Special Issue "Watershed Hydrology and Water Quality: Emerging Environmental Issues and Novel Modeling Techniques"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3606

Special Issue Editors

Dr. Haw Yen
E-Mail Website
Guest Editor
Blackland Research and Extension Center, Texas A&M University, Temple, TX 76502, USA
Interests: water resources engineering; optimization; uncertainty analysis; watershed modeling; flood control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aleksey Sheshukov
E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, Kansas State University, 1016 Seaton Hall, Manhattan, KS 66506, USA
Interests: soil erosion; ephemeral gully erosion; watershed modeling; climate change impacts; hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past two decades, there have been significant advances in computer modeling tools and technology that improved our understanding of hydrological processes and water quality transport at hillslope, catchment, and watershed scales. This resulted in better management of natural resources and targeted implementation of conservation practices in many regions of the world. Special considerations have been given to the evaluation of climate variability and extremes in the agro- and socio-economic sectors. In this Special Issue, we invite submissions that incorporate watershed scale studies on hydrologic and nutrient processes with innovative approaches to solve environmental problems and novel applications of modern technology. This  Special Issue will cover research on the impacts of climate change on watershed hydrology (surface, subsurface, groundwater) and water quality (nutrients, pesticide, bacteria, etc.). Studies of emerging environmental issues at watershed scale that require novel modeling approaches, development of new tools, or improvement of the existing models are especially welcome.

Dr. Haw Yen
Dr. Aleksey Y. Sheshukov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water resources engineering
  • watershed modeling
  • environmental assessment
  • climate change
  • conservation practices
  • model development

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data
Water 2021, 13(21), 2947; https://doi.org/10.3390/w13212947 - 20 Oct 2021
Cited by 1 | Viewed by 1285
Abstract
Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most [...] Read more.
Watershed-scale hydrological models have become important tools to understand, assess, and predict the impacts of natural and anthropogenic-driven activities on water resources. However, model predictions are associated with uncertainties stemming from sources such as model input data. As an important input to most watershed models, land use/cover (LULC) data can affect hydrological predictions and influence the interpretation of modeling results. In addition, it has been shown that the use of soft data will further ensure the quality of modeling results to be closer to watershed behavior. In this study, the ecologically relevant flows (ERFs) are the primary soft data to be considered as a part of the modeling processes. This study aims to evaluate the impacts of LULC input data on the hydrological responses of the rapidly urbanizing Upper Cahaba River watershed (UCRW) located in Alabama, USA. Two sources of LULC data, i.e., National Land Cover Database (NLCD) and Digitized Landsat 5 Thematic Mapper (TM) images, were used as input in the Soil and Water Assessment Tool (SWAT) model for the years 1992 and 2011 using meteorological data from 1988 to 2013. The model was calibrated at the watershed outlet against daily streamflow from 1988 to 1993 using the 1992 LULC data and validated for the 2008–2013 period using the 2011 LULC datasets. The results show that the models achieved similar performances with both LULC datasets during the calibration and validation periods according to commonly used statistical rating metrics such as Nash Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and model percent bias (PBIAS). However, LULC input information had substantial impacts on simulated ERFs such as mean monthly streamflow, maximum and minimum flows of different durations, and low flow regimes. This study demonstrates that watershed models based on different sources of LULC and applied under different LULC temporal conditions can achieve equally good performances in predicting streamflow. However, substantial differences might exist in predicted hydrological regimes and ERF metrics depending on the sources of LULC data and the LULC year considered. Our results reveal that LULC data can significantly impact the simulated flow regimes of the UCRW with underlaying influences on the predicted biotic and abiotic structures of aquatic and riparian habitats. Full article
Show Figures

Figure 1

Article
Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula
Water 2021, 13(12), 1709; https://doi.org/10.3390/w13121709 - 21 Jun 2021
Cited by 4 | Viewed by 838
Abstract
Long-term streamflow simulations of the Land Surface Models (LSMs) are necessary for the comprehensive evaluation of hydrological responses to climate change. The high complexity and uncertainty in the LSM modelling require the model calibration to improve the simulation performance and stability. Objective functions [...] Read more.
Long-term streamflow simulations of the Land Surface Models (LSMs) are necessary for the comprehensive evaluation of hydrological responses to climate change. The high complexity and uncertainty in the LSM modelling require the model calibration to improve the simulation performance and stability. Objective functions are commonly used in the calibration process, and the choice of objective functions plays a crucial role in model performance identification. The Kling and Gupta Efficiency (KGE) has been widely used in the hydrological model calibration by the measure of the three components (variability, bias, and correlation) decomposed from the Nash and Sutcliffe Efficiency (NSE). However, there is a clear tendency of systematic errors in the peak flow and/or water balance of streamflow time series optimized by the KGE calibration when the correlation between simulations and observations is relatively low. For a more balanced optimal solution of the KGE, this study has proposed the adjusted KGE (aKGE) by substituting the normalized correlation score in the KGE. The proposed aKGE was assessed by long-term daily streamflow simulation results from the Common Land Model (CoLM) for the calibration (2000–2009) and validation (2010–2019) periods in the Nakdong River Watershed, Korea. The case study demonstrated that the aKGE calibration can improve the simulation performance of high flow and annual average flow with a slightly inferior correlation of flows compared with the KGE and NSE criteria. Full article
Show Figures

Figure 1

Article
Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace
Water 2021, 13(9), 1241; https://doi.org/10.3390/w13091241 - 29 Apr 2021
Cited by 1 | Viewed by 824
Abstract
This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for [...] Read more.
This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years. Full article
Show Figures

Figure 1

Back to TopTop