Comparing the Results of HEC-RAS and DHM for Two Dimensional Diffusion Wave

By Theodore .V. Hromadka II (1) and Prasada Rao (2)

(1) Professor, Department of Engineering-Mathematics, United States Military Academy, West Point, NY.

(2) Professor, Department of Civil and Environmental Engineering, California State University, Fullerton, CA.

Abstract

For simulating flows where inertial forces dominate over frictional forces, as in many types of floods, the solution of the two dimensional diffusion wave equation will suffice (compared to a one dimensional simulation). The associated numerical models are reliable and computationally efficient, thus not warranting the amount of resources that are required for solving the full momentum equations. In this work, the reliability of the USGS Diffusion Hydrodynamic Model (DHM) is cross checked by comparing its results with the more widely used HEC-RAS model. The close agreement between the two sets of computational results underscores the reliability of the legacy DHM.

Key words: Two dimensional models, Diffusive wave, HEC-RAS, DHM

Introduction

Numerical models developed in the 1980’s and earlier, during which period, computer memory and speed were significant modeling constraints, are increasingly called “legacy” models. The mathematical underpinnings in these models coupled with their reliability has made their solution to serve as a benchmark solution for the modern software. That is, providing another computational opinion to the problem under study using the prior technology. While some legacy models have been altered to accommodate more complex fluid dynamics (by adding modules to simulate flow turbulence, simplifying the data input requirements, and/or enhancing the model output/visualization modules), other legacy models have focused on access to their core algorithms, laying a foundation to newer models. DHM [1] is an example of such a legacy, first generation, hydraulics model developed for the USGS in the mid 1980’s time period, which led to publication of a USGS Technical Report in 1987. The model was written in Fortran 77, and has been extensively applied to different overland, coupled channel, and overbank flow scenarios. The DHM also served as a foundation for other finite-difference algorithms [2] resulting in additional computational programs for solving a variety of transport problems.

DHM solves the two-dimensional overland flow equations coupled with one-dimensional open channel flow equations and includes interfaces between these two flow regimes using source and sink term approximations. It is one of the first general purpose computational solutions to a two dimensional formulation of the Navier-Stokes equations. The model is capable of approximating such hydraulic effects as backwater, drawdown, channel overflow, storage, and ponding, among other hydraulic topics of interest.

The application of legacy hydraulic models, including DHM, for large-scale applications on modern computers is constrained by multiple factors. One is related to recompiling the code to take into account new processors and architecture. Porting the Fortran 77 codes to the new computing platforms and recompiling them can be a challenge and may warrant rewriting some coding statements that are not supported by the new compilers. Changes to the legacy codes may require adding new functionalities. In this case, the DHM code was re-compiled using the Intel visual Fortran compiler. The end windows environment executable file is robust, and the time required to run the model, because of the various optimization modules in the compiler, has been significantly reduced. Another factor is that DHM was written when memory requirements in computers were limited. This limitation translated to using a smaller size of the arrays, and hence its application over large-scale computational domains was not feasible. To address this, the legacy codes can be modified to include the array standards that were introduced to Fortran in the 2000’s timeframe. The size of the dimension array in the DHM was increased from 250 to 9999. Larger arrays are possible as well. Similarly, for codes in which the system of equations is assembled into the matrix form Ax=B, the solver phase can incur a large cost. Matrices are often so large that the standard numerical methods become unsatisfactory and cannot be implemented on even high-performance computers. In a typical code, the largest portion of CPU time is spent in solving systems of linear algebraic equations [4]. It is noted that for an applied researcher, a solution module merely represents a means to an end of solving the flow equations, while for a “solver” developer, the application is a source of sparse equations to be solved. In such cases, using an appropriate solver coupled with a pre-conditioner can make the legacy codes more computationally efficient.

In this work, the results from DHM are compared with those of HEC-RAS (ver. 5.07) for a flow scenario accompanied by a flood. Our primary goal is to underscore the reliability of DHM.

Overview of the Models

DHM was developed for the USGS in the late 1970’s and is one of the first computational hydraulics programs. It solves the simplified Navier-Stokes equations written in diffusion form. For the uniform grid elements, the integrated finite difference version of the nodal domain integration method is used for solving the equations. Characteristic features of the DHM include (a) computational domain is made of square cells; (b) flow can enter or leave the cell through any of the four interfaces (c) minimum and maximum time step size that can be used in the computation needs to be specified, and (d) flow variables are calculated at the center of the cell. The required input conditions at the center of each cell are bottom elevation, initial water depth, and roughness value; The model uses an integrated finite difference numeric scheme for solving the flow equations. The model’s companion website www.diffusionhydrodynamicmodel.com has the relevant source codes, executable files, and complete documentation along with the results from various case studies of applications.

