Parameters of selected runoff events for numerical simulations.
\r\n\t
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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"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"51702",title:"Empowering Diabetes Patient with Mobile Health Technologies",doi:"10.5772/64620",slug:"empowering-diabetes-patient-with-mobile-health-technologies",body:'\nAging and chronic conditions represent a huge burden on the health care budgets. Moreover, in the future this burden will only increase [1]. At the same time, the patients require a better service, while there are fewer health care professionals and lesser resources. The states currently act mainly in two dimensions. On the one hand, they are strengthening their efforts on prevention. They develop or update existing programs, which promote healthy and active life styles. On the other hand, they are transforming the existing health care. Namely, the health care systems as we know them were developed to treat the acute diseases. However, the chronic diseases spent more than 70% of the health care budgets [2, 3]. The prevention programs can prolong the healthy period of each individual, but some chronic diseases, such as diabetes, cannot eliminate. Therefore, there is a need for transformation of the existing health care whose goals are better health results, better quality of service and quality of patient life, and economic feasibility. This transformation is as follows [2]:
from the health care model centered on acute medical care to the model adopted to the needs of chronic patients,
from reactive model to proactive model to cure, care for and prevent based on risk factors,
from passive patients to a model with a patient in the center, actively managing his disease,
from a fragmented model with lack of coordination to a model enabling continuity of care, and
from activities primarily in acute hospitals to activities in more suitable environments, such as homes.
The key enablers for such transformation are patient empowerment, use of information and communications technology (ICT), integrated care, and adopted business models.
\nThis chapter explores the concept of empowerment of diabetes patients by presenting current and future possibilities of mobile health technology.
\nNumber of diabetes patients around the world has reached 415 million and it is predicted to climb up to 642 million by the year 2040. Diabetes can also be linked to around 5 million deaths each year and it is associated with high financial burden, since health spending on diabetes accounts for around 12% of total health expenditure worldwide. The costs include increase use of health services as well as loss of productivity or disability and are estimated to range from 673 billion USD to 1197 billion USD [4]. With such troubling predictions, we are obligated to look for new methods to facilitate patient care. Introduction of new methods should be done with the understanding that more than 95% of diabetes care is done by the patients themselves [5]. This is just one of several reasons why diabetes patients are excellent candidates for managing their disease with the help of mobile health technologies (mHealth) and why this may improve many aspects of personal and public health.
\nDiabetes is medically defined by the following criteria: patient fasting plasma glucose level ≥126 mg/dl (7.0 mmol/l) or HbA1c (glycated hemoglobin) ≥6.5% or patient plasma glucose level 2-h after OGTT (oral glucose tolerance test) ≥200 mg/dl (11.1 mmol/l) or patient random plasma glucose level ≥200 mg/dl (11.1 mmol/l) [6]. After the diagnosis, diabetes patients need to endure life-long management of their disease, which include medications and significant lifestyle changes. Disease progress should be monitored with the help of health care professionals in order to ensure prevention and quick diagnosis of long-term complications of hyperglycemia. Most commonly seen diabetes complications are retinopathy with a potential loss of vision, nephropathy that can result in renal failure, peripheral neuropathy leading to foot ulcers, Charcot foot and amputation, autonomic neuropathy that can causes gastrointestinal, genitourinary, and cardiovascular symptoms. The patients with a chronically elevated glucose level have high incidence of atherosclerotic vascular changes, which cause development of peripheral arterial disease, cerebrovascular, and cardiovascular complications [6, 7]. Number of complications can be reduced, if patients maintain good glycemic control. Every patient who reduces HbA1c level for 1% decreases the risk of microvascular complication for 37% and the risk for diabetes related deaths for 21% [8].
