Key words: Soil and Water Assessment Tool, Integrated Coastal Zone and Watershed Management, GIS.
Resumen
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La gran cuenca del Río Grande, ubicada en el noroeste de Jamaica, crítico para el desarrollo, particularmente para vivienda, turismo, agricultura y minería. Es una fuente de sedimentos y nutrientes de recarga para el ambiente costero incluyendo el Parque Marino Bahía Montego. Proponemos un marco integrado de modelado utilizando la herramienta de evaluación de suelo y agua (SWAT) y SIG. Las estadísticas de rendimiento del modelo calculadas para la descarga de alto flujo rindió una eficacia de ]]> R2 de 0.70 sugiriendo una buena medición y correlación de descarga simulada (calibrada). Los estados insulares con frecuencia toman decisiones basándose en los impactos de la cuenca. Esto requiere un profundo entendimiento y análisis de factores como los recursos hídricos, uso del suelo/cobertura, sedimentos y nutrientes de recarga entre otros factores a nivel de cuenca. Con financiamiento del Instituto Interamericano para la investigación del Cambio Global (IAI) se ]]>
Palabras clave: Herramienta de ]]>
Increasing population along with increasing pressure on land for food, expansion, and the need for infrastructure facilities have given rising alarm to conflicting demands on finite land and water resources (Biswas, Sudhakar & Desai, 2002). Additionally, anthropogenic land use changes tend to ]]>
To adequately handle the stresses to our natural resources from climate change (e.g., flooding) and ‘burgeoning populations’ innovative methods have been conceptualized over the years. As it relates to challenges at the watershed scale, ]]>
The primary focal point of watersheds is the river systems. Rivers provide the hydrologic link and ]]>
In an effort to achieve environmental sustainability, an integrated watershed and coastal zone management (IWCZM) approach must be incorporated, particularly as it relates to coastal zones irrespective of their definition by geographic or political boundaries. It is critical that an ecosystem-based approach to management is taken that will ensure a holistic management ]]>
According to the Coral Reef Alliance, coral reefs are among the world’s most productive ecosystems (Goreau & Hayes, 2008). They are a major natural resource providing coastal protection, fisheries, and tourism income. The survival of coral reefs is largely dependent on a set of environmental parameters ]]>
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The location of the island, its geography and geology make Jamaica susceptible to several natural hazards such as earthquakes, landslides, hurricanes, floods and droughts. The country has also acknowledged the increasing importance of climate change due to the high vulnerability attributed to the high concentration of development and infrastructure within the coastal zone. Coupled with this, human induced pressures on ecosystem goods and services are significant and highlighted within the major national ]]>
The current study examines the applicability of the Soil, Water and Assessment Tool (SWAT) on a tropical island watershed to evaluate the ability of the model to predict stream flow, and impacts of landuse changes on stream flow to allow for better understanding of how these tools can aid in water resources management. The following objectives were established:
Calibration and validation of the hydrologic component of SWAT model in the Great River Watershed;
Investigation of fluctuations in ]]>
The Great River Watershed is located in the northwestern section of the island of Jamaica and is one of 26 watersheds in the island. The Great River is approximately 74km (46mi) long with an area of 327.27km2 and has five major tributaries: ]]>
Regular monitoring of water quality within the watershed is not routinely carried out and as such monitoring data are inconsistent (Hayman, 2001; Greenaway, 2004). The water quality throughout the watershed is generally good with the exception of fecal coliform contamination triggered by human and animal fecal waste. The most recent assessment conducted from April 2002 to ]]>
There are relatively few peer-reviewed, published SWAT model applications in tropical regions (Gassman, Reyes, Green & Arnold, 2007; Oestreicher, 2008). This is primarily due to the diversity of soils, species and climate of these regions in comparison to those of temperate zones. The model is very flexible and can be applied to a wide range of different environmental conditions (Arnold & Fohrer, 2005). The SWAT model, a freeware, was ]]>
Although the model has significant advantages, it is important to recognize that limitations exist. SWAT unfortunately is lacking in relation to the spatial representation of ]]>
Of the two main ]]>
Materials and Methods
In order to setup SWAT various ]]>
The SWAT 2005 model was used through the ArcSWAT interface embedded in the ArcGIS software. This allows one to employ all available tools of ArcGIS in handling spatial datasets. SWAT allows for the discretisation of a watershed by dividing it into multiple sub-watersheds, which can then be further subdivided into hydrologic response units (HRUs) that consist of homogeneous land ]]>
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The modeling process generated 30 HRUs that represent the entire GRW. Calibration of the model was performed by comparing the simulated discharge with the monitoring (measured) discharge data in situ. The measured data were divided into two time periods covering the period 1998 to 2006 and represented complete data for the longest time period with the most meteorological stations in the watershed. The period 1998 to 2002 was selected for calibration and the period 2002 to 2006 for validation.
