Uncertainty analysis for nonpoint source pollution modeling: implications for watershed models

Shen, Zhenyao; Xie, Hui; Chen, L.; Qiu, Jiali; Zhong, Yulong

doi:10.1007/s13762-014-0598-8

Cited by 17 publications

(4 citation statements)

References 73 publications

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“…The last decade witnessed some of the major milestone researches targeting the subject of uncertainty in remote sensing, GIS, spatial models and geographical sciences (Congalton 1991;Sinha et al 2012;Liang et al 2013;Shen et al 2015). The three challenges identified by Dungan (2002) include (a) the unavailability of appropriate reference data due to expensive and time-consuming issues, (b) challenge to represent or visualize spatially varying intervals and (c) inadequacy of statistical models that assume errors to be independent and spatial units to be dependent.…”

Section: Limitations and Uncertaintiesmentioning

confidence: 99%

A review of radar remote sensing for biomass estimation

Sinha¹,

Jeganathan²,

Sharma

et al. 2015

Int. J. Environ. Sci. Technol.

242

116

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Forest plays a vital role in regulating climate through carbon sequestration in its biomass. Biomass reflects the health and environmental conditions of a forest ecosystem. In context to the climate change mitigation mechanisms like REDD (reducing emissions from deforestation and forest degradation), an extensive forest monitoring campaign is especially important. Remote sensing of forest structure and biomass with synthetic aperture radar (SAR) bears significant potential for mapping and understanding forest ecological processes. Limitations of the conventional forest inventory procedures, like the extensive cost, labor and time, can be overcome through integrated geospatial techniques. Optical sensor or SAR data are suitable for extracting information about simple and homogeneous forest stand sites. However, optical sensors face serious limitations, specifically in tropical regions, like the cloud cover that SAR can overcome along with targeting saturation and penetration aspects. Simultaneous use of spectral information and image texture parameters improves the biomass assessment over undulating terrain and in radical conditions. Also, synergic use of multi-sensor optical and SAR has better potential than single sensor. Interferometric (InSAR) and polarimetric (PolSAR) SAR or a combination of the both (PolInSAR) serves as effective alternatives. These techniques could serve as valuable methods for biomass assessment of heterogeneous complex biophysical environments. However, SAR data have its own limitations and complexities. Identifying, understanding and solving major uncertainties in different stages of the biomass estimation procedure are critical. In this regard, the current study provides a review of radar remote sensing-based studies in forest biomass estimation.

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Section: Limitations and Uncertaintiesmentioning

confidence: 99%

A review of radar remote sensing for biomass estimation

Sinha¹,

Jeganathan²,

Sharma

et al. 2015

Int. J. Environ. Sci. Technol.

242

116

View full text Add to dashboard Cite

show abstract

“…Simplification of some processes (e.g., infiltration, interception, and evapotranspiration) is inevitable. Development or modification of currently used method is a common approach addressing structure uncertainty [86]. For assessment of VFSs in SWAT model, the empirical calculation of trapping efficiency was improved from those only considering filter width to equations that combine modeling results and field experiments [8].…”

Section: Incorporation Of Uncertainty Analysismentioning

confidence: 99%

Assessment of Agricultural Best Management Practices Using Models: Current Issues and Future Perspectives

Xie

Chen

Shen

2015

Water

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Best management practices (BMPs) are the most effective and practicable means to control nonpoint source (NPS) pollution at desired levels. Models are valuable tools to assess their effectiveness. Watershed managers need to choose appropriate and effective modelling methods for a given set of conditions. This paper considered state-of-the-art modelling strategies for the assessment of agricultural BMPs. Typical watershed models and specific models were analyzed in detail. Further improvements, including simplified tools, model integration, and incorporation of climate change and uncertainty analysis were also explored. This paper indicated that modelling methods are strictly scale dependent, both spatially and temporally. Despite current achievements, there is still room for future research, such as broadening the range of the pollutants considered, introducing more local BMPs, improving the representation of the functionality of BMPs, and gathering monitoring date for validation of modelled results. There is also a trend towards agricultural decision support systems (DSSs) for assessing agricultural BMPs, in which models of different scales are seamlessly integrated to bridge the scale and data gaps. This review will assist readers in model selection and development, especially those readers concerned about NPS pollution and water quality control.

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“…The dispersion of emissions leads to the identification difficulties of its boundary and critical source area (Panagopoulosa et al 2011). The complex interaction between precipitation and other factors determines that NPS pollution has strong randomness and uncertainty (Shen et al 2015). Monitoring difficulties result in the missing of monitoring data with long time series (Wu 2012).…”

Section: Introductionmentioning

confidence: 99%

Estimating nonpoint source pollution load using four modified export coefficient models in a large easily eroded watershed of the loess hilly–gully region, China

2016

Environ Earth Sci

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The quantitative estimation of nonpoint source (NPS) pollution load in large river basins is one of the important issues in watershed modeling. This study focuses on Beiluohe River watershed and takes Johnes export coefficient model as a starting point to establish three kinds of modified export coefficient model and estimate NPS total nitrogen (TN) and total phosphorus (TP) load of the watershed from 1995 to 2010. Results indicate that: (1) The first proposed export coefficient model considering effects of soil loss has characteristics of high precision, simple algorithm and less parameters; it is more suitable for loess hilly and gully region with a sparse vegetation cover and severe soil erosion. (2) NPS TN load in Beiluohe River watershed was 12,778.17 tons in 1995; in 2010, it fell to 4673.37 tons, a decrease of 63.42 %. NPS TP load in Beiluohe River watershed was 643.52 tons in 1995; by the year of 2010, it fell to 254.93 tons, a decrease of 60.38 %.(3) The highest contribution of NPS TN load is from farmland (42.16 %); the contribution rates of agricultural population, big livestock, pig, forestland and grassland are 18.14, 14.05, 8.03, 8.02 and 6.17 %, respectively; The highest contribution of NPS TP load is also from farmland (31.41 %); grassland, agricultural population and pig also account for large proportions, which are 21. 80, 19.58 and 16.72 %, respectively. (4) The top priority for improving water environment quality is to further strengthen the source control of NPS pollution. The following is the migration control and then the end control of waste and wastewater treatment. Results may provide a theoretical basis and data reference for quantitative estimation and source control of NPS pollution at watershed scales.

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Uncertainty analysis for nonpoint source pollution modeling: implications for watershed models

Cited by 17 publications

References 73 publications

A review of radar remote sensing for biomass estimation

A review of radar remote sensing for biomass estimation

Assessment of Agricultural Best Management Practices Using Models: Current Issues and Future Perspectives

Estimating nonpoint source pollution load using four modified export coefficient models in a large easily eroded watershed of the loess hilly–gully region, China

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