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Atmosphere
Special Issues
Rainfall-Runoff Modelling
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Rainfall-Runoff Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8506

Special Issue Editors

Dr. Muhammad Jehanzaib

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: climate change; machine learning; drought propagation; rainfall-runoff modeling; climate extremes
Special Issues, Collections and Topics in MDPI journals
Dr. Kristian Förster

E-Mail Website
Guest Editor
Institut für Hydrologie und Wasserwirtschaft, Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany
Interests: hydrology; climate variability and change; seasonal to decadal prediction; hydrologic engineering; nature-based solutions; urban hydrology; snow and glaciers
Dr. Muhammad Ajmal

E-Mail Website
Guest Editor
Department of Agricultural Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan
Interests: hydrological modeling; rainfall-runoff modeling

Special Issue Information

Dear Colleagues,

Surface runoff is responsible for the majority of stream damage; therefore, modelling runoff is critical for preventing and managing its harmful consequences. Runoff modelling helps in gaining a better knowledge of hydrologic phenomena and how changes affect the hydrological cycle. Runoff models depict what happens in water systems as a result of changes in pervious surfaces, vegetation, and weather events. The phenomena of runoff formation is considered to be highly nonlinear, time-varying, and spatially distributed. Many hydrologic models have been constructed specifically for this purpose such as empirical, conceptual, and physical structures. Empirical black-box models are completely devoid of an explicitly well-defined characterization of the physical processes involved in the conversion of rainfall into runoff, while collection of suitable data with sufficient accuracy is a challenge with physical models. Due to increased computation power and deeper knowledge of hydrological cycle, runoff models have become increasingly sophisticated. Modeling runoff can assist in understanding, controlling, and monitoring the quality and quantity of water resources.

Dr. Muhammad Jehanzaib
Prof. Dr. Kristian Förster
Dr. Muhammad Ajmal
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • rainfall-runoff
  • data-driven approach
  • runoff Prediction
  • conceptual event-based modelling
  • satellite-based rainfall data
  • distributed model
  • calibration
  • black box

Published Papers (2 papers)

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Research

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19 pages, 2642 KiB  
Article
Hydroclimatology of the Chitral River in the Indus Basin under Changing Climate
by Zain Syed, Shakil Ahmad, Zakir Hussain Dahri, Muhammad Azmat, Muhammad Shoaib, Azhar Inam, Muhammad Uzair Qamar, Syed Zia Hussain and Sarfraz Ahmad
Atmosphere 2022, 13(2), 295; https://doi.org/10.3390/atmos13020295 - 09 Feb 2022
Cited by 12 | Viewed by 2878
Abstract
Biased distribution of hydro-climate stations in high elevations are major obstacles for reliable appraisal of the hydro-climatic regime of the Chitral Basin located in the extreme north of Pakistan. We modeled this regime in the ARC-SWAT hydrological model forced with the latest gridded [...] Read more.
Biased distribution of hydro-climate stations in high elevations are major obstacles for reliable appraisal of the hydro-climatic regime of the Chitral Basin located in the extreme north of Pakistan. We modeled this regime in the ARC-SWAT hydrological model forced with the latest gridded reanalysis ERA5 Land dataset, bias-corrected against a good quality reference dataset. The performance of the gridded dataset was cross-validated by comparing the model flow simulation against the observed flows. The ERA5 Land overall provided reasonably good estimates. The calibrated model on the daily time scale was able to provide excellent values of the employed statistical measures (NSE, KGE, PBIAS, RMSE and MAE). For a future climate change analysis, climate series was devised using two future projection scenarios (RCP4.5 and RCP8.5) using the best performing GCM (MIROC5_rlilp1) out of five investigated GCMs. The results of the climate change analysis reveal increment in the average temperature up to +3.73 °C and +5.62 °C for RCP4.5 and RCP8.5, respectively, while the analysis of precipitation suggests an annual decrease up to −16% and −35% against RCP4.5 and RCP8.5, respectively, by the end of century. A future simulated flow analysis showed an increment of +0.25 % and decrease of −6.82% for RCP4.5 and RCP8.5, respectively. Further analysis of climate suggests seasonal deflections especially in precipitation and flow regimes. A notable climb in flow quantities was observed during spring season (MAM) in spite of the major reduction in precipitation amounts for that season. This implicitly supports a high rate of glacial/snow melt especially in the spring season during that period. Frequent droughts and floods are also projected by examining flow durations at each interval of the 21st century. Full article
(This article belongs to the Special Issue Rainfall-Runoff Modelling)
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Review

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15 pages, 1730 KiB  
Review
A Systematic Review on Farmers’ Adaptation Strategies in Pakistan toward Climate Change
by Naeem Saddique, Muhammad Jehanzaib, Abid Sarwar, Ehtesham Ahmed, Muhammad Muzammil, Muhammad Imran Khan, Muhammad Faheem, Noman Ali Buttar, Sikandar Ali and Christian Bernhofer
Atmosphere 2022, 13(8), 1280; https://doi.org/10.3390/atmos13081280 - 11 Aug 2022
Cited by 7 | Viewed by 4563
Abstract
Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the [...] Read more.
Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer’s adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers’ adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources. Full article
(This article belongs to the Special Issue Rainfall-Runoff Modelling)
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