\nWorld Health Organization defines adherence as an extent to which a person’s behavior: taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider [9]. One of the key challenges in diabetes management is a lack of adherence to medication regime and lack of lifestyle changes. Adherence to oral hypoglycemic agents is between 36 and 93% for the first 9–24 months of treatment and adherence to insulin treatment for type 2 diabetes patients is between 62 and 64%. Patients even less complain when it comes to dietary and other lifestyle recommendations [10, 11]. Regardless of the type of treatment, it was proven that introduction of self-monitoring of blood glucose level is associated with better glycemic control [12], but there are still around 29% patients treated with insulin, 65% patients treated with oral hypoglycemic agents, and 80% patients treated with dietary restriction, who do not practice self-monitoring or they do it less than once a month [13]. Poor adherence is also a public health issue. For every 10% increase in adherence, there is 8.6% decrease in annual health care cost. Furthermore, there is evidently a link between number of hospitalizations and adherence to medication therapy, which is reduced by 23.3% when adherence had increased from 50 to 100%. Even more evident reduction of 46.2% is seen in number of emergency department visits. Both events, i.e., number of hospitalization and emergency department visits are associated with lower costs [14, 15]. When dealing with nonadherence, it is valuable to consider various reasons why this phenomenon occurs. It can be attributed to demographic factors, psychological factors, social factors, medical system factors, disease and treatment characteristic, but mostly it is a result of combinations of multiple factors [11, 16]. For example, glycemic control and treatment outcomes are less promising among racial minorities, men and people with depression or anxiety disorders [17, 18]. Those differences emphasize the importance of individualized and patient-centered care.
\nPatient empowerment is a paradigm of transferring the responsibility of patient’s health care in the hands of patients. Such paradigm is in contradiction with the traditional health care where the care was in the hands of the medical staff.
\nCompliance and adherence are concepts that arise from an idea of patient submitting and agreeing with health professional, who acts as an authority, whereas empowerment promotes patient active involvement and control over their own health [19].
\nThe empowerment can be achieved through education, self-management, and shared decision making.
\nTo obtain satisfactory outcome, there is more to be done than just encourage patients to self-manage their chronic disease. Patients need to be educated about their disease, motivated, provided with patient-centered care, which means that self-management plan needs to be tailored to fit patient priorities, goals, resources, culture, and lifestyle [19].
\nThe education typically occurs at the clinic by the doctor and/or nurse. At Stanford School of Medicine, a Chronic Disease Self-Management Program (CDSMP) [20] was developed which empowers patients through a series of workshops in community settings. Its success is evident in Denmark, which decided to implement it in its health care. As a result, Danish patients are the most empowered [21]. This program was also used in EU project EMPOWER [22].
\nWhile face-to-face patient education has positive effects, not much work has been done to evaluate the effect of virtual education. Moreover, patient knowledge is a necessary, but not a sufficient factor for change in the self-care behavior [23]. Patients require self-management support. It can be achieved through face-to-face interaction, through self- and telemonitoring and virtually [24–27].
\nCollaborative decision making represents collaboration and exchange of knowledge among patients, formal and informal care givers. Because it currently occurs in face-to-face meetings in most cases, there is no evidence for the effects of virtual collaborative decision making.
\nThe goal of such efforts is to shape individuals that make rational health care decisions regarding their health and wellness, are less depended on health care service and contribute to more cost-effective use of health care resources [28].
\nFinally, in the diabetes treatment, notions of compliance and adherence were replaced with the concept of patient empowerment [29]. With Diabetes Empowerment Scale, significant correlation between level of empowerment and better medication adherence, extensive diabetes knowledge, improved general diet and exercise, blood glucose control, and foot care, can be established [30]. In diabetes management, mobile health technologies are already offering different means for introduction of the concept of empowerment into patients’ everyday life.
\nPatients with chronic diseases need to monitor and record several biometric health parameters. For this purpose, in current health care system, they mainly note observations on a paper, although most devices in use enable storage of such biometric measurements. While there are issues with reading noted values from the papers or even with losing the papers, there are issues with transferring the stored data on the devices to general practitioners (GPs) or specialists. Moreover, the doctors would appreciate to monitor more parameters that are relevant for a holistic care.
\nOn the market, there are devices that can automatically measure and transfer the measurements to the smartphones and dedicated servers: Fitbit wristlets measure user activity and sleep periods [31]; BitBite measures user nutrition habits [32]; iHealth [33], VitaDock [34], VPD [35], and Abbott [36] products measure temperature, blood pressure, pulse, blood oxygen saturation, and glucose level. The current trend is geared toward wearables and gadgets that help diagnose very specific diseases, such as peripheral neuropathy [37] and retinopathy [38], toward gadgets that can measure several parameters [39] and toward integration of functionalities from gadgets to smartphone applications, such as in Google Fit [40] and in Moves [41]. These applications perform comparably well as standalone devices [42].
