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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 20549

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Special Issue Editors

Prof. Dr. Vahid Nourani

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Guest Editor

1. Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard 99138, Turkey
2. Faculty of Civil Engineering, University of Tabriz, Tabriz 51368, Iran
Interests: climate change modeling; artificial intelligence in hydrology; numerical methods in water sciences; geostatistics; stochastic hydrology; GIS and RS applications in water science
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Dr. Aida H. Baghanam

E-Mail Website
Guest Editor

Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz 51368, Iran
Interests: hydro-environmental modeling; climate change modeling; artificial intelligence in hydrology; water quality assessment; landfill leachate

Special Issue Information

Dear Colleagues,

Water is the vital component of everyday life through which climate change affects human health, environment and the economy. In this regard, sustainable water resource planning and management should be the essential concentration of policy makers to cope with the changing climate. Therefore, to achieve the sustainable development goals, realization of the nonlinear impact of climate change on water demand and supply is necessary. The greenhouse effect on the hydrologic cycle has led to alterations in the spatiotemporal characteristics of precipitation and, subsequently, the hydrological balance in water resources has been disturbed. Examples of such dire disarrays are freshwater resource reductions, alterations in the timing and magnitude of runoff and soil moisture leading to severe floods and drought, change in snow accumulation and melt timings and patterns, seawater level changes, poor water quality, increased sediment delivery, decrease in groundwater replenishment, changes in summer atmospheric circulation patterns, etc. Such changes raise the possibility of environmental, social, and economical problems, and they have significant impact on future water resources planning and management. Moreover, the mentioned climate change impacts on the hydrologic cycle accompanied with anthropogenic effects, which endanger a resilient and sustainable water resource management plan. Therefore, accurate identification of problems that have occurred, and the estimation of future challanges induced by climate change, can pave the way for proper mitigation and adoption to acheive sustainability goals. This Special Issue of Sustanability calls for innovative research papers to contribute to assessing the climate change impact over the hydrological processes at different scales, from catchment, to region, and to globe considering two strategies: 1) modeling research to recognize climate change quality and quantity on hydroclimatologic variables to incorporate the various aspects of climate change impacts, and 2) developing decision-making policies to prevail against the adverse consequences of climate change.

Ultimately, topics such as downscaling techniques, integration of downscaling with hydrological modeling, climate extremes and impacts on water resources, multicriteria analyses, water availability assessment, management of aquifer systems, and adaptation strategies for water resources in a changing climate are welcome in this Special Issue.

Prof. Dr. Vahid Nourani
Dr. Aida H. Baghanam
Guest Editors

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Keywords

climate change impact
hydrological processes
water resources management and planning
decision making
downscaling of climate models
watershed models
sustainability
droughts
floods
ecosystems
surface and groundwater vulnerability
evapotranspiration
socioeconomic effects

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Published Papers (7 papers)

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Research

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19 pages, 8554 KiB

Open AccessArticle

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

by Aida Hosseini Baghanam, Vahid Nourani, Ehsan Norouzi, Amirreza Tabataba Vakili and Hüseyin Gökçekuş

Sustainability 2023, 15(21), 15209; https://doi.org/10.3390/su152115209 - 24 Oct 2023

Cited by 4 | Viewed by 1687

Abstract

Climate model (CM) statistical downscaling requires quality and quantity modifications of the CM’s outputs to increase further modeling accuracy. In this respect, multi-resolution wavelet transform (WT) was employed to determine the hidden resolutions of climate signals and eliminate bias in a CM. The results revealed that the newly developed discrete wavelet transform (DWT)-based bias correction method can outperform the quantile mapping (QM) method. In this study, wavelet coherence analysis was utilized to assess the high common powers and the multi-scale correlation between the predictors and predictand as a function of time and frequency. Thereafter, to rate the most contributing predictors based on potential periodicity, the average variance was calculated, which is named the Scaled Average (SA) measure. Consequently, WT along with Artificial Neural Network (ANN) were applied for bias correction and identifying the dominant predictors for statistical downscaling. The CAN-ESM5 data of Canadian climate models and INM-CM5 data of Russian climate models over two climatic areas of Iran with semi-arid (Tabriz) and humid (Rasht) weather were applied. The projection of future precipitation revealed that Tabriz will experience a 3.4–6.1% decrease in precipitation, while Rasht’s precipitation will decrease by 1.5–2.5%. These findings underscore the importance of refining CM data and employing advanced techniques to assess the potential impacts of climate change on regional precipitation patterns. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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27 pages, 5639 KiB