Calibration of the discharge was achieved by adjusting the input parameters. The top ten parameters were selected based on ranking achieved from the sensitivity analysis conducted. Adjusting the selected parameters allowed for a better match of measured and simulated discharges. The most sensitive parameters were used to calibrate the model for the GRW. The first year data (1998) was used as start-up/warm-up in the calibration process and was ]]>
Land use scenarios used in this study were designed to offer management planning for the protection of the watershed by assessing the potential impact of land use changes on hydrological parameters such as surface runoff, stream flows, and potential evapotranspiration. Land use scenarios were designed around real macro development possibilities within the watershed. Three scenarios were designed as follows: (1) an increase in agriculture to ]]>
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Results
Watershed modelling & streamflow: The measured and simulated monthly discharges (Fig. 1) ]]> -1 (std. dev.= 9.94) slightly lower than the mean simulated monthly flow volume of 10.25cm s-1 (std. dev.= 7.06). The model was run during the validation period on the basis of the parameters from the calibration process only. The mean validation measured monthly flow ]]> s-1 (std. dev.= 13.32), slightly higher than the mean simulated monthly flow volume of 10.66cm s-1 (std. dev.= 7.72).
The calculated model ]]> R2 value of 0.70 suggesting a fairly good correlation between measured and simulated (calibrated) discharge. The validation period similarly demonstrated good model performance. The NSE value of 0.61 and R2 value of 0.67 also suggests a fairly ]]>
Landuse scenarios modelling – Impact on stream flow: The land-use change scenarios performed reasonably well in comparison to the simulated baseline when calculated for mean monthly stream flow (Fig. 2). Simulated mean annual stream flow was less than measured stream flow using the validated period with ]]> s-1 (agriculture) to 10.86cm s-1 (urban). These changes represented a 5.5% (urban) to 7.17% (agriculture) reduction in annual mean stream flow when compared with the baseline measured flow at the single discharge gauge station located approximately 3km from the river sea interface. The simulated land-use changes suggest no noticeable impact on mean stream flow with no more than a 3% increase ]]>
Although changes in simulated stream flow are fairly small in real values, the percent change in stream flow during the dry season for the forest and urban land-use scenarios are more exaggerated, ranging from an increase of 5% to 9% primarily during the dry season. Interestingly, the forest land-use scenario reflected similar fluctuations in reduction in stream flow during the same period particularly in the months of February and March while the agriculture scenario showed little or no change from the baseline throughout both dry and wet seasons.
Land-use scenarios modelling – Impact in stream nutrients: Simulated agriculture and urban land-use changes produced consistent increases in organic nutrients for most months with the greatest increase (106%) being predicted under simulated urban land use changes in March. The greatest increase in organic nitrogen for the agriculture land-use change was observed for the dry season month of January (~56%) when stream flow is lower and outside the main growing season of April-May. Increases exceeded 12% for all months in the year. Increases of 20-40% were also observed for wet season months of May to September when average monthly stream flow increases (Fig. 3).
Simulated agriculture and urban land-use changes produced consistent increases in organic phosphorous for each month with urban land use changes in March having the highest % change. Increases of 28-48% were also observed for wet season months of May to November for organic phosphorous within the stream (Fig. 4). Similar to organic nitrogen, phosphate contributions to stream flow were greatest during low-flow period.
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Land-use scenarios modelling – Impact on surface runoff to streams: Increase in surface runoff were generally observed for all three land-use changes, however forest land-use change recorded marginal decreases in some dry season months. While agriculture land-use scenario projected small increases in surface runoff contribution to stream flow ranging from 3.13% to 12.70%, forest land use change contributions ranged from -5.1% to 52%. The urban land-use scenario unlike the others projected greater monthly increases in surface runoff contributions. During the dry season the increase in surface runoff was in excess of 150% for the urban scenario. During the wet season the increase in surface runoff had a range of 98% to 234% (Fig. 5).