\nFurthermore, there are numerous smartphone applications, working with manual data input or data from previously mentioned devices, that assist users in managing their health [43, 44] or diabetes in particular [45, 46]. Such applications do not only display the status of health parameters, but also provide personalized recommendations based on the input data. They mostly encourage users to change their behavior [47].
\nHowever, wearable devices and smartphone applications are only facilitators and not drivers of patient empowerment. The design of engagement strategies is more important for successful use and potential health benefits of these devices than the features of technology [48].
\nSeveral pilots have been conducted, suggesting positive effects on health and diabetes care, and a need for 24/7 support [49–51]. However, in use, there are mainly only solutions that enable patients to informatively monitor their health status. Solutions that would be used as a part of the general health care service are in the stage of pilots and are only very rarely deployed as part of the standard practice.
\nMobile health is defined as a use of mobile communication devices, which include mobile phones, patient monitoring devices, tablets, personal digital assistants, and other wireless devices for health services, and information [52]. Currently, growing use of those devices can be seen in practically every part of the world. Number of mobile phone owners in the United States has reached 92% of a population and even number of smartphone owners has grown to 68%, while 45% of people own a tablet computer. Desktop or laptop computers were bought by 73% of Americans [53]. Surprisingly, similar rise in mobile technology use is also recorded among people in developing countries, where average share of people with mobile phones is around 83% [54]. With a vast majority of world population having an access to some type of mobile technology, this can certainly become a widely used to deliver deferent health care solutions to people. Role of mHealth is very broad and includes education and rising awareness, remote data collection, remote monitoring, communication with health care workers, support with diagnostic and treatment, and tracking diseases and epidemic outbreaks [55]. Most of these tasks are already performed by various mHealth applications for diabetes self-management.
\nThere are different classifications of diabetes app features and in this section we present some of them.
\nReview of accessible diabetes applications has shown that they mostly focus on blood glucose monitoring, medications, physical exercise, and diet management, while they also include other features such as education, communication, weight or BMI and blood pressure tracking, integration with public health records, decision support systems, and social networking. Blood glucose monitoring is available in all reviewed applications, while other features are more rarely present. Educational tools are brought to use in just 18% of applications and only around 30% of applications offer means to monitor weight, blood pressure, and physical exercise [56]. Still, among all medical conditions, diabetes with weight control represents the most addressed medical issue by mHealth applications in mHealth research [57].
\nIn a recent systematic review, 53% of apps offered documentation function (recording and displaying data), 17.8% analysis function (the possibility to analyze the recorded data and to graphically display the results), 11.4% reminder function (reminds the user of its periodic, predefined medication), 34.5% of apps offered an information function (inform about the illness). Data forwarding/communication function (opportunity to send the recorded data) was present in 31.1% of apps. Surprisingly, only 8.8% of the diabetes apps provided an advisory function (use of the recorded data to create individualized advice) or any other kind of therapeutic support. Besides, the previously described functions, 14.5% of the apps included suggestions for recipes suitable for the needs of diabetics. The majority of apps, i.e., 54.1% were limited to only one function, while there were only 28.2% with two and 17.7% with three and more functions [58]. In another classification, features of apps were grouped into three classes on the basis of prevalence. In class A, there were insulin and medication management, communication and patient monitoring by primary care providers, diet management, and physical activity. Class B included weight management, blood pressure management, and connection to personal health record (PHR). In class C, there were education, social media, and alerts. Class A comprised four major features and class B had significantly higher prevalence than class C [59].
\nmHealth research platform Few Touch Application (FTA) was developed to support management of diabetes. Applications and studies based on FTA allow automatic monitoring of blood glucose information, receiving short message service information about type 1 diabetes, mobile diary for type 2 diabetes, sharing diaries with doctor or nurse, mobile diary for type 1 diabetes, a food picture data, transfer of physical activity data on mobile phone, nutrition advices, context sensitivity, and modeling of blood glucose. Performance of each of the 10 FTA-based apps was analyzed and the conclusion was that all FTA apps are beneficial [60].
\nIn the next sections, we will present 11 problems of diabetes disease that can be addressed by mHealth.
