Special Issue "Response and Simulation of Watershed Hydrological Cycle under Climate Change"

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 1732

Special Issue Editors

College of Agriculture and Human Sciences, Prairie View A&M University, Prairie View, TX 77446, USA
Interests: watershed hydrology; SWAT model; groundwater hydrology; catchment hydrology; hydrology; hillslope hydrology; environmental engineering
Cooperative Agricultural Research Center, Prairie View A&M University, Prairie View, TX 77446, USA
Interests: water security; agricultural water management; environmental sustainability; hydrological modeling; climate change; drought
Dr. Di Liu
E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing, China
Interests: climate change; climate modeling; land-atmosphere interaction; hydrological cycle; hydrological modeling; data assimilation; machine learning; climate-water-food nexus

Special Issue Information

Dear Colleagues,

Climate change has significantly influenced the movement of water through the land, oceans, and the atmosphere. It has many implications on watershed hydrological processes, resulting in severe droughts, floods, sea-level rise, and storms. To overcome such unprecedented climate change impacts on the watershed hydrologic cycle, modeling is one of the best tools to simulate watershed hydrological processes. In recent decades, hydrologic models have been used to simulate hydrologic processes based on historical data. However, the response and simulation of the watershed hydrological cycle under climate change have not been well studied. An advanced simulation of the watershed hydrologic cycle is needed to better predict the impacts of climate change using robust models and machine learning.

This Special Issue, “Response and Simulation of Watershed Hydrological Cycle under Climate Change”, will focus on better understanding future watershed hydrologic simulation cycles with more accurate and reliable information. Therefore, new research studies are required to investigate the impacts of climate change on watershed hydrologic processes. Hence, we invite article submissions that contribute but are not limited to the following thematic areas:

  • Response and simulation of watershed processes under climate change;
  • Watershed models and machine learning techniques to simulate watershed hydrologic cycles under different land-use changes;
  • Impacts of extreme events and climate changes on water resources;
  • Water–energy–food–land nexus as a framework for achieving sustainable water management and their impacts.

Dr. Nigus Demelash Melaku
Dr. Anoop Valiya Veettil
Dr. Di Liu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.


  • watershed process
  • process-based models
  • climate change
  • hydrologic cycle
  • evapotranspiration
  • temperature
  • runoff
  • streamflow
  • groundwater
  • machine learning
  • climate extremes
  • water–energy–food–land nexus

Published Papers (1 paper)

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The Revised Curve Number Rainfall–Runoff Methodology for an Improved Runoff Prediction
Water 2023, 15(3), 491; https://doi.org/10.3390/w15030491 - 26 Jan 2023
Cited by 1 | Viewed by 1487
The Curve Number (CN) rainfall–runoff model is a widely used method for estimating the amount of rainfall and runoff, but its accuracy in predicting runoff has been questioned globally due to its failure to produce precise predictions. The model was developed by the [...] Read more.
The Curve Number (CN) rainfall–runoff model is a widely used method for estimating the amount of rainfall and runoff, but its accuracy in predicting runoff has been questioned globally due to its failure to produce precise predictions. The model was developed by the United States Department of Agriculture (USDA) and Soil Conservation Services (SCS) in 1954, but the data and documentation about its development are incomplete, making it difficult to reassess its validity. The model was originally developed using a 1954 dataset plotted by the USDA on a log–log scale graph, with a proposed linear correlation between its two key variables (Ia and S), given by Ia = 0.2S. However, instead of using the antilog equation in the power form (Ia = S0.2) for simplification, the Ia = 0.2S correlation was used to formulate the current SCS-CN rainfall–runoff model. To date, researchers have not challenged this potential oversight. This study reevaluated the CN model by testing its reliability and performance using data from Malaysia, China, and Greece. The results of this study showed that the CN runoff model can be formulated and improved by using a power correlation in the form of Ia = Sλ. Nash–Sutcliffe model efficiency (E) indexes ranged from 0.786 to 0.919, while Kling–Gupta Efficiency (KGE) indexes ranged from 0.739 to 0.956. The Ia to S ratios (Ia/S) from this study were in the range of [0.009, 0.171], which is in line with worldwide results that have reported that the ratio is mostly 5% or lower and nowhere near the value of 0.2 (20%) originally suggested by the SCS. Full article
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