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 چکیده
1 مقدمه
2 مواد و روشها
1 2 توصیف منطقه
2 2 نمونه گیری از آب باران و جمع آوری اطلاعات
3 2 توصیف مدل و گنجایش LID
4 2 محاسبه مدل و تجزیه حساسیت
5 2 بهینه سازی اندازه LID
1 5 2 میزان بارش نشانگر بهینه سازی اندازه LID
2 5 2 هدف بهینه سازی
3 نتایج و بحث
1 3 کنترل آب باران
2 3 محاسبه – اعتبارسنجی و تحلیل حساسیت
3 3 رویداد بارشی انتخاب شده
4 3 اندازه LID – کاهش SS برای الگوی بارش
5 3 تصمیم برای اندازه بهینه LID با در نظر گرفتن MFF
4 نتیجه گیری

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در این تحقیق ،روش جدیدی را برای مطرح کردن اندازه بهینه LID معرفی کردیم که با مدلسازی عددی و نظارت فشرده بر جریان آب باران در ارتباط است.بادرنظر گرفتن MMFn،LID برای کاهش FFE درآب های ورودی طراحی شده اند.این روش برای بهینه سازی اندازه انواع مختلف LID در یک منطقه تجاری در کره بررسی شده است.یافته های اصلی می توانند به صورت زیر لیست شوند:
1- SWMM ترکیب شده با MATLAB تجزیه و تحلیل حساسیت و محاسبات اتوماتیک را با استفاده از ابزار تحقیقی الگو انجام می دهد و بیانگر قابلیت شبیه سازی میزان بارش-جریان آب باران و جامدات معلق در منطقه تحقیق می باشد.
2- ما با در نظر گرفتن IETD ، یک رویداد نشانگر میزان بارش باران- الگوی بارش مشاهده شده و منحنی هاف برای منطقه داده شده تولید کردیم .مقدار IETD(تعریف زمان در داخل رویداد) برابر 17 ساعت ، میانگین بارش 32.4 میلی متر و میانگین دوره بارش 8.6 ساعت است.
3- شبیه سازی swmm بیان می کند که EMC در واکنش به اندازه های مختلف LID بسیار متغییر است که دلالت بر بهینه سازی اندازه LID دارد.
4- اندازه های بهینه LID بر اساس حجم اولیه اب مشخص شده اند که شاخصی برای تعیین کمیت FFE می باشد که در قالب عمق اب از 1.2 تا 3 میلی متر مرتب شده است.
در این تحقیق LID با استفاده از روش مدلسازی و با کنترل اب باران طراحی می شود که تلاشی است برای تقویت کیفیت اب و نه کاهش سیلاب .فرایند بهینه سازی با واکنش های اب شناسی مختلف و دینامیک های مختلف کیفیت اب برای 6 LID مختلف همراه است.امیدواریم که روش مطرح شده بتواند برای استراتژی های LIDموثر باشد.