\nMonitoring of blood glucose level is a base function of all available mHealth diabetes applications, because even without technology interventions self-monitoring of blood glucose (SMBG) is still an integral component of daily diabetes management, especially for insulin-treated patients [56, 61]. Unstructured SMBG is not recommended and does not produce the same results as structured SMBG, which links to behavioral changes, optimization of therapy, and improved clinical outcome. An example of such structured SMBG is a 7-point glucose profile, where blood glucose is measured every day of the week preprandially, postprandially, and at bedtime [62]. A pilot of such structured SMBG demonstrated a reduction in HbA1c levels up to 1.2% in 12 months [63]. To help patients in keeping up with structured SMBG, mHealth offers personal goal setting and various types of reminders [64]. Most commercially available devices for glucose monitoring enable patients to store and follow their blood glucose pattern. For this, patients need to transfer measurements to a computer through an USB cable or to a mobile phone with a direct connection through Bluetooth or Wi-Fi. Even more accurate glucose profile can be obtained with a use of continuous glucose monitor [61]. Regardless of a type of a mHealth intervention used, there is evidence in its positive impacts on reduction of HbA1c values by a mean of 0.5% over 6 months [65]. However, a review of 24 papers has shown that the effectiveness of mHealth interventions (blood glucose reading and transmission to server) measured in HbA1c value was inconsistent for both types of diabetes [66].
\nFor education, most of the technologies used for nutrition therapy rely on videoconferencing, while for food tracking and food selection various mobile apps [47]. Distinct features are being formed among behavior mHealth modalities. Food intake can be recorded to determine the quantity of calories consumed each day and the targeted quantity of calories automatically adjusted, based on patient’s daily physical activities [67]. More widely adopted are different nutrition databases containing a rage of food items, including different brands and restaurant food, and real-time calculations of consumed calories. Similarly, there exists also a possibility to scan the barcode of brand names to see the nutrient content [47]. Growing number of possibilities provide a new generation of mHealth devices also known as wearables. For example, in diet self-management wrist monitors and electronic utensils can be used to track the amount and speed of bites, but such devices are practically not used yet [68]. Furthermore, mHealth may enable calories calculations with recognition of food from photography in free-living conditions. Even more promising are mobile applications that suggest appropriate meal based on preprandial blood glucose reading, which can facilitate patients’ educated decision making [64].
\nSixty nine percent of diabetes patients describe their exercise practices as nonexistent or less than recommended level [69]. It is recommended that adult patient with diabetes perform at least 150 min of moderate-intensity aerobic physical activity per week, spread over at least 3 days and with no more than two consecutive days without exercise [70]. For patients to monitor the extent of their daily physical activity, mHealth offers solutions in a form of body-worn activity monitors. Most easily accessible are pedometers, but besides number of steps taken they do not measure other forms of physical activity [64]. Meanwhile, accelerometers with a combination of gyroscope can record wider range of movements and accuracy of recordings is not dependent on person’s body position [71]. When tested, people with access to fully automated system performed on average for 2 h and 18 min per week more of physical activity than people without it [72]. Wearable sensors still need to be complemented with education, planning, and feedback tools to successfully promote physical activity. Effectiveness of mHealth intervention was shown in improved daily number of steps, which was done by setting an achievable goal, providing real-time feedback about the amount of burned calories, and showing recorded progress. This raised the number of steps by 22% in 8 weeks [73].
\nIt was observed that insufficient number of currently evaluable mHealth applications incorporate evidence-based behavior change techniques. This is especially true for techniques, such as relapse prevention, teaching to use props, time management, and agreement to form behavior contract [74].
\nObesity should be diagnosed according to body mass index (BMI). BMI classes are normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), obesity class I (30–34.9 kg/m2), obesity class II (35–39.9 kg/m2), and obesity class III (≥40 kg/m2). For Southeast Asians and Asian Indians, lower BMI cut-points may be appropriate. Lifestyle modifications including behavioral changes, reduced calorie diets, and appropriately prescribed physical activity should be implemented as the cornerstone of obesity management [75]. Weight loss can be achieved with 500–750 kcal/day reduction that means intake of 1200–1800 kcal/day depending on sex and baseline body weight [76]. Raised BMI, i.e., above 25 kg/m2, is seen in more than 75% of diabetes patients [77]. Patients with high BMI and diabetes are significantly more likely to have poor glycemic control [78]. Overweight individuals with diabetes are encouraged to lose at least 5–10% of their weight, because this importantly reduces most cardiovascular risk factors, but it is worth mentioning that lager weight loss (10–15%) has even greater benefits [79]. Reviewed mHealth applications offer means to help achieve this recommendation by monitoring and facilitating physical activity (41% of the applications) and by improving users’ diet (68% of the applications) [56]. An evaluation of 137 diabetes apps showed, that only 39% of them offered weight tracking [59]. Self-monitoring of weight and of body composition by using weight scales can now be accomplished wirelessly with mHealth apps or computer applications. This minimizes the burden on the user, while it also minimizes the error in data transcription. Tracked weight and fat mass can be graphically analyzed by the patient or health care practitioner [47].
