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Surface
Water-Quality
Modeling
Steven C. Chapra
Tufts University
WAVELAND
PRESS, INC.
Long Grove, Illinois
CONTENTS
Preface
x
v
i
i
PART
1
Completely Mixed Systems
1
LECTURE 1
Introduction
3
1.1 Engineers and Water Quality
4
1.2 Fundamental Quantities
6
1.3 Mathematical Models
10
1.4 Historical Development of Water-Quality Models
14
1.5 Overview of This Book
19
Problems
20
LECTURE 2
Reaction Kinetics
24
2.1 Reaction Fundamentals
24
2.2 Analysis of Rate Data
29
2.3 Stoichiometry
38
2.4 Temperature Effects
40
Problems
42
LECTURE 3
Mass Balance, Steady-State Solution, and Response Time
47
3.1 Mass Balance for a Well-Mixed Lake
47
3.2 Steady-State Solutions
52
3.3 Temporal Aspects of Pollutant Reduction
57
Problems
62
LECTURE 4
Particular Solutions
65
4.1 Impulse Loading (Spill)
66
4.2 Step Loading (New Continuous Source)
68
4.3 Linear ("Ramp") Loading
70
4.4 Exponential Loading
71
4.5 Sinusoidal Loading
73
4.6 The Total Solution: Linearity and Time Shifts
76
4.7 Fourier Series (Advanced Topic)
80
Problems
83
LECTURE 5
Feedforward Systems of Reactors
86
5.1 Mass Balance and Steady-State
86
5.2 Time Variable
91
vii
viii CONTENTS
5.3 Feedforward Reactions
95
Problems
99
LECTURE 6
Feedback Systems of Reactors
101
6.1 Steady-State for Two Reactors
101
6.2 Solving Large Systems of Reactors
103
6.3 Steady-State System Response Matrix
107
6.4 Time-Variable Response for Two Reactors
111
6.5 Reactions with Feedback
113
Problems
117
LECTURE 7
Computer Methods: Well-Mixed Reactors
120
7.1 Euler's
Method
121
7.2 Heun's Method
124
7.3 Runge-Kutta Methods
126
7.4 Systems of Equations
128
Problems
131
PART II
Incompletely Mixed Systems
135
LECTURE 8
Diffusion
137
8.1
Advection and Diffusion
137
8.2 Experiment
138
8.3 Fick's First Law
141
8.4 Embayment Model
143
8.5 Additional Transport Mechanisms
149
Problems
153
LECTURE 9
Distributed Systems (Steady
-
State)
156
9.1
Ideal Reactors
156
9.2 Application of the PFR Model to Streams
164
9.3 Application of the MFR Model to Estuaries
168
Problems
171
LECTURE
10
Distributed Systems (Time
-
Variable)
173
10.1 Plug Flow
173
10.2 Random (or "Drunkard's") Walk
177
10.3 Spill Models
180
CONTENTS ix
10.4
Tracer Studies
186
10.5
Estuary Number
189
Problems
190
LECTURE
11
Control-Volume Approach: Steady-State Solutions
192
11.1
Control-Volume Approach
192
11.2
Boundary Conditions
194
11.3
Steady-State Solution
195
11.4
System Response Matrix
197
11.5
Centered-Difference Approach
198
11.6
Numerical Dispersion, Positivity, and Segment Size
201
11.7
Segmentation Around Point Sources
207
11.8
Two- and Three-Dimensional Systems
208
Problems
209
LECTURE 12
Simple Time-Variable Solutions
212
12.1
An Explicit Algorithm
212
12.2
Stability
214
12.3
The Control-Volume Approach
215
12.4
Numerical Dispersion
216
Problems
221
LECTURE 13
Advanced Time-Variable Solutions
223
13.1
Irnplicit Approaches
223
13.2
The MacCormack Method
229
13.3
Summary
230
Problems
232
PART 111
Water-Quality Environments
233
LECTURE 14
Rivers and Streams
235
14.1
River Types
235
14.2
Stream Hydrogeometry
238
14.3
Low-Flow Analysis
243
14.4
Dispersion and Mixing
245
14.5
Flow, Depth, and Velocity
247
14.6
Routing and Water Quality (Advanced Topic)
250
Problems
257
x CONTENTS
LECTURE 15
Estuaries
260
15.1 Estuary Transport
260
15.2 Net Estuarine Flow
262
15.3 Estuary Dispersion Coefficient
263
15.4 Vertical Stratification
270
Problems
272
LECTURE 16
Lakes and lmpoundments
276
16.1 Standing Waters
276
16.2 Lake Morphometry
278
16.3 Water Balance
282