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Introduction Currently, continued urbanization and development result in an increase of impervious area and surface runoff. This also increases the potential for floods and can cause severe water quality degradation by increasing associated pollutants, including suspended solids, fine particles, heavy metals, nutrients, and organic chemicals (Kayhanian et al., 2012; Lee et al., 2010; Davis and McCuen, 2005; Barbosa et al., 2012). One of the greatest issues in pollutant emissions is the first flush effect (FFE), which implies a greater discharge rate of pollutant mass or concentration in the early part of the runoff volume as compared with later in the storm (Ma et al., 2002; Sansalone and Cristina, 2004). A number of monitoring studies and modeling studies have been conducted to characterize the stormwater runoff from various pollution types, including nutrient (Lee et al., 2002; Sollera et al., 2005; Huang et al., 2007; Lee et al., 2011; Beecham and Razzaghmanesh, 2015), heavy metal (Lee et al., 2002, 2004; Soller et al., 2005; Huang et al., 2007; Lee et al., 2011), oil and grease (Lee et al., 2011); and E. coli (Muirhead et al., 2011). Recently, low impact development (LID) practices have been mentioned as a promising strategy for urban stormwater runoff control, pollution prevention and better urban ecosystems (Elliott and Trowsdale, 2007; Kayhanian et al., 2012; Ahiablame et al., 2012; Randhir and Raposa, 2014). LID is a green approach for managing stormwater that seeks to preserve the pre-development hydrology of a given site using decentralized micro-scale control measures (Coffman, 2002; HUD, 2003). However, it requires much experimental efforts to test LID characteristics and propose an adequate guideline for optimizing LID performance (Ahiablame et al., 2012). Mathematical modeling can be used to determine LID type and placement with limited stormwater management funds (Prez-Pedini et al., 2005) and can encourage the wider application of LID (Beecham, 2002). As well, it can be useful for implementation of LID in more efficient ways (Elliott and Trowsdale, 2007). Previously, stormwater management practices associated with LID have primarily focused on extreme event control for reducing the flood potential where a certain return period is an important criterion to design LID for given areas (Guo and Adams, 1998; Park et al., 2013). Water Quality Capture Volume (WQCV) was proposed to manage stormwater in urban areas in an attempt to prevent receiving water pollution from stormwater events (WEF and ASCE, 1998; Park et al., 2013; Guo and Urbonas, 2002). It has been applied to LID design in many places in the United States (WEF and ASCE, 1998; Park et al., 2013; Guo and Urbonas, 1996, 2002; Guo and Adams, 1998). Because WQCV is based on total volume, it does not consider the FFE on the receiving water quality. FFE provides an opportunity for Low Impact Development to be designed more effectively by optimizing removal efficiency for the early part of runoff (Kang et al., 2008). As well, Li et al. (2006) and Abrishamchi et al. (2010) emphasized first flush (FF) treatment for effective stormwater management, rather than just treating total water volume. In addition, different LIDs have their own hydrologic responses and water treatment characteristics; thereby the performance of each LID substantially varies. A few previous studies have focused on the sizing of different LIDs. Here, we propose a novel methodology to design LID for minimizing FFE, incorporated with intensive monitoring programs and modeling efforts in a commercial area in Korea. The aims of this study are 1) to monitor/characterize stormwater runoffs from the study site, 2) to develop the stormwater model with observations, and 3) to optimize the sizing of different LIDs for mitigating FFE. 2. Material and methods 2.1. Site description We selected a commercial area (35
090 33.1200N, 126
500 49.6300E) in Gwangju, Korea for characterizing stormwater runoff and FFE (Fig. 1). The area (0.0125 km2 ), including the separated sewer system, can be characterized by high imperviousness (approximately 85%) and includes offices, restaurants, a parking lot, a car repair shop, and a residential area. The pervious area is located only at a parking lot and occupied small part of the study area (approximately 15%).

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چکیده
1 مقدمه
2 توصیف منطقه دهکده المپیک
3 چارچوب انالیز طراحی LID BMP
1 3 تجزیه اساسی حوضه
2 3 اصول کاربرد LIDBMP
3 3 انتخاب BMP مناسب
4 3 طرح اولیه BMP
5 3 ارزیابی و بهینه سازی BMP
6 3 طرح نهایی ساخت و چشم انداز و اجرای BMP
4 تجزیه و تحلیل طراحیBMP
1 4 انتخاب ابزار تجزیه و تحلیل
2 4 ترکیب مدل های SWMM و BMPDSS
3 4 اماده سازی اطلاعات ورودی
5 تجزیه سناریوها و نتایج
1 5 تجزیه سناریوها
2 5 نتایج شبیه سازی سناریو
6 نتیجه گیری

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6 نتیجه گیری

همراه با شهرگرایی سریع در چین، نگرانی هایی رخ داد و تبدیل به نگرانی کلیدی بسیاری از مسئولان شهری و مردم عامه می شود.از میان این مشکلات شهری،تاثیرات منفی جریان های شهری بر آب شناسی و کیفیت آب از جدی ترین موارد هستند.این تحقیق استفاده از فعالیت های توسعه کم اثر LID را بر کاهش تاثیرات جریان شهری بررسی می کند. BOV که به عنوان عامل تشریح مفهوم جامعه سبز مطرح شده است ، به عنوان مطالعه موردی برای تجزیه مزایای LID BMP بهینه بر کاهش حجم جریان و میزان پیک جریان می باشد. مدل مرکب SWMM-BMPDSS برای آنالیز مورد استفاده قرار می گیرد.نتایج نشان داد که در مقایسه با شرایط موجود، برنامه پیشنهاد شده  BMP باعث کاهش 27 و21 درصدی حجم جریان آب باران  و میزان پیک جریان می شود.با استفاده از مدل BMPDSS ، اندازه BMP می تواند کاهش یابد و هدف کاهش هزینه یا افزایش مزایا را به دنبال داشته باشد. نتالیج تحقیق می تواند منجر به مطرح کردن پیشنهادات مفید برای اصلاح سیستم مدیریتی موجود در منطقهBOV،و دیگر مناطق شود تا بتوان به کنترل بیشتر جریان دست یافت.