\nSMS interventions were investigated to promote change in diet and physical activity. Small and short randomized controlled trials proved significant weight loss, while larger and longer studies showed no statistical significance [80].
\nResearchers investigated dietary self-monitoring-based electronic interventions using personal digital assistants (PDAs), electronic portable devices that share some of the features of mobile phones. PDA in the study was equipped with dietary and exercise software with and without feedback message. Patients were enrolled in three groups: PDA alone, PDA with feedback (feedback algorithm that provided daily messages tailored to their entries and provided positive reinforcement and guidance for goal attainment), and paper diary/record. All participants had statistically significant weight loss, but PDA group combined with feedback had the highest proportion of participants achieving greater than 5% weight loss in six months [81].
\nStudies incorporating podcasts compared to podcasts that included prompting by mobile app and interaction with study counselors and other participants on Twitter, did not show enhanced weight loss in the latter group [82].
\nInterventions delivered by smartphone app, website, or paper diary were also compared. App incorporated goal settings, self-monitoring of diet and activity, and feedback via weekly text message. The website group used commercially available slimming website. Trial retention, adherence, and achieved weight loss were significantly higher in the smartphone app group [80].
\nElevated blood pressure is a common condition coexisting with diabetes and it is a clear independent risk factor for the cardiovascular complications. To reduce the risk, blood pressure should be routinely monitored and maintained at a targeted level. Recommended systolic pressure for diabetes patient is <140 mmHg and diastolic pressure <90 mmHg. Home-monitoring is greatly encouraged, because it is a way to exclude white coat hypertension and because research suggested better correlation between at home measurements and cardiovascular risk than office measurements [83]. Among reviewed diabetes applications, 23% of them currently offer means of self-monitoring of blood pressure [56]. Monitoring blood pressure with the help of mHealth program may detect hypertension patterns that would otherwise gone unreported [22]. Fully automatized blood pressure readings, which are immediately stored in mHealth device and send to a health care provider, enabled to 51% of diabetes patient to reach the targeted blood pressure level. This is a significant improvement compared to 37.5% of general diabetes population that succeed in lowering their blood pressure. Such improvement was achieved also due to by inclusion of daily reminders, alerts in a case of concern-reassign measurement and linkage to patient support system [84]. Home-monitoring alone does not produce the same results, proving telemedicine equal, but not more effective than standard approach, so this needs to be taken into consideration for future mHealth design [85].
\nPreviously, already discussed lack of adherence to pharmacological intervention in diabetes patients is an issue addressed by approximately 76% of reviewed mHealth applications [56]. One of the commonly reported barriers is patient forgetfulness, when it comes to medication intake regime. Mobile health technologies can offer different solution to this problem. Daily automatic electronic or text-message reminders may improve medication intake [86]. Those reminders can be upgraded by the use of real-time medication monitoring, which is possible with a use of electronic medication dispenser that records date and time of each opening. Consequentially, alert is only send in a case of forgotten medication dose. A trail confirmed that a baseline adherence of around 62% improved to 79.5% adherence after 1 year of use. Long-term effectiveness of this mHealth method peaked at 80.4% medication adherence, whereas control group adherence remained in a range of 68.4%. Majority of patients that used real-time medication monitoring also agreed that this method supported higher awareness of their medication use and reported positive experience with receiving SMS reminders [87, 88]. Considerable amount of patient, who tested real-time monitoring devices, were glad that physician knew if they took their medication and were reassured by technology supported communication [89].
