Antimicrobial groups based on mechanism of action.
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54056",title:"Antimicrobial Mechanisms of Escherichia coli",doi:"10.5772/67363",slug:"antimicrobial-mechanisms-of-i-escherichia-coli-i-",body:'Increasing resistance to antimicrobial drugs is of major concern worldwide. For many developing countries, the possibility of having cheap antibiotics available may now be threatened. There are antimicrobial resistance issues with most pathogenic bacteria and in virtually all of the opportunistic bacterial-caused infections. This translates into increased healthcare costs. These costs include extended length of hospital stay and increased costs for medical supplies including more expensive antimicrobial drugs. In addition, antimicrobial drug-resistant infections lead to higher rates of mortality, especially in patients who had recent prior exposure to antibiotics [1].
Antimicrobial drugs are classified into groups according to the type of antimicrobial activity. These groups include drugs that inhibit bacterial cell wall synthesis, depolarize the cell membrane, inhibit bacterial protein synthesis, inhibit bacterial nucleic acid synthesis, and inhibit bacterial metabolic pathways. The improper use of these drugs has helped to create resistant bacterial strains. Factors that contribute to resistance include the increased use of all antimicrobial drugs and improper antimicrobial prescribing. Many of the less expensive drugs that have fewer side effects have been used too commonly. Improper prescribing may be choosing broad spectrum or ineffective drugs [2–4].
Many strains of Escherichia coli are not harmful. In fact, these commensal strains in the gut are necessary for the synthesis of vitamin K2, which is an important clotting factor [5]. However, there are pathogenic strains, and these strains may become a larger threat if they possess or acquire certain antimicrobial mechanisms. The main ones of concern are the extended-spectrum β-lactamase (ESBL)-producing strains and the carbapenem-resistant Enterobacteriaceae (CRE) strains. The ESBL strains are resistant to most β-lactam drugs, and the CRE strains are resistant to most carbapenem drugs. Greatly increased healthcare costs are associated with the ESBL and CRE strains. Various studies have shown a hospital stay of up to twice as long and increased costs of 1.5–2.5 times as much [6–8]. One study in the United States estimated the increased costs to be $16,450 per patient [6].
All of the pathogenic strains of E. coli are armed with the same types of potential virulence factors. These factors include a capsule (in some strains), flagella, the lipopolysaccharide (LPS) cell wall, fimbriae, outer membrane proteins (OMPs), a hemolysin, cytolysins, and siderophores. The specific types of some of these virulence factors plus the possession of other toxins and effectors may vary with each individual pathogenic strain [9].
Commensal strains of E. coli are the predominant facultative organism in the human gut. Even though greatly outnumbered by the anaerobic organisms, the E. coli are vital to human health, playing roles in biofilm communities and subsequent digestion of oligosaccharides and polysaccharides, among other things [10, 11]. Unfortunately, there are also several pathogenic strains of E. coli. The classification names of these strains may vary some depending on the source, but for the purposes of this chapter, we will use the following names. There are six strains of potentially pathogenic intestinal-based diarrhea causing E. coli: diffusely adherent E. coli (DAEC), enteroaggregative E. coli (EAEC), enterohemorrhagic E. coli (EHEC), enteroinvasive E. coli (EIEC), enteropathogenic E. coli (EPEC), and enterotoxigenic E. coli (ETEC). In addition, there is one extraintestinal pathogenic strain, uropathogenic E. coli (UPEC), which causes urinary tract infections. There is some evidence that a second extraintestinal strain exists, the meningitis-associated E. coli (MNEC) strain. Findings on MNEC indicate that the infection starts as a blood infection and then gains access to the central nervous system. A majority of MNEC possess the K1 capsular antigen, and there is a high rate of mortality from the meningitis [12]. This chapter will focus on the six intestinal and the UPEC strains.
These strains are sometimes referred to as enteroadherent E. coli (EAEC) (not to be confused with the enteroaggregative strains, which are sometimes referred to as EAggEC). While not known to cause severe diarrheal disease, the DAEC, which are probably a group of related strains, are thought to be responsible for some types of persistent diarrhea in infants. Whether they possess true virulence factors is still under debate, but these bacteria are able to bind to enterocytes (probably via adhesins such as Afa/Dr) and elicit a response in which the microvilli extend and wrap around the bacteria. Diarrhea in association with DAEC has also shown an ability to induce the production of inflammatory cytokines, such as IL-8 [13–15].
The EAEC strains were so named because of their tendency to adhere to enterocytes in dense clusters. The bacteria attach to the microvilli and also to other EAEC bacteria. The EAEC are also a heterogeneous group of strains with a similar pathogenesis, resulting in non-bloody diarrhea. Infection is established by adhering to the microvilli via fimbriae (the aggregative adherence fimbriae—AAF), inducing increased mucus production and biofilm formation, inducing an inflammatory response and production of toxins. The main toxins of the EAEC strains are the EAEC heat-stable enterotoxin (EAST1), which enters the enterocytes and activates guanine cyclase, resulting in increased levels of cGMP in the cell and loss of fluid into the intestinal lumen; the plasmid-encoded toxin (Pet), which disrupts the enterocyte cytoskeleton, resulting in cell detachment; and a Shigella-like enterotoxin (ShET1), a heat-stable toxin which may also result in fluid secretion [16–18].
The most publicized strain of pathogenic E. coli is the EHEC strain. EHEC serotype O157:H7 is well known as the causative agent of outbreaks of food-associated severe diarrhea. Infection with O157:H7 results in sever abdominal cramps and bloody diarrhea and may lead to hemolytic-uremic syndrome (HUS) which can be life-threatening. The most common foods associated with transmission of these bacteria are undercooked meat (especially ground beef), raw milk, and raw vegetables. The EHEC strains do not directly invade the enterocytes, but produce toxins that do enter and severely damage these cells. The responsible cytotoxins are verotoxins I and II (designated as Shiga toxins, Stx-1 and Stx-2). The Shiga toxins are capable of inactivating ribosomes, blocking protein synthesis, and emerging through the basolateral membrane into the subepithelial region. Stx-2 is seen most often in the EHEC strains that cause HUS [19–21].
In infections with EIEC, the bacteria invade by directly entering M cells. The bacteria pass through these cells and then are able to invade the enterocytes via the basolateral membrane, causing severe damage to the intestinal mucosa. The bacteria are also able to spread laterally through the cell side walls to adjacent cells (via actin). This damage results in dysentery (watery diarrhea with pus, mucus, and blood). The EIEC bacteria do not produce toxins, but participate in direct damage and induce production of IL-1 and IL-8. The pathogenic mechanisms and disease symptoms associated with EIEC are so similar to Shigella spp. that differential diagnosis can be difficult. Diagnosis is usually based on physiological and biochemical characteristics that can be detected in the clinical laboratory [19, 22, 23].
