you have specified.

```
self.function = _functionWrapper(function, numberOfVariables, numberOfObjectives, numberOfConstraints, directions)
else:
# More simulation inputs are specified and can be passed to the simulation handle

```

After the above, the last step is to modify the _functionWrapper method in borg.py:

```
def _functionWrapper(function, numberOfVariables, numberOfObjectives, numberOfConstraints, directions=None, addl_inputs=None):
# addl_inputs will be passed into the simulation model
def innerFunction(v,o,c):
global terminate
try:
result = function(*[v[i] for i in range(numberOfVariables)])
else:
result = function([v[i] for i in range(numberOfVariables)], addl_inputs)

```

# Solving non-linear problems using linear programming

This week’s post comes from recent conversations we’ve had around the Reed group concerning tools to quickly solve (approximately) non-linear programming problems.  First, some context.

As part of a simulation model our group is building, a drinking water allocation sub-problem must be solved.   Figure 1 is a simplified example of the sort of problem we are solving.

Figure 1: Mock water distribution network

There are three utilities that each have a demand ($d_{1}$, $d_{2}$, and $d_{3}$). The utilities are connected via some infrastructure, as shown in Figure 1.  When our total available water ($R$) is in excess of the demand ($d_{1}+d_{2}+d_{3}$), no rationing is needed.  When we do need to ration, we want to allocate the water to minimize the percent supply deficits across the three utilities:

Equation 1

Subject to:

The last constraint here describes the a limitation of the distribution network.  The real problem is much more complicated, but we needn’t detail that here.

This problem needs to be solved thousands, or hundreds of thousands of times in each simulation, so we want any solution technique to be fast.  The natural solution is linear programming (LP), which can solve problems with tens of thousands of variables and constraints nearly instantaneously.

We won’t discuss LP in great detail here, except to say that LP requires an objective and constraints that are linear with respect to the decision variables.  These restrictive requirements significantly reduce the number of potential optimal solutions that must be searched.  By systematically testing and pivoting between these potential optimal solutions, the popular Simplex Algorithm quickly converges to the optimal solution.

As stated in equation 1, our rationing scheme is indifferent to imposing small deficits across all three utilities, or imposing one large deficit to a single utility.  For example, the objective value in equation 1 is the same, whether each utility has a deficit of 5%, or if utility 1 has a deficit of 15%, and utilities 2 and 3 have no deficit.  In reality, many small deficits are likely preferable to one large one.  So what are we to do?

We could square our deficits.  In that case, our rationing scheme will prefer small distributed deficits over one large deficit:

Equation 2

BUT, we can’t use LP to solve this problem, as our objective is now non-linear! There are non-linear programming algorithms that are relatively fast, but perhaps not fast enough.  Instead we could linearize our non-linear objective, as shown in Figure 2.

The strategy here is to divide a single allocation,  $x_{1}$ for instance, into many decision variables, representing different ranges of the actual allocation $x_{1}$.  In each range, a linear segment approximates the actual quadratic objective function.  Any actual release $x_{1}$ can be achieved by assigning the appropriate values to the new decision variables ($k_{1}$, $k_{2}$, and $k_{3}$), and the contribution to the objective function from that release can be approximated by:

Equation 3

Subject to:

If a more accurate description is needed, the range of $x_{1}$ can be divided into more segments.  For our purposes just a few segments are probably sufficient.  A similar strategy can be adopted for $x_{2}$ and $x_{3}$.  Of course the constraints from the original optimization problem would need to be translated into terms of the new decision variables.

Now we are adding many more decision variables and constraints, but this is unlikely to slow a modern LP algorithm too much; we are still solving a relatively simple problem.  BUT, how does the LP algorithm know to increase $k_{1}$ to its maximum threshold before applying $k_{2}$?  Do we need to add a number of conditional constraints to ensure this is done properly?

It turns out we don’t!  Because our squared deficit curve in Figure 2 is monotonic and convex, we know that slope of the linear segments making up the approximation are increasing (becoming less negative).  Thus, in a minimization problem, the marginal improvement in the objective is highest for the $k_{1}$ segment, followed by the $k_{2}$ segment, followed by the $k_{3}$ segment, and so on.  In other words $a < b < c$.For this reason, the algorithm will increase $k_{1}$ to its maximum threshold before assigning a non-zero value to $k_{2}$, and so forth.  No need for complicated constraints!