\nDiabetes education is a key element in patient care. To reassure adequate results, effective education strategies can be found in National Standards for Diabetes Self-Management Education and are worth applying to mHealth methods [90]. Even limited amount of education can result in improved weight control and potentially reduced cardiovascular risk [91]. Initial comparisons between in person diabetes education and education administrated through telemedicine already demonstrated a feasibility and equal effectiveness of technology supported methods [92]. Most diabetes self-management applications do not integrate educational information. When available, such information is often generic and is not personalized to the individual patient. This is more prominent in commercial applications [56]. Education and personalized feedback are still underdeveloped features, included in less than one third of reviewed mHealth applications. Only 20% of reviewed applications had an education module, and only 26% of these met the criteria for personalized education or feedback. Task of personalizing rapidly growing number of information is challenging, but it may be largely beneficial for diabetes patient [59]. Most widely used mHealth method for diabetes education is SMS. Meta-analysis of current findings has shown that mobile SMS education can improve glycemic control. The glycemic control is even better if diabetes education is done by a combination of SMS and internet methods, i.e., 86% effect in comparison with 44% that is achieved by SMS alone [93]. Positive results of e-mail and SMS education can also be seen in improved quality of life [94].
\nNumerous applications are available helping healthy people or people with risk factors to assess their risk for developing diabetes type 2 in the future. Only a few of these apps disclose the name of the risk calculator used for assessing the risk of diabetes, therefore the quality of their calculations is questionable [95].
\nDiabetic retinopathy represents most frequent cause of newly occurring adult blindness. Incidence of diabetic retinopathy is highly depended on duration of diabetes itself. Among population with type 2 diabetes cumulative incidence after 4 years is estimated to be 26%, 38.1–41.0% after 6 years and 66% after 10 years [96]. Comprehensive eye examination should be performed after diabetes diagnosis and repeated every 2 years, if there are no visible changes, or annually, if initial retinopathy changes are already present [97]. To keep up with this requirement even with patient in remote and isolated areas, low cost smartphone-based intelligent system integrated with microscopic lens was developed and tested. System detects retinal abnormalities by a method of comparison with medical image database. Early testing has promisingly shown more than 87% accuracy rate in retinal disease detection [98].
\nDiabetic foot is a main cause for nontraumatic lower limb amputation and precedes 85% of the cases. Approximately 15% of diabetes patients will develop diabetic foot ulcers in their lifetime [99]. It is recommended for all diabetes patients to perform annual extensive foot examination to identify risk factors predictive of foot ulcers. Patient should be screened for signs of peripheral neuropathy and peripheral vascular disease, simultaneously paying attention to identify other risk factors such as foot deformities, past foot ulcers, visual impairment, and cigarette smoking [97]. Currently, researched mHealth method to facilitate foot care is using high quality photography to diagnose foot ulcers and preulcerative lesions. Trained professional can diagnose visible diabetic foot changes in valid and reliable manner, which implies methods usability as a monitoring tool in home environment [100]. Originally developed was a method for wound area measurement. The wound margins are recognized with the help of smartphone. First, the wound contour is copied on the foil. The area is then measured with smartphone app and compared with previous measurements [101].
\nPatients with diabetes should regularly be assessed for their psychosocial well-being. Care should include assessment of their attitudes about the illness and prognosis, mood changes, satisfaction with quality of life, financial, social and emotional conditions, and possible psychiatric disorders (depression, distress, anxiety, eating disorders, dementia). Screening is recommended for depression and cognitive impairment for older than 65 [76].
\nTelemedicine study researching depression and glycemic control in elderly showed a weak relationship between depression and HbA1C, but depression did not prospectively predict change in glycemic control [102]. In another study, web-based depression treatment for diabetics using cognitive behavior therapy was effective in reducing depressive symptoms, diabetes-specific emotional distress, while it had no benefit on glycemic control [103].
\nTelephone-based cognitive assessment (TBCA) was previously performed by conventional telephone. Because of better understanding of cognitive impairment, there is a possibility of more accurate TBCA. It needs more complex features of telephone which are easily achieved with the use of smartphones [104].
\nPHR is an internet-based set of tools that allows people to access and coordinate their lifelong health information and make appropriate parts of it available to those who need it [105]. Electronic medical record (EMR) is an electronic record with documents of patient’s treatment in a clinic. Electronic health record (EHR) is a summary of individual’s lifetime health status and care. Terms EMR and EHR are often used interchangeably [106].
\nOverall, 21% of commercial applications support synchronization of data with PHRs. Half of reviewed studies have integrated PHR with EMR and provide both patient and physician Web portal, whereas other included either a patient view or a clinician view of the EMR [56].