16.4 Near-Shore Models (Advanced Topic)
287
Problems
293
LECTURE17
Sediments
295
17.1 Sediment Transport Overview
295
17.2 Suspended Solids
297
17.3 The Bottom Sediments
302
17.4 Simple Solids Budgets
304
17.5 Bottom Sediments as a Distributed System
307
17.6 Resuspension (Advanced Topic)
312
Problems
315
LECTURE 18
The "Modeling" Environment
317
18.1 The Water-Quality-Modeling Process
317
18.2 Model Sensitivity
327
18.3 Assessing Model Performance
335
18.4 Segmentation and Model Resolution
339
Problems
341
PART IV Dissolved Oxygen and Pathogens
345
LECTURE 19
BOD and Oxygen Saturation
347
19.1 The Organic Production/Decomposition Cycle
347
19.2 The Dissolved Oxygen Sag
348
19.3 Experiment
351
19.4 Biochemical Oxygen Demand
353
19.5 BOD Model for a Stream
355
CONTENTS xi
19.6 BOD Loadings, Concentrations, and Rates
357
19.7 Henry's Law and the Ideal Gas Law
360
19.8 Dissolved Oxygen Saturation
361
Problems
365
LECTURE20
Gas Transfer and Oxygen Reaeration
367
20.1 Gas Transfer Theories
369
20.2 Oxygen Reaeration
376
20.3 Reaeration Formulas
377
20.4 Measurement of Reaeration with Tracers
384
Problems
386
LECTURE 21
Streeter-Phelps: Point Sources
389
21.1 Experiment
389
21.2 Point-Source Streeter-Phelps Equation
391
21.3 Deficit Balance at the Discharge Point
391
21.4 Multiple Point Sources
393
21.5 Analysis of the Streeter-Phelps Model
396
21.6 Calibration
398
21.7 Anaerobic Condition
399
21.8 Estuary Streeter-Phelps
401
Problems
403
LECTURE 22
Streeter-Phelps: Distributed Sources
405
22.1 Parameterization of Distributed Sources
405
22.2 No-Flow Sources
407
22.3 Diffuse Sources with Flow
410
Problems
417
LECTURE 23
Nitrogen
419
23.1 Nitrogen and Water Quality
419
23.2 Nitrification
421
23.3 Nitrogenous BOD Model
424
23.4 Modeling Nitrification
426
23.5 Nitrification and Organic Decomposition
428
23.6 Nitrate and Ammonia Toxicity
430
Problems
432
LECTURE 24
Photosynthesis/Respiration
433
24.1 Fundamentals
433
xii CONTENTS
24.2 Measurement Methods
437
Problems
448
LECTURE 25
Sediment Oxygen Demand
450
25.1 Observations
451
25.2 A "Naive" Streeter-Phelps SOD Model
455
25.3 Aerobic and Anaerobic Sediment Diagenesis
457
25.4 SOD Modeling (Analytical)
459
25.5 Numerical SOD Model
470
25.6 Other SOD Modeling Issues (Advanced Topic)
474
Problems
480
LECTURE 26 Computer Methods
482
26.1 Steady-State System Response Matrix
482
26.2 The QUAL2E Model
486
Problems
500
LECTURE 27
Pathogens
503
27.1 Pathogens
503
27.2 Indicator Organisms
504
27.3 Bacterial Loss Rate
506
27.4 Sediment-Water Interactions
510
27.5 Protozoans:
Giardia
and
Cryptosporidium
512
Problems
516
PART V
Eutrophication and Temperature
519
LECTURE 28
The Eutrophication Problem and Nutrients
521
28.1 The Eutrophication Problem
522
28.2 Nutrients
522
28.3 Plant Stoichiometry
527
28.4 Nitrogen and Phosphorus
530
Problems
533
LECTURE 29 Phosphorus Loading Concept
534
29.1 Vollenweider Loading Plots
534
29.2 Budget Models
536
29.3 Trophic-State Correlations
539
CONTENTS
xiii
29.4
Sediment-Water Interactions
545
29.5
Simplest Seasonal Approach
551
Problems
558
LECTURE 30
Heat Budgets
560
30.1
Heat and Temperature
561
30.2
Simple Heat Balance
563
30.3
Surface Heat Exchange
565
30.4
Temperature Modeling
571
Problems
575
LECTURE 31
Thermal Stratification
577
31.1
Thermal Regimes in Temperate Lakes
577
31.2
Estimation of Vertical Transport
580
31.3
Multilayer Heat Balances (Advanced Topic)
585
Problems
588
LECTURE 32
Microbe/Substrate Modeling
590
32.1
Bacterial Growth
590
32.2 Substrate Limitation of Growth
592
32.3
Microbial Kinetics in a Batch Reactor
596
32.4
