Four optimization scenarios were evaluated, involving a uniform, holistic, prioritization, and targeted reduction approaches. A uniform reduction approach evaluated each subwatershed to meet a reduction goal. Using specific land use contributions, an annual cost of $5.9 million would be required to meet N and P reduction goals on 14 of the 17 subwatersheds. The holistic approach is a scenario whereby the entire watershed’s nutrient reduction strategy is evaluated to meet the nutrient reduction goal at the Opequonwatershed mouth. However, no optimal solution was found for this approach using agricultural BMPs. When BMPs were implemented on all acres of crop and pasture land, a total cost of $19.3 million was computed with only 43% of the reduction goal is achieved for P and 42% for N. In the third scenario, a prioritization approach targets priority subwatersheds. High priority subwatersheds were identified using the WCMS nutrient levels and public participation prioritization exercise in watershedmanagement. The same three subwatersheds were identified as high priority by both methods: Mill, Tuscarora and Middle Creeks. Using P as the only constraint, the total cost of BMP implementation for these three subwatersheds under the Chesapeake Bay values was approximately $1.1 million compared to $282,000 using specific land use specific values. This result showed that nutrient reduction costs are much lower under specific land use contributions than using the Chesapeake Bay wide averages. The final scenario involved a targeted approach where reduction goals are to be met for both the Virginia and West Virginia parts of the Opequonwatershed. No optimal solution exists for these two points of evaluation. As with the second scenario, when BMPs were
and considers more mechanisms than those on a site. In a review by Ahiablame et al. (2012) , simulations in which plot scales are scaled up to larger scales were identified as critical for advancing LID practices. Elliott and Trowsdale (2007)  reviewed 10 stormwater models that were relevant to LID simulations and concluded that up-scaling at the catchment level and catchment scale predictions are needed for further model development. To support decision-making in watershed-scale design, the USEPA developed a decision-support system called the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) [12–14]. The SUSTAIN model is a powerful model that can evaluate LID performance at different spatial scales and determine the optimal LID design based on cost efficiency. A major limitation of the model is that the system has to run on an ArcGIS platform , which may be unavailable or unfamiliar to users.
The Water-Energy-Food nexus (WEF) has been initially introduced in the international community as an adaptive managementapproach in response to climate change (Schmidt and Matthews, 2018; Zhang et al., 2018; Endo et al., 2015). In fact, the goal of adaptation is to reduce vulnerability to climate and human impacts in terms of the security challenges of water, energy, and food. It is therefore essential for sustainable development (Rasul and Sharma, 2016). The first report on the need for the WEF nexus model dates back to the Bonn Conference in 2011 focused on the interdependency of water, energy, and food security that need to be explicitly identified in decision making (Mohtar et al., 2015). By definition, nexus consists of basic concepts for the dynamics of water, energy and food inter-relationships (Elsayed et al., 2018; Smajgl et al., 2016). Numerous studies have been reported on water- energy-food nexus (e.g., Daher et al., 2019; Li et al., 2019; Serrano-Tovar et al., 2019; Hussien et al., 2018;), water–food–energy nexus (e.g., Moioli et al., 2018), water- energy nexus (e.g., Murray and Holbert, 2020; Liu et al., 2019; Pan et al., 2018), food-energy nexus (e.g., Amjath-Babu et al., 2019), and food-energy-water nexus (e.g., Nie et al., 2019). Applying a food- energy-water nexus approach for land use optimization (Nie et al., 2019) reported that the framework works effectively to balance multiple objectives and benchmarks the competitions for systematic decisions. The water-energy-food nexus assessment of a local case study of desalination system in the Canary Islands, Spain, was investigated by Serrano-Tovar et al. (2019). This latter study illustrated the potential of the multi- scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) in providing an integrated assessment of the WEF nexus in relation to sustainability. The illustration of the MuSIASEM approach showed the possibility of producing a robust quantitative estimate of the energy, water, and food nexus utilizing insights from complexity theory. This was accomplished through establishing connections over processors conveying
hierarchical approach adds flexibility to man- substantial costs and risks (some ir- agement, breaking the usual rigid connection reversible) to some groups; (4) the technical between policy and scale. In most cases the ecological and sociological facts are highly scale is driven by the policy problem, and it uncertain; and (5) policy decisions will have is usually unclear who should formulate the effects outside the scope of the problem. policy question and at what scale (Lackey, He concludes that ‘solving these kinds of 1997). With the hierarchy provided by the problems in a democracy has been likened watershedapproach, the scale of the targeted to asking a pack of four hungry wolves and management object becomes less crucial, as a sheep to apply democratic principles to long as it is presented as an element of the deciding what to eat for lunch’ (p. 22). The whole hierarchical structure. The smaller wa- outcome may seem quite obvious, except tersheds are embedded into the larger ones, that with people there is always less cer- and various policies formulated can be tainty about how problems are resolved, treated in the appropriate level. The hier- and in the long run there is still a chance archy in this case is not imposed on the for the sheep to persuade the wolves to system from the outside, as in case of ad- become vegetarians. The success of this ministrative divisions, but it is embedded in endeavor becomes very much dependent the physical characteristics of the system and on how efficiently the new technology is offers a much larger variety of scales. developed and used, since it is our scientific, The potential of the watershed man- cultural and social development which agement approach may be illustrated by the makes Homo sapiens special and leaves fact that the US Environmental Protection certain space for optimism. In this context Agency (EPA) has currently adopted it as its we do not view technology as a panacea that primary approach to addressing remaining can cure all the problems of environmental waterquality problems (NRMRL, 1999). The degradation and resource depletion, but US Geological Survey (USGS) has defined rather as a means of understanding, edu- a multi-digit classification system for water- cating and resolving conflict.
