The Coupled Impacts of Climate and Land Use and Land Cover Changes on Watershed Hydrology

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 4243

Special Issue Editor


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Guest Editor
Department of Geography, Earth and Environmental Sciences, Faculty of Environment, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada
Interests: climate change and dynamics; hydrology; hydroclimate prediction; data analysis; numerical modeling

Special Issue Information

Dear Colleagues,

Climate change directly impacts regional water resources through changes in precipitation, temperature, and energy balance. These changes pose significant risks to river streamflow by accelerating snowmelt and by increasing spring rain, leading to more frequent flooding events in downstream locations. In addition, these risks can be further intensified if the watershed and its hydrology change over time due to land use land cover (LULC) modifications, especially through urbanization and deforestation. While many efforts have been made to quantify the changes in watershed hydrology caused by past and future climate change, the current literature lacks detailed hydrological impact assessments considering both climate and LULC changes. To design an efficient water resource management system for the current and future state of the water supply, scientific progress must be accelerated to achieve a paradigm that represents the coupled impacts of climate and LULC changes on watershed hydrology.

This Special Issue will focus on the cumulative effects of climate change and LULC modifications on watershed hydrology. It will provide a platform to showcase the latest research advancements in the field of watershed hydrology. The aim is to compile studies highlighting applications of data-driven methodologies or numerical modeling tools to study regional hydrology. The information collected in this Special Issue will add valuable information to our current knowledge, directing future decision-making processes regarding water resource management and land-use planning. I welcome original research articles, case studies, and reviews in the several research areas focusing watershed hydrology, including (but are not limited to):

  • Climate change impact assessments;
  • Atmospheric and land-coupled interactions with water;
  • Forest disturbances such as wildfires, beetle intensification, and logging;
  • Remote sensing and field-based monitoring of LULC changes;
  • Application of hydrological modeling using new datasets and methods.

I look forward to receiving your contributions.

Dr. Siraj Ul Islam
Guest Editor

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Keywords

  • climate change
  • forest disturbance
  • numerical modeling
  • watershed hydrology
  • hydrological extremes
  • water management

Published Papers (2 papers)

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Research

15 pages, 2882 KiB  
Article
Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI
by Shiva Gopal Shrestha and Soni M. Pradhanang
Water 2023, 15(23), 4194; https://doi.org/10.3390/w15234194 - 04 Dec 2023
Viewed by 2144
Abstract
The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment tool (SWAT) demand spatio-temporal data and expert knowledge. Also, the difficulty and [...] Read more.
The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment tool (SWAT) demand spatio-temporal data and expert knowledge. Also, the difficulty and complexity is compounded in the smaller watershed due to data constraint and models’ inability to generalize hydrologic processes. Data-driven models can bridge this gap with their mathematical formulation. Long Short-Term Memory (LSTM) is a data-driven model with Recurrent Neural Network (RNN) architecture, which is better suited to solve time series problems. Studies have shown that LSTM models have competitive performance in watershed hydrology studies. In this study, a comparative analysis of SWAT and LSTM models in the Cork Brook watershed shows that results from LSTM were competitive to SWAT in flow prediction with NSE of 0.6 against 0.63, respectively, given the limited availability of data. LSTM models do not overestimate the high flows like SWAT. However, both these models struggle with low values estimation. Although interpretability, explainability, and use of models across different datasets or events outside of the training data may be challenging, LSTM models are robust and efficient. Full article
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19 pages, 8582 KiB  
Article
Watershed Hydrological Responses to Land Cover Changes at Muger Watershed, Upper Blue Nile River Basin, Ethiopia
by Dawit Samuel Teshome, Megersa Kebede Leta, Habitamu Taddese, Alene Moshe, Terefe Tolessa, Gebiaw T. Ayele and Songcai You
Water 2023, 15(14), 2533; https://doi.org/10.3390/w15142533 - 10 Jul 2023
Cited by 1 | Viewed by 1477
Abstract
Changes in land cover (LC) are the major factors influencing the hydrological processes within a watershed. Understanding the impacts of LC on watershed hydrology is crucial for planning and predicting land resource utilization, water resources, and sustaining hydrological balance. This study assesses the [...] Read more.
Changes in land cover (LC) are the major factors influencing the hydrological processes within a watershed. Understanding the impacts of LC on watershed hydrology is crucial for planning and predicting land resource utilization, water resources, and sustaining hydrological balance. This study assesses the hydrological responses of LC changes in the Muger watershed located in the Upper Blue Nile River Basin (UBNRB) from 1986 to 2020. We used the Soil and Water Assessment Tool (SWAT) hydrological model to investigate the effects of LC on the hydrological process. The simulations were driven by several datasets, such as watershed elevations, mean climatology, hydrology and soil datasets, and LC satellite maps for three time periods (i.e., satellite imagery taken in 1986, 2003, and 2020). We found that the key LC changes that affected hydrological parameters in the Muger watershed are changes in cultivation land, forest land, and settlement. The expansion of cultivation land and shrinkage of forest and shrub lands triggered surface runoff and a reduction in groundwater between 1986 and 2003. Additionally, settlement was identified as the primary factor contributing to increases in evapotranspiration (ET) and surface runoff. The LC changes that occurred between 1986 and 2020 reduced the average annual, wet season, and dry season streamflow. Between 2003 and 2020, surface runoff decreased by 3.71% due to the effect of land landscape restoration interventions. The outcome of the study can assist decision-makers and planners in preparing adaptable strategies under changing LC conditions within a watershed. Full article
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