Microbial Kinetics in a CSTR
598
32.5
Algal Growth an a Limiting Nutrient
600
Problems
602
LECTURE 33
Plant Growth
and Nonpredatory Losses
603
33.1
Limits to Phytoplankton Growth
603
33.2 Temperature
605
33.3
Nutrients
607
33.4
Light
609
33.5
The Growth-Rate Model
612
33.6
Nonpredatory Losses
613
33.7
Variable Chlorophyll Models (Advanced Topic)
615
Problems
621
LECTURE 34
Predator
-
Prey and
Nutrient/Food-Chain Interactions
622
34.1
Lotka-Volterra Equations
622
34.2
Phytoplankton-Zooplankton Interactions
626
34.3
Zooplankton Parameters
629
xiv CONTENTS
34.4 Nutrient/Food-Chain Interactions
629
Problems
631
LECTURE 35
Nutrient/Food-Chain Modeling
633
35.1 Spatial Segmentation and Physics
633
35.2 Kinetic Segmentation
634
35.3 Simulation of the Seasonal Cycle
637
35.4 Future Directions
641
Problems
642
LECTURE 36
Eutrophication in Flowing Waters
644
36.1 Stream Phytoplankton/Nutrient Interactions
644
36.2 Modeling Eutrophication with QUAL2E
649
36.3 Fixed Plants in Streams
658
Problems
663
PART VI Chemistry
665
LECTURE 37
Equilibrium Chemistry
667
37.1 Chemical Units and Conversions
667
37.2 Chemical Equilibria and the Law of Mass Action
669
37.3 Ionic Strength, Conductivity, and Activity
670
37.4 pH and the Ionization of Water
672
37.5 Equilibrium Calculations
673
Problems
676
LECTURE
38
Coupling Equilibrium Chemistry and Mass Balance
677
38.1 Local Equilibrium
677
38.2 Local Equilibria and Chemical Reactions
680
Problems
682
LECTURE 39
pH
Modeling
683
39.1 Fast Reactions: Inorganic Carbon Chemistry
683
39.2 Slow Reactions: Gas Transfer and Plants
686
39.3 Modeling pH in Natural Waters
689
Problems
691
CONTENTS xv
PART VII Toxics
693
LECTURE 40
Introduction to Toxic-Substance Modeling
695
40.1 The Toxics Problem
695
40.2 Solid-Liquid Partitioning
697
40.3 Toxics Model for a CSTR
700
40.4 Toxics Model for a CSTR with Sediments
705
40.5 Summary
713
Problems
713
LECTURE 41
Mass-Transfer Mechanisms: Sorption and Volatilization
715
41.1
Sorption
715
41.2 Volatilization
727
41.3 Toxicant-Loading Concept
732
Problems
737
LECTURE 42
Reaction Mechanisms: Photolysis, Hydrolysis,
and Biodegradation
739
42.1 Photolysis
739
42.2 Second-Order Relationships
751
42.3 Biotransformation
751
42.4 Hydrolysis
753
42.5 Other Processes
755
Problems
756
LECTURE 43
Radionuclides and Metals
757
43.1 Inorganic Toxicants
757
43.2 Radionuclides
758
43.3 Metals
761
Problems
768
LECTURE 44
Toxicant Modeling in Flowing Waters
769
44.1 Analytical Solutions
769
44.2 Numerical Solutions
778
44.3 Nonpoint Sources
779
Problems
782
xvi
CONTENTS
LECTURE 45 Toxicant/Food-Chain Interactions
784
45.1 Direct Uptake (Bioconcentration)
785
45.2 Food-Chain Model (Bioaccumulation)
788
45.3 Parameter Estimation
790
45.4 Integration with Mass Balance
794
45.5 Sediments and Food Webs (Advanced Topic)
795
Problems
797
Appendixes
798
A
Conversion Factors
798
B Oxygen Solubility
801
C Water Properties
802
D
Chemical Elements
803
ENumerical Methods Primer
805
F Bessel Functions
817
G
Error Function and Complement
820
References
821
Acknowledgments
834
Index
835
... Within a body of water, the physical, chemical, and biological variables can change in a short period, even in hours [1], such as the variations of dissolved oxygen between day and night [2], the variations due to the border entries they have greater importance within the analysis of the water body [3], when studying dynamic systems such as Andean rivers due to their flow and the loads of pollutants that enter them, these variations should be considered to evaluate the scenario. ...
