Modeling potential herbicide loss to surface waters on the Swiss plateau

Siber, Rosi; Stamm, Christian; Reichert, Peter

doi:10.1016/j.jenvman.2009.08.019

Cited by 17 publications

(12 citation statements)

References 41 publications

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“…The use of herbicides affects all types of weeds, including those that might have positive effects on the soil physical properties (soil structure and nutrients) as well as soil fauna. In addition, the use of herbicides causes pollution of ground and surface (Siber et al ., 2009). More than 17 per cent of the respondents affirmed the usage of herbicides.…”

Section: Results and Discussion: Causes And Consequences Of Land Degrmentioning

confidence: 99%

Land degradation: socioeconomic and environmental causes and consequences in the eastern Mediterranean

Hammad¹,

Tumeizi²

2010

Land Degrad Dev

148

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Land degradation is a natural and socioeconomic cause-effect phenomenon that is widespread all over the world. This study investigated the socioeconomic factors and causes of land degradation (e.g. population growth and urbanization, poverty, overgrazing, pollution, biodiversity, erosion) in the eastern part of the Mediterranean region. The study revealed a significant land use change from agricultural and natural vegetation to urbanized areas due to the high population increase during the last ca. 80 years (51-times the magnitude of the total built-up area). The high poverty rate that exists in the area (57 per cent of the population can be classed as 'poor') has resulted in damaging environmental practices (overgrazing and intensive usage of natural plants); we found 83 per cent of the farmers admitted to the adoption of these practices. Poverty has also resulted in an inverse and significant correlation (at the 95 per cent confidence level) between holding size and the sale of land for urban uses. We also noted that 75 per cent of the smallholder farmers surveyed had sold their lands for immediate benefits to cope with poverty. This relation was affected by the education level of the farmers; we found a direct and significant (95 per cent confidence level) correlation existed between these factors. Overgrazing was practised by 70 per cent of the farmers and was found to be one of the most important environmental consequences of land use change (1600 ha have been transformed from natural grazing to built-up (urban) in the study area).Socio-economic induced land degradation demands efforts to improve farmer environmental awareness as well as environmental standards, laws and bylaws and the reduction of mismanagement of land.

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Section: Results and Discussion: Causes And Consequences Of Land Degrmentioning

confidence: 99%

Land degradation: socioeconomic and environmental causes and consequences in the eastern Mediterranean

Hammad¹,

Tumeizi²

2010

Land Degrad Dev

148

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“…The hydrology of the catchment is characterized by a relatively low base flow index (0.42–0.48, Siber et al . []). Annual average precipitation was 1220 mm in the study period with a mean discharge of 0.98 m 3 s −1 (672 mm yr −1 ) at the monitoring station.…”

Section: Methodsmentioning

confidence: 99%

Integrated uncertainty assessment of discharge predictions with a statistical error model

Honti

Stamm

Reichert

2013

Water Resources Research

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[1] A proper uncertainty assessment of rainfall-runoff predictions has always been an important objective for modelers. Several sources of uncertainty have been identified, but their representation was limited to complicated mechanistic error propagation frameworks only. The typical statistical error models used in the modeling practice still build on outdated and invalidated assumptions like the independence and homoscedasticity of model residuals and thus result in wrong uncertainty estimates. The primary reason for the popularity of the traditional faulty methods is the enormous computational requirement of full Bayesian error propagation frameworks. We introduce a statistical error model that can account for the effect of various uncertainty sources present in conceptual rainfall-runoff modeling studies and at the same time has limited computational demand. We split the model residuals into three different components: a random noise term and two bias processes with different response characteristics. The effects of the input uncertainty are simulated with a stochastic linearized rainfall-runoff model. While the description of model bias with Bayesian statistics cannot directly help to improve on the model's deficiencies, it is still beneficial to get realistic estimates on the overall predictive uncertainty and to rank the importance of different uncertainty sources. This feature is particularly important if the error sources cannot be addressed individually, but it is also relevant for the description of remaining bias when input and structural errors are considered explicitly.Citation: Honti, M., C. Stamm, and P. Reichert (2013), Integrated uncertainty assessment of discharge predictions with a statistical error model, Water Resour. Res., 49,[4866][4867][4868][4869][4870][4871][4872][4873][4874][4875][4876][4877][4878][4879][4880][4881][4882][4883][4884]

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“…Conceptually, the approach shares similarities with the Surface Water Attenuation Model developed by Brown and Hollis (1996) In contrast to their model, the approach presented here is intended as a first‐tier screening tool based on discharge measurements only and is not suited to predict environmental concentrations during single events. Based on similar reasoning as presented here, Siber et al (2009) have used the FFI across the Swiss Plateau as an indicator for catchment vulnerability for herbicide losses. The base flow index (BFI) has also been identified as one relevant characteristic for nutrient concentrations in surface waters (Davies and Neal, 2007; Schärer et al, 2006).…”

mentioning

confidence: 99%

Estimating Catchment Vulnerability to Diffuse Herbicide Losses from Hydrograph Statistics

2010

Self Cite

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The prediction of diffuse herbicide losses to surface waters using process-based models is data and time demanding. There is a need for simpler and more efficient catchment screening tools. We developed and tested a new proxy for screening catchments for their vulnerability to diffuse herbicide losses relying on widely available river flow data only. The proxy combines the fast flow index (FFI) (i.e., the long-term average proportion of fast flow to total discharge) and the fast flow volume (FFVs) (i.e., the stormflow component of the hydrograph during the spring flush period). We tested the proxy and the underlying hypotheses by regression analyses of existing high-frequency, multiannual monitoring datasets of atrazine, metolachlor, alachlor, and acetochlor concentrations from six US and European catchments. The percentages of applied amounts of three of the four herbicides lost to surface water were positively correlated with the FFV, and the slope of the correlation was positively correlated with the FFI. Based on the empirical data, we derived quantitative proxies to estimate atrazine and metolachlor losses based on the measured FFI and FFV values. The application to 65 European catchments revealed that many of them had a lower vulnerability than the six test catchments. The observed FFI dependency of the slope seems reasonable because with increasing FFI less rain is needed to trigger fast flow and more herbicide is available in the topsoil. The proposed FFI-FFVs proxy can guide researchers and authorities in selecting monitoring areas, setting monitoring results into context, and prioritizing mitigation strategies according to catchment vulnerability.

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Modeling potential herbicide loss to surface waters on the Swiss plateau

Cited by 17 publications

References 41 publications

Land degradation: socioeconomic and environmental causes and consequences in the eastern Mediterranean

Land degradation: socioeconomic and environmental causes and consequences in the eastern Mediterranean

Integrated uncertainty assessment of discharge predictions with a statistical error model

Estimating Catchment Vulnerability to Diffuse Herbicide Losses from Hydrograph Statistics

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