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Climate Change Effects at Watershed, Estuary and In-Stream Scales: Implications on Water Quality and Water Management

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 3324

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Northern Gulf Institute, MSU Science and Technology Center, NASA Stennis Space Center, 1021 Balch Blvd., Hancock County, MS 39529, USA
Interests: water and water quality management
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Special Issue Information

Dear Colleagues,

Understanding how environmental variables subjected to increased climatic variability (attributed to climate change) affect the hydrologic and hydrodynamic regimes of watersheds and water bodies, and their related water quality processes, is of paramount importance. Current and future water management strategies to be implemented depend on having practical methods for predicting the involved phenomena. However, climate change effects on the hydrologic, hydrodynamic, and water quality processes occurring at watershed, estuary, and in-stream scales are difficult to assess and predict. Issues such as forcing and state variables measurement, spatial and temporal data downscaling, and linking global and local models are topics that are just beginning to be studied. This Special Issue aims to collect scientific and review papers addressing all of these topics. Papers that discuss the implications of climate change for water quality and sustainable water management are also welcomed, as long as the geographical container in which the research was undertaken is at the watershed, estuary, or in-stream scale.

Dr. Vladimir Jose Alarcon Calderon
Guest Editor

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Keywords

  • Sediments
  • nutrients
  • water quality
  • hydrodynamics
  • hydrology
  • watershed
  • estuary
  • in-stream

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Published Papers (1 paper)

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Research

18 pages, 4690 KiB  
Article
Hindcasting and Forecasting Total Suspended Sediment Concentrations Using a NARX Neural Network
by Vladimir J. Alarcon
Sustainability 2021, 13(1), 363; https://doi.org/10.3390/su13010363 - 3 Jan 2021
Cited by 9 | Viewed by 2498
Abstract
Estimating and forecasting suspended sediments concentrations in streams constitutes a valuable asset for sustainable land management. This research presents the development of a non-linear autoregressive exogenous neural network (NARX) for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile). Details [...] Read more.
Estimating and forecasting suspended sediments concentrations in streams constitutes a valuable asset for sustainable land management. This research presents the development of a non-linear autoregressive exogenous neural network (NARX) for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile). Details are presented on input data selection, data splitting, selection of model architecture, determination of model structure, NARX training (optimization of model parameters), and model validation (hindcasting and forecasting). The study explored if the developed artificial neural network model is valid for forecasting daily suspended sediment concentrations for a complete year, capturing seasonal trends, and maximum and baseflow concentrations. Francia Creek watershed covers approximately 3.24 km2. Land cover within the catchment consists mainly of native and exotic vegetation, eroded soil, and urban areas. Input data consisting of precipitation and stream flow time-series were fed to a NARX network for forecasting daily suspended sediments (SST) concentrations for years 2013–2014, and hindcasting for years 2008–2010. Training of the network was performed with daily SST, precipitation, and flow data from years 2012 and 2013. The resulting NARX net consisted of an open-loop, 12-node hidden layer, 100 iterations, using Bayesian regularization backpropagation. Hindcasting of daily and monthly SST concentrations for years 2008 through 2010 was successful. Daily SST concentrations for years 2013 and 2014 were forecasted successfully for baseflow conditions (R2 = 0.73, NS = 0.71, and Kling-Gupta efficiency index (K-G) = 0.84). Forecasting daily SST concentrations for year 2014 was within acceptable statistical fit and error margins (R2 = 0.53, NS = 0.47, K-G = 0.60, d = 0.82). Forecasting of monthly maximum SST concentrations for the two-year period (2013 and 2014) was also successful (R2 = 0.69, NS = 0.60, K-G = 0.54, d = 0.84). Full article
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