Now this is not always the case.  If the function were not monotonic, or if it were convex for a maximization, or concave for a minimization, this would not work.  But, this trick works for a surprising number of applications in water resources systems analysis!

If nothing else this simple example serves as a reminder that a little bit of thought in formulating problems can save a lot of time later!

# Using Borg in Parallel and Serial with a Python Wrapper – Part 1

Simulation and optimization are frequently used to solve complex water resources and environmental systems problems. By itself, a simulation model begs the question “what to simulate?” Similarly, by itself, an optimization model begs the question “is the solution really best?” For this reason, simulation and optimization models are frequently coupled.

This blog post is part 1 of a multi-part series that will demonstrate how I have coupled a pure Python simulation model with the multi-objective evolutionary optimization algorithm Borg. In this post, I will show how you can access Borg’s serial and/or parallelized (master-slave) implementations through a Python wrapper (borg.py).

Please see this previous blog post for some background about Borg, and how to obtain it. My instructions below assume you have access to the Borg files.

In the setup I will describe below, Borg parameterizes and iteratively refines solutions (e.g., reservoir operating policies) to a problem, optimizing them in response to their simulated performance with respect to multiple objectives.

• Serial (i.e., borg.c, libborg.so, etc.) and/or master-slave (i.e., borgms.c, libborgms.so, etc.) implementations of Borg, depending upon your ability to parallelize.
• Python wrapper for Borg (borg.py), which will allow you to to access Borg easily in Python.

You will need to create the following files yourself (I provide sample code below for these files):

• example_sim_opt.py—A python module that should contain two main functions:
1. A simulation caller, which takes decision variables and returns multi-objective performance. This function is called “Simulation_Caller” in the example_sim_opt.py code below.
2. An optimization function, which calls the Borg MOEA through its python wrapper borg.py. This borg.py wrapper provides extensive docstring documentation regarding required arguments, returned values, etc., so I do suggest reading through the wrapper if you have questions (e.g., about the python data types of arguments and returns).

Note that the file and function names above are just example names. You can name the above files whatever you want. Just be sure to modify the code I provide below to reflect the new names.