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6. Conclusions

Along with the rapid urbanization in China, urban woes have occurred and have been great concerns of many city officials and the general public. Among these urban problems, the negative impact of urban runoff on hydrology and water quality is among the most serious ones. The present study examined the use of low impact development (LID) practices in mitigating urban runoff impacts. The BOV, which was built as a demonstration of the ‘‘green community’’ concept, was selected as a case study to analyze the benefits of optimized LID BMP implementation on reducing runoff volume and peak rates. The coupled SWMM–BMPDSS model was used for the analysis. The results showed that compared to the existing condition, the recommended BMP plan would cause a 27% and 21% reduction for total runoff volume and the peak flow rate, respectively. Using the optimization module of BMPDSS, the sizes of BMPs could be further reduced and still meet the target of minimizing cost or maximizing benefits. The research results could lead to useful recommendations for modifying the present stormwater management system at the BOV, and also at other urban sites, in order to achieve more runoff control benefits.

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چکیده
مقدمه
روشها
داده های اقلیمی
مدل های سرمادهی
انتخاب مدل گردش اتمسفری-اقیانوسی
درجه حرارت های پیش بینی شده ساعتی
نتایج
بحث
نتیجه گیری

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افزایش دما به دلیل متصاعد شدن گازی های گلخانه ای در نتیجه فعالیت های انسانی بر ابعاد کلیدی تولیدات باغی تاثیر می گذارد.تاثیر بالقوه درجه حرارت های بالاتر بر توانایی درختان میوه و خشکبار برای شکستن خواب زمستانه که مستلزم قرار گیری در معرض درجه حرارت های پایین زمستانه است در نظر گرفته شد.سه مدل سرمادهی( ۰-۷، درجه حرارت ۲ سانتی گراد،مدل اصلاح شده یوتاه و دینامیک) برای بررسی تغییرات در انباشت سرما در ۱۳ مکان در استرالیا بر طبق تغییرات موضعی درجه حرارت مربوط به افزایش ۱،۲ و ۳ درجه سانتیگراد در میانگین درجه حرارت جهانی مورد استفاده قرار گرفت. این روش وابسته به برایند های واکنش های متصاعد شدن GHG که تغییر می کنند و یا در معرض تغییر می باشند نمی باشد.تاثیرات منطقه ای و و سرعت کاهش دمای سرد کننده در میان همه مدل های سرمادهی متفاوت می باشد که درآن مدل ۷–.۲۰°C که بیشترین کاهش و مدل دینامیک(پویا) کم ترین سرعت کاهش را نشان دادند. مکان های مرتفع و با عرض جغرافیایی بالا در استرالیا به مقدار کم ترین میزان و در حالی که سه منطقه بحری در غرب استرالیا با ارتفاع کم تر به مقدار بیشترین میزان از گرمایش آینده متحمل شدند.

۱ مقدمه

اقلیم نقشی اساسی در تولید موفق محصولات باغی و خشکبار در مقیاس تجاری ایفا می کند.خواب زمستانه یک بعد مهم و کلیدی چرخه سالانه درختان میوه خزان کننده(برگ ریز) و به تبع آن شکستن حالت خواب محسوب می شود. این وضعیت در دوره زمستانه برای حفاظت درختان در برابر صدمات دمای پایین حفظ می شود(سار ۱۹۸۵). درختان جهت خارج شدن از حالت خواب، بایستی در معرض مقادیر از پیش تعیین شده دماهای سرد در فرایندی موسوم به بهاره سازی(ورنالیزاسیون) قرار بگیرند.سرمادهی ناکافی می تواند موجب رشد نامنظم و جوانه زنی اندک، نمو ضعیف میوه،کوچک ماندن اندازه میوه و زمان های رسیدگی غیر یکنواخت می شود(اکالبی و و همکاران ۲۰۰۳،پتری و لیت ۲۰۰۴، سار ۱۰۸۵،ولر ۱۹۸۶). افزایش درجه حرارت در آینده ناشی از تغییر اقلیم تحت فعالیت های انسانی بر فرایند های سرمادهی یا بهاره سازی تاثیر گذاشته و موجب وارد آمدن اثرات نامطلوب بر تولید می شود.

