How weather conditions and physico-chemical properties control the leaching of flufenacet, diflufenican,

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Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provide an entryway of agrochemicals into subsurface water bodies and increase nutrition loss in soils. For maintenance and infrastructural development, accurate maps of tile drainage pipe locations and drained agricultural land are needed. However, these maps are often outdated or not present. Different remote sensing (RS) image processing techniques have been applied over the years with varying degrees of success to overcome these restrictions. Recent developments in deep learning (DL) techniques improve upon the conventional techniques with machine learning segmentation models. In this study, we introduce two DL-based models: i) improved U-Net architecture; and ii) Visual Transformer-based encoder-decoder in the framework of tile drainage pipe detection. Experimental results confirm the effectiveness of both models in terms of detection accuracy when compared to a basic U-Net architecture. Our code and models are publicly available at .

Drainage ponds are a useful measure to manage water resources. However, these small water bodies are characterized by highly dynamic internal processes. This article discusses a simple process-oriented model developed to simulate temporal dynamics of internal processes within drainage ponds. The PondR model is able to simulate the relevant hydrological processes of the pond by using commonly available input data. For model development, data from a 3-year monitoring campaign of the investigated drainage pond served to validate the newly developed model for the autumn and winter time periods. A temporal parameter sensitivity analysis (TEDPAS) revealed that groundwater parameters are predominant during the whole year. The model performed well in simulating outflow together with simulated pond volume and improved the understanding of the hydrological regime for drainage ponds. Regarding the practical benefit, the developed PondR model could be useful in future studies for more precise planning of pond dimensions and water resource management in the field of research and engineering services.