A sample of code for example_sim_opt.py is as follows:

```import numpy as np
import pysedsim # This is your simulation model
import platform  # helps identify directory locations on different types of OS

def Simulation_Caller(vars):
'''
Purpose: Borg calls this function to run the simulation model and return multi-objective performance.

Note: You could also just put your simulation/function evaluation code here.

Args:
vars: A list of decision variable values from Borg

Returns:
performance: policy's simulated objective values. A list of objective values, one value each of the objectives.
'''

borg_vars = vars  # Decision variable values from Borg

# Reformat decision variable values as necessary (.e.g., cast borg output parameters as array for use in simulation)
op_policy_params = np.asarray(borg_vars)
# Call/run simulation model, return multi-objective performance:
performance = pysedsim.PySedSim(decision_vars = op_policy_params)
return performance

def Optimization():

'''

Purpose: Call this method from command line to initiate simulation-optimization experiment

Returns:
--pareto approximate set file (.set) for each random seed
--Borg runtime file (.runtime) for each random seed

'''

import borg as bg  # Import borg wrapper

parallel = 1  # 1= master-slave (parallel), 0=serial

# The following are just examples of relevant MOEA specifications. Select your own values.
nSeeds = 25  # Number of random seeds (Borg MOEA)
num_dec_vars = 10  # Number of decision variables
n_objs = 6  # Number of objectives
n_constrs = 0  # Number of constraints
num_func_evals = 30000  # Number of total simulations to run per random seed. Each simulation may be a monte carlo.
runtime_freq = 1000  # Interval at which to print runtime details for each random seed
decision_var_range = [[0, 1], [4, 6], [-1,4], [1,2], [0,1], [0,1], [0,1], [0,1], [0,1], [0,1]]
epsilon_list = [50000, 1000, 0.025, 10, 13, 4]  # Borg epsilon values for each objective

# Where to save seed and runtime files
main_output_file_dir = 'E:\output_directory'  # Specify location of output files for different seeds
os_fold = Op_Sys_Folder_Operator()  # Folder operator for operating system
output_location = main_output_file_dir + os_fold + 'sets'

# If using master-slave, start MPI. Only do once.
if parallel == 1:
bg.Configuration.startMPI()  # start parallelization with MPI

# Loop through seeds, calling borg.solve (serial) or borg.solveMPI (parallel) each time
for j in range(nSeeds):
# Instantiate borg class, then set bounds, epsilon values, and file output locations
borg = bg.Borg(num_dec_vars, n_objs, n_constrs, Simulation_Caller)
borg.setBounds(*decision_var_range)  # Set decision variable bounds
borg.setEpsilons(*epsilon_list)  # Set epsilon values
# Runtime file path for each seed:
runtime_filename = main_output_file_dir + os_fold + 'runtime_file_seed_' + str(j+1) + '.runtime'
if parallel == 1:
# Run parallel Borg
result = borg.solveMPI(maxEvaluations='num_func_evals', runtime=runtime_filename, frequency=runtime_freq)

if parallel == 0:
# Run serial Borg
result = borg.solve({"maxEvaluations": num_func_evals, "runtimeformat": 'borg', "frequency": runtime_freq,
"runtimefile": runtime_filename})

if result:
# This particular seed is now finished being run in parallel. The result will only be returned from
# one node in case running Master-Slave Borg.
result.display()

# Create/write objective values and decision variable values to files in folder "sets", 1 file per seed.
f = open(output_location + os_fold + 'Borg_DPS_PySedSim' + str(j+1) + '.set', 'w')
f.write('#Borg Optimization Results\n')
f.write('#First ' + str(num_dec_vars) + ' are the decision variables, ' + 'last ' + str(n_objs) +
' are the ' + 'objective values\n')
for solution in result:
line = ''
for i in range(len(solution.getVariables())):
line = line + (str(solution.getVariables()[i])) + ' '

for i in range(len(solution.getObjectives())):
line = line + (str(solution.getObjectives()[i])) + ' '

f.write(line[0:-1]+'\n')
f.write("#")
f.close()

# Create/write only objective values to files in folder "sets", 1 file per seed. Purpose is so that
# the file can be processed in MOEAFramework, where performance metrics may be evaluated across seeds.
f2 = open(output_location + os_fold + 'Borg_DPS_PySedSim_no_vars' + str(j+1) + '.set', 'w')
for solution in result:
line = ''
for i in range(len(solution.getObjectives())):
line = line + (str(solution.getObjectives()[i])) + ' '

f2.write(line[0:-1]+'\n')
f2.write("#")
f2.close()

print("Seed %s complete") %j

if parallel == 1:
bg.Configuration.stopMPI()  # stop parallel function evaluation process

def Op_Sys_Folder_Operator():
'''
Function to determine whether operating system is (1) Windows, or (2) Linux

Returns folder operator for use in specifying directories (file locations) for reading/writing data pre- and
post-simulation.
'''

if platform.system() == 'Windows':
os_fold_op = '\\'
elif platform.system() == 'Linux':
os_fold_op = '/'
else:
os_fold_op = '/'  # Assume unix OS if it can't be identified

return os_fold_op
```

The following is an example of how you would submit a batch script on a Linux cluster to run a parallelized simulation-optimization experiment using the example_sim_opt.py and borg.py files. Note that in the parallelized version, simulations (i.e., “function evaluations”) are being run in parallel by separate processors.

You would need the following two files:

1. example_sim_opt_caller.py. This is a python file that is used to call example_sim_opt.py
2. example_sim_opt_batch_script.pbs. This is batch script that runs example_sim_opt_caller.py in parallel on a cluster using open MPI.