The EPEC strains do not directly invade enterocytes. Instead, these bacteria adhere to the microvilli and inject effector proteins into the cell via a type III secretion system (T3SS). One of these effectors is the translocated intimin receptor (Tir), which initiates recruitment of the host cell actin to form a pedestal under the bacteria. The recruitment of actin and formation of the pedestals result in destruction of the rest of the microvilli and also inhibit the transport of Na+ and Cl- in the cell, which results in the subsequent exodus of water into the intestinal lumen. Another effector, the E. coli secreted proteins (Esps), interacts with the host cell cytoskeleton and results in disruption of the cell tight junctions [24–26].
The ETEC strains are a common cause of acute travelers’ diarrhea. These strains usually colonize the proximal small intestine, adhering to the microvilli via various colonization factors including fimbrial, nonfimbrial, helical, and fibrillar types. The EPEC strains secrete two types of toxin: a heat-labile toxin (LT) and heat-stable toxins (STs). The LT is an AB toxin, and the B subunits bind to the monosialoganglioside GM1, which induces the cell to take in the toxin. The LT toxin activates adenylyl cyclase, which increases the cAMP in the cell, resulting in hypersecretion of water and electrolytes into the intestinal lumen. The STs bind to guanylyl cyclase receptors on the microvilli, which stimulate guanylate cyclase and activate the cystic fibrosis transmembrane receptor (CFTR). This results in an increase in cGMP in the cell and impaired absorption of Na+, which causes hypersecretion of water into the intestinal lumen [19, 24, 27].
The UPEC strains are responsible for most uncomplicated urinary tract infections (UTIs). These strains possess a capsule and bind to uroepithelial cells via fimbriae. The interaction of the bacteria with the host cell induces the internalization of the bacteria where the bacteria multiply rapidly and form biofilm-like intercellular bacterial communities (IBCs). The bacteria are shed intermittently from the uroendothelial cells into the lumen of the bladder. UPEC strains produce several types of toxins including hemolysin A (HlyA) which has pore-forming capability and two cytotoxins, cytotoxic necrotizing factor (CNF-1) and secreted auto-transporter toxin (Sat) [19, 28, 29].
As mentioned above, antimicrobial drugs are often classified in groups based on their mechanism of antimicrobial action. Table 1 displays those groups along with examples of the antimicrobial drugs included in each group. The β-lactam drugs, which were among the first antimicrobials to be discovered, target the bacterial cell wall (via peptidoglycan synthesis) and are most useful against gram-positive bacteria (having little effect on gram-negative bacteria because of the lipopolysaccharide cell envelope that protects the thin peptidoglycan cell wall in these bacteria). Over the years, because the β-lactam drugs were readily available and inexpensive and caused few side effects, physicians commonly treated their patients initially with these drugs.
Inhibit cell wall synthesis | β-Lactams Carbapenems Cephalosporins Monobactams Penicillins Glycopeptides |
Depolarize cell membrane | Lipopeptides |
Inhibit protein synthesis | Bind to 30S ribosomal subunit Aminoglycosides Tetracyclines Bind to 50S ribosomal subunit Chloramphenicol Lincosamides Macrolides Oxazolidinones Streptogramins |
Inhibit nucleic acid synthesis | Quinolones Fluoroquinolones |
Inhibit metabolic pathways | Sulfonamides Trimethoprim |
Antimicrobial groups based on mechanism of action.
When bacterial resistance to the β-lactam drugs became an issue (very early on), scientists developed synthetic versions of penicillin such as ampicillin, amoxicillin, and methicillin. In addition, scientists discovered the natural cephalosporin β-lactam drugs. The initial cephalosporins (first generation) were most useful against gram-positive cocci, with some activity against a few gram-negative bacilli. Further development of these drugs has produced second generation (less effective against gram-positive cocci, more effective against gram-negative bacilli); third generation (generally with a broad spectrum of activity against gram-negative organisms); fourth generation (extended-spectrum activity against gram-positive cocci and gram-negative bacilli); and recently, fifth generation (hopefully effective against various multidrug-resistant organisms), with more still in development. Other β-lactam drugs developed during this time frame were the carbapenems (broad-spectrum activity) and monopenems (aztreonam—activity against gram-negative aerobic bacteria) [30–32].
Differences in structure, metabolism, virulence factors, etc., between gram-negative and gram-positive bacteria predict which antimicrobial drug groups may be effective. Fewer of the drug groups have good activity against gram-negative bacteria. Those groups include some of the β-lactam drugs (especially second-, third-, and fourth-generation cephalosporins): aminoglycosides, fluoroquinolones, trimethoprim/sulfamethoxazole (TMP/SXT), and nitrofurans (for UTIs) [33]. Intestinal infections with E. coli are most commonly self-limiting and require supportive therapy (antiemetics, antidiarrhetics, rehydration) only. Severe or recurring infections (e.g., traveler’s diarrhea) may be treated with fluoroquinolone drugs. Acute dysentery caused by EIEC strains may be treated with fluoroquinolones or appropriate cephalosporins. For infections caused by EHEC, antimicrobial therapy is contraindicated as it greatly increases the risk for development of HUS. UTIs caused by UPEC strains are usually treated with antimicrobial drugs and uncomplicated UTIs with nitrofurantoin or TMP/SXT; complicated UTIs may also be treated with fluoroquinolones [34–37]. Infections with ESBL or CRE strains severely limit treatment options. For ESBL strains, carbapenems may still be an option or newer β-lactam/β-lactamase inhibitor drug combinations; for CRE strains, gentamicin, amikacin, colistin, tigecycline, and fosfomycin may be options. Unfortunately, some ESBL and CRE strains may be resistant to even some of these drugs [38–40].
At the beginning of antimicrobial drug resistance, physicians did not realize how the various drugs affected the bacteria. In addition, in an effort to begin antimicrobial therapy as quickly as possible, physicians often ordered a broad-spectrum drug before knowing the causative agent of the infection. These issues (among others) have led to a large amount of resistance to β-lactam drugs, especially among the gram-negative bacteria. Bacteria that produced β-lactamases (enzymes that inactivate β-lactam drugs) were identified as early as the 1940s (around the same time as penicillin was discovered), and the number of different β-lactamases produced has increased over the years to around 1000 [2–4, 41].
The issue of resistance is not just with the β-lactam drugs. Over the 70 plus years that antimicrobial drugs have been in existence, resistance mechanisms have been seen for most of these drugs. It does not seem to take the bacteria very long from initial use of a drug to development of resistance to that drug. Important resistant milestones include resistance to aminoglycosides and tetracycline in the 1960s, vancomycin in the 1980s, fluoroquinolones in the 1990s, and linezolid in the 2000s [42]. In addition, some of the bacteria have become resistant to multiple antimicrobial agents from many of the drug classes. These multidrug-resistant bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), are currently a major cause of morbidity and mortality [43].