HEC-RAS allows the user to perform one-dimensional steady flow, one and two-dimensional unsteady flow calculations, sediment transport, and water quality modeling [4]. It gives the option of either solving the two dimensional diffusion wave (default mode) or the full dynamic equations. HEC-RAS and DHM share many similarities in that both models calculate the flow variables at the center of the computational cell with the given input details at the center of the cell (elevation, roughness value, initial depth). HEC-RAS uses an implicit finite volume solution of the flow equations. The model provides the flexibility of generating either uniform square, hexagonal, or adaptive mesh, based on the terrain characteristics. It also offers many other features for solving one and two dimensional steady and unsteady river flow simulations like coupling one and two dimensional models, computational mesh development and refinement tools, geometric and hydraulic input and output tools, multiple boundary options, moving sediment and water quality analysis tools, and other options as discussed in the current version of the HEC-RAS user manual [4].

Case Study Test Problem and Flow Details

In the case under study, a rectangular channel is specified that is 41 miles long and 800ft wide with a longitudinal slope of 2.48%. The computational domain is discretized using uniform square cells of dimensions 160ft x 160ft. There are a total of 9471 cells defined in the test model domain. The two channel “walls” defined by the DHM were modeled by cells with high bottom elevations. The simulation was performed over 73 hours. An initial water depth of 0ft in all the cells was specified. The inflow hydrograph (Figure 1) was specified at the center cell of the upstream end. The inflow has a peak value of 79141 cfs at time = 36 hours and a flow of 16500 cfs at time = 72 hours. A critical depth boundary condition was specified along the downstream boundary nodes. Of interest is the cumulative outflow at the downstream boundary.

Computational Results

Figure 1 plots the inflow hydrograph and the two outflow hydrographs for a channel bottom Mannings roughness (n) value of 0.035. The peak outflow and its time of arrival, which are of primary interest in the analysis, are similar for both the configured models. The program’s default values (like tolerance variables, time step, threshold values, etc.) were used, and no effort was made to optimize them.

Figure 1. Inflow and Outflow hydrographs for n = 0.035
Figure 1. Inflow and Outflow hydrographs for n = 0.035

Figure 2 plots the corresponding outflow hydrographs for a channel bottom roughness (n) value of 0.06.

Figure 2. Inflow and Outflow hydrographs for n = 0.06
Figure 2. Inflow and Outflow hydrographs for n = 0.06

The demonstration problems show that the considered two computational models can produce very similar outcomes. This is an important result because the two models differ in their underpinnings. The respective partial differential equations being modeled differ, and the computational efforts also differ, causing a difference between the two modeling outcomes. However, the computational effort needed to use the HEC-RAS model is substantially more than the DHM formulation, which calls into question the use of the HEC-RAS in similar practical problems. It is noted that a recent advance in the DHM is a code that runs on a hand-held calculator.

Conclusions

In this effort, the performance characteristics of two well-known hydraulic models, HEC-RAS and DHM are analyzed. The focus was on comparing their respective solution of the two dimensional diffusion flow equation. Given that the Diffusive Wave formulation of the flow equations is the HEC-RAS default option, and the Diffusive Wave formulation is also the default flow routing option in the legacy program DHM, a unique opportunity exists to test both computational programs and compare outcomes. The reliability of DHM, being one of the first computational models for solving the simplified overland flow equations, was reinforced by comparing its output with the HEC-RAS output. It is concluded that other computational outcomes from legacy models like DHM can act as a baseline analysis for new computational models and paradigms that continue to evolve.

Author Details

Hromadka & Associates’ Principal and Founder, Theodore Hromadka II, Ph.D., Ph.D., Ph.D., PH, PE, has extensive scientific, engineering, expert witness, and litigation support experience. His frequently referenced scientific contributions to the hydrologic, earth, and atmospheric sciences have been widely published in peer-reviewed scientific literature, including 30 books and more than 500 scientific papers, book chapters, and government reports. His professional engineering experience includes supervision and development of over 1500 engineering studies. He is currently a faculty member at the United States Military Academy at West Point, New York.

Prasada Rao is a Professor in the Civil and Environmental Engineering Department at California State University, Fullerton.