Example code for example_sim_opt_caller.py:

```
'''
Purpose: To initiate the optimization process, which will iteratively call the simulation model.
'''

import example_sim_opt  # Import main optimization module that uses borg python wrapper

# Module within example
example_sim_opt.Optimization()

```

Example code for example_sim_opt_batch_script.pbs:

```
#PBS -l nodes=8:ppn=16
#PBS -l walltime=24:00:00
#PBS -j oe
#PBS -o pysedsim_output.out

cd \$PBS_O_WORKDIR
source /etc/profile.d/modules.sh
mpirun python example_sim_opt_caller.py

```

You could then run the above batch script with a command such as:

qsub example_sim_opt_batch_script.pbs

# Calculating Risk-of-Failures as in the Research Triangle papers (2014-2016) – Part 1

There has been a series of papers (e.g., Palmer and Characklis, 2009; Zeff et al., 2014; Herman et al., 2014) suggesting the use of an approximate risk-of-failure (ROF) metric, as opposed to the more conventional days of supply remaining, for utilities’ managers to decide when to enact not only water use restrictions, but also water transfers between utilities. This approach was expanded to decisions about the best time and in which new infrastructure project a utility should invest (Zeff at al., 2016), as opposed to setting fixed times in the future for either construction or options evaluation. What all these papers have in common is that drought mitigation and infrastructure expansion decisions are triggered when the values of the short and long-term ROFs, respectively, for a given utility exceeds those of pre-set triggers.

For example, the figure below shows that as streamflows (black line, subplot “a”) get lower while demands are maintained (subplot “b”), the combined storage levels of the fictitious utility starts to drop around the month of April (subplot “c”), increasing the utility’s short-term ROF (subplot “d”) until it finally triggers transfers and restrictions (subplot “e”). Despite the triggered restriction and transfers, the utility’s combined storage levels crossed the dashed line in subplot “c”, which denotes the fail criteria (i.e. combined storage levels dropping below 20% of the total capacity).

It is beyond the scope of this post to go into the details presented in all of these papers, but even after reading them the readers may be wondering how exactly ROFs are calculated. In this post, I’ll try to show in a graphical and concise manner how short-term ROFs are calculated.

In order to calculate a utility’s ROF for week m, we would run 50 independent simulations (henceforth called ROF simulations) all departing from the system conditions (reservoir storage levels, demand probability density function, etc.) observed in week m, and each using one of 50 years of streamflows time series recorded immediately prior to week m. The utility’s ROF is then calculated as the number of ROF simulations in which the combined storage level of that utility dropped below 20% of the total capacity in at least one week, divided by the number of ROF simulations ran (50). An animation of the process can be seen below.

For example, for a water utility who started using ROF triggers on 01/01/2017, this week’s short-term ROF (02/13/2017, or week m=7) would be calculated using the recorded streamflows from weeks 6 through -47 (assuming here a year of 52 weeks, for simplicity) for ROF simulation 1, the streamflows from weeks -48 to -99 for ROF simulation 2, and so on until we reach 50 simulations. However, if the utility is running an optimization or scenario evaluation and wants to calculate the ROF in week 16 (04/10/2017) of a system simulation, ROF simulation 1 would use 10 weeks of synthetically generated streamflows (16 to 7) and 42 weeks of historical records (weeks 6 to -45), simulation 2 would use records for weeks -46 to -97, and so on, as in a 50 years moving window.

In another blog post, I will show how to calculate the long-term ROF and the reasoning behind it.

Works cited

Herman, J. D., H. B. Zeff, P. M. Reed, and G. W. Characklis (2014), Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty, Water Resour. Res., 50, 7692–7713, doi:10.1002/2014WR015338.

Palmer, R., and G. W. Characklis (2009), Reducing the costs of meeting regional water demand through risk-based transfer agreements, J. Environ. Manage., 90(5), 1703–1714.

Zeff, H. B., J. R. Kasprzyk, J. D. Herman, P. M. Reed, and G. W. Characklis (2014), Navigating financial and supply reliability tradeoffs in regional drought management portfolios, Water Resour. Res., 50, 4906–4923, doi:10.1002/2013WR015126.