Conducting ﬁeld experiments or collection of long- term data is very expensive and time consuming. There are uncertainties/errors associated with the measured data and also diﬃculty in repeating the monitoring process without additional resources and time when corrections are warranted. With nonpoint source pollution emerging from a large watershed with mixed land uses and soil, it is quite diﬃcult to associate waterquality improvements to speciﬁc BMPs using the monitoring data, unless extensive sampling points are available. In this context, an application of a watershed simulation model becomes useful. Because the climate, land use, soil, topography and geological conditions vary within a watershed, a watershed based modeling approach (with spatial or geographic information system capability) allows for the consideration of these variations, and quantifying the impacts of BMPs at diﬀerent locations. Hence, the objective of this article is to demonstrate the utility of a modeling approach to
the overall waterquality by combining several waterquality parameters. It is easier to see the waterquality by having one value than having several values for several parameters. Therefore, the waterquality parameters that are included in the WQI should be selected properly so that they can concisely represent the real waterquality of a river. In Malaysian WQI, there are only six parameters included. The analysis of the rivers in the Skudai watershed showed that all parameters that are not included in the index did not show alarming values except for total phosphorus (TP) and nitrate (N). The Skudai River (Natural), the Skudai River (Head) and the Senai River that were classified as clean category from the calculated WQI values showed relatively high values of nitrate compared to other rivers. The Skudai River (Tail) was classified as slightly polluted river with the highest value of total phosphorus. Further research must be done by developing a new WQI derivation that may include total phosphorus and nitrate so that we can check whether the river class will be affected when we consider those parameters.
27 Levels in the environment are increasing due to industry (Lu, 2010). The pattern of distribution and bioaccumulation of heavy metals in organisms was not only influenced by the environmental sorption-desorption characteristics, but also by physiological status, feeding strategy, biochemistry, and capacity to accumulate heavy metals in their bodies (Noegrohati, 2005). Highest Pb concentration was found in the downstream zone pollutants flows from urban runoff and municipal sewage that increased the concentration of Pb in stream water (Malik, 2011). A good monitoring program is an essential program for managing the coastal areas based on ecological and sustainability (Jayaprabha et al., 2014). In addition to publishing Government Regulation No.
Vineyards 0.80 0.80 0.2
Overall Average Rooting Depth 0.50 1.0
The water balance model is generally applied as a single-storage model. In cases where immediate storm runoff makes up a large fraction of total streamflow, however, the use of more than one storage component may be necessary. In order to determine whether Carneros Creek is one of these cases, a rough baseflow separation was carried out by graphical means, on the basis of a recent limited record of flow in Carneros Creek (described under data, below). The results indicated that approximately 40% of the streamflow during the measured period was direct stormflow. Since direct stormflow appears to be significant, the Thornthwaite model was altered to include two parallel storages, one quick and one slow.
