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Precision Management of Water Resources under Changing Climate and Weather Dynamics: Data, Simulation, Modeling, and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 6144

Special Issue Editor

Biological Sciences Engineering, University of Wisconsin-Madison, Madison, WI 53703, USA
Interests: water resources management; water quality and quantity; uncertainty quantification; climate change; hydrologic modeling; stochastic modeling; time series analysis; sustainability

Special Issue Information

Dear Colleagues,

On March 22, 2018, the UN General Assembly declared the decade from 2018 through 2028 as the “Water Action Decade” due to the limited access that the geometrically progressing population has to good quality and sufficient water. With this in mind, managing water resources for better sustenance is now more than ever. Henceforth, we would like to invite you to contribute your novel research, findings, and observations to this Special Issue, titled “Precision Management of Water Resources under Changing Climate and Weather Dynamics: Data, Simulation, Modeling, and Sustainability” by 10 December 2022.

To answer questions that are critical to water-related issues is not easy and largely dependent the availability of a large number of data. Data can be collected, or simulated using certain inputs to plan a proactive preparedness program where data are limited, to draw conclusions. With precision measurement calculated using drones, sensors, hyperspectral remote sensing, hydrologic models, stochastic and advanced mathematics, and statistics, it is now possible to quantify impact, put resources in place to mitigate those impacts, and make sustainable plans for the future.

The changes that have been recorded short-term weather patterns and in the long-term climate pose a number of threats in terms of water surplus (flash floods) and drought in different parts of the world and require novel solutions to mitigate the implications caused by these natural and somewhat anthropogenic led disasters, especially in terms of their effects on agriculture.

With this vision in mind, this Special Issue is dedicated to the following themes, and we extend an invitation for the submission of your research and to help further advance science to solve these critical water issues:

  1. Agricultural water management—In situ, plot-scale studies on large watershed modeling solutions;
  2. Urban water management—planning safe, sufficient, and equitable water distribution in towns and cities;
  3. Data and water—modeling/simulation studies addressing water problems;
  4. Water, policy, and sustainability—planning, implementation, and adoption of feasible water management plans: successes and failures.

I look forward to receiving your contributions.

Dr. Sushant Mehan
Guest Editor

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. Sustainability 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 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

  • water resources management
  • sustainability
  • agricultural and urban watersheds
  • data
  • policy

Published Papers (3 papers)

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Research

22 pages, 25199 KiB  
Article
A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India
by Vasala Saicharan and Shwetha Hassan Rangaswamy
Sustainability 2023, 15(7), 5758; https://doi.org/10.3390/su15075758 - 25 Mar 2023
Cited by 3 | Viewed by 1767
Abstract
Precise rainfall measurement is essential for achieving reliable results in hydrologic applications. The technological advancement has brought numerous rainfall datasets that can be available to assess rainfall patterns. However, the suitability of a given dataset for a specific location remains an open question. [...] Read more.
Precise rainfall measurement is essential for achieving reliable results in hydrologic applications. The technological advancement has brought numerous rainfall datasets that can be available to assess rainfall patterns. However, the suitability of a given dataset for a specific location remains an open question. The objective of this study is to find which rainfall datasets perform well in India at various spatial resolutions: pixel level, meteorological sub-divisions (MSDs) level, and India as a whole and temporal resolutions: monthly and yearly. This study performs skill metrics analysis on seven widely used rainfall datasets—GPM, CRU, CHIRPS, GLDAS, PERSIANN-CDR, SM2RAIN, and TerraClimate—using the Indian Meteorological Department’s (IMD) gridded data as a reference. The rule-based decision tree techniques are employed on the obtained skill metrics analysis values to find the good-performing rainfall dataset at each pixel value among all the datasets used. The MSD and pixel-wise analyses reveal that GPM performs well, while TerraClimate performed the most poorly in almost all MSDs. The analysis suggests that of the satellite-derived, gauged, and merged datasets, merged-type are the good-performing datasets at the MSD level, with approximately 17 MSDs demonstrating the same. The temporal analysis (in both month- and year-wise scales) also suggests that GPM is a good-performing dataset. This study obtained the optimal dataset for each pixel among the seven selected datasets. The GPM dataset typically ranks as a good-performing fit, followed by CHIRPS and then PERSIANN-CDR. Despite its finer resolution, the TerraClimate dataset ranks lowest at the pixel level. This research will aid in selecting the optimal dataset for MSDs and pixels to obtain reliable results for hydrologic and agricultural applications, which will contribute to sustainable development. Full article
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15 pages, 3579 KiB  
Article
Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling
by Erdem Küçüktopcu, Emirhan Cemek, Bilal Cemek and Halis Simsek
Sustainability 2023, 15(7), 5689; https://doi.org/10.3390/su15075689 - 24 Mar 2023
Cited by 5 | Viewed by 1360
Abstract
Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when [...] Read more.
Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET0). The proposed models are developed and tested using daily ET0 data collected over 11 years (2010–2020) in the Samsun province of Türkiye. The results show that the ARIMA–GRNN model reduces the root mean square error by 48.38%, the ARIMA–ANFIS model by 8.56%, and the ARIMA–ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET0, which can be beneficial for many branches such as agriculture and water management that require dependable ET0 estimations. Full article
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18 pages, 2366 KiB  
Article
Pre and Post Water Level Behaviour in Punjab: Impact Analysis with DiD Approach
by Yogita Sharma, Baljinder Kaur Sidana, Sunny Kumar, Samanpreet Kaur, Milkho Kaur Sekhon, Amrit Kaur Mahal and Sushant Mehan
Sustainability 2023, 15(3), 2426; https://doi.org/10.3390/su15032426 - 29 Jan 2023
Viewed by 1912
Abstract
Punjab Agriculture is trapped in the complex nexus of groundwater depletion and food insecurity. The policymakers are concerned about reducing groundwater extraction at any cost for irrigation without jeopardizing food security. In this regard, the Government of Punjab introduced the “Punjab Preservation of [...] Read more.
Punjab Agriculture is trapped in the complex nexus of groundwater depletion and food insecurity. The policymakers are concerned about reducing groundwater extraction at any cost for irrigation without jeopardizing food security. In this regard, the Government of Punjab introduced the “Punjab Preservation of Subsoil Water Act, 2009”. The present paper examines the impact of the “Preservation of Sub Soil Water Act, 2009” on pre- and post-water levels in Punjab using the difference-in-difference (DiD) approach. The state has witnessed a severe fall of 0.50 m per year and 0.43 m per year for the post-monsoon and pre-monsoon season, respectively. Only 2.62 per cent of wells were in the range of 20–40 m depth in the state in 1996, which increased to 42 per cent and 67 per cent in 2018 for the pre-monsoon period, and post monsoon period respectively, depicting an increase of 25 times. The groundwater depth in high rice-growing(treated) districts declined by 1.53 and 1.39 m than the low rice-growing (control) districts in the pre-monsoon and post-monsoon periods respectively post the enactment of PPSW Act, 2009. A groundwater governance framework is urgently needed to manage the existing and future challenges connected with the groundwater resource. Full article
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