A Generic Approach for Live Prediction of the Risk of Agricultural Field Runoff and Delivery to Watercourses: Linking Parsimonious Soil-Water-Connectivity Models With Live Weather Data Apis in Decision Tools
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| Titel: | A Generic Approach for Live Prediction of the Risk of Agricultural Field Runoff and Delivery to Watercourses: Linking Parsimonious Soil-Water-Connectivity Models With Live Weather Data Apis in Decision Tools |
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| Autoren: | Comber, Alexis, Collins, Adrian L., Haro-Monteagudo, David, Hess, Tim, Zhang, Yusheng, Smith, Andrew, Turner, Andrew |
| Weitere Verfasser: | Natural Environment Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), Haro-Monteagudo, David 0000-0002-7885-8248 |
| Verlagsinformationen: | Frontiers Media Frontiers Media SA Frontiers |
| Publikationsjahr: | 2019 |
| Schlagwörter: | big data & analytics, spatial data integration, pesticides, metaldehyde, web-based model, API (application program interface), United Kingdom, envir |
| Beschreibung: | 14 Pags.- 4 Figs.- 4 Tabls. © 2019 Comber, Collins, Haro-Monteagudo, Hess, Zhang, Smith and Turner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. ; This paper describes the development and application of a novel and generic framework for parsimonious soil-water interaction models to predict the risk of agro-chemical runoff. The underpinning models represent two scales to predict runoff risk in fields and the delivery of mobilized pesticides to river channel networks. Parsimonious field and landscape scale runoff risk models were constructed using a number of pre-computed parameters in combination with live rainfall data. The precomputed parameters included spatially-distributed historical rainfall data to determine long term average soil water content and the sensitivity of land use and soil type combinations to runoff. These were combined with real-time live rainfall data, freely available through open data portals and APIs, to determine runoff risk using SCS Curve Numbers. The rainfall data was stored to provide antecedent, current and future rainfall inputs. For the landscape scale model, the delivery risk of mobilized pesticides to the river network included intrinsic landscape factors. The application of the framework is illustrated for two case studies at field and catchment scales, covering acid herbicide at field scale and metaldehyde at landscape scale. Web tools were developed and the outputs provide spatially and temporally explicit predictions of runoff and pesticide delivery risk at 1 km2 resolution. The model parsimony reflects the driving nature of rainfall and soil saturation for runoff risk and the . |
| Publikationsart: | article in journal/newspaper |
| Sprache: | English |
| Relation: | http://hdl.handle.net/10261/208226 |
| Verfügbarkeit: | http://hdl.handle.net/10261/208226 |
| Rights: | undefined |
| Dokumentencode: | edsbas.BF9C1576 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | 14 Pags.- 4 Figs.- 4 Tabls. © 2019 Comber, Collins, Haro-Monteagudo, Hess, Zhang, Smith and Turner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. ; This paper describes the development and application of a novel and generic framework for parsimonious soil-water interaction models to predict the risk of agro-chemical runoff. The underpinning models represent two scales to predict runoff risk in fields and the delivery of mobilized pesticides to river channel networks. Parsimonious field and landscape scale runoff risk models were constructed using a number of pre-computed parameters in combination with live rainfall data. The precomputed parameters included spatially-distributed historical rainfall data to determine long term average soil water content and the sensitivity of land use and soil type combinations to runoff. These were combined with real-time live rainfall data, freely available through open data portals and APIs, to determine runoff risk using SCS Curve Numbers. The rainfall data was stored to provide antecedent, current and future rainfall inputs. For the landscape scale model, the delivery risk of mobilized pesticides to the river network included intrinsic landscape factors. The application of the framework is illustrated for two case studies at field and catchment scales, covering acid herbicide at field scale and metaldehyde at landscape scale. Web tools were developed and the outputs provide spatially and temporally explicit predictions of runoff and pesticide delivery risk at 1 km2 resolution. The model parsimony reflects the driving nature of rainfall and soil saturation for runoff risk and the . |
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