 

References

1. Hromadka II TV, Yen CC. A diffusion hydrodynamic model. Water resources investigations report. U.S. geological survey; 1987: 87– 4137, https://pubs.er.usgs.gov/publication/wri874137

2. O’Brien JS, Julien PY, Fullerton WT. Two‐dimensional water flood and mudflow simulation. J Hydraulic Eng ASCE. 1993; 119: 244–261.

3. Eriksson K, Estep D, Hansbo P, and Johnson C. Computational Differential Equations. Cambridge University Press, New York, 1996.

4. Brunner, GW., CEIWR-HEC, US Army Corps of Engineers, HEC-RAS River Analysis System, 2D Modeling User’s Manual, Version 5.07, March 2021. https://www.hec.usace.army.mil/software/hec-ras/

Simulating the Water Storage Benefits of Cover Crops Using the Gridded Surface Subsurface Hydrological Analysis (GSSHA) Model

By Salam Murtada, Daniel Reinartz, and Steve Kloiber

OVERVIEW

Cover crops provide many benefits that include improving soil health, providing storage to reduce runoff, preventing soil erosion, and protecting water quality. They benefit both the environment and farm operators by optimizing the use of fertilizers, allowing runoff treatment through the unsaturated soil, and preventing the loss of nutrients to the rivers.

Cover crops are typically applied between harvest and growing seasons. They can also be interseeded with cultivated crops during the growing season. However, they have the greatest potential benefit in early spring, before planting, when the ground is otherwise fallow and soil is vulnerable to intense rainfall events.

This study compares the effects of cover crops between growing and non-growing seasons and investigates the processes that drive them, using continuous and synthetic events simulations. These processes include infiltration, surface runoff, storage capacity, and soil moisture. This paper quantifies the benefits of cover crop application in the Shakopee watershed, Minnesota through hydrological modeling using the Gridded Surface Subsurface Hydrological Analysis (GSSHA) model.

Haruna et al., 2020, summarized studies published in the last 20 years addressing the benefits of cover crop on soil physical properties. According to these studies, cover crops significantly reduced soil density and increased soil organic content leading to reductions in soil loss and surface runoff, and increases in water holding capacity, infiltration and potential carbon sequestration. In another study, Basche and DeLong, 2017, presented the benefits of cover crops and other continuous living crops in combating rainfall infiltration variability, after statistically determining their effects on increasing porosity (8.0 + 2.2%) and soil water retention at field capacity (9.3 + 2.7).

The Gridded Surface Subsurface Hydrological Analysis (GSSHA) model is a physically-based, distributive model that simulates the interactions between the complex hydrological processes taking place on the surface and subsurface media at fine temporal and spatial scales. It uses finite difference in space to the second order, and forward difference in time to the first order. The model interfaces with the Watershed Modeling System (WMS) graphical user interface. GSSHA was developed by Dr. Charles Downer of the Environmental Research and Development Center (ERDC), United States Army Corps of Engineers (USACE) and is currently supported by Aquaveo, Inc.

The Shakopee watershed (323 mi2) extends across three counties: Kandiyohi, Chippewa, and Swift counties in central Minnesota (Figure 1). It is located in predominantly agricultural areas, where corn and soybean crops comprise approximately 68% of the total watershed. The soils are primarily fine-textured and poorly drained.

Figure 1: Shakopee Creek watershed land use and location.

METHODS

In this study, the effects of cover crops on the subsurface and surface processes were simulated to evaluate changes to surface water runoff, infiltration, and evapotranspiration. After calibrating and validating the model, cover crops were introduced as a hypothetical scenario in the form of small grains applied over all agricultural areas during growing and non-growing seasons. Examples of small grains are winter-hardy cultivars of rye, wheat, and triticale that can survive cold weather. Input climate variables were held constant in the model.

Large grid cells (9.88-acre) were used to accommodate the computation time to simulate the large, 323 square-mile watershed. The model consists of three layers to represent the upper tillage layer and lower soil layers as reported by the Soil Web. Key model assumptions and parameters are summarized in Table 1.

The simulation was broken down into two connected and continuous cycles, non-growing and growing, where different processes controlled the hydrology. This enabled us to compare the effects of cover crops between the two cycles in order to highlight their benefits more accurately (Figure 2).