... They are tools for the development of management plans and policies to protect water resources [4]. Mathematical quality models are management tools that will make it possible to understand the behavior and cause-effect of the processes suffered by the receiving environment to evaluate different alternatives [1,5]. To achieve adequate management and use of the model in an initial phase, it must be calibrated and validated; thus, it can predict the concentration of pollutants for different scenarios [1,6]. ...
... Mathematical quality models are management tools that will make it possible to understand the behavior and cause-effect of the processes suffered by the receiving environment to evaluate different alternatives [1,5]. To achieve adequate management and use of the model in an initial phase, it must be calibrated and validated; thus, it can predict the concentration of pollutants for different scenarios [1,6]. ...
-
![Carlos Matovelle]()
Using models of organic matter degradation and dissolved oxygen consumption, the concentrations of these compounds are analyzed in two stretches of a river after a discharge of raw sewage. The analyzed river has low drafts and widths, so the velocity is high and the aeration coefficient kr calculated with the Covar method is high, this indicates a rapid recovery of oxygen from the water consumed by the organic matter degradation processes, the river has been instrumented to measure flows and organic matter at various points to calibrate the model. The hydraulic parameters of the river section are analyzed in three control points, in each one sample are taken to analyze oxygen consumption by organic matter and nitrification through laboratory tests to determine and adjust the kinetics of the processes (kd; knit). This kinetics have been used in the development of a water quality model to verify its adjustment, obtaining higher RMSE results than with kinetics from secondary sources. It is observed that the river has an excellent capacity for self-purification due to the high income of dissolved oxygen, with a kr > 9 d-1.
... Linking process-based models that simulate lake and stream physical and biogeochemical processes can provide an effective means of both understanding past events and forecasting future scenarios. A mathematical model is an idealized formulation simulating the response of a system to given conditions (Chapra, 2008). ...
- Nicholas J. Messina
Lake Auburn, Maine, USA, is a historically unproductive lake that has experienced multiple algal blooms since 2011. The lake is the water supply source for a population of ~60,000. We modeled past temperature, and concentrations of dissolved oxygen (DO) and phosphorus (P) in Lake Auburn by considering the watershed and internal contributions of P as well as atmospheric factors, and predicted the change in lake water quality in response to future climate and land-use changes. A stream hydrology and P-loading model (SimplyP) was used to generate input from two major tributaries into a lake model (MyLake) to simulate physical mixing, chemical dynamics, and sediment geochemistry in Lake Auburn from 2013 to 2017. Simulations of future lake water quality were conducted using meteorological boundary conditions derived from recent historical data and climate model projections for high greenhouse-gas emission cases. The effect of future land development on lake water quality for the 2046 to 2055 time period under different land-use and climate change scenarios were also simulated. Our results indicate that lake P enrichment is more responsive to extreme storm events than increasing air temperatures, mean precipitation, or windstorms; loss of fish habitat is driven by windstorms, and to a lesser extent an increasing water temperature; and watershed development further leads to water quality decline. All simulations also show that the lake is susceptible to both internal and external P loadings. Simulation of temperature, DO, and P proved to be an effective means for predicting the loss of water quality under changing land-use and climate scenarios.
