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Drought and Sustainable Water Management

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8209

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Dr. Mir Jafar Sadegh Safari

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Guest Editor

Associate Professor, Department of Civil Engineering, Yaşar University, Izmir, Turkey
Interests: drought; hydraulics; hydrology; sediment transport; urban drainage

Dr. Gokmen Tayfur

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Guest Editor

Professor, Department of Civil Engineering, Izmir Institute of Technology, Izmir, Turkey
Interests: drought; flood; rainfall-runoff; sediment transport; dam break; water resources

Special Issue Information

Dear Colleagues,

