Aerial photograph-based delineation of artificially drained areas as a basis for water balance and phosphorus modelling in large river basins

Tetzlaff, Björn; Kuhr, Petra; Vereecken, Harry; Wendland, Frank

doi:10.1016/j.pce.2009.02.002

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…Most previous studies that have attempted to map SD systems focused on delineating subsurface drained lines (or areas) at a field or catchment scales and used Geographical Information System (GIS)‐based analyses and/or aerial image processing techniques (Naz et al, ; Naz & Bowling, ; Sugg, ; Tetzlaff et al, ; Tetzlaff et al, ; Zhang et al, ). The 1992 National Resource Inventory (NRI) data set provided potential extents of subsurface drains in continental United States (Wieczorek, ).…”

Section: Introductionmentioning

confidence: 99%

Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

Cho

Jacobs

Jia

et al. 2019

Water Resources Research

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Human‐induced landscape changes affect hydrologic responses (e.g., floods) that can be detected from a suite of satellite and model data sets. Tapping these vast data sets using machine learning algorithms can produce critically important and accurate insights. In the Red River of the North Basin in the United States, agricultural subsurface drainage (SD; so‐called tile drainage) systems have greatly increased since the late 1990s. Over this period, river flow in the Red River has markedly increased and 6 of 13 major floods during the past century have occurred in the past two decades. The impact of SD systems on river flow is elusive because there are surprisingly few SD records in the United States. In this study, Random Forest machine learning (RFML) classification method running on Google Earth Engine's cloud computing platform was able to capture SD within a field (30 m) and its expansion over time for a large watershed (>100,000 km2). The resulting RFML classifier drew from operational multiple satellites and model data sets (total 14 variables with 36 layers including vegetation, land cover, soil properties, and climate variables). The classifier identified soil properties and land surface temperature to be the strongest predictors of SD. The maps agreed well with SD permit records (overall accuracies of 76.9–87.0%) and corresponded with subwatershed‐level statistics (r = 0.77–0.96). It is expected that the maps produced with this data‐intensive machine learning approach will help water resource managers to assess the hydrological impact from SD expansion and improve flood predictions in SD‐dominated regions.

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Section: Introductionmentioning

confidence: 99%

Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

Cho

Jacobs

Jia

et al. 2019

Water Resources Research

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“…While preserving the same design parameters of the drainage system and the same drainage hydraulic effect, this lowered depth of soil saturated layer will unequivocally lead to a reduction of the drainage runoff as proved experimentally or on models [21,40,58,73]. On the other hand, the drainage system will keep all water management functions and criteria for which it has been designed.…”

Section: Discussion Of the Effectiveness And Adequacy Of Measures On mentioning

confidence: 94%

“…Methods of remote sensing used for the identification of drainage systems were described e.g. by [71][72][73]. To classify the visualization of a drainage system on aerial photos according to the accuracy and surface area, we used the five following categories: 1) Surface-integrated system, clear identification 2) Non-integrated system, clear identification 3) Disjointed parts (up to 50%) 4) Disjointed parts (up to 50%), unclear 5) Disjointed, poorly visible, isolated parts…”

Section: Evaluating the Present State And Defect Causes Of Land Drainmentioning

confidence: 99%

Adaptation Options for Land Drainage Systems Towards Sustainable Agriculture and the Environment: A Czech Perspective

Kulhavý¹,

Fučík²

2015

Pol. J. Environ. Stud.

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Agricultural drainage systems have traditionally served the optimization of soil water balance for crop production and soil management. In the Czech Republic (CR), installation of drainage systems started in the second half of the 19 th century [1]. At that time, an exact engineering approach was applied, which proposed principles for drain spacing and depths in mineral agricultural soils based on foreign and domestic experience. However, the majority of Czech agricultural drainage systems was built in the 20 th century, during three discrete periods. The first period came shortly before World War I, the second boom took place between the World Wars, and the third wave-and by far the largest

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“…Reference values from literature (e.g. Wessolek and Facklam, 1997;Wendland et al, 2003;Tetzlaff et al, 2009) are used as starting points for the assignment of grid related BaseFlow Index (BFI)-values.…”

Section: Methodsmentioning

confidence: 99%

Area-differentiated modeling of water balance in Pinios Basin, central Greece

2015

Global NEST Journal

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<div> GROWA is a modular grid based empirical hydrological model that is being used to address practical water resources management related issues. It has been successfully applied in typical hydrological and hydrogeological setups of northern European basins ranging typically between meso-scale river basins of about 1000 km2 to entire States or river catchments of 100,000 km2 or more. This paper examines the applicability of this model in a typical southern European environment where the typical water resources management pattern and also the controlling hydrodynamic evolution patterns differ considerably. As such, the River Pinios basin in central Greece, which has a spatial extent of some 11.000km2, is studied. Model compilation was based on long-term average climatic and hydrological data for a reference period of 23 years (1980-2002) and validation performed against 12 river discharge gauging stations distributed in the study basin. Specific discrepancies that were denoted are attributed to the importance of secondary processes in the evolution of the studied system (e.g. overall water use, groundwater crossflows). It is concluded that prior to model recalibration efforts special attention should be given to the inclusion of such processes (water gains and losses) into the model runs. Total runoff and groundwater recharge should consequently be linked to the overall water use and secondary inputs before they are used for strategic water resources management. Overall, GROWA was successfully applied in this typical Mediterranean Basin, despite the totally different climatic, pedological, hydrological and hydrogeological conditions for which it was originally designed and tested. </div>

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Aerial photograph-based delineation of artificially drained areas as a basis for water balance and phosphorus modelling in large river basins

Cited by 16 publications

References 20 publications

Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine

Adaptation Options for Land Drainage Systems Towards Sustainable Agriculture and the Environment: A Czech Perspective

Area-differentiated modeling of water balance in Pinios Basin, central Greece

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