... A formulação original de Streeter-Phelps é composta, de forma genérica, por duas equações diferenciais ordinárias: uma modela a desoxigenação, ou seja, a oxidação da matéria orgânica biodegradável, e a outra a reaeração atmosférica (STREETER; PHELPS, 1925). Ao longo dos anos, a formulação clássica foi passando por aperfeiçoamentos e incorporações de diferentes processos (COX, 2003;CHAPRA, 2008). Segundo Wang et al. (2013), o processo de desenvolvimento dos modelos de qualidade de água pode ser dividido em três fases. ...
- Thiara Cezana Gomes
- Antonio Sérgio Ferreira Mendonça
- José Antonio Tosta dos Reis
-
![Rodrigo Alvarenga Rosa]()
No Brasil, os níveis de cobertura dos serviços de tratamento de esgotos ainda são considerados baixos. Os custos de implantação, operação e manutenção de sistemas de tratamento de esgotos são, em geral, elevados e variam consideravelmente conforme o tipo de tecnologia a ser implementada. Assim, modelos matemáticos capazes de auxiliar o processo de alocação da carga orgânica e o consequente processo de seleção dessas tecnologias são de grande valia para a gestão adequada dos recursos hídricos. Diante da relevância do assunto, este artigo tem como objetivo realizar uma revisão sistemática das principais publicações relacionadas ao Problema de Alocação de Efluentes Sanitários (PAES). O intuito é analisar publicações recentes e identificar abordagens de solução, cenários de aplicação, características incorporadas aos modelos de otimização e lacunas científicas existentes. Palavras-chave: Problema de alocação de efluentes sanitários. Modelo de qualidade de água. Tratamento de esgoto. Sistemas de águas residuárias. Otimização.
... After the second half of the twentieth century, urban river rehabilitation was of increasing concern in these countries. Generally, by the 1970s, countries in Europe and the United States had basically ended the direct discharge of sewage into urban rivers by developing almost complete sewer systems and wastewater treatment plants (WWTPs) (Bernhardt et al., 2005;Chapra, 2008). Despite rehabilitation of the urban water bodies is a well-established trend in developed countries (Feio et al., 2021), urban river pollution control is still a very big challenge in developing countries, especially arising from improper coordination between incomplete sewer network and rapid urbanization as well as population growth (Xu et al., 2019). ...
- Hailong Yin
-
![Md Sahidul Islam]()
- Mengdie Ju
Rapid urbanization and economic development caused urban river pollution globally. In the developing countries' megapolis, the situation is especially critical in response to the United Nation's sustainable development goal for 2030. Dhaka, Bangladesh, is one of the most densely populated cities in the world. Around 21 million people are living in this city of 1464 km2. Currently, 98% of untreated domestic sewage as well as significant amounts of industrial wastewater discharge from over 7000 industries are contributing to the presence of black and foul water in the urban rivers. In this study, a driving force-impact-challenge-response (DICR) framework is formed to create a big picture of urban river pollution situation, which incorporates nine factors of social, environmental, and governmental aspects. Using this framework, a comparative study was performed between Dhaka rivers and other rivers in developing countries with successful rehabilitation experiences. Accordingly, a strategy for Dhaka's urban river pollution control was developed, which included improvement of sewerage systems, appropriate planning of industrial enterprises, construction of hydraulic structures to modify and improve the water flow, and an efficient management framework and finance investments for river rehabilitation. The key actions necessary to further improve the river water quality in the far future are also suggested, under the condition that dry-weather black and foul occurrence in the Dhaka rivers could be eliminated. The river assessment framework and associated strategies may also help policymakers, the government, and researchers to develop a plan for the rehabilitation of seriously polluted rivers in other developing countries.
... The standard equation describing concentration of DO was formulated by Chapra [1], which is based on the study of Ohio River done by Streeter and Phelps [8]. This model has been amended in various ways [4,6,7] to incorporate different phenomena that occur naturally in a stream of river. ...