Zeff, H. B., J. D. Herman, P. M. Reed, and G. W. Characklis (2016), Cooperative drought adaptation: Integrating infrastructure development, conservation, and water transfers into adaptive policy pathways, Water Resour. Res., 52, 7327–7346, doi:10.1002/2016WR018771.

# Synthetic streamflow generation

A recent research focus of our group has been the development and use of synthetic streamflow generators.  There are many tools one might use to generate synthetic streamflows and it may not be obvious which is right for a specific application or what the inherent limitations of each method are.  More fundamentally, it may not be obvious why it is desirable to generate synthetic streamflows in the first place.  This will be the first in a series of blog posts on the synthetic streamflow generators in which I hope to sketch out the various categories of generation methods and their appropriate use as I see it.  In this first post we’ll focus on the motivation and history behind the development of synthetic streamflow generators and broadly categorize them.

### Why should we use synthetic hydrology?

The most obvious reason to use synthetic hydrology is if there is little or no data for your system (see Lamontagne, 2015).  Another obvious reason is if you are trying to evaluate the effect of hydrologic non-stationarity on your system (Herman et al. 2015; Borgomeo et al. 2015).  In that case you could use synthetic methods to generate flows reflecting a shift in hydrologic regime.  But are there other reasons to use synthetic hydrology?

In water resources systems analysis it is common practice to evaluate the efficacy of management or planning strategies by simulating system performance over the historical record, or over some critical period.  In this approach, new strategies are evaluated by asking the question:  How well would we have done with your new strategy?

This may be an appealing approach, especially if some event was particularly traumatic to your system. But is this a robust way of evaluating alternative strategies?  It’s important to remember that any hydrologic record, no matter how long, is only a single realization of a stochastic process.  Importantly, drought and flood events emerge as the result of specific sequences of events, unlikely to be repeated.  Furthermore, there is a 50% chance that the worst flood or drought in an N year record will be exceeded in the next N years.  Is it well advised to tailor our strategies to past circumstances that will likely never be repeated and will as likely as not be exceeded?  As Lettenmaier et al. [1987] reminds us “Little is certain about the future except that it will be unlike the past.”

Even under stationarity and even with long hydrologic records, the use of synthetic streamflow can improve the efficacy of planning and management strategies by exposing them to larger and more diverse flood and drought than those in the record (Loucks et al. 1981; Vogel and Stedinger, 1988; Loucks et al. 2005).  Figure 7.12 from Loucks et al. 2005 shows a typical experimental set-up using synthetic hydrology with a simulation model.  Often our group will wrap an optimization model like Borg around this set up, where the system design/operating policy (bottom of the figure) are the decision variables, and the system performance (right of the figure) are the objective(s).

(Loucks et al. 2005)

### What are the types of generators?

Many synthetic streamflow generation techniques have been proposed since the early 1960s.  It can be difficult for a researcher or practitioner to know which method is best suited to the problem at hand.  Thus, we’ll start with a very broad characterization of what is out there, then proceed to some history.

Broadly speaking there are two approaches to generating synthetic hydrology: indirect and direct.  The indirect approach generates streamflow by synthetically generating the forcings to a hydrologic model.  For instance one might generate precipitation and temperature series and input them to a hydrologic model of a basin (e.g. Steinschneider et al. 2014).  In contrast, direct methods use statistical techniques to generate streamflow timeseries directly.

The direct approach is generally easier to apply and more parsimonious because it does not require the selection, calibration, and validation of a separate hydrologic model (Najafi et al. 2011).  On the other hand, the indirect approach may be desirable.  Climate projections from GCMs often include temperature or precipitation changes, but may not describe hydrologic shifts at a resolution or precision that is useful.  In other cases, profound regime shifts may be difficult to represent with statistical models and may require process-driven models, thus necessitating the indirect approach.

Julie’s earlier series focused on indirect approaches, so we’ll focus on the direct approach.  Regardless of the approach many of the methods are same.  In general generator methods can be divided into two categories: parametric and non-parametricParametric methods rely on a hypothesized statistical model of streamflow whose parameters are selected to achieve a desired result (Stedinger and Taylor, 1982a).  In contrast non-parametric methods do not make strong structural assumptions about the processes generating the streamflow, but rather rely on re-sampling from the hydrologic record in some way (Lall, 1995).  Some methods combine parametric and non-parametric techniques, which we’ll refer to as semi-parametric (Herman et al. 2015).