Similar to the threat of MRSA, members of the gram-negative Enterobacteriaceae, in particular E. coli and Klebsiella pneumoniae, include strains that are ESBLs and CREs. These organisms can be resistant to most commonly used antimicrobials, which makes the infections they cause extremely difficult to treat, leading to increased morbidity and mortality (and healthcare costs) [44, 45].
There are four general antimicrobial resistance mechanisms that bacteria use. These are limiting uptake of the drug, modifying the target of the drug, inactivating the drug, and active efflux of the drug. These mechanisms may be located on the bacterial chromosome and occur naturally in all members of a species (intrinsic) or come from other bacteria, usually via a plasmid (acquired). Intrinsic resistance genes may be expressed constitutively (usually at a low level) or be induced by the presence of antimicrobial drugs. Gram-negative bacteria widely use all four of these mechanisms and are very capable of horizontal transfer of resistance elements. Table 2 shows which resistance mechanisms and genes are associated with resistance to various antimicrobial drugs [43, 46, 47].
Antimicrobial agents | Mechanisms of resistance | Genetic basis |
---|---|---|
β-lactams Penicillins Cephalosporins Monobactams Carbapenems | β-lactamases—inactivate drugs Active efflux | ampC bla genes—plasmid (TEM, SHV, CTX-M, NDM) acrAB(tolC), acrAD(tolC) |
Aminoglycosides Amikacin Gentamicin Tobramycin | Aminoglycoside modifying enzymes Modify target—16S rRNA Active efflux | aac, ant, aph—plasmid amrA, rmtB mdtEF(tolC) |
Tetracyclines Tetracycline | Limiting uptake Active efflux | ompF acrAB(tolC) tetA, tetB—plasmid |
Chloramphenicol | Limiting uptake Active efflux | ompF acrAB(tolC) |
Fluoroquinolones Ciprofloxacin Norfloxacin | Limiting uptake Modified target—gyrase Modified target—topoisomerase IV Active efflux | ompF gyrA parC acrAB(tolC), acrEF(tolC), mdtABC(tolC) |
Metabolic pathway inhibitors Trimethoprim/Sulfamethoxazole | Target enzyme modification | TMP—dhfr SXT—dhps |
Common antimicrobial resistance genes and mechanisms in Escherichia coli.
Gram-negative bacteria have an advantage in combating drugs because of the structure and functions of the LPS cell wall, which provides a natural barrier to certain molecules. The LPS is generally hydrophobic which limits access to small hydrophilic drugs, such as the β-lactams. These hydrophilic drugs gain access by traveling through the OMPs. The main OMPs in E. coli are OmpF and OmpC. In addition to the β-lactams, other drugs that may use the porin channels are chloramphenicol, fluoroquinolones, and tetracycline. Hydrophobic drugs such as the aminoglycosides and the macrolides gain access by permeating through the LPS layer. There are two main mechanisms that are used to limit access to drugs via porins: a decrease in the number of porins or a change in charge, within the porin channel, which reduces its function or binding properties. In E. coli porin production may be reduced dramatically or even stopped, or a different porin may be produced instead [48, 49].
Gram-negative bacteria make use of the modifying of drug targets against several of the antimicrobial groups including β-lactams, aminoglycosides, fluoroquinolones, and the combination drug TMP/SXT. Even though not as widely used as in gram-positive bacteria, the gram-negative bacteria are able to produce penicillin-binding proteins (PBPs) that are resistant to some β-lactam drugs. PBPs are actually peptidases that are involved in the making of the peptidoglycan cell wall. Penicillin drugs that are able to bind to PBPs inhibit the assembly process. There are several different native PBPs produced by E. coli, some of which have reduced binding affinity for some of the β-lactam drugs. No acquired modified form of PBP has been shown to be significant in β-lactam resistance in E. coli [50, 51].
The aminoglycoside drugs inhibit protein synthesis by binding to the bacterial 30S ribosomal subunit at the A-site of the 16S rRNA. Bacteria are able to modify the ribosomal subunit via acquisition of plasmids carrying 16S rRNA methyltransferases. The methyltransferases are able to modify the structure of the 16S rRNA, which decreases the ability of the drug to bind to it. Several of these methyltransferases have been identified and characterized. The genes involved include armA (for aminoglycoside resistance methylase) and several rmt (for ribosomal methyltransferase) genes, with rmtB being the most common. The bacteria quite often possess several of these genes simultaneously. These genes most often confer clinically significant resistance to amikacin, gentamicin, and tobramycin, among other aminoglycosides [52–56].
The fluoroquinolone drugs interfere with nucleic acid synthesis during DNA replication by inhibiting either DNA gyrase or topoisomerase IV. Resistance to these drugs occurs commonly from mutations in either the chromosomally encoded GyrA subunit of gyrase (gyrA gene) or the ParC subunit of topoisomerase IV (parC gene). These mutations decrease the binding ability of the drugs, most commonly ciprofloxacin and norfloxacin. There is also some evidence that low-level resistance may be acquired via plasmids carrying quinolone resistance (qnr) genes [56–58].
The combination drug TMP/SXT is currently a common choice for treatment of UTIs. Both of these drugs target enzymes in the bacterial folate biosynthesis pathway via competitive inhibition. Trimethoprim is an analog of the natural substrate of the dihydrofolate reductase (DHFR) enzyme, and SXT is an analog of p-amino-benzoic acid, the natural substrate of the dihydropteroate synthase (DHPS) enzyme. This competitive binding blocks the binding of the natural substrate and stops the pathway at that point. Since TMP and SXT affect two different enzymes on the same pathway, the combination drug makes an effective treatment. Chromosomal mutations (often single point mutations) in the dhfr or dhps genes are commonly the cause of resistance to these drugs [59, 60].
Drug inactivation is accomplished in one of two ways: by actual degradation of the drug or by transfer of a chemical group to the drug. Gram-negative bacteria use drug inactivation against β-lactams and aminoglycosides. The β-lactam drugs are universally inactivated by β-lactamase enzymes, which degrade the drugs, and E. coli produces several of these. The aminoglycoside drugs are inactivated fairly universally by enzymes that transfer one of three small chemical groups to the drug. These enzymes include the acetyltransferases (AACs, aac genes), nucleotidyltransferases (ANTs, ant genes), and the phosphotransferases (APHs, aph genes) [43, 49, 61].
The β-lactam drugs all share a specific core structure, which consists of a four-sided β-lactam ring. The β-lactamases (also originally called penicillinases and cephalosporinases) are capable of inactivating β-lactam drugs via hydrolyzation of a specific site in the β-lactam ring structure causing the ring to open. The drugs are then not able to bind to their target proteins, the PBPs. Within the large number of β-lactamases which have been identified, there are enzymes which can inactivate any of the current β-lactam drugs. The production of β-lactamases is the most common resistance mechanism used by gram-negative bacteria against β-lactam drugs [46, 62].