- Aayushi Jain
- Viquar Husain Badshah
- Vandana Gupta
The present paper addresses a diffusion-reaction equation describing the dynamics of dissolved oxygen in a polluted stream of a river. The diffusion-reaction equation is a mass-balanced partial differential equation which relates the concentration of dissolved oxygen with the effect of other natural processes, viz. diffusion, natural aeration and reaction with pollutants. The well-known method of lines is used to solve the one-dimensional non-steady state case with Dirichlet boundary conditions. The study is motivated by the miserable condition of most of the rivers in India. Water pollution has now become a global concern and this study furnishes a better apprehension of complex phenomenon of maintaining desired level of oxygen and will aid water resource management.
... Chlorophyll-a concentrations, an indicator of algal biomass/primary productivity (Chapra 2008) in this current study registered no observable change from 2015 to 2016. The reason could be that nutrient inputs into the reservoir had been relatively constant yearly and seasonally confirming the report of Shaw et al. (2000) that chlorophyll-a concentration only varies throughout the growing season and from year to year depending on nutrient input and weather. ...
- Daniel Nsoh Akongyuure
-
![Elliot Haruna Alhassan]()
Water quality is essential for fish survival and growth in reservoirs. However, little information is known about the water quality status and its relation with fish production in the Tono Reservoir. This study sought to assess water quality parameters and examine association among them as well as determine the correlation between the water quality parameters and fish catch per unit effort of the Tono Reservoir. A three-level stratified sampling was adopted and samples were collected on a monthly basis. Water quality parameters such as water level, temperature, pH, electrical conductivity, transparency, turbidity, dissolved oxygen, chloride, sulphate, phosphate-phosphorus, silica, nitrate-nitrogen, nitrite-nitrogen, chlorophyll-a, and fish catches were measured simultaneously from each of the three strata of the reservoir. The water quality parameters of the reservoir fell within the recommended range for fish production. Concentrations of water quality parameters for the riverine, transitional and lacustrine zones showed no significant difference (p > 0.05). Catch per unit effort correlated significantly positive with only chloride (r = 0.61, p < 0.05) attributable to fertilisers used on surrounding farm lands and carried by runoff or floods to the reservoir. The reservoir could be classified as mesotrophic based on chlorophyll-a concentration. It was recommended that the reservoir water quality should be monitored quarterly by the Ministry of Fisheries and Aquaculture Development to ensure safe fish production.
... The mathematical model QUALAKE is a one-dimensional, eddy diffusion, 96 finite element, water quality prediction model which was developed to simulate the seasonal 97 temperature cycle, oxygen distribution and productivity for Lake Vegoritis as well as its 98 evaporation and heat budget (Antonopoulos and Gianniou, 2003;Gianniou and Antonopoulos, 99 2007a,b; 2014). 100 The heat transport is based on the vertical diffusion equation of the form (Henderson-Sellers, 101 1984; Chapra, 1997;Gianniou and Antonopoulos, 2007a,b): ...
- Vassilis Z. Antonopoulos
- Soultana K. Gianniou
The knowledge of micrometeorological conditions on water surface of impoundments is crucial for the better modeling of the temperature and water quality parameters distribution in the water body and against the climatic changes. Water temperature distribution is an important factor that affects most physical, chemical and biological processes and reactions occurring in lakes. In this work, different processes of water surface temperature of lake's estimation based on the energy balance method are considered. The daily meteorological data and the simulation results of energy balance components from an integrated heat transfer model for two complete years as well as the lake's characteristics for Vegoritis lake in northern Greece were used is this analysis. The simulation results of energy balance components from a heat transfer model are considered as the reference and more accurate procedure to estimate water surface temperature. These results are used to compare the other processes. The examined processes include a) models of heat storage changes in relationship to net radiation (Qt(Rn) values, b) net radiation estimation with different approaches, as the process of Slob's equation with adjusted coefficients to lake data, and c) ANNs models with different architecture and input variables. The results show that the model of heat balance describes the water surface temperature with high accuracy (r²=0.916, RMSE=2.422oC). The ANN(5,6,1) model in which Tsw(i-1) is incorporated in the input variables was considered the better of all other ANN structures (r²=0.995, RMSE=0.490oC). The use of different approaches for simulating net radiation (Rn) and Qt(Rn) in the equation of water surface temperature gives results with lower accuracy.