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Increases in temperature as a result of anthropogenically generated greenhouse gas (GHG) emissions are likely to impact key aspects of horticultural production. The potential effect of higher temperatures on fruit and nut trees’ ability to break winter dormancy, which requires exposure to winter chilling temperatures, was considered. Three chill models (the 0–7.2°C, Modified Utah, and Dynamic models) were used to investigate changes in chill accumulation at 13 sites across Australia according to localised temperature change related to 1, 2 and 3°C increases in global average temperatures. This methodology avoids reliance on outcomes of future GHG emission pathways, which vary and are likely to change. Regional impacts and rates of decline in chilling differ among the chill models, with the 0–7.2°C model indicating the greatest reduction and the Dynamic model the slowest rate of decline. Elevated and high latitude eastern Australian sites were the least affected while the three more maritime, less elevated Western Australian locations were shown to bear the greatest impact from future warming. Keywords Climate change . Vernalisation . Fruit . Nut Introduction Climate plays a fundamental role in the successful production of commercial scale fruit and nut products. Winter dormancy is one key aspect of the annual cycle of deciduous fruit and nut trees along with the subsequent breaking of the dormant state. This state is maintained through the winter period each year to protect against damaging cold temperatures (Saure 1985). To be released from dormancy trees require exposure to a predetermined quantity of cold temperatures in a process known as winter chilling or vernalisation. Insufficient chilling can lead to sporadic and light bud-break, poor fruit development, small fruit size and uneven ripening times (Oukabli et al. 2003; Petri and Leite 2004; Saure 1985; Voller 1986). Expected future increases to temperature as a result of anthropogenically induced climate change may impact the vernalisation process leading to these adverse effects on production. While the chilling process is not fully understood (Dennis 1994), the physiological response is often estimated by temperature based models (e.g. Cesaraccio et al. 2004; Fishman et al. 1987; Linsley-Noakes et al. 1994; Linvill 1990; Richardson et al. 1974; Shaltout and Unrath 1983; Weinberger 1950). Of the available models, the following are used commonly by researchers and growers; the 0–7.2°C (Bennett 1949; Weinberger 1950), Utah (Richardson et al. 1974) and Modified Utah (Linvill 1990), and the Dynamic (Erez et al. 1990; Fishman et al. 1987) models. All these models, although they contain differing levels of complexity, accumulate chill according to hourly temperature exposure and, once a threshold amount of chill has been amassed, define chilling as satisfied. Different species, and varieties within species, require different amounts of chill to break dormancy. Varietal chill requirements, or thresholds, have been defined according to different chill models resulting in chill thresholds reported in different units (e.g. Table 1). Few studies have quantitatively investigated projected impacts of increased temperatures on chill accumulation, although many discuss potential negative outcomes (Darbyshire et al. 2011; Harrington et al. 2010; Legave et al. 2008; Wand et al. 2008). Hennessy and Clayton-Greene (1995) conducted one of the first investigations into chill accumulation under climate warming conditions. Their study was for Australia and used the Modified Utah model. They implemented two methods to investigate future chill conditions, a sensitivity approach as well as a range of scenarios for the year 2030. The sensitivity study involved adding 1, 2 and 3°C to historical temperature records, meaning a constant temperature increase was applied across all locations. Comparison between sites using this method was not possible, as the rate of warming is likely to differ between regions. Additionally, uniform minimum and maximum temperatures increases were applied, this is also unlikely to eventuate. To allow investigation into site differences they also considered scenarios for 2030 using five climate models and two emission scenarios; however, these projection data was produced in 1992 and are now dated. Recently, Luedeling et al. (2011a) conducted a global analysis of projected changes to chill accumulation according to the Dynamic model only. This model has been shown to equal or out-perform other chill models (Alburquerque et al. 2008; Campoy et al. 2011a; Erez et al. 1990; Luedeling et al. 2009b; Perez et al. 2008; Ruiz et al. 2007; Viti et al. 2010); however, the