The β-lactamase enzymes can be classified based on their primary structure or functional characteristics. Structurally they are placed into four main categories (A, B, C, or D). There are three functional groupings: the cephalosporinases, the serine β-lactamases, and the metallo-β-lactamases. These enzymes are also commonly referred to by their enzyme family, for example, the TEM (named after the first patient) family, the sulphydryl variable (SHV) family, and the CTX (preferentially hydrolyze cefotaxime) family [56, 63].
The first β-lactamase to be characterized was from E. coli and is chromosomally encoded by the ampC gene (so named for ampicillin resistance). This gene is constitutively expressed at a low level, but mutations may result in overexpression of the gene. The AmpC β-lactamases are most effective against the penicillins and some first-generation cephalosporins. There are also many plasmid-borne β-lactamases, which carry a variety of bla genes (β-lactamase genes). Because these β-lactamases confer resistance to later generation cephalosporins, they were designated as ESBLs and include the TEM, SHV, and CTX-M enzyme families. The most commonly seen of these in E. coli are the CTX-Ms. The ESBLs may also be resistant to multiple drug classes but are generally sensitive to β-lactamase inhibitors. The β-lactamase inhibitors are structurally similar to β-lactamases and have weak antimicrobial ability alone but work synergistically in combination with a β-lactam drug [56, 64–67].
Recently, there has been emergence of β-lactamases that are active against the carbapenems (carbapenemases), found primarily in the Enterobacteriaceae. Bacterial strains that carry these are known as CRE strains. The carbapenemases are all metallo-β-lactamases (MBLs), and the most widely distributed are the IMP-1 (for imipenem resistance) and VIM-1 (Verona integron-encoded MBL) types. A new MBL has recently been identified, mainly in strains of E. coli. It has been designated as New Delhi MBL (NDM-1). The CRE strains are usually resistance to all the β-lactam drugs and are not inactivated by the standard β-lactam/β-lactamase inhibitor combination drugs. There is a newer β-lactamase inhibitor, avibactam, which has been approved for use with ceftazidime against gram-negative bacteria. In addition, avibactam is being tested for use with aztreonam against CREs [62, 66–68].
Bacteria possess methods for disposal of toxic substances to the outside of the cell. The most commonly used mechanism is the efflux pump. Most bacteria have chromosomally encoded efflux pump genes. Some of these pumps are expressed constitutively, and expression of others is induced by various environmental stimuli. Many of these pumps are capable of transporting a variety of substances and are also described as multidrug (MDR) efflux pumps. There are five efflux pump family groups: the ATP-binding cassette (ABC) family, the multidrug and toxic compound extrusion (MATE) family, the major facilitator superfamily (MFS), the small multidrug resistance (SMR) family, and the resistance-nodulation-cell division (RND) family. The RND pumps are generally found only in gram-negative bacteria as these pumps are multicomponent pumps that function in association with an OMP [69–72].
There is only one ABC efflux pump in E. coli that is known to contribute to antimicrobial resistance. That is the MacAB transporter that confers resistance to some macrolides [73]. There is also only one MATE efflux pump found in E. coli, the NorE pump which is able to transport fluoroquinolones. It is still in question if the NorE pump has a clinically significant impact on antimicrobial resistance [74, 75]. There are five known MFS efflux pumps found in E. coli. These are capable of transporting macrolides (MefB and MdfA pumps), fluoroquinolones (QepA2, EmrAB-TolC, and MdfA pumps), tetracycline (EmrAB-TolC and MdfA pumps), trimethoprim (Fsr pump), and chloramphenicol (MdfA pump). In addition, there are several MFS pumps that may be acquired by E. coli (e.g., via plasmids) that are specific for tetracyclines, with tetA and tetB being the most common [76, 77]. There are no clinically significant SMR efflux pumps found in E. coli [78].
The RND efflux pumps are the most clinically significant pumps found in gram-negative bacteria. These pumps consist of three components (tripartite): an inner membrane transporter, an outer membrane porin, and a periplasmic accessory protein that functions to connect the other two components. In E. coli, the OMP that is associated with all of the antimicrobial efflux pumps is TolC. There are five known RND pumps in E. coli: AcrAB-TolC, AcrAD-TolC, AcrEF-TolC, MdtABC-TolC, and MdtEF-TolC. AcrAD-TolC has been shown to efflux aminoglycosides and β-lactams. AcrEF-TolC has been shown to efflux quinolones and tigecycline. MdtABE-TolC has been shown to efflux quinolones. MdtEF-TolC has been shown to efflux erythromycin. The level of expression of these four pumps is relatively low, and if operating alone, the amount of antimicrobials effluxed would probably not be significant. Because E. coli has five efflux systems plus multiple other types of antimicrobial resistance mechanisms in play, these pumps undoubtedly help out. The other RND efflux pump in E. coli, AcrAB-TolC, is the most clinically significant and accounts for major antimicrobial efflux. This pump has been shown to efflux β-lactams, fluoroquinolones, tetracyclines, chloramphenicol, and lincosamides [72, 79, 80].
For many strains of pathogenic E. coli, the most common course of therapy is supportive and does not require the use of antimicrobial drugs, or in the case of EHEC, antimicrobial therapy is not recommended. For severe intestinal infections and UTIs, antimicrobial therapy may be necessary. Unfortunately with the issue of ever increasing antimicrobial resistance, the antimicrobial options are becoming fewer. With the emergence of ESBL and CRE E. coli strains, the options have gotten extremely limited, and antimicrobial development has not been able to keep up with the demand. Hopefully the newer carbapenem/β-lactamase inhibitor combination drugs and other drugs being developed under the tetracycline and aminoglycoside drug classes will prove to be equal to the task or at least keep the bacteria under control until better options become available.
Numerical simulation is increasingly used for studying overland flows. Since runoff drives soil erosion and landscape evolution, the runoff models provide a foundation for modeling soil erosion, rill erosion, and related processes at the watershed scale [1, 2]. Models involving different levels of abstraction have been proposed [3, 4, 5]. Two commonly used models are the diffusion wave (DW) and kinematic wave (KW) models [6, 7, 8, 9]. The KW models set the friction slope to be equal to the bed slope and ignore the inertial terms [10]. The method has been successfully used to describe overland flows [11, 12, 13, 14]. The governing equations are highly nonlinear and do not have general analytical solutions, so one has to solve them numerically for practical cases [15]. The models based on full Saint-Venant (SV) equations have also been applied and produced better results.
Two-dimension models are generally used for cases with irregular domains. A distributed rainfall-runoff model using the KW approximation solved by an implicit finite difference scheme was developed [16], but channel flows are computed using a separate KW model. Fully two-dimensional shallow water equations are being utilized for modeling overland flows in late 1980s [17]. A two-dimensional finite difference (FD) runoff model was developed by solving 2D SV equations [18]. Shallow water equation-based 2D models [19] were used for runoff over an irregular topography of experimental scale with infiltration processes considered and in rural semiarid watersheds for overland flows generated by storms [20].