Figure 2: Continuous simulation broken down into three cycles where different processes dominate. Note that an increase in interception was needed for the October event to represent the late growing season just before harvest. The model interception was based on early growing season values that overestimated the observed peak and volume.
Figure 2: Continuous simulation broken down into three cycles where different processes dominate. Note that an increase in interception was needed for the October event to represent the late growing season just before harvest. The model interception was based on early growing season values that overestimated the observed peak and volume.

The model simulated the effects of cover crops as follows:

Hydraulic Conductivity

Hydraulic conductivity is an important factor to characterize the effects of cover crops. Based on cover crop ability to decrease soil compaction and increase the organic content of the soil within its root zone, empirical relationships from the Water Erosion Prediction Project (WEPP) method (USDA-WEPP, 1995) were used to determine the saturated hydraulic conductivity. The WEPP method combines the effects of soil properties based on the Hydrologic Soil Group (HSG) and soil texture, as well as land use features based on a specific agricultural practice (e.g. conservation tillage) or the curve number (CN). The model used the hydraulic conductivity to simulate infiltration using the Green & Ampt method. Soil textures were obtained from the Soil Survey Geographic Database (SSURGO) to compute the other parameters using Saxton & Rawls equations (2006) and Brooks & Corey equations.

Roughness

Roughness characterizes the ability of cover crops to slow water flow and create micro-storage on the surface. By increasing residency time due to roughness, cover crops increased storage and allowed more time for water to infiltrate. The model included roughness to simulate surface water flow using the Diffusive Wave equation.

Evapotranspiration (ET)

The model used the Penman-Monteith equation, where the canopy stomatal resistance and vegetation heights were adjusted to account for the effects of small grains. In addition, the evapotranspiration was adjusted seasonally in the model through a multiplier called the Canopy Resistance Amplification Factor that increased the stomatal resistance during the winter months and decreased it to 1.0 during the growing season.

Interception and Retention

These parameters were used in the late growing season to account for canopy effects in intercepting rain and retaining it on the surface.

RESULTS AND DISCUSSION

Runoff volume reduction

According to this study, the application of cover crops reduced discharge volume at the watershed outlet by an average of 11% and 41% for growing and non-growing seasons, respectively (Figure 3). Cover crop applications achieved maximum benefits during the non-growing season after they were compared with fallow ground conditions. This was attributed primarily to increases in the saturated hydraulic conductivity and roughness by 2 and 3 times, respectively. In the growing season, however, the cover crops were competing with the cultivated crops, showing improvements based on hydraulic conductivity, but not necessarily roughness. As a result, surface runoff reduction due to cover crop application was higher for the non-growing season than the growing season by a factor of 3.6 (Figure 3). Furthermore, the rate of infiltration increase due to cover crop application was higher for the non-growing season by a factor of 4.7 (Figure 3).

Figure 3: Comparing the benefits of crop cover for non-growing versus growing seasons.
Figure 3: Comparing the benefits of crop cover for non-growing versus growing seasons.

Effects of cover crops on storage

According to the model results, extensive cover crop application removed an average of approximately 17,500 acre-feet of runoff volume computed at the watershed outlet (Figure 4) (equal to approximately 1-inch of runoff over the watershed) during the non-growing season. Most of the net volume removed infiltrated through the soil during the fall and early spring when the ground was not frozen. Water storage was spatially distributed across the landscape, yielding significant cumulative benefits at the watershed outlet (Figure 5).

Figure 4: Effects of cover crops on net storage and infiltration volumes for the non-growing cycle.
Figure 4: Effects of cover crops on net storage and infiltration volumes for the non-growing cycle.
Figure 5: Watershed showing net gain in infiltration for most areas.
Figure 5: Watershed showing net gain in infiltration for most areas.

Peak flow and discharge volume sensitivity to model parameters

The simulation for the non-growing period was broken down further into separate simulations to investigate the influence of different model parameters on both peak flows and the discharge volume. Model parameters for hydraulic conductivity and roughness were analyzed independently using synthetic rainfall events. The results showed that peak flow was reduced by 30% from the combined effects of roughness and hydraulic conductivity. Discharge volume was reduced by 24%, but the relative importance and the interaction between hydraulic conductivity and roughness were different. Hydraulic conductivity and roughness were both important in reducing the volume of discharge when considered separately. However, their effects did not combine as they did for peak flow reduction. For peak flow reduction, the rate of infiltration alone is overwhelmed by the rate of precipitation. However, an increase in roughness would help slow the flow and give it more residence time for infiltration to occur. For volume reduction, both parameters are as important independently and in combination, because they affect storage on the surface as well as the subsurface in the form of recharge into the groundwater or plant uptake. In the end, the total volume at the watershed outlet will also include infiltrated flows that will ultimately exfiltrate into the stream network as groundwater baseflow.