results may have limited application as few varietal chill thresholds have been measured in chill portions. Luedeling and Brown (2010) compared the output of the 0–7.2°C, Utah and Dynamic models globally and verified that conversion factors between the chill models are regionally dependent and therefore inconsistent. Consequently, thresholds determined for varieties using one chill model cannot be interpreted using output from a different chill model. Some authors have investigated chill concurrently using two or more chill models (Alburquerque et al. 2008; Luedeling et al. 2009b, d, 2011b; Ruiz et al. 2007; Sunley et al. 2006; Viti et al. 2010) but conversion factors between the models were conflicting. Here, three common chill models are assessed for Australian conditions, allowing investigation into chill model sensitivity to warming, consideration of projected chill accumulation measured in different units (e.g. Table 1) and comparison to other studies. Growing support for the Dynamic model and previous research highlighting the higher sensitivities of alternate chill models to warming (Luedeling et al. 2009a) indicate that results from the Dynamic model will be most applicable. Methodology regarding interpretation of climate projection data is a major consideration in this study. Appropriate representation of Atmosphere-Ocean General Circulation Model (AOGCM) uncertainty is important for projection analyses as models can differ greatly in output (Jun et al. 2008; Watterson 2011). Emission scenarios (Nakicenovic and Swart 2000) are intentionally excluded from the analysis. This is because the IPCC’s Special Report on Emission Scenarios (SRES) greenhouse gas (GHG) emission scenario storylines may not eventuate, especially if GHG production continues unabated or if mitigation policies are implemented (e.g. Meinshausen et al. 2009). To improve the applicability of the results, SRES pathways are used for interpretation of results rather than being embedded in the methodology. In this study, chill projections for 13 sites across Australia using three chilling models were calculated. Six AOGCMs were selected, cross-validated against existing model skill assessments (Suppiah et al. 2007; Watterson 2011), to ensure the maximum range of likely outcomes were included in the results. Temperature projections were created using localised monthly minimum and maximum temperature perturbations relating to 1, 2 and 3°C global average temperature increases. Thus, results are comparable across locations and are independent of GHG emission projection uncertainty. Methods Future chill conditions were evaluated at 13 perennial horticultural production locations in Australia (Fig. 1 and Table 2) as used by Darbyshire et al. (2011) for historical chilling analysis. Climate data Historical daily minimum and maximum data from 1911 to 2009 were sourced from 0.05° by 0.05° grids (Jones et al. 2009). This dataset was used by Darbyshire et al. (2011) to investigate historical chilling conditions in Australia as quality historical in situ meteorological data are not available for the major production areas. Climate projection output from 21 AOGCMs were provided by the Queensland Climate Change Centre of Excellence (QCCCE). The projection data were provided as localised monthly minimum and maximum temperature perturbations per 1°C global temperature increase from the 1975–2004 baseline. The pattern scaling methodology used to produce future climates, developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), is described in Page and Jones (2001) and Ricketts and Page (2007). Where climate variables for some AOGCMs were not available from CSIRO, they were in-filled by QCCCE using regression methods. Chill models The 0–7.2°C model (Bennett 1949; Weinberger 1950) is a simple step-function which records one chill hour for every hour that temperature is within the 0–7.2°C interval and nil chill hours otherwise. The Modified Utah model (Linvill 1990) builds on the simplicity of the 0–7.2°C model. It incorporates an optimum chilling temperature, which is assigned one chill unit, with temperatures either side of the optimum declining in ability to contribute to the chilling process. This model additionally accounts for the negation effect of high temperatures on chilling, with temperatures over 14°C reversing previously accumulated chill, an aspect lacking in the 0–7.2°C model. These two models are time independent, meaning the effectiveness of chilling temperatures are constant across the chill period. Independence from time using the Modified Utah model means chill accumulated early in winter can be negated by late season warming.

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