Open AccessArticle

Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment

by Arathy Nair Geetha Raveendran Nair, Shamla Dilama Shamsudeen, Meera Geetha Mohan and Adarsh Sankaran

Sustainability 2023, 15(19), 14148; https://doi.org/10.3390/su151914148 - 25 Sep 2023

Cited by 5 | Viewed by 3093

Abstract

Accurate prediction of future streamflow in flood-prone regions is crucial for effective flood management and disaster mitigation. This study presents an innovative approach for streamflow projections in deep learning (DL) environment by integrating the quantitative Land-Use Land-Cover (LULC) overlaid with flow accumulation values and the various Global Climate Model (GCM) simulated data. Firstly, the Long Short Term Memory (LSTM) model was developed for the streamflow prediction of Greater Pamba River Basin (GPRB) in Kerala, India for 1985 to 2015 period, considering the climatic inputs. Then, the flow accumulation-weighted LULC integration was considered in modelling, which substantially improves the accuracy of streamflow predictions including the extremes of all the three stations, as the model accounts for the geographical variety of land cover types towards the streamflow at the sub-basin outlets. Subsequently, Reliability Ensemble Averaging (REA) technique was used to create an ensemble of three candidate GCM products to illustrate the spectrum of uncertainty associated with climate projections. Future LULC changes are accounted in regional scale based on the sub-basin approach by means of Cellular-Automata Markov Model and used for integrating with the climatic indices. The basin-scale streamflow projection is done under three climate scenarios of SSP126, SSP245 and SSP585 respectively for lowest, moderate and highest emission conditions. This work is a novel approach of integrating quantified LULC with flow accumulation and other climatic inputs in a DL environment against the conventional techniques of hydrological modelling. The DL model can adapt and account for shifting hydrological responses induced by changes in climatic and LULC inputs. The integration of flow accumulation with changes in LULC was successful in capturing the flow dynamics in long-term. It also identifies regions that are more likely to experience increased flooding in the near future under changing climate scenarios and supports decision-making for sustainable water management of the Greater Pamba Basin which was the worst affected region in Kerala during the mega floods of 2018. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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32 pages, 11185 KiB

Open AccessArticle

Evaluation of Geospatial Interpolation Techniques for Enhancing Spatiotemporal Rainfall Distribution and Filling Data Gaps in Asir Region, Saudi Arabia

by Ahmed M. Helmi, Mohamed Elgamal, Mohamed I. Farouk, Mohamed S. Abdelhamed and Bakinam T. Essawy

Sustainability 2023, 15(18), 14028; https://doi.org/10.3390/su151814028 - 21 Sep 2023