... Rainfall input data was given with a resolution of 60 min. The lower time step was used to ensure the Courant condition for numerical stability, i.e. that the model could adequately simulate flow velocities of up to 1 m/s (hence a 25 m cell requires a simulation timestep of 25 s) (Chapra, 2008;Van Leeuwen et al., 2016). ...
Wildfires can have strong negative effects on soil and water resources, especially in headwater areas. The spatially explicit OpenLISEM model was applied to a burned catchment in southern Portugal to quantify the individual and combined impacts of wildfire and rainfall on hydrological and erosion processes. The companion paper has calibrated and assessed model performance in this area before and after a fire. In this study, the model was applied with design storms of six different return periods (0.2, 0.5, 1, 2, 5, and 10 years) to simulate and evaluate pre- and post-wildfire hydrological and erosion responses at the catchment scale. Our results show that rainfall amount and intensity played a more important role than fire occurrence in the catchment discharge and sediment yields. Fire occurrence was found to be an important factor for peak discharge, indicating that high post-fire hydro-sedimentary responses are frequently related to extreme rainfall events. The results also suggest a partial shift from runoff to splash erosion after fire, especially for higher return periods. This can be explained by increased splash erosion in burned upstream areas saturating the sediment transport capacity of surface runoff, limiting runoff erosion in downstream areas. Therefore, the pre-fire erosion risk in the croplands of this catchment was partly shifted to a post-fire erosion risk in upper slope forest and natural areas, especially for storms with lower return periods, although erosion risks in croplands were important both before and after fires. These findings have significant implications to identify areas for post-wildfire stabilization and rehabilitation, which is particularly important given the predicted increase in the occurrence of fires and extreme rainfall events with climate change.
Eutrophication and excessive algal growth pose a threat on aquatic organisms and the health of the public, environment, and the economy. Understanding what drives excessive algal growth can inform mitigation measures and aid in advance planning to minimize impacts. We demonstrate how simulated data from weather, hydrological, and agroecosystem numerical prediction models can be combined with machine learning (ML) to assess and predict chlorophyll a (chl a) concentrations, a proxy for lake eutrophication and algal biomass. The study area is Lake Erie for a 16-year period, 2002–2017. A total of 20 environmental variables from linked and coupled physical models are used as input features to train the ML model with chl a observations from 16 measuring stations. Included are meteorological variables from the Weather Research and Forecasting (WRF) model, hydrological variables from the Variable Infiltration Capacity (VIC) model, and agricultural management practice variables from the Environmental Policy Integrated Climate (EPIC) agroecosystem model. The consolidation of these variables is conducive to a successful prediction of chl a. Aside from the synergistic effects that weather, hydrology, and fertilizers have on eutrophication and excessive algal growth, we found that the application of different forms of both P and N fertilizers are highly ranked for the prediction of chl a concentration. The developed ML model successfully predicts chl a with a coefficient of determination of 0.81, bias of −0.12 μg/l and RMSE of 4.97 μg/l. The developed ML-based modeling approach can be used for impact assessment of agriculture practices in a changing climate that affect chl a concentrations in Lake Erie.
This paper presents analysis and estimation of the longitudinal dispersion coefficient, a key hydrologic parameter for transport of contaminants in rivers and streams. The longitudinal dispersion coefficient varies spatially in streams with changes in the hydrologic parameters, e.g., the cross-sectional width, depth, sinuosity, and velocity of flow. Many theoretical and empirical equations are reported in the literature. After a comprehensive review, 30 equations for prediction of the longitudinal dispersion coefficient were selected from published research. These equations were used in this analysis. Hydrologic data from 59 river reaches were used for estimation of the dispersion coefficient. The estimated values of the dispersion coefficient were compared statistically and graphically with the observed values. Results showed that sinuosity significantly impacts estimation of the dispersion coefficient. Computations that include sinuosity improve the performance of the dispersion equation. Observed and estimated values were compared, and the equations were ranked based on the accuracy of estimation. The nine top-ranked equations were used for field validation using the ICWater model. The Sahay equation provides the best results when used in the calculation of concentration in the advection dispersion equation.
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Source: https://www.researchgate.net/publication/48447645_Surface_Water-Quality_Modeling
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