Both parametric and non-parametric methods have advantages and disadvantages.  Parametric methods are often parsimonious, and often have analytical forms that allow easy parameter manipulation to reflect non-stationarity.  However, there can be concern that the underlying statistical models may not reflect the hydrologic reality well (Sharma et al. 1997).  Furthermore, in multi-dimensional, multi-scale problems the proliferation of parameters can make parametric models intractable (Grygier and Stedinger, 1988).  Extensive work has been done to confront both challenges, but they may lead a researcher to adopt a non-parametric method instead.

Because many non-parametric methods ‘re-sample’ flows from a record, realism is not generally a concern, and most re-sampling schemes are computationally straight forward (relatively speaking).  On the other hand, manipulating synthetic flows to reflect non-stationarity may not be as straightforward as a simple parameter change, though methods have been suggested (Herman et al. 2015Borgomeo et al. 2015).  More fundamentally, because non-parametric methods rely so heavily on the data, they require sufficiently long records to ensure there is enough hydrologic variability to sample.  Short records can be a concern for parametric methods as well, though parametric uncertainty can be explicitly considered in those methods (Stedinger and Taylor, 1982b).  Of course, parametric methods also have structural uncertainty that non-parametric models largely avoid by not assuming an explicit statistical model.

In the coming posts we’ll dig into the nuances of the different methods in greater detail.

### A historical perspective

The first use of synthetic flow generation seems to have been by Hazen [1914].  That work attempted to quantify the reliability of a water supply by aggregating the streamflow records of local streams into a 300-year ‘synthetic record.’  Of course the problem with this is that the cross-correlation between concurrent flows rendered the effective record length much less than the nominal 300 years.

Next Barnes [1954] generated 1,000 years of streamflow for a basin in Australia by drawing random flows from a normal distribution with mean and variance equal to the sample estimates from the observed record.  That work was extended by researchers from the Harvard Water Program to account for autocorrelation of monthly flows (Maass et al., 1962; Thomas and Fiering, 1962).  Later work also considered the use of non-normal distributions (Fiering, 1967), and the generation of correlated concurrent flows at multiple sites (Beard, 1965; Matalas, 1967).

Those early methods relied on first-order autoregressive models that regressed flows in the current period on the flows of the previous period (see Loucks et al.’s Figure 7.13  below).  Box and Jenkins [1970] extended those methods to autoregressive models of arbitrary order, moving average models of arbitrary order, and autoregressive-moving average models of arbitrary order.  Those models were the focus of extensive research over the course of the 1970s and 1980s and underpin many of the parametric generators that are widely used in hydrology today (see Salas et al. 1980; Grygier and Stedinger, 1990; Salas, 1993; Loucks et al. 2005).

(Loucks et al. 2005)

By the mid-1990s, non-parametric methods began to gain popularity (Lall, 1995).  While much of this work has its roots in earlier work from the 1970s and 1980s (Yakowitz, 1973, 1979, 1985; Schuster and Yakowitz, 1979; Yakowitz and Karlsson, 1987; Karlson and Yakowitz, 1987), improvements in computing and the availability of large data sets meant that by the mid-1990s non-parametric methods were feasible (Lall and Sharma, 1996).  Early examples of non-parametric methods include block bootstrapping (Vogel and Shallcross, 1996), k-nearest neighbor (Lall and Sharma, 1996), and kernel density methods (Sharma et al. 1997).  Since that time extensive research has made improvement to these methods, often by incorporating parametric elements.  For instance, Srinivas and Srinivasan (2001, 2005, and 2006) develop a hybrid autoregressive-block bootstrapping method designed to improve the bias in lagged correlation and to generate flows other than the historical, for multiple sites and multiple seasons.  K-nearest neighbor methods have also been the focus of extensive research (Rajagopalan and Lall, 1999; Harrold et al. 2003; Yates et al. 2003; Sharif and Burn, 2007; Mehortra and Sharma, 2006; Prairie et al. 2006; Lee et al. 2010, Salas and Lee, 2010, Nowak et al., 2010), including recent work by our group  (Giuliani et al. 2014).