Discussion
Despite the slight over-estimation ]]>
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Stream flow is projected to be impacted most with an increased urban landscape. An increase in the forest and agricultural land-use change does not suggest any real measureable increases in stream flow. Simulated agriculture land-use change has less of an impact and may be attributed to the regions within the watershed where substantive simulated changes were made. The lower-reach had little or no change in agriculture land-use, significant changes were made to the mid- and upper-reach, particularly ]]>
Little or no data were available for hydrological parameters throughout most of the watershed. Where available the data were often incomplete due to faulty or non-functional equipment. No adequate historical land-use maps for the watershed were available to observe change in land-use over a prolonged ]]>
Nutrient contamination is a very important aspect of water quality monitoring in rivers and coastal environments globally. In Jamaica, the impact of nutrient contamination has been evident in some rivers and along the coast particularly in ]]> 4 L-1 were recorded in June 2008 and October 2009 as well as nitrates routinely at or near the upper limit standard of 7.5mg PO4 L-1. Land-use change influences suggest local water ]]>
Phosphorus is generally present in stream flow as dissolved or particulate matter, and is a vital plant nutrient and possibly the most limiting nutrient to plant growth in fresh water. It is rarely found in significant concentrations in ]]>
Many bodies of freshwater are currently experiencing influxes of phosphorus and nitrogen from outside sources. The increasing concentration of available phosphorus allows plants to assimilate more nitrogen before the phosphorus is depleted. ]]>
Predictably, surface runoff is projected to increase greatly once the land cover is dominated by hard surface which is a characteristic of urbanization or where forested land cover has reduced allowing for greater sheet flow where agriculture is not dominated by tree crops. This is well represented using the simulated urban land-use scenario wherein surface runoff ]]>
The reduced surface runoff potential for agriculture and forest scenarios may translate to reduced ]]>
]]> The significant increase in surface water observed for the urban scenario may have huge impacts on stream health that will negatively impact on the treatment services of potable water. This is required bearing in mind the expected increase in population, and also the projected increase in tourism related services such as hotels and eco-adventure tours. Improved watershed management is recognized as a critical area of need in Jamaica. These improvements are geared towards providing reliable and adequate supplies of clean water for agriculture, industry, tourism, urban and rural populations, ]]>
Future watershed hydrologic changes due to land conversion are expected to be site-specific, and climate variability is an important factor controlling basin hydrologic processes (Qi et al., 2009). Agricultural activities on steep slopes have long been recognized as the single most important cause of the ]]>
The increase run-off in urban areas (particularly residential) and maintenance and potential increase in agriculture within the GRW has and will impact significantly on the dynamics of suspended sediments and nutrient contribution to stream flow. The GRW is dependent on the episodic and seasonal flows to maintain watershed health. Suspended sediment transport is greatest ]]>
Based on these dynamics it is possible that there is an efficient system of material transport and limited impact from channel sedimentation and de-nitrification processes (Brodie & Mitchell, 2005). However, with an increase in ]]>
The integrated watershed and coastal zone management approach must be used to tackle issues such as watershed health, stream water quality, coastal zone and sensitive ]]>
The performance of ]]> R2) provided confidence that the model is adequate for use in tropical island watersheds with karts networks. Model predictions are as accurate as the mean of the measured stream flow data with the E values indicating the model is particularly sensitive to low flows but still performs fairly well to peak flows. This ]]>
The modelled urban ]]>
]]> Although this research puts forward impact to watershed based on projected macro changes in land use with projected climate change, it should be highlighted that it is very much theoretical. There is still large uncertainty in predicting future impacts due to climate and land-use changes. The pace at which technology will be developed to arrest the increasing threat of climate change is still unknown. Similarly, the importance of development (economic and social) must be factored and may even be a greater limiting factor. The dynamic shifts in land-use beyond the extent of ]]>
Acknowledgments
The authors would like to thank the ]]>
References
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Arnold, J. G., &. Fohrer, N. (2005). SWAT2000: Current Capabilities and Research Opportunities in Applied Watershed Modelling. Hydrological Processes, 19(3), 563-572. doi: 10.1002/hyp.5611. [ Links ]