Conclusion

Using the GSSHA model, this study quantified and characterized the water storage benefits of cover crop application for reducing both peak flow and volume discharge. It highlights the importance of cover crops as a potential tool for flood mitigation and watershed management.

The results of the analysis presented here show that the hydrologic benefits of cover crops are particularly important for the non-growing season. Cover crop applications in the non-growing season produced significant reductions in discharge volume of up to 41% when compared to fallow ground conditions. The GSSHA simulation also demonstrated volume reductions up to 11% in discharge volume for the growing season.

Both saturated hydraulic conductivity and surface roughness were the main drivers in controlling the peak flow and volume of discharge reductions, according to the simulation. Their combined effects amplified peak flow reductions.

Author Details

Salam Murtada is a civil and environmental engineer working as floodplain hydrologist for the Minnesota Department of Natural Resources. His job includes developing and reviewing hydrological and hydraulic models for watershed studies, FEMA and flood related projects, and geomorphic evaluation of culvert designs and stream restoration projects. He graduated from West Virginia University with a Master of Science degree in Civil and Environmental Engineering, and from the University of Texas at Austin in Bachelor of Science degrees in Civil Engineering and Petroleum Engineering.

Daniel Reinartz has worked for the Lake Ecology Unit of the Minnesota Department of Natural Resources for the past 9 years. He retired from the U.S. Army Corps of Engineers as a Hydrologic Engineer after 35 years. He has a total of 49 years in civil engineering with a BCE Degree from the University of Minnesota.

Steve Kloiber supervises the Lake Ecology Unit of the Minnesota Department of Natural Resources. He has over 30 years of experience in water resource science and environmental analysis. He received his masters and PhD from the University of Minnesota in environmental engineering with a minor in water resources science.

References

  1. Agricultural Research Science. (2009) Soil Water Characteristics (SWC). USDA.
  2. Agricultural Research Service. (1995). Water Erosion Prediction Project (WEPP). Chapter 7, Soil Component.
  3. Basche, A. D., DeLonge, M. (2017). The Impact of Continuous Living Cover on Soil Hydrologic Properties: A Meta-Analysis. Agronomy & Horticulture, Faculty Publications.
  4. Downer, C. W., Ogden, F. L., Byrd, A. R. (2008). GSSHAWIKI User’s Manual, Gridded Surface Subsurface Analysis Version 7.13 for WMA 10.1. ERDC Technical Report. Engineer Research and Development Center, Vicksburg, Mississippi.
  5. Haruna, S., Anderson, S. H., Udawatta, R. P., Gantzer, C. J., Phillips, N. C., Cui, S., Gao, Y. (2020). Improving soil physical properties through the use of cover crops: A review. Agrosystems, Geosciences & Environment.
  6. Singer, J., Kaspar, T., Pederson, P. (2005). Small Grains Cover Crops for Corn and Soybean. Iowa State University Extension and Outreach.
  7. Minnesota Geospatial Commons. (2013). https://gisdata.mn.gov/dataset/quick-layers
  8. US Army Corps of Engineers. HEC-DSSVue Version 1.2. (2006).
  9. University of Minnesota Extension. (2021). Cover Crops. https://extension.umn.edu/soil-and-water/cover-crops

AIH Call for Articles

The next issue of the AIH Bulletin is scheduled to be published in the winter of 2021, for which the editorial team invites contributions from members.

Original articles on any aspect of hydrology (e.g., administrative, technical, socioeconomic) will be considered for publication. It is not required that the article be based on academic or scientific work; however, it should not be published elsewhere. Book reviews may also be submitted under this category.

  • Please provide an un-formatted word document of your story without embedded images. You can signify where you’d like a submitted image using brackets.
  • Images you wish to be included with your article must not be embedded in the Word document; send them separately and labeled with names corresponding to where you’d like them used in the Word document.
  • Articles must have a brief title and a byline.
  • Supply a high-resolution head-shot of the author.
  • Article length must be between 500 – 1000 words.
  • Please include an “About the Author” post script, to provide our audience with the context of your perspectives. Include how you would like your name and title to be presented.
  • Avoid using too many bulleted lists, diagrams or graphs in your article.