Emerging work focuses on stochastic streamflow generation using copulas [Lee and Salas, 2011; Fan et al. 2016], entropy theory bootstrapping [Srivastav and Simonovic, 2014], and wavelets [Kwon et al. 2007; Erkyihun et al., 2016], among other methods.

In the following posts I’ll address different challenges in stochastic generation [e.g. long-term persistence, parametric uncertainty, multi-site generation, seasonality, etc.] and the relative strengths and shortcomings of the various methods for addressing them.

### Works Cited

Barnes, F. B., Storage required for a city water supply, J. Inst. Eng. Australia 26(9), 198-203, 1954.

Beard, L. R., Use of interrelated records to simulate streamflow, J. Hydrol. Div., ASCE 91(HY5), 13-22, 1965.

Borgomeo, E., Farmer, C. L., and Hall, J. W. (2015). “Numerical rivers: A synthetic streamflow generator for water resources vulnerability assessments.” Water Resour. Res., 51(7), 5382–5405.

Y.R. Fan, W.W. Huang, G.H. Huang, Y.P. Li, K. Huang, Z. Li, Hydrologic risk analysis in the Yangtze River basin through coupling Gaussian mixtures into copulas, Advances in Water Resources, Volume 88, February 2016, Pages 170-185.

Fiering, M.B, Streamflow Synthesis, Harvard University Press, Cambridge, Mass., 1967.

Giuliani, M., J. D. Herman, A. Castelletti, and P. Reed (2014), Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management, Water Resour. Res., 50, 3355–3377, doi:10.1002/2013WR014700.

Grygier, J.C., and J.R. Stedinger, Condensed Disaggregation Procedures and Conservation Corrections for Stochastic Hydrology, Water Resour. Res. 24(10), 1574-1584, 1988.

Grygier, J.C., and J.R. Stedinger, SPIGOT Technical Description, Version 2.6, 1990.

Harrold, T. I., Sharma, A., and Sheather, S. J. (2003). “A nonparametric model for stochastic generation of daily rainfall amounts.” Water Resour. Res., 39(12), 1343.

Hazen, A., Storage to be provided in impounding reservoirs for municipal water systems, Trans. Am. Soc. Civ. Eng. 77, 1539, 1914.

Herman, J.D., H.B. Zeff, J.R. Lamontagne, P.M. Reed, and G. Characklis (2016), Synthetic Drought Scenario Generation to Support Bottom-Up Water Supply Vulnerability Assessments, Journal of Water Resources Planning & Management, doi: 10.1061/(ASCE)WR.1943-5452.0000701.

Karlsson, M., and S. Yakowitz, Nearest-Neighbor methods for nonparametric rainfall-runoff forecasting, Water Resour. Res., 23, 1300-1308, 1987.

Kwon, H.-H., U. Lall, and A. F. Khalil (2007), Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature, Water Resour. Res., 43, W05407, doi:10.1029/2006WR005258.

Lall, U., Recent advances in nonparametric function estimation: Hydraulic applications, U.S. Natl. Rep. Int. Union Geod. Geophys. 1991- 1994, Rev. Geophys., 33, 1093, 1995.

Lall, U., and A. Sharma (1996), A nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res. 32(3), pp. 679-693.

Lamontagne, J.R. 2015,Representation of Uncertainty and Corridor DP for Hydropower 272 Optimization, PhD edn, Cornell University, Ithaca, NY.

Lee, T., J. D. Salas, and J. Prairie (2010), An enhanced nonparametric streamflow disaggregation model with genetic algorithm, Water Resour. Res., 46, W08545, doi:10.1029/2009WR007761.

Lee, T., and J. Salas (2011), Copula-based stochastic simulation of hydrological data applied to Nile River flows, Hydrol. Res., 42(4), 318–330.

Lettenmaier, D. P., K. M. Latham, R. N. Palmer, J. R. Lund and S. J. Burges, Strategies for coping with drought Part II: Planning techniques for planning and reliability assessment, EPRI P-5201, Final Report Project 2194-1, June 1987.