The presence of E. coli bacteria in a stream suggests a relatively fresh fecal source entering the water. Although monitoring indicated that bacteria concentrations tended to be high in Hoover Creek, the monitoring did not identify the sources of these elevated bacteria levels. Rather than conduct expensive microbial source tracking studies in the watershed, a targeted sampling approach was conducted. This approach entailed taking numerous samples along the stretch of the stream to determine locations with elevated bacteria concentrations. Two sampling events occurred in August and September of 2006 during which 14-17 samples were collected and analyzed for bacteria. Fluorometry was then used to sample the levels of optical brighteners in the water. Samples were taken at a rate of one sample per 150 meters of stream length or where tile lines were ﬂ owing. Sites were numbered 1 through 17 and are mapped and described in Figure 7 and Table 1. Additionally, a smaller sampling event was conducted on a subset of these sites in October. The presence of optical brighteners and
Computation of Weight Parameter (Wp)
Weight parameter is the ratio of Wi of every waterquality measure to the sum of all relative weights. Weight parameter enables to know about the relative share of each waterquality measure on overall waterquality. The Wp is given by the equation;
studious attention to develop system dynamics model in order to either provide action plans and management scenarios or enlighten the behavior of the system causes such problems (Winz et al., 2009; Gastélum et al., 2009; Gastélum et al., 2010; Ahmad and Prashar, 2010; Xi and Kim, 2013). Furthermore, hydrologic simulation is also in the center of attention of water resources modelers and planners. They applied SD models to simulate and calculate surface runoff, changes in ground water level, base flow, evaporation, eutrophication modeling, and etc. (Tisdale, 1996; Wang et al., 2005; Elshorbagy and Ormsbee, 2006; Ghashghaei et al., 2012). Focusing on internally generated dynamic behavior of a system, emphasizing on understanding patterns of behavior generated by systems’ structures instead of focusing on point precise predictions, high level of generality and scale robustness which is allowing to cope with a wide range of variables in the model (Radzicki, 2009), and providing a user friendly graphical environment to develop the model are specific features makes SD highly beneficial in modeling integrated dynamic systems and assessing diverse scenarios. SD models may be developed using lots of software including Stella (http://www.iseesystems.com), Vensim (http://www.vensim.com), or Powersim (http://www.powersim.com). These programs use numerical methods (e.g. Euler and Runge-Kutta) to solve nonlinear equations (Ford, 2011). We used Vensim software to translate and couple the hydrologic and economic models.
Selection of site for implementing watershed techniques. Collection of data of site condition and surrounding area. To manage and utilize run-off water for useful purpose, the suitable structures on water outlet points were suggested. 3.0 AREA SELECTED FOR STUDY:
Adarsha watershed has served as a benchmark or nucleus watershed, and has already demonstrated the benefits of integrated watershedmanagement. The technology has been adopted in watersheds of neighbouring villages and other areas by farmers with little technical support from the consortium. The satellite watersheds, which are similar in terms of soils, climate and socioeconomic patterns, can achieve broad impacts by adopting these technologies. The ICRISAT consortium focuses on training farmers, development agencies and NGOs through demonstrations of different technologies on benchmark watersheds, and acts as a mentor for technology backstopping. The farmers’ community, through village institutions, takes responsibility for all activities of implementation and monitoring. Government and non-government agencies catalyse the process. The key factor while evaluating and scaling-up this approach is that the concerned line departments of the government need to be included in the consortium from the beginning, along with other partners.
land-use from all 17 basins showed annual average chl-a had no statistically significant correlations.
Multiple regression. However, a multiple regression analysis revealed that for every
percent increase in Other Open Lands Rural (LULC 260) it causes a 1.410 ug/L decrease in chl- a; for every percent increase in Wet Prairies (LULC 643) it causes a 1.625 ug/L increase in chl-a and for every percent increase in Emergent Aquatic Vegetation (LULC 644) it causes a 78.886 ug/L increase in chl-a. This model explains 87.3% of the variation in chl-a (Appendix I). Chl-a is often used as a waterquality indicator since they are an indirect measurement of the planktonic algae within the water body and because they respond quickly to environmental changes: temperature, light, and nutrients (EPCHC, 2000). Therefore, it is interesting that the model shows increases in chl-a with increases in natural wetland land-use types as opposed to urban land-uses as it would be expected that chl-a should be negatively correlated to land use types known to increase temperature and nutrients such as urban and agriculture (Tong & Chen, 2002; Pusey & Arthington, 2003; Atasoy et al., 2006; Walsh et al., 2005). Also, The Florida Department of Environmental Protection determined the Alafia River is impaired by chlorophyll-a (FDEP, 2014). The results for chl-a could be less reliable as there were only 19 samples taken during the 10-year study period, meaning only about 2 samples per year.