\nSocial media integration is also emerging function of diabetes apps. It can help patients find similar users and communities in a dynamic fashion. But the majority of apps only provides links to their groups in well-known social networking sites such as Facebook and Twitter or maybe provides an account to a forum [59].
\nIn a study of feasibility and acceptability of PDA-based (personal digital assistant-based) dietary self-monitoring of diabetes patients at the time of advent of the first smartphone high percentage of participants reported that they found PDA-monitoring useful, that the app was easy to use and that feedback was easily understandable [107]. In another study with PDAs, several limitations were found that may have contributed to perceived frustration including usability, data loss/errors (especially mistyped numbers) and time constraint (time consuming and tedious handling) [108]. A new study revealed that the perceived additional benefit and the perceived ease of use had the strongest impact on acceptance of diabetes smartphone technology by patients 50 or older. Less important factors were previous experiences, health status, support, confidence in own technical knowledge, perceived data security, and fault tolerance. The target group of diabetes patients aged 50 or older is a rather heterogeneous one and their needs are highly heterogeneous due to differences in previous knowledge, age, type of diabetes, and therapy. For that reason, it is impossible to address the needs of all diabetes patients adequately with one diabetes app in order to gain an additional benefit. Therefore, the contents of a helpful diabetes app should be individually adaptable [109]. There is a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. The first attempts were made to create personalized, contextualized, actionable suggestions automatically from self-tracked information [110].
\nTen percent of all available apps in 2013 were evaluated within usability evaluation by three experts considering the special requirements of diabetes patients age 50 years or older. Four main criteria were evaluated, being ”comprehensibility,” “presentation,” “usability,” and “general characteristics.” The main criteria, “comprehensibility,” rated best. In particular, the elderly benefit from easy, understandable semantics and easy, comprehensible, and interpretable images and depictions. It can lower inhibition thresholds, especially during the first time of use. The same is true for the influence of “easily understandable feedback” and an “intuitive usability” (main criterion “usability”). However, these two subcriteria performed worse within evaluation. Test of accessibility features indicated a very good operability of the screen readers. The criterion “fault tolerance” rated worst. Especially, inexperienced (elderly) users often have difficulties with inputting data. Some errors are unrecoverable or even cause the application to shut down [58].
\nWe have to say some words about type 1 diabetes mellitus. We described until now mobile health interventions irrespective of the diabetes type. Because type 2 diabetes is much more widespread, studies included mostly or exclusively type 2 diabetes patients.
\nYoung people with type 1 diabetes have many ideas and can help improve services and their own health-related quality of life. However, their lifestyle and their use of Web and smartphones to cope the disease are not well researched [111].
\nA systematic review was carried out, focused on the ability of mobile health tools to grant patients with type 1 diabetes greater glycemic control. The tools investigated took a variety of forms and provided a number of different services to a diabetic patient. The indicator demonstrating the intervention to be successful was HbA1c and it was decreased in majority of studies, but not all values were statistically significant. In addition, prospective studies were predominantly used instead of randomized controlled trials [112].
\nQualitative interviewing and exploring how young people with diabetes type 1 make use of technology in their lives and in relation to their condition and treatment, was made. On that basis, many suggestions to develop apps were found including issues such as alcohol and diabetes, hypoglycemia and diabetes, illness and diabetes, and Twitter use for diabetics. All listed suggestions were taken forward for prototyping, with alcohol and diabetes being developed as clinically approved app. There were also many other issues suggested, that were not prototyped [111].
\nIn UK, a competition for teams including at least one young person with diabetes to develop an app, that might help this group of patients in preparation for their diabetes appointments, was conducted. After the development, other young people with diabetes were invited to choose, test and review new apps. The competition proved successful, showing the app designers and developers a need to develop a range of new functions [113].
\nInsulin calculator apps for patients with diabetes were scrutinized, because self-medication errors are recognized source of avoidable harm. Users are at risk of both catastrophic overdose and more subtle—suboptimal glucose control. In a research, considering input, only 59% calculators included clinical disclaimer and only 30% documented the calculation formula. 91% lacked numeric input validation, problems were also with calculation with missing values, ambiguous terminology, even with numeric precision. Considering output, 67% of calculators carries a risk of inappropriate output dose recommendation that either violated basic clinical assumptions or did not match a stated formula or correctly update in response to changing user input. It is advised, that health care professionals should exercise substantial caution in recommending unregulated dose calculators to patients and take care for proper education about possible threats [114].