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Allen, P. M. (1999). Continental Scale Simulation of the Hydrologic Balance. Journal of the American Water Resources Association, 35(5), 1037-1051. doi: 10.1111/j.1752-1688.1999.tb04192.x. [ Links ]
Batchelor, J. (2013). Using GIS and SWAT analysis to assess water scarcity and WASH services levels in rural Andhra Pradesh. IRC International Water and Sanitation Centre. Working Paper 10. [ Links ]
Biswas, S., Sudhakar, S., & Desai, V. R. (2002). Remote Sensing and Geographic Information System Based Approach for Watershed Conservation. Journal of Surveying Engineering, 128(3), 108-124. [ Links ]
Brodie, J., & Mitchell, A. (2005). Nutrients in Australian Tropical Rivers: Changes With Agricultural Development and Implications for Receiving Environments. Marine and Freshwater Research, 56(3),279-302. doi: 10.1071/MF04081. [ Links ]
Dunne, T., & Leopold, L. B. (1978). Water in Environmental Planning. San Francisco: W. H. Freeman. [ Links ]
Easton, Z. M., Fuka, D. R., Walter, M. T., Cowan, D. M., Schneiderman, E. M., & Steenhuis, T. S. (2008). Re-Conceptualizing the Soil and Water Assessment Tool (SWAT) Model to Predict Runoff from Variable Source Areas. Journal of Hydrology, 348(3-4), 279-291. [ Links ]
Espeut, P. (2012). Cutting Out Contamination in Kingston Harbour. Sunday Observer, February 9, 2012. [ Links ]
Erturk, A. L. I., Melike Gurel, Mansoor Ahmed Baloch, Teoman Dikerler, Evren Varol, Neslihan Akbulut, and Aysegul Tanik. 2006. “Application of Watershed Modeling System (WMS) for Integrated Management of a Watershed in Turkey.” Journal of Environmental Science and Health, Part A no. 41 (9):2045-2056. doi: 10.1080/10934520600780693. [ Links ]
Evelyn O.B. 2009. “Utilizing geographic information system (GIS) to determine optimum forest cover for minimizing runoff in a degraded watershed in Jamaica.” International Forestry Review no. 11 (3):375-393. doi: 10.1505/ifor.11.3.375. [ Links ]
Ferreyra, C., & Beard, P. (2007). Participatory Evaluation of Collaborative and Integrated Water Management: Insights from the Field. Journal of Environmental Planning and Management, 50(2), 271-296. doi: 10.1080/09640560601156532. [ Links ]
Fohrer, N., Möller, D., & Steiner, N. (2002). An Interdisciplinary Modelling Approach to Evaluate the Effects of land Use Change. Physics and Chemistry of the Earth, Parts A/B/C, 27(9-10), 655-662. [ Links ]
Garg, K. K., Karlberg, L., Barron, J., Wani, S. P., & Rockstrom, J., (2012). Assessing the Impacts of Agricultural Interventions in the Kothapally Watershed, Southern India. Hydrological Processes, 26(3), 387-404. [ Links ]
Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, G. (2007). The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Vol. 50, Transactions of the ASABE. St. Joseph, MI, ETATS-UNIS: American Society of Agricultural Engineers. [ Links ]
Goreau, T. J., & Hayes R. L. (2008). Effects of Rising Seawater Temperature on Coral Reefs, in Fisheries and Aquaculture. Retrieved from http://www.globalcoral.org/coral_reefs.htm. [ Links ]
Graiprab, P., Pongput, K., Tangtham, N., & Gassman, P. W. (2010). Hydrologic Evaluation and Effect of Climate Change on the At Samat Watershed, Northeastern Region, Thailand. International Agricultural Engineering Journal, 19(2), 12-22. [ Links ]
Greenaway, A. (2004). Water Quality of the Great River Watershed St. James/Hanover/Westmoreland. Kingston, Jamaica: National Environment and Planning Agency and the United States Agency for International Development. [ Links ]
Harden, C., Foster, W., Morris, C., Chartrand, K., & Henry, E. (2009). Rates and Processes of Streambank Erosion in Tributaries of the Little River, Tennessee. Physical Geography, 30(1), 1-16. [ Links ]
Hayman, A. (2001). Rapid Rural Appraisal of the Great River Watershed: Ridge to Reef Watershed Project. Burlington, VT: National Environment and Planning Agency and the United States Agency for International Development. [ Links ]
Heathwaite, A. L., & Johnes, P. J. (1996). Contribution of Nitrogen Species and Phosphorus Fractions to Stream Water Quality in Agricultural Catchments. Hydrological Processes, 10(7), 971-983. doi: 10.1002/(sici)1099-1085(199607)10:7<971::aid-hyp351>3.0.co;2-n. [ Links ]
Heuvelmans, G., Garcia-Qujano, J. F., Muys, B., Feyen, J., & Coppin, P. (2005). Modelling the Water Balance with SWAT as Part of the Land Use Impact Evaluation in a Life Cycle Study of CO2 Emission Reduction Scenarios. Hydrological Processes, 19(3), 729-748. doi: 10.1002/hyp.5620. [ Links ]
Hooper, B. P. (2003). Integrated Water Resources Management and River Basin Governance. Water Resources Update, 126, 8. [ Links ]
Jakeman, A. J., & Letcher, R. A. (2003). Integrated Assessment and Modelling: Features, Principles and Examples for Catchment Management. Environmental Modelling & Software, 18(6), 491-501. doi: 10.1016/s1364-8152(03)00024-0. [ Links ]
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1. Centre for Marine Sciences, University of the West Indies, Mona, Kingston 7; opgjunior@gmail.com
2. Department of Earth & Environment, Florida International University (FIU) Miami, FL 33199
Received 17-X-2013 Corrected 07-II-2014 Accepted 24-III-2014
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