Optimum Allocation of Discharged Pollutant Loads from Nonpoint Sources in a Watershed using GIS

1. OptimumAllocation of Discharged Pollutant Loads from Nonpoint Sources in a Watershed using GIS Alok Kumar Laboratory of Water Resources Engineering Division of Environmental Science and Technology Graduate School of Agricultural Science

2. Salient features related to pollutant loads from nonpoint sources: • Nonpoint sources contribute significantly to water pollution; mainly from agricultural, domestic, and industrial sectors • Relatively difficult to manage due to their wide spatial distribution and being influenced by varying climatic and geological factors • Without significant control over nonpoint sources, even huge expenditure on point source control measures can not improve water quality much • Less research works have been carried out addressing nonpoint source pollutant control

3. Objectives of this research: • To develop a model to optimize the allocation of pollutant loads from nonpoint sources in a watershed by combining the application of optimization theory and GIS technique considering each polygon of land use as an individual land management unit(LMU) and to apply the model to an area of interest. • To formulate a multiobjective optimization model in order to allocate maximum allowable pollutant loads from nonpoint sources in a watershed, from each LMU represented by a grid of uniform and same size, considering equity among the LMUs in the watershed. • To develop a multiobjective model to optimally allocate the pollutant load emitted from nonpoint sources in a watershed considering surface and subsurface flows and apply the formulated model to an area of interest to demonstrate its applicability.

4. Objectives of the part of study covered in Chapter 3 of dissertation: • To develop a model to optimize the allocation of pollutant loads from nonpoint sources in a watershed by combining the application of optimization theory and GIS technique considering each polygon of land use as an individual land management unit (LMU) and • To apply the formulated model to a selected area of interest in a river basin in Shiga prefecture, Japan

5. Flow chart showing Steps followed in GIS

6. Pollutant load transport: • During transfer from sources to water bodies, pollutant loads normally reduce in amount due to self-purification characteristics of watershed. • Reduction rate per unit length can be assumed to be proportional to amount of discharged pollutant load from sources and can be expressed as: • This can be integrated to form:

7. Optimization model: • Objective function: Maximize • Constraints: A.Effluent limitation at the outlet of a watershed

8. Optimization model (continued) Constraints: B.Relations among mean effluents from different LMU types (i) (iii) (ii) C.Lower limit of effluent in each LMU

9. Application: Study area in Yasu river basin

10. Elevation map of the study area

11. Flow length in study area

12. Land use pattern in study area

13. Value of λ for T-N 0.0001 m-1 Value of L 1.294 Mg/month Optimization parameters:

14. Optimum load without lower limit

15. Optimum load with definedlower limit

16. Conclusions of this part of study: • Model for allocating optimum discharged pollutant loads from nonpoint sources, by maximizing allowable total loads, is developed, considering each polygon of different land use as individual land management unit (LMU). • ArcView GIS is used to model study area and compute flow length and area of LMUs, to consider the spatial variation in parameters. • Values of optimum discharged pollutant load from different LMUs are observed to be sensitive to preference order of land use type of LMUs. • Model can be used for allocating optimum discharged pollutant loads from LMUs in a drainage basin based on preference order of decision maker’s choice and users need.

17. Aim of the part of study described in Chapter 4: • Till now • Polygons of land use considered as LMU and average value of flow length taken for each LMU. But flow length can vary within same polygon of large size • Aims • To formulate a multiobjective optimization model in order to allocate maximum allowable pollutant loads from nonpoint sources in a watershed, from each LMU represented by a grid of uniform and same size, considering equity among the LMUs in the watershed • To apply the formulated model to the same selected area of interest in Shiga prefecture

18. Optimization model: • Objective function: Minimize • Constraints: A.Effluent limitation at the outlet of a watershed Maximizing loads Equity among LMUs

19. Optimization model (continued) Constraints: B.Relations among mean effluents from different LMU types (i) (ii) C.Lower limit of effluent in each LMU

20. Land use and grids for which optimization considered

21. Value of λ for T-N 0.0001 m-1 Value of L 1.151 Mg/month Optimization parameters:

22. Optimum allocated pollutant loads for ω1 =0.5 and ω2 =0.5

23. Optimum allocated pollutant loads for ω1 =0.1 and ω2 =0.9 Difference between the maximum and minimum allocated loads decreases as more weightage is given to equity among LMUs

24. Trade-off among the objectives:

25. Conclusions of this part of study: • Model for optimum allocation of discharged pollutant loads from nonpoint sources, by maximizing allowable total loads, is developed, treating uniform grids of identical size as individual LMU and considering equity among each LMUs. • The model can be used for optimally allocating discharged pollutant loads from LMUs in a watershed on the grid basis for a set of weightage values to the two objectives and for a preference of land use type, defined by user. • The model could help making policies and trading effluents among dischargers for a sound water quality management, based on total maximum daily loads.

26. Aim of the part of study covered in Chapter 5: • Till now • No discrimination has been made between pollutant loads through overland and subsurface components. • Aims • To develop a multiobjective model to optimally allocate the pollutant load emitted from nonpoint sources in a watershed considering surface and subsurface flow and • To apply the formulated model to same selected area in Shiga prefecture • Data on subsurface geology in each LMU is used to differentiate the ratio of overland and subsurface flows occurring in each LMU.

27. Optimization model: • Objective function: Minimize • Constraints: A.Effluent limitation at the outlet of a watershed Maximizing loads Equity among LMUs

28. Optimization model (continued) Constraints: B.Relations among mean effluents from different LMU types (i) (ii)

29. Optimization model (continued) Constraints: C.Relation between effluents through overland and subsurface flows in each LMU for ji on loamy soils for ji on sandy soils D.Lower limit of effluent in each LMU

30. Land use and grids for which optimization considered

31. Subsurface geology for study area

32. Parameter L (Mg/month) α β Values 1.151 0.5 2 Optimization parameters:

33. Optimum load for ω1 =0.05 and ω2 =0.95 with flow discrimination Trend of LMUs with higher flow length getting higher pollutant load allocation is broken Over-estimation of load can be avoided

34. Optimum load for ω1 =0.05 and ω2 =0.95 without flow discrimination

35. Conclusions of this part: • Model for allocating optimum discharged pollutant loads from NPS, by maximizing allowable total loads considering equity, is developed. • Process of pollutant load transfer through overland and subsurface flows is differentiated by taking different values of self-purification coefficient and deciding ratio of flows based on subsurface geology. • Model can be used for allocating optimum discharged pollutant loads from LMUs in a catchment on the grid basis which might be useful for effluent trading among dischargers for an efficient water quality management.

36. Summary: • All 3 Models, presented here, could provide decision-makers with optimum strategies forwater quality control in a basin or watershed scale. • Decision-makers can produce one or more optimum solutions, by manipulating preference parameters for land use type and equity parameters for objectives, depending on the need of sound water quality management practices. • All these models can be used for allocating optimum discharged pollutant loads from nonpoint sources in a catchment on different scales, which might be useful for deciding on policies related to Total Maximum Daily Loads and effluent trading among dischargers for an efficient water quality management. • Further studies will be needed to make better estimation of few parameters and incorporating point sources components or linking with point source models.