In addition to finite difference method (FDM), the two-dimensional finite element (FEM) and finite volume methods (FVM) have been used for overland flow simulations. A FEM KW model was developed by Liu et al. [21] for simulating runoff generation and concentration over an irregular bed and reproduced experimental results. Tests [15] indicated that the FVM-based 2D SV model performed better than that of FDM. Costabile et al. [22] solved the shallow water equations using the FVM and applied the resulting model to simulate a real event on a watershed of 40 km2. Nunoz-Carpena et al. [23] solved the KW equation using the Petrov-Galerkin method. Venkata et al. [24] developed a Galerkin DW FEM and applied it to a small watershed. Singh et al. [25] simulated runoff processes by solving the 2D shallow water equations with a shock-capturing scheme and the FVM. Shirmeen et al. [26] showed results of a validated, FEM 2D model in predicting runoff from a flat agricultural watershed.
In order to check numerical models’ mathematical correctness and physical applicability, the developed computational models have been tested with analytical solutions, experimental, and field data. Iwagaki [27] studied runoff using analytical methods and experimental data; several specific solutions were developed based on the characteristic method. Govindaraju et al. [28] developed analytical solutions using KW and DW approximations. Comparisons of analytical solutions, numerical solutions, and experimental data were discussed. Singh [29] detailed the KW model’s analytical and numerical solutions and their wide applications. Cea [30] tested FVM using an experimental watershed with a complex shape. These overland flow models use simplified equations and need to specify pre-existing channel networks, which make it difficult to simulate soil erosion cases with hill-slope evaluation and mixed sheet-channel flow conditions.
CCHE2D is a physically based model, which treats the entire watershed including the channels and ditches as one continuous domain. One does not need to differentiate overland sheet flow and channel flow calculation areas using grid cells and 1D channel networks as is done in GSSHA [31], WASH123D [32], NIKE-SHE [33], and SHETRAN [34]. It is also not necessary to employ arbitrarily shaped sub-watersheds and 1D channel networks as is done in the CCHE1D model [35]. In these models, 2D DW equations or KW equations are solved for the overland flow using finite difference methods, and the 1D SV equation is solved in the prescribed channel networks. In contrast to these models, in CCHE2D, hydrodynamics over the entire watershed is simulated using only 2D equations discretized on an irregular quadrilateral finite element mesh, which is generated using digital elevation model (DEM) data. The simulated overland sheet flow and channel flow are seamlessly connected everywhere in the domain and the channel network is formed automatically. This method may be more applicable when sediment transport, rill erosion, or gully erosion processes in watersheds are considered.
In this study, the CCHE2D model is modified and applied to simulate watershed hydrological processes. CCHE2D is a general hydrodynamic model for unsteady, turbulent free flows, sediment transport, and pollutant transport. It has been validated and applied widely to simulations of channel flow, flooding, coastal flow, bed topographic change, and chemical contamination in aquatic environments [36, 37, 38, 39, 40].
The major objectives of the present paper are to assess the accuracy and the effectiveness of this FEM in predicting overland runoff processes, and its applicability to practical agricultural watersheds with ditches and natural stream channels. The approach of the study followed the recommendations of [41] for quality assurance that numerical models have to be verified and validated using analytical solutions, physical experimental data, and field data. The validated numerical model was used to simulate and characterize the hydrological processes of an agricultural watershed in the Mississippi River alluvial plain where farm fields are drained and separated by ditches and stream channels. A limitation was found in the interpolation method when it is applied to the water surface elevation of the sheet runoff. A numerical scheme was developed and implemented for improving the bilinear interpolation. The present study focused on watershed surface flow processes over bare soils; interception, evapotranspiration, and infiltration were not considered.
Surface runoff due to precipitation is typically quite shallow and can be aptly represented by the 2D shallow water equations within the CCHE2D model [36, 38]. The water surface elevation of the runoff flow, η, is calculated by the continuity equation in a Cartesian coordinate system
in which h is the local water depth, t is time; R is rainfall intensity, which may vary in time and space, and u and v are depth-averaged velocity components in x and y directions, respectively. The depth-integrated 2D momentum equations for turbulent flows are as follows:
in which g is the gravitational acceleration, ρ is water density, τxx, τxy, τyx, and τyy are depth-averaged Reynolds stresses, and τbx, τby are bed shear stresses. In the overland runoff area, the Reynolds stress terms vanish, and Eqs. (2) and (3) become the shallow water equations. The Reynolds stress terms remain significant in the part of the domain with channel and concentrated flows. A special finite element method called the efficient element method is adopted in the model, in which a collocation approach is used to discretize the equations in a structured quadrilateral nonorthogonal mesh system. A partially staggered grid is used for solving these equations. A velocity correction method is used to couple the continuity equation and the momentum equations. More details about this model’s numerical methodology and techniques can be found in earlier publications [36, 38, 42].
The full Eqs. (1)–(3) are applicable for general flow conditions. In realistic cases where runoff and channel flow conditions coexist, a general flow model is necessary. Under the sheet flow condition, the advection and turbulence stress terms in the momentum equations vanish because the dominant forcing for the overland flow is the gravity and bed shear stress. The water depth is very small, and water surface slope and bed slope become almost the same:
in which b is the bed elevation. The general flow equations then become the KW equations. Under this condition, the flow is completely dominated by the bed slope. Shear stresses on the bed are evaluated in conjunction with the Manning equation as:
in which n is the Manning roughness coefficient and
CCHE2D uses a partially staggered method: the velocities are solved at collocation points and the pressure (water surface) is solved at cell centers [36]. A bilinear interpolation method is used to interpolate the water surface elevation solution to the collocation nodes where the momentum equations are solved. The bilinear interpolation works well for general channel flow simulations because the water depth is large in comparison with the variation of bed surface and the mesh size. When overland sheet runoff is simulated; however, the water depth is very small; it is often less than the microelevation variation of bed topography represented in an element. In this case, the interpolated water surface elevation may be lower than the bed if the bed is concave down and vice versa. This is a limitation of the interpolation method. In the concave down case, dry nodes are created artificially; in the concave up case, artificial masses of water could be erroneously created. Figure 1 illustrates this problem in one dimension. The problem occurs whenever irregular bed topographies are encountered. A correction is therefore necessary to the interpolation over the surface runoff area.
The error of underestimation and overestimation caused by linear interpolation of water surface elevation from cell centers to collocation nodes.