Beside original articles, members may also submit leads to items of interest to the hydrologists’ community. Such items may include news related to the field of hydrology, conferences, new publications, etc.

If you are interested in contributing, please send articles or other items of interest via the Dropbox link below by Friday, October 15, 2021. Please ensure submissions are identified properly (example: TitleofArticle-FirstLastName.doc) and that supporting graphics/images are of the highest possible quality. Be sure to include your contact information within your submission as well.

Submit Your Article Today!

Should you have any questions, do not hesitate to contact our office at admin@aihydrology.org.

AIH President’s Message

Your AIH leadership team and member volunteers are working hard on exciting initiatives for AIH. Thank you to all who have stepped forward to take on roles to help advance the mission of AIH. We rely on our members’ participation, and we are eager to engage more members in AIH activities. Even if not interested in taking on a leadership role for AIH or getting involved in various subcommittees or groups, we request all our members to be ambassadors for AIH and its certified members. Please contact me or others on our leadership team to get involved.

We are approaching an important pivot point for the focus of AIH’s leadership team. Much energy has been dedicated to improving fundamental processes for AIH over the past few years. While we continue our work to address challenges, changes are underway that we are confident will improve our processes. Examples include rollout of new online member application and database system, and upcoming solicitation for examination support services. Concurrently, we are advancing initiatives related to member engagement, along with collaboration and engagement with other organizations (e.g., American Water Resources Association (AWRA); Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI); etc.).

Noted in my previous message, we established a new Diversity, Equity, and Inclusion (DEI) Committee along with a new Webinars Subcommittee. Kudos to: Ed Baquerizo, PH; Megan Gehrke, PH; Ramanitharan Kandiah, PH; Amesha Morris, Matt Naftaly, PH; John Ramirez Avila, PH; and Michelle Woolfolk, PH, and along with AIH leadership team members (Sarah Erck, CMP; Salam Murtada, PH; and Julé Rizzardo, PH) for stepping up to lead AIH’s DEI initiatives. Our Webinars Subcommittee members include member volunteer, Mike Talbot, HIT, and AIH leadership team members (Sarah Erck; Yige Gao, PH; Salam Murtada, PH; and Brennon Schaefer, PH). We’re excited for the rollout of actions from these two groups over the next few months.

On a matter related to inclusion, our Executive Committee (EC) advanced an action earlier this year to eliminate the constraint of nominations for serving on AIH’s EC to only certified Professional Hydrologists (PH)–all certified members (PHs, Hydrologists-in-Training (HITs), and Hydrologic Technicians (HTs) may be nominated to serve for positions on the EC. Subsequently, Chance Fulk, HT III, was appointed as Treasurer, the first HT to serve on AIH’s EC.

Please look for upcoming announcements regarding membership engagement. We’re planning a virtual “meet and greet” event during September and, if all goes well, an in-person social event in Sacramento, California to ring in the New Water Year on September 30. I’m very excited for these events!

 

PS: Note deliberate effort to include AIH certified members’ acronyms with names. I’m calling on all AIH certified members, as ambassadors of AIH, to take pride and flaunt your AIH acronym. Be HIT-, HT-, and PH-proud!

Sincerely,

Jamil S. Ibrahim PH, PMP, ENV SP
AIH President, 2021-2022

Congratulations to New Members

Congratulations to those who have been recently certified as Professional members of the American Institute of Hydrology!

Matthew Burnette – PH Surface Water

David Ho – PH Surface Water

Bill Szafranski – PH Surface Water

Megan Gehrke – PH Water Quality

Ji Qi – PH Surface Water

Vignon Houenou – PH Surface Water

Justin Coffman – PH Surface Water

Andrew Earles – PH Surface Water

Robert Parrish – PH Surface Water

Sarah Harris – PH Surface Water

Sean Aucion – Hydrologic Technician I

Nikolaos Apsilidis – PH Surface Water

John Ramirez-Avila – PH Surface Water 

Call for Photos

Our industry-diverse membership often finds themselves in a variety of interesting locations either performing research, working on a project, or attending a conference. And, with some of us now working from home our additional ‘hydro-office’ serves as another “interesting” location to add to the collection.

We want to broadcast the diversity of the hydrology industry and specifically showcase our AIH certified members. Take a moment to snap a few photos of your surroundings so we can paint a clearer picture of what hydrology really looks like. Are your cats or family part of your work from home life? Include them! Do you spend time surrounded by nature and breathtaking environments? We want to see it all!