\nLittle attention has been paid to physicians’ intentions to adopt mobile diabetes monitoring technology. Japan study showed that overall quality (system quality, information quality, and service quality) assessment does affect doctors’ intention to use this technology, but only indirectly through perceived value. Net benefits (both ubiquitous control and health improvement) seem to be also a strong driver in both a direct and indirect manner [115].
\nCombined smartphone-based logging of different health parameters (e.g., blood sugar logging, insulin dose logging, bread unit logging, activity logging) can of course help doctor (diabetologist) in solving glycemic control problems. With these data, diabetologist can make individualized recommendations for every patient [116].
\nIt was establish that standard, technology unsupported, diabetes interventions are cost-effective. Effective base therapy typically costs up to $50,000 per each quality adjusted life year gained [117]. Activities that focus on intensive lifestyle changes, universal opportunistic screening for undiagnosed type 2 diabetes, intensive glycemic control, annual screening for diabetic retinopathy were proven to cost ≤$25,000 per life year gained or per quality-adjusted life year, what categorizes them as very cost-effective [118]. If there is to be expected that mHealth interventions will be introduced in everyday diabetes patients’ care, they need to show themselves to be more cost-effective than standard treatment. In other words, they should cost less than an amount that we are already willing to pay for diabetes treatment. Current diabetes cost-effectiveness studies are sparse, but promising. Findings of one such study demonstrated annual cost decrease by using mHealth glucose meter with a support of disease management call center that outweighed higher program costs by several-folds. Implementation of technology supported care meant $50 per patient per month higher expenses than standard care, however in a year’s time it was possible to register $3384 cost decrease compared with an increase of $282 among those with previously established\t course of treatment [119]. Immediate cost reduction after implementation of telehealth is primarily due to the absence of transportation costs per patient visit to outpatient clinic and productivity savings, because of eliminated need for frequent work absences. More substantial medical savings can be seen with a long term use [120]. Furthermore, it is reasonable to predict lowering of medical cost with growing number of diabetes patient included in automated telephone-linked interventions. For illustration, delivery of mHealth solution to 10,000 patients instead of 1000 can reduce expenses from £444 per patient to £301 [121]. In other economic evaluations, new management methods were determine to be associated with higher cost per quality adjusted life year and not cost-effective addition to standard care. This economic model argues that even with 80% reduction in equipment cost and full utilization of the telehealth service the probability of cost-effectiveness would only reach 61% at the £30,000 threshold of willingness to pay [122]. Still, individual research results are too heterogeneous to enable extraction of significant meaning regarding a possible medical expenditure reduction with continuous use of mHealth solution [123].
\nClinical decision support systems are active knowledge systems using two or more items of patient’s data to generate case specific advice. It is in majority of cases standalone technology or integrated in provider’s information system and is used by doctors or other medical staff [124]. Many mobile decision support software apps for smartphones are now available for diabetes and are intended to assist patients to make decisions for themselves in real time without having to contact their health care provider. For many minute-to-minute decisions, the questions are not sufficiently significant to warrant contacting a health care provider and there is insufficient time to wait for a reply. Mobile decision support apps can be helpful to assist patients to identify data patterns and make it easier for them to come to an immediate decision on their own [52]. With the advent of minimally invasive subcutaneous continuous glucose monitoring increasing academic and industrial effort has been focused on the development of SC-SC (subcutaneous-subcutaneous) artificial pancreas systems, using continuous glucose monitoring coupled with continuous subcutaneous insulin infusion. Next step is use of mobile system as user interface which is controlled by the patient. The interface is based on patient’s commercial mobile phone [125].
\nUse of mobile health technology for empowerment of patients with diabetes is an emerging way to improve their health and wellbeing. It can address almost every problem of diabetic patient. But approaches are diverse and every app has its own properties and functionalities. There are many apps on the market, but only few of them are adequately certified by health care authorities. Therefore, their quality is questionable. But many studies showed, mHealth is effective and even cost-effective, though more research is needed.
\nThe future applications should be more personally oriented, improved regarding usability and accessibility, and based on accepted clinical guidelines.
\nNumerical 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).
Authors are listed below with their open access chapters linked via author name:
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\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
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\n\nFrede Blaabjerg 2015-18
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