A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

Abstract

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid's Exporting and Delivery model, was introduced to estimate annual instream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid's features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model's validity and good performance in estimating the annual NPS nutrients' fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.

A Topography Analysis Incorporated Optimization Method for the Selection and Placement of Best Management Practices

“…The slope gradient was calculated by the elevations data and slope length of the drainage cells at the sub-watershed level [7]. The land use indicator was directly associated with the type of land use, which was determined from the land use - land cover map [41], [42]. In this study, the interpreted land use was classified into forests, paddy lands, grasslands, drylands, residential areas, bare lands and waters [29].…”

Decision support systems for scheduling tasks in Biosystems Engineering

“…However, predominant decision support systems prevailed. -Forest management model [21] -Impact of water resources on forest productivity [22] -Watershed Management Priority Indices (WMPI) [22] -Forest Road Evaluation System (FRES) [22] -Harvest Schedule Review System (HSRS) [22] Agriculture -Planning the harvest of tomatoes [23] -Dynamic Bayesian Network [23] -Optimization of the sugar cane harvest [24] -Regression analysis [24] -Optimization of grass harvest [25] -Mixed Integer Programming [25] Decision problem Method / algorithm / framework to support scheduling -evaluate the DSS for Agrotechnology Transfer Cropping System Model (DSSAT-CSM) for assessing grain sorghum yield and water productivity [26] -DSS for Agrotechnology Transfer Cropping System Model (DSSAT-CSM) [26] -Predictive irrigation planning system [27,28] -Artificial Neural Network (ANN) [27] -Predictive methods [28] -Basin Irrigation Design with Multi-Criteria Analysis Focusing on Water Saving and Economic Returns [29] -(meta-)heuristics [29] -A simulation tool which integrate the energy efficiency of the pumping station taking into account irrigation events distribution according to the crop irrigation scheduling at each plot [30] -GREDRIP [30] -Quantify the effects of The El Nino Southern Oscillation (ENSO) phenomenon on tomato crop water requirements [31] -AgroClimate irrigation tool [31] -Development of an integrated decision support system (IDSS) based on wireless sensor networks (WSN) and simulation procedures [32] -Platform Matlab (R) i Opnet (R) [32] -A decision support system based on the combination of the wireless sensor and actuation network technology and the fuzzy logic theory [33] -Fuzzy Logic [33] -Modeling the water and nitrogen productivity of sunflower using OILCROP-SUN model in Pakistan [34] -DSS for Agro-Technology Transfer (DSSAT) [34] , 0 (2019) https://doi.org/10.1051/e3sconf /2019 0 E3S Web of Conferences 132 10 1320100 POLSITA 2019 8 8 -A flexi...…”

Development and application of watershed-scale indicator to quantify non-point source P losses in semi-humid and semi-arid watershed, China

“…The ECM has the advantage of requiring less data and has fewer parameters, but doesn't consider the influence of spatial heterogeneity of rainfall and underlying surfaces (Ding et al, 2010). The mechanistic models can provide accurate results, but often encounter difficulties in application because of their complicated structures and strict requirements for the input data (Zhang, 2010b). This phenomenon was particularly prominent in countries without long term and spatially dense monitoring data and basic or empirical field studies, such as China (Ongley et al, 2010;Shen et al, 2012).…”