Upload your photos to this link [https://www.dropbox.com/request/jLQB3udMBGFaAJLw0LEB] and label the file with your name, the location, and your agency.

Thank you for assisting us as we enhance and strengthen the standing of hydrology as a science and profession.

AIH Meet & Greet Event

AIH is planning a Meet and Greet interactive virtual event on September 9, 2021 from 3:00 to 4:00 PM (PDT). The event is designed for AIH’s members to meet and interact with AIH’s Executive Committee, Executive Director, and management office. We will feature opening remarks by AIH President, Jamil Ibrahim, and Executive Director, Sarah Erck, and introductions of AIH’s Executive Committee members. The agenda will also include an overview of AIH’s current initiatives and future activities. We look forward to meeting you and hearing your feedback!

2021 AIH Membership Report

By: Jolyne Lea, Acting Secretary

The 2021 American Institute of Hydrology (AIH) currently has 410 members. An overwhelming majority of the members specialize in surface water hydrology, which is nearly an equal mix of longtime members (who have been certified for over 20 years) and newer members. AIH currently has 15 Hydrologist-in-Training members. There are currently 14 Certified Hydrologic Technician members. Additionally, there are 23 Emeritus members who support the Institute and their profession. Figure 1 shows the Professional, student, and emeritus members by certification identification.

[Figure 1. AIH members by membership category.]

It is also interesting to look at the longevity of our members. The AIH certification identification can be determined when each person was originally certified by AIH. Members range from those who were founding members at the forming of AIH in 1982, to new members. Figure 2 shows current members’ length of membership. There has been a steady influx of new members since 2005, where over ten new members, per year, were certified.

[Figure 2. AIH membership year of certification.]

In figure 3, the membership is grouped in ten-year increments. Over half of the current members (233) have joined within the last twenty years. Long time members of 20-40 years of membership number 151. In the last ten years, AIH has added 112 new members.

[Figure 3. AIH membership grouped by length of membership.]

Lastly, AIH membership is widely distributed across the U.S., Mexico, and Canada. The largest number of members are located in California, Colorado and Texas. In addition, the Institute has ten international members: three from Mexico and seven from Canada.  Figure 4 shows the members by state/country.

[Figure 4. AIH member location]

In summary, based on a review of membership in 2021 compared to historical numbers, the AIH membership numbers appear to be strong and continue to expand with new certified members. The AIH Executive Committee is committed to improving the membership certification process, increasing membership benefits, and expanding student membership to keep the Institute strong for the future. But, we urge our members to contact the AIH Executive Committee regarding input on how AIH membership can continue to be improved and to be active by volunteering for roles where you can.

Interview with Amesha Morris, DEI Committee

Interviewed by: Jule Rizzardo, President-Elect

I sat down with Amesha Morris over a virtual cup of coffee.  Amesha has submitted her application to obtain her Professional Hydrologist certification with AIH, and she serves on the newly formed AIH Diversity, Equity and Inclusion (DEI) Committee. Amesha is currently the stormwater program manager for the City of McKinney’s Stormwater Management Program in North Central Texas.

What is the most challenging thing about your job?

The most challenging part of my job is communication. My work requires coordination with 10 different departments and every year our permit has new requirements. It can be hard to juggle communicating with multiple personnel about changing parameters and changing program requirements.

Describe the most fun project team you’ve been part of at work?

Our department has been upgrading our data to be visualized using GIS. I have been nerding out, because we now have so many more options to display and analyze stormwater data.

What’s something people would be surprised to find out about you outside of work?

I started watching kdramas while I was in graduate school.  It was the perfect way to forget about my thesis for a few hours.

What is one thing you’re glad you tried but would never do again?

The Portland Saint Patty’s Fest Celebration – the event started with running a marathon and ended with lots of singing, dancing, and people celebrating in green.

What’s your favorite hydrologic feature and why?

Honestly, I enjoy a well-designed bioswale with diverse landscaping. Working in stormwater, I’ve learned that green infrastructure can be functional and aesthetically pleasing.

What is the best vacation you’ve taken?

I’m currently on a getaway vacation in Paso Robles California with my best friends, but the best vacation I’ve ever taken was visiting my dad stationed in Korea.

Where in the world do you want to travel next?

I really want to go to Portugal!  It seems like the perfect blend of metro and nature. If I could, I would love to retire there when the time comes.