The nutrients, phosphorous and nitrogen, of PS can be managed on-site at each plant by controlling the SROOXWDQW¶V level a llo wed to be discharged also known as Permit Co mp liance System (PCS). Meanwhile managing the impact of NPS where the pollutant distributed within the watershed is somehow difficult. The a mount of nutrient in the streams, wh ich affects the waterquality, however is both from PS and NPS. The state of Texas , under the Texas Co mmission on Environmental Qua lity (TCEQ) and the Texas State Soil and Water Conservation Board (TSSWCB), ma intains the waterquality in the impaired river
management philosophy create a level of tagging that becomes useless when as departments merge or split.
Geographical assignments of equipment provides stability for hard assets however; this method of classification often causes confusion for the appropriate location of mobile equipment and tools. The key to making the system usable for all those integral to its success revolves around user accessibility and ease of use. Simple, easy to follow structures enable more input and quality of input from staff rendering better results for all staff within the utility that depend on the system outputs. The key approach to this concept includes involving and developing the system (typically maintenance and operations staff) around the main users of the system while customizing the valuable asset reports utilized by utility leaders.
Watershed Forum 6 months
Coordinate within the stakeholder group to determine the feasibility and recommended structure for a SDRW Data Repository to ensure the best available data is accessible to entities involved with management efforts.
In this era of ever increasing water demands and rapidly depleting water resources coupled with overpopulation, it has become necessary to develop the means to recharge the ground water resources which are necessary for future requirements. This paper presents one such case study where large amount of rainwater is directed to recharge ground water resources. Somwar Peth is a small village located at distance of 15 Kms. from Kolhapur city. Under Social Forestry Department, some measures have been adopted to recharge the ground water resources, but it has been found that these measures don’t work with full capacity in some cases. Hence it is planned to take such engineering and biological measures which will direct this extra runoff to ground water storage. The most significant feature of the work is that if such technologies are developed and adopted at larger scale in rural areas, it will prevent thousands of villages of the country from water supply by tankers. Moreover this will also help us to tackle the issue of flood which mainly occurs due to excess runoff.
al., 2017). Multi-paddock grazing can decrease high flow events, leading to reduction in flooding frequency (Park et al., 2017).
Research showed conflicting results regarding waterquality issues about grazing management. Increased suspended solids and nitrate loads were not noticeable with grazing practices but bacteria densities increased in a Colorado front range stream (Gary et al., 1983). Other studies reported that grazing operations on grassland degrades waterquality (Lyons et al., 2000; O’reagain et al., 2005; Owens et al., 1989). For example, intensive rotational grazing resulted in streambank erosion and fine substrate reduction in the channel compared to continuous grazing (Lyons et al., 2000). Heavily continuous (all-year round) grazing lead to increased organic nitrogen, total organic carbon, and sediment in streamflow in a North Appalachian watershed near Coshocton, Ohio (Owens et al., 1989). Similarly, summer rotational grazing and winter-feeding grazing increased sediment by 60% compared to summer rotational grazing only in Wisconsin (Lyons et al., 2000). In North Carolina, pollutant loads from grazed grassland fields slightly decreased with installation of off-stream water sources for cattle (Line et al., 2000). Regulating and managing the intensity of grazing practices can also lead to waterquality improvement (Mosley et al., 1997; Sheffield et al., 1997). Research showed that intensive grazing may have negative impacts on waterquality (Stout et al., 2000). Grazing
Calibrating the SLEUTH Model
The calibration phase used an expert-weighting approach in which county planners identified a set of factors that had acted to either exclude or attract development between 1986 and 2005 (Jantz et al. 2009). For example, stakeholders in both Pike County, Pennsylvania, and Sullivan County, New York, identified their proximity to the New York urban core as a driver for growth pressure, so we weighted factors to reflect higher growth pressure in the southeastern part of the watershed and lower growth pressure in the northwestern part (our assumption is that this growth pressure will persist through the 2030 forecast period). In contrast, in central and southeastern Delaware County, New York, growth is largely restricted to reflect the protection of watersheds that supply water to the New York Metropolitan Area. For each county, we assigned each factor a weight and combined all factors into a single map that reflected growth pressures over the time period used for calibration. Based on tests of the model’s performance both with and without the use of the expert-weighted exclusion–attraction layer, we note that the exclusion–attraction map developed in conjunction with county planners significantly improved model performance (Jantz et al. 2009).