Cited by 11 | Viewed by 2750

Abstract

Providing an accurate spatiotemporal distribution of rainfall and filling data gaps are pivotal for effective water resource management. This study focuses on the Asir region in the southwest of Saudi Arabia. Given the limited accuracy of satellite data in this arid/mountain-dominated study area, geospatial interpolation has emerged as a viable alternative approach for filling terrestrial records data gaps. Furthermore, the irregularity in rain gauge data and the yearly spatial variation in data gaps hinder the creation of a coherent distribution pattern. To address this, the Centered Root Mean Square Error (CRMSE) is employed as a criterion to select the most appropriate geospatial interpolation technique among 51 evaluated methods for maximum and total yearly precipitation data. This study produced gap-free maps of total and maximum yearly precipitation from 1966 to 2013. Beyond 2013, it is recommended to utilize ordinary Kriging with a J-Bessel semivariogram and simple Kriging with a K-Bessel semivariogram to estimate the spatial distribution of maximum and total yearly rainfall depth, respectively. Additionally, a proposed methodology for allocating additional rain gauges to improve the accuracy of rainfall spatial distribution is introduced based on a cross-validation error (CVE) assessment. Newly proposed gauges in the study area resulted in a significant 21% CVE reduction. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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18 pages, 3716 KiB

Open AccessArticle

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

by Han Chen, Ziqi Zhou, Han Li, Yizhao Wei, Jinhui (Jeanne) Huang, Hong Liang and Weimin Wang

Sustainability 2023, 15(12), 9826; https://doi.org/10.3390/su15129826 - 20 Jun 2023

Cited by 2 | Viewed by 1635

Abstract

The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-K_c model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R²) of 0.75). The FAO-56 dual K_c model performed best in the ET simulation at the suburb area site (R² of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R² of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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12 pages, 2610 KiB

Open AccessArticle

Establishment of Monterrey Pine (Pinus radiata) Plantations and Their Effects on Seasonal Sediment Yield in Central Chile

by Roberto Pizarro, Pablo García-Chevesich, Ben Ingram, Claudia Sangüesa, Juan Pino, Alfredo Ibáñez, Romina Mendoza, Carlos Vallejos, Felipe Pérez, Juan Pablo Flores, Mauricio Vera, Francisco Balocchi, Ramón Bustamante-Ortega and Gisella Martínez

Sustainability 2023, 15(7), 6052; https://doi.org/10.3390/su15076052 - 31 Mar 2023

Cited by 1 | Viewed by 1919

Abstract

Sediment production and transport in a basin are generally a function of the degree of soil protection, normally represented by plant cover. In this study, two basins located at similar latitudes but with different hydrological regimens and plant covers were studied, one with a pluvial regimen and forest plantations (Purapel) and another one with the pluvio-nival regimen and native forest (Ñuble). For this purpose, sediment yield was analyzed in both drainage areas using the Mann-Kendall statistical test. Both basins showed larger amounts of sediment production during winter months. In addition, sediment yield trends did not show significant variation in the case of the Ñuble, most likely due to non-relevant changes in plant cover over time. However, there is a sustained decrease in annual sediment release at Purapel, coinciding with the afforestation in the basin, so it is logical to attribute the referred reduction to this process. For the first time, the behavior of two watersheds is contrasted, one covered with native forest and the other one with forest plantations, appreciating that the basin covered with plantations presents a reduction in sediment production over time, which means that forest plantations are efficient in sediment retention, even in contrast to native forest. However, both basins have different types of soil, topography, etc., meaning that more studies are needed to support this theory. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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14 pages, 2907 KiB

Open AccessEditor’s ChoiceArticle

The Skills of Medium-Range Precipitation Forecasts in the Senegal River Basin

by Mekonnen Gebremichael, Haowen Yue, Vahid Nourani and Richard Damoah

Sustainability 2022, 14(6), 3349; https://doi.org/10.3390/su14063349 - 12 Mar 2022

Cited by 5 | Viewed by 3081

Abstract

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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Review

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23 pages, 2738 KiB

Open AccessReview

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

by Dilayda Soylu Pekpostalci, Rifat Tur, Ali Danandeh Mehr, Mohammad Amin Vazifekhah Ghaffari, Dominika Dąbrowska and Vahid Nourani

Sustainability 2023, 15(7), 6080; https://doi.org/10.3390/su15076080 - 31 Mar 2023

Cited by 30 | Viewed by 5172

Abstract

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey. Full article

(This article belongs to the Special Issue Hydrological Management Adopted to Climate Change)

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