Loucks, D.P., Stedinger, J.R. & Haith, D.A. 1981, Water Resources Systems Planning and Analysis, 1st edn, Prentice-Hall, Englewood Cliffs, N.J.

Loucks, D.P. et al. 2005, Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications, UNESCO, Delft, The Netherlands.

Maass, A., M. M. Hufschmidt, R. Dorfman, H. A. Thomas, Jr., S. A. Marglin and G. M. Fair,

Design of Water Resource Systems, Harvard University Press, Cambridge, Mass., 1962.

Matalas, N. C., Mathematical assessment of synthetic hydrology, Water Resour. Res. 3(4), 937-945, 1967.

Najafi, M. R., Moradkhani, H., and Jung, I. W. (2011). “Assessing the uncertainties of hydrologic model selection in climate change impact studies.” Hydrol. Process., 25(18), 2814–2826.

Nowak, K., J. Prairie, B. Rajagopalan, and U. Lall (2010), A nonparametric stochastic approach for multisite disaggregation of annual to daily

streamﬂow, Water Resour. Res., 46, W08529, doi:10.1029/2009WR008530.

Nowak, K., J. Prairie, B. Rajagopalan, and U. Lall (2010), A nonparametric stochastic approach for multisite disaggregation of annual to daily

streamﬂow, Water Resour. Res., 46, W08529, doi:10.1029/2009WR008530.

Nowak, K., J. Prairie, B. Rajagopalan, and U. Lall (2010), A nonparametric stochastic approach for multisite disaggregation of annual to daily

streamﬂow, Water Resour. Res., 46, W08529, doi:10.1029/2009WR008530.

Nowak, K., J. Prairie, B. Rajagopalan, and U. Lall (2010), A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow, Water Resour. Res., 46, W08529, doi:10.1029/2009WR008530.

Prairie, J. R., Rajagopalan, B., Fulp, T. J., and Zagona, E. A. (2006). “Modified K-NN model for stochastic streamflow simulation.” J. Hydrol. Eng., 11(4), 371–378.

Rajagopalan, B., and Lall, U. (1999). “A k-nearest-neighbor simulator for daily precipitation and other weather variables.” Water Resour. Res., 35(10), 3089–3101.

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Salas, J.D., 1993, Analysis and Modeling of Hydrologic Time Series, Chapter 19 (72 p.) in The McGraw Hill Handbook of Hydrology, D.R. Maidment, Editor.

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Sharif, M., and Burn, D. H. (2007). “Improved K-nearest neighbor weather generating model.” J. Hydrol. Eng., 12(1), 42–51.

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Srinivas, V. V., and Srinivasan, K. (2001). “A hybrid stochastic model for multiseason streamflow simulation.” Water Resour. Res., 37(10), 2537–2549.

Srinivas, V. V., and Srinivasan, K. (2005). “Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows.” J. Hydrol., 302(1–4), 307–330.

Srinivas, V. V., and Srinivasan, K. (2006). “Hybrid matched-block bootstrap for stochastic simulation of multiseason streamflows.” J. Hydrol., 329(1–2), 1–15.

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Steinschneider, S., Wi, S., and Brown, C. (2014). “The integrated effects of climate and hydrologic uncertainty on future flood risk assessments.” Hydrol. Process., 29(12), 2823–2839.

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Vogel, R.M., and J.R. Stedinger, The value of stochastic streamflow models in over-year reservoir design applications, Water Resour. Res. 24(9), 1483-90, 1988.

Vogel, R. M., and A. L. Shallcross (1996), The moving block bootstrap versus parametric time series models, Water Resour. Res., 32(6), 1875–1882.

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Yakowitz, S., Nonparametric estimation of markov transition functions, Ann. Stat., 7, 671-679, 1979.

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Yates, D., Gangopadhyay, S., Rajagopalan, B., and Strzepek, K. (2003). “A technique for generating regional climate scenarios using a nearest-neighbor algorithm.” Water Resour. Res., 39(7), 1199.