Improved Targeting Technique and Parsimonious Optimization as Synergistic Combination for Nitrate Hot Spots Identification and Best Management Practices Implementation in a watershed of the Midwestern USA
Notice bibliographique
Résumé
The contamination of rivers with nitrate from agricultural diffuse sources is not just a risk for ecosystems and their services, but also a health risk for water users. The Great Lakes (USA and Canada) are suffering from eutrophication problems. The Midwest is one of the richest farming land and one of the most productive areas on the planet. Thus, agriculture is one of the biggest drivers of local economies, accounting for billions of dollars of exports and thousands of jobs. The Midwest encompasses the Corn Belt region, a specialised system in corn production. Many of its agricultural basins drain into the Great Lakes. Corn requires a heavy amount of fertilizer to keep the best-yielding varieties. Some of the soils also require artificial drainage due to their low permeability, and to enable agriculture. The Cedar Creek watershed (CCW) in northeastern Indiana in the Corn Belt region is used as a case study area in this dissertation. Intensive agriculture in the CCW is characterised mainly by corn and soybean production. Tile drains are used, ejecting nitrate directly into the water. To find hotspots of nitrate is, then, crucial to avoid water quality deterioration. Identification of critical source areas of nitrate (CSAs) impairing waters is challenging. There are, mainly, two methodologies to identify hotspots of nitrate for the implementation of Best Management Practices (BMP): the targeting technique and the optimization approach. The targeting technique tends to identify hotspots based on loads of nitrate, omitting geomorphological watershed characteristics, costs for BMP implementation, and their spatial interactions. On the other hand, the parsimonious strategy does contemplate the trade-off of the economic and environmental contribution but requires sophisticated computational resources and it is more data-intense. This research presents a new framework based on the synergistic combination of both methodologies for the identification of CSAs in agricultural watersheds. Changes in watershed response due to alternative BMP applications were assessed using the model Soil and Water Assessment Tool (SWAT). Outputs in SWAT (nitrate export rates and nitrate concentration at the subbasin level) were used to evaluate the changes in water quality for the CCW. The newly developed targeting technique (HosNIT) considers SWAT outputs and intrinsic watershed parameters such as stream order, crop distance to the draining stream, and downstream nitrate enrichment/dilution effects within the river network. HosNIT establishes a workflow, based on a threshold system for the parameters considered, in order to spatially identify priority areas from where nitrate is reaching water. The more precise hotspots of nitrate are identified, the more improved the allocation of limited resources for conservation practices will be. HosNIT allows for a more spatially accurate CSAs identification, which enables a parsimonious optimization for BMP implementation. This parsimonious strategy will test BMP’s performance based on the environmental contribution and cost at the hotspots determined by HosNIT. The optimised solution for the CCW comes from the environmental contribution (decrease percentage of nitrate concentration at outlets) per dollar spent. For this case study means a year average of 3.7% of nitrate reduction with the optimised selection of scenarios for the studied period.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».