Abstract
In this study, in order to determine the efficiency of estimating annual water pollution loads from remote-sensed land cover classification and ground-observed hydrological data, an empirical model was investigated. Remote sensing data imagery from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer were applied to an 11 year (1994–2004) water quality dataset for 30 different rivers in Japan. Six water quality indicators—total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO)—were examined by using the observed river water quality data and generated land cover map. The TN, TP, BOD, COD, and DO loads were estimated for the 30 river basins using the empirical model. Calibration (1994–1999) and validation (2000–2004) results showed that the proposed simulation technique was useful for predicting water pollution loads in the river basins. We found that vegetation land cover had a larger impact on TP export into all rivers. Urban areas had a very small impact on DO export into rivers, but a relatively large impact on BOD and TN export. The results indicate that the application of land cover data generated from the remote-sensed imagery could give a useful interpretation about the river water quality.
Highlights
Changing land use and land management practices are regarded as amongst the most important factors that can alter hydrological systems and water quality, which have become increasingly important to catchment stakeholders, such as management groups, land owners and government departments [1,2,3,4]
In a previous study we have analyzed the relationship between land cover types and potential annual water pollution loads and improved an empirical model to successfully calculate potential annual water pollution loads in 30 river basins in Japan by using the collected dataset in the year of 1996 [27]. Based on these previous results of estimating total water pollution loads in year 1996 for 30 river basins, the objectives of the present study are to (1) test the model prediction capability with long term dataset; (2) modify the original empirical model to account for the water pollution loads from each land cover classification; (3) examine and analyze the capability of this method for apportioning long term potential annual loads of water quality indicators such as total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO) in river basins
TP, BOD, COD, and DO, respectively, indicating that water pollution loads estimates had sufficient reproducibilityfor forthe thecalibration calibrationperiod period (Figure (Figure 3)
Summary
Changing land use and land management practices are regarded as amongst the most important factors that can alter hydrological systems and water quality, which have become increasingly important to catchment stakeholders, such as management groups, land owners and government departments [1,2,3,4]. Different studies have increasingly recognized that human action at the landscape scale is a principal threat to the ecological integrity of river ecosystems and water quality [9,10,11,12,13,14,15]. It is important to understand the relationships between catchment characteristics and river water chemistry, which provides a base for determining how future changes in land cover and use and climate will impact on river water quality.
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