A numerical scheme has been developed and implemented in CCHE2D to correct the interpolation error [43]. Figure 2 illustrates how the scheme is formulated in one dimension with an exaggerated vertical scale. Eq. (7) is the formulation to compute the correction value Δb for nonuniform meshes, and it is simplified to Eq. (8) if the mesh is uniform. It is straightforward to extend Eqs. (7) and (8) to two dimension. Water depth at the cell centers is positive, without this correction, the depth at the middle point would become negative because the interpolated water surface elevation is below the bed. This scheme is necessary and effective when cases with irregular topography are simulated
Definition sketch for the formulation of the correction (Eqs. (7) and (8)) to linear water surface elevation interpolation. b1, b2, and b3 are bed elevation. Δb is the interpolation correction and Δx1 and Δx2 are mesh spacing.
where b1, b2, and b3 are bed elevation, Δb is the interpolation correction,
Two analytical solutions were obtained by solving a one-dimensional kinematic equation analytically for rain-generated runoff by [44, 45]. The solution of sustained rains for the runoff to reach a steady state [44] and the solution for rainfall that stops before the runoff becomes steady [45], including the tailing stage solution after rainfall stops, were provided. The governing one-dimensional kinematic equation for deriving these solutions is:
in which q is the discharge of water per unit width (m2/s), k is an exponent (=5/3), and α (=5) is a coefficient (m2−k/s). These analytical solutions were realized for a few simple cases: runoff due to steady rainfall intensity on a uniform planar area of 200 × 1 m with a slope of 1.0%. The rainfall intensity was R = 2.7 × 10−5 m/s, and the Manning’s coefficient was n = 0.02 m−1/3s. For comparison, the same case was simulated using CCHE2D and a 10 × 100 point 2D mesh with uniform spacing. The solutions were recorded at cross sections located at 50, 100, 150, and 200 m, from the upstream end of the plane.
Figure 3 shows the comparisons of the simulated runoff and the analytical solutions for the sustained rain collected at the four cross sections. Hydrographs at each cross section indicate that equilibrium runoff (steady state) is reached before the rain stops at T = 1000 s. The runoff is always nonuniform, and the peak discharge increases in the downstream direction. At first, the flow is unsteady (rising limb), then becomes steady until T = 1000 s, and finally becomes unsteady in the falling limb. The runoff reaches equilibrium earlier at locations closer to upstream. The simulation is a little less than the analytical solution at the time approaching the peak discharge, particularly near the downstream. The solution can be improved by reducing the local mesh size effectively.
Comparisons of the simulated runoff hydrographs and analytical solutions. The sustained rain stopped at T = 1000 s after the steady states have reached everywhere on the slope. Comparisons at four cross sections are shown. Δx is the mesh spacing in the runoff direction.
Figure 4 shows a case in which the rainfall stops before runoff reaches steady state (T = 200 s); the hydrographs, thus, have a different pattern. The peak discharge is reached at the time the rain stopped and is the same for all cross sections. The peak discharge for the lower cross sections lasts longer because the flows at the lower locations are sustained by upstream contributions. The runoff recession is earlier for upstream locations. The shape of the two sets of simulated hydrographs at all cross-section locations corresponded well with the analytical solutions.
Comparisons of the simulated runoff hydrographs and analytical solutions. The rain stopped at T = 200 s, before the flow at any of the four cross sections reached steady state. Δx is the mesh spacing in the runoff direction.
CCHE2D model was validated using experimental data sets collected from the literature. All of these cases were carried out on impervious overland flow planes. The only quantity measured in these experiments was the downstream runoff discharge.
Morgali and Linsley [46] obtained two sets of experimental runoff data. Their tests were carried out over a straight turf surface of 21.95 m long with a constant slope (0.04) and width. The Manning’s coefficient, n, was found to be 0.5 m−1/3s. The rains had two different intensities and were uniform along the slope for 1200 s (20 min). Figure 5 compares the experimental data and the numerical simulations. The analytical solution for this test condition [44] is also presented in Figure 5. It was found that these runoff experiments fit well with the analytical solution. A 110 × 10 points uniform mesh and 0.01 s time step were used for the numerical simulation. The CCHE2D numerical results showed good agreements with the analytical solution as well as the experimental results (Figure 5). The rising limb of the discharge hydrograph and the peak discharge were captured very well by the simulations. The processes of the two experiments, 1A (R = 92.96 mm/h) and 1B (R = 48.01 mm/h), look similar because the only difference in the experiments was rainfall intensity. The peak discharges of the experiments occurred at approximately 850 and 1100 s, respectively, for Case 1A and Case 1B. The numerical solutions of CCHE2D agreed well with the experimental data. The peak discharges for Case 1A and Case 1B are 5.67×10−4 and 2.93 × 10−4 m3/s, respectively. The times to peak discharge for Case 1A resulted from the analytical solution, CCHE2D and the experiment, are 760, 850 and 950 s, respectively. The differences among the three are less for the Case 1B (Figure 5).
Comparisons of measured data with analytical solution, results of CCHE2D, and other numerical models.
Figure 5 also compares the simulation results of CCHE2D and the model results by Govindaraju et al. [28]; the two numerical solutions agree well for the case with the higher rain (1A), but the fit of their solutions based on the SV equations does not correspond well for the case with the smaller rainfall (1B). The results of CCHE2D also outperform the analytical solution of the DW approximation [28].
Cea et al. [30] conducted three runoff experiments of complex topography and simulated these cases using a 2D unstructured FVM. The experimental watershed was a rectangle (2 × 2.5 m) made by three planes of stainless steel, each of them with a slope of 0.05 (Figure 6). Two dikes (1.86 and 1.01 m in length) were placed in the watershed to vary the topography. Rainfall intensity, duration, and runoff hydrographs were measured. As a result, the runoff direction, distribution, and pattern of the hydrograph were affected. The runoff was accumulated and became channel flows along intercepting lines of slopes and dikes. Since both overland flow and channel flow are involved, faithful simulation requires solving full governing Eqs. (1)–(3). The rainfall applied to each test case was different. In the first test (2A), the rainfall intensity was 317 mm/h for 45 s. In the second test (2B), the rainfall intensity was 320 mm/h for 25 s; then it was stopped for 4 s and restarted for an additional 25 s with the same intensity. In the third test (2C), rainfall intensity was 328 mm/h. The rainfall was applied for 25 s; then it was stopped for 7 s and then restarted for another 25 s.
Topography of the experimental watershed [30].
In this study, CCHE2D was applied and the numerical results were compared with experimental data. The watershed was modeled using an irregular structured mesh with the cell size ranging from 0.034 to 0.009 m; the mesh was refined near the main channel and the outlet for improving results. The Manning’s roughness coefficient was set equal to 0.009 m−1/3s. The simulation time was 120 s for each case. The channel flow and runoff sheet flow coexisted: the runoff from the watershed surface was accumulated in the bottom of the watershed channel with a triangle-shaped cross section formed by the side slopes. Results of cases 2A and 2C are shown in Figures 7 and 8, respectively.
Comparison of measured and simulated hydrographs using rainfall with one peak (Case 2A).
Comparison of measured and simulated hydrographs using rainfall with two peaks (Case 2C).