Hydrologic Impacts of Water Resources Development

(a review of commonly overlooked hydrologic impacts)

By: Anand Prakash, Ph.D., P.E., P.H., F.ASCE, FIE

Capital and operation costs and the ensuing benefits are by far the most dominant determinant for the viability of any water resources development project. However, a cursory review of the analyses of a few recently completed projects indicates that some, relatively obscure, quantifiable and non-quantifiable hydrologic impacts and associated environmental costs are not adequately accounted for in the decision-making process. Examples of such implicit, though inherent, hydrologic impacts are identified herein. 

Quantifiable Perpetual Watershed Management – Perpetual floodplain management is necessitated due to continuing long-term alterations of the floodplain and potential future (almost permanent) damage to the hydrologic environment attributable to the project. A simple approach for continuing watershed management is to ensure that a constant amount of annual floodplain management fund, R1, is available in perpetuity. This amount has to be estimated by the project team based on judgment and experience. With an annual discount rate, i, the capitalized value over the project life, N, is given by, C1 = R1 [{(1+i) N – 1}/{ i (1+i)N }]. For perpetuity, with a large value of N, this gives C1 ≈ [ R1 / i ].      

Quantifiable Infrequent Flood Damages – These are damages due to flood events exceeding the design basis, which may occur during the project life. This cost may not be explicitly specified in project estimates, but has to be incurred periodically as special repairs. Quantification of such damages includes hydrologic modeling and economic analysis to develop a table of flood exceedance probability, Pk, versus damage, Dk, for selected return periods, k, above the design basis flood, arranged in descending order of Pk; and computation of incremental probability, ΔPk = (Pk – Pk+1); and the corresponding average damage, Davk = [(Dk + Dk+1)/2]. The expected annual damage, R2, is estimated as R2 = ∑ (Davk ΔPk) = ∫ Dk dPk and capitalized over the project life as C2 = R2 [{(1+i)N – 1}/{ i (1+i)N }].  The summation or integral goes from a low value of Pk indicative of catastrophic damages to a threshold (high) value of Pk, above which flood damages are considered insignificant. The lower limit may correspond to the design basis flood for the project. 

Quantifiable Consequences of Hydrologic Failure – A scary psychological concern related to major hydraulic structures, particularly dams, is that a major component may fail during the hydrologic event of probability, P, which exceeds the design basis flood. It may be worthwhile to consider insurance, indemnification, or some other compensation for such a hydrologic failure. Even though failure may or may not occur during the project life, the scare, though intangible, can result in real and involuntary hydrologic damage, and its causation is the project. Conceptually, the hydrologist, with the help of an economist, may estimate the present value, V, of failure consequences due to a flood of probability P. Statistically, the probability of failure in the nth year with no failure up to year n-1, is Pn = P (1-P) (n-1). So, expected present value of failure in the nth year = V P [(1-P) (n-1)] and expected insurance cost, C3, for N years of project life is, ∑ V P[(1-P) (n-1)]; the summation is from n = 1 to N. This gives, C3 = V{1-(1-P) N}.                                                                              

Non-Quantifiable Impacts – In addition to quantifiable impacts, there are hydrologic impacts which are not amenable to numeric quantification. Qualitative assessment of such impacts would require detailed hydrologic modeling for the affected surface and groundwater environment. One example of such impacts includes resettlement for which the hydrologist delineates the zone of evacuation to accommodate project structures. The compensation for evacuees must include a one-time payment to legal landowners and social cost for resettlement, rehabilitation, and development of the displaced population to an equivalent or better living standard. This must also include remedial or compensatory measures for the loss or dislocation of wildlife, fisheries, aquatic biota, forests, vegetation, unique historic features, and threatened or endangered plant and animal habitat. Other examples include project impacts on the floodplains and water quality including enhanced waterlogging potential downstream and safe decommissioning of project features at the end of the project life to ensure minimal impacts on future hydrologic environment. Despite being intractable in terms of present value, these impacts must be included in project costs.

Prudent decision making requires that due weight be given to all conceivable tangible and intangible hydrologic and environmental impacts, benefits, and leverage, and a consensus-based iterative process be used to finalize project planning in preference to the commonly used benefit-cost ratio based solely on quantifiable investments and revenues.

About Author

The author is a water resources engineer and has been working in the field for over sixty years. His professional activities include hydrologic and hydraulic analyses involving surface and groundwater flows, contaminant transport and designs of hydraulic structures (dams, spillways, tailings dams, riverine structures, groundwater pumping and dewatering wells, etc.) related to about 200 projects worldwide.