Figure 7 shows the comparison between the numerical solution and experimentally observed runoff hydrograph of Case 2A. The solution of the CCHE2D model agrees very well with the experimental results. The flow discharge increased continuously once the rain started. The peak discharge occurred at the time the rainfall stopped (at 45 s). Although the rising and the falling limbs of the hydrograph were slightly overestimated, the shape of the hydrograph and the peak discharge were aptly predicted.
Figure 8 shows the comparison between the numerical and experimental runoff hydrographs of Case 2C. The shape of the hydrograph was successfully predicted. The interval between the two rainfall peaks was 7 s. The first runoff peak discharge occurred at the time the rainfall stopped, at 25 s. The runoff discharge decreased for approximately 10 s and then increased. The second runoff peak discharge occurred at approximately 57 s. The simulated processes and the observed physical processes showed a good general agreement; it also matched well with the model results of [30].
Figure 9 shows the simulation results at t = 54 s (the peak of the second rainfall) for Case 2C: (a) simulated water depth contour distribution, (b) simulated flow unit discharge pattern and (c) velocity vector distribution in the watershed. The distributions indicate how the overland sheet flow, under the influence of dikes and topography, concentrates into channels and flows out of the watershed. The flows over the slopes are sheet runoff, but complex recirculations are developed in the main channel. The water surface is no longer parallel to the bed surface. These flows cannot be represented by KW, DW, and SV models.
Distributions of simulated (a) water depth contours (b) flow (unit discharge) distribution and (c) velocity vectors at t = 54 s for test Case 2C.
This section presents the application of CCHE2D to a sub-watershed of the Howden Lake watershed, an 18 km2 agricultural watershed in the Mississippi River alluvial plain (Figure 10). In this region of low relief, watersheds are configured by farm fields drained by culverts, ditches, and intermittently flowing streams called bayous. During periods between runoff events, the channels contain standing water. The studied sub-watershed was upstream of a gaging station on an intermittently flowing bayou. The average annual precipitation in this region is about 1440 mm. Precipitation occurs as intense thunderstorms or low-intensity rains associated with major frontal movements. The latter type of events may stretch over several days of drizzle and sporadic showers. During growing seasons, channels experience some flow and stage fluctuation due to irrigation withdrawals and return flows.
Location and topography of the Howden Lake watershed. Dashed curve encloses the runoff simulation area, and the dark closed curve is the gaged watershed.
Watershed topography was surveyed by airborne LiDAR with a 1.5 m horizontal resolution. The vertical accuracy was 15.0 cm RMSE or better. The watershed elevation ranges from approximately 43.89–48.99 m. A nearly uniform fine mesh (mesh spacing = 3.76–4.98 m) was generated for the simulation with the ditches and small streams between the plots further refined locally. Cultivated fields are connected to the streams and ditches with drainage culverts, which often convey water from one sub-watershed to another. The locations of culverts in the study watershed were identified in a field survey and incorporated in the numerical mesh.
Soil data were obtained from the Soil Survey Geographic (SSURGO) database [47]. The watershed is covered mostly by soils with high clay content, which is typical of the region [48]. Infiltration is, therefore, negligible and was not considered in the simulation. Precipitation and flow stage data were measured by field instrumentation. Because the stream instrumented with the gage station has complex conditions, it was difficult to collect reliable velocity data during a rain event. Only stage data were available. As a result, the gage station does not have a discharge-stage rating curve. Development of a rating curve using simulations and measured data for this site would be helpful for understanding the hydrologic processes in these watersheds.
Because the Howden Lake watershed is of low-relief, it was often difficult to determine the boundaries between sub-watersheds in field surveys or on topographic contour maps. For example, the runoff from a piece of field may flow in two directions into two sub-watersheds, and the location of the divide line might be identified only from the runoff flow distribution during a simulation. Normally, the outline of a watershed is a given condition for a hydrology study. In this study, the exact boundary outline was not firmly established even after field surveys. A larger area containing the studied watershed was simulated, and the watershed boundary and area were finally defined by the simulated runoff and channel flow patterns. The boundary outline of the studied watershed (Figure 11) contributing to the gage was identified by visually checking the simulated overland flow directions of CCHE2D.
Numerical simulation identified watershed for the gage station. Simulation results in the dashed rectangle area are shown in Figure 12.
In the simulations, the streams and ditches between farming plots were represented using DEM elevations like flat surface areas. No channel networks were prescribed, but the simulated surface runoff flowed logically to the ditches and to the stream channel. No other watershed analysis tools were needed. Although the study results presented later are for this identified watershed, the spatial domain of numerical simulations was several times larger (Figure 10). The northern side of the stream channel had been blocked by farmers, so the overland flow from the watershed entered the stream in the middle and flowed in a southwesterly direction (Figure 11). The water from this identified watershed pasted the gage, while runoff from the region outside this watershed was discharged from the simulation domain via other ditches and streams. The area of this watershed, including cultivated land, drainage ditches and a stream segment, was found to be 973,700 m2. In this area, the topographic elevation ranges from approximately 46.77–47.49 m in one plot and from 47.27 to 48.09 m in another. The mean slope of the fields is 0.0097 and 0.0098, respectively.
Several observed storm events were selected for the model application. To reduce minor losses of water due to evaporation, soil wetting and infiltration, etc., only large rain events were considered. The rainfall event in April 2011 (Table 1) was first used for simulation. Figure 12a shows the detailed ground elevation contour of a small simulation area (dashed rectangle area in Figure 11). The elevation of this area ranges from about 46.8 to about 47.4 m. Figure 12b shows the direction vectors of the runoff near the end of the simulation. Because the water is very shallow, the flow direction is highly affected by the ground topography. Figure 12c and d shows the direction vectors and water depth distribution at the peak time of the rainfall.
Event | Measured rainfall (mm) | Runoff volume* (m3) | z | r | L0 (m) |
---|---|---|---|---|---|
4/27–4/28/2011 | 88.39 | 85,817 | 2.4 | 1.223 | 0.45 |
10/30–11/4/2013 | 53.59 | 52,182 | 1.9 | 4.24 | 0.78 |
11/21–25/2011 | 62.99 | 61,333 | 1.4 | 1.613 | 0.45 |
5/20–24/2013 | 48.77 | 47,483 | 1.0 | 1.436 | 0.59 |
9/25–27/2011 | 52.32 | 50,946 | 1.8 | 5.211 | 0.48 |
Parameters of selected runoff events for numerical simulations.
Computed from the main bulk of the rain event.
Information and simulation results in an area indicated in Figure 11 in a dashed rectangle: (a) bed elevation contours, (b) velocity direction distribution near the end of the simulation, (c) velocity direction, and (d) the water depth distribution at the peak of the April 2011 rainfall.
Although the variation of the bed surface topography is very small, the simulation shows how the runoff is controlled by microtopography (Figure 12a). At the peak time of the rainfall, the overall water depth in this area (Figure 12d) is much deeper, and the flow directions (Figure 12c) are less affected by the local microtopographic features. The flow on the right side of the domain is still sheet runoff under the KW condition; while on the left side, the water depth is more than 0.2 m, and the flow is no longer governed by the KW condition. This model provides the outflow hydrograph as well as the temporal and spatial distribution of the water depth and flow velocity, which can be used for studying soil erosion, agro-pollutant transport, and water quality.
The gage station (Figure 11) recorded the channel water surface elevation at regular time intervals, but velocities were generally too low for accurate measurement, and therefore, water discharge was not measured. In order to better understand the watershed hydrology, a rating curve of the form:
was developed using simulated discharge, in which L is the measured water surface elevation, r and z are parameters, and L0 is the initial water surface elevation prior to a rainfall event. Eq. (11) has two unknown parameters, but there is only one relationship available for determining their values. The total volume of runoff, obtained by numerically integrating Eq. (11) in time, is equal to the rain volume, VR:
in which Li is the measured water surface elevation at the gage station. With Eq. (12) satisfied, values of r and z that best fit the shape of the discharge hydrograph computed using Eq. (11), and that of the numerical simulation, were determined for each event by trial and error.
Attempts were made to fit all simulated curves using a single set of values for r, z and a mean base stage L0, but the result showed unacceptable discrepancies. L0 varied due to antecedent precipitation, downstream discharge control, sedimentation, and water usage between events. The range of L0 for the studied events is 0.33 m (Table 1). Given the complexities of the hydraulic regime in the water body, varying from standing to moving state and with varying downstream controls, variable rating curve parameters are sensible. Event-specific rating curve parameters are not ideal but are useful in a research context.
Manning’s roughness coefficient (n) is a major factor in the determination of watershed runoff characteristics and generally reflects ground cover and management. The event on April 27–28, 2011 was used for initial calibration of Manning’s coefficient. The studied watershed is cultivated with soybeans (Glycine max L. Merr.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), and rice (Oryza sativa L.). The sensitivity of the CCHE2D model in Howden Lake watershed to Manning’s n was examined using a wide range of values from 0.030 to 0.30 m−1/3s. Smaller Manning’s n results in a higher runoff peak discharge and an earlier peak flow arrival time. A visual comparison of discharge hydrographs based on stage measurements and numerical simulation (Figure 13) indicates that n = 0.3 m−1/3s is the most appropriate choice for the overland runoff area because the peak times of these runoff events are consistent. Considering that the depths of the sheet runoff are much smaller than the microtopographic irregularities over the fields, the calibrated n represents not only the bed resistance but also form drags due to the microbed forms, crop residue, and vegetation. This n value agrees with the recent runoff studies [25, 31, 49] in cases of overland flows, including those in the Goodwin Creek Experimental Watershed in Northern Mississippi. There are numerous trees, bushes, and weeds growing along and within the channel, thus, n = 0.16 m−1/3s was used for the channel and kept unchanged for other rain event cases.
Sensitivity of simulated hydrograph to Manning’s coefficient.
The total observed rainfall volume for the April 27–28, 2011 event (Figure 13) was approximately 86,000 m3 (88.32 mm). The total simulated runoff volume is about 80,600 m3 (83.78 mm), which is reasonable because the hydrograph recession limb extended past the simulation termination at 47 h. There were several small rain events that occurred before the event shown in Figure 13, so the runoff volume based on the observed water surface elevation may include recession of the earlier events.
Figure 14 compares the discharge hydrographs of several additional runoff events computed using Eq. (12) and that of the numerical simulations. The identified parameters for these events, r and z, are listed in Table 1. Events 9/2011 and 10/2013 have one major peak, while those of 11/2011 and 5/2013 each have two major peaks. The simulated hydrographs fit well with those computed using Eq. (11). The two rain peaks of the 5/2013 event were separated by about 2 h, but those of the 11/2011 event were separated by 15 h. The runoff of the 5/2013 event showed only one peak because the two rain peaks were very close, and the runoff peaks were superimposed. However, the temporal separation of the two peaks of the 11/2011 event was much longer. Therefore, the superimposed hydrologic response also displayed two peaks. These watershed responses were reproduced by the numerical simulations.
Comparisons of simulated runoff and Eq. (11).
As noted above, the watershed has multiple field ditches that convey runoff into the channel (Figures 10 and 11). Ditch and channel flow were simulated together with the overland sheet runoff. Figure 15 shows the simulated flows in the channel network of the watershed. The contours represent the distribution of the unit flow discharge. The vectors in the ditches and in the stream formed a channel network indicated by the large velocity vectors; those in the runoff area are too small to be seen. The flows in the stream are turbulent when the rainfalls are large. Because no velocity data were acquired, the simulated velocity results in the channel were not validated.
Simulated flow in the network of drainage ditches and the stream in the watershed.
The numerical model CCHE2D was used to model sheet runoff from watersheds, large and complex enough to include both overland and channel flow processes. The model was systematically verified and validated using analytical solutions and experimental data due to steady and unsteady rainfall intensity, and applied to a real world watershed. Good agreement between the analytical solutions, experimental data, and numerical simulations were obtained. For the experimental cases involving complex watershed shapes, the numerical model has the ability to simulate runoff over the slope surfaces and the channel flows.
A numerical scheme was developed to correct the bilinear interpolation of the water surface elevation from its solutions at the staggered cell centers to the collocation nodes. The scheme was necessary and effective for obtaining good sheet runoff simulation results in watersheds with irregular topography. One would have to smooth the ground topography if a model requires the interpolation of water surface solution under this condition.
The model was applied to an agricultural watershed in the Mississippi River alluvial plain. It was useful to identify the boundary of the monitored watershed and develop the rating curve at the gage station of the watershed. Several significant runoff events were selected for simulation. Each of the simulated runoff hydrographs and the rating curves agreed well with those observed in the field. The sensitivity of the model to overland sheet flow friction was studied. An increase in the bed surface friction coefficient significantly diminishes the peak of runoff discharge, delaying its time of arrival. Values of n = 0.2–0.3 m−1/3s for overland flow were found to be adequate to best fit the numerical simulations and the observed data in the studied watershed. With a high-resolution mesh, the model can predict the complex surface runoff pattern over the agricultural land. Ditch and stream channels in the domain are a connected channel network. The model is able to simulate sheet runoff, turbulent channel flow, and their transitions seamlessly. The simulated hydrological processes for several storm events fit well to those observed at the gage station. The capability would be useful for studies related to soil erosion and agro-pollutant transport. The model is currently used for watershed applications without considering interception, evapotranspiration, and infiltration. Additional work is needed to further extend the research in these areas.
This work is supported in part by USDA Agriculture Research Service under the Research Project No. 6060-13000-025-00D (NCCHE) monitored by the USDA-ARS National Sedimentation Laboratory (NSL). Support is also in part by the Southeast Region Research Initiative (SERRI) project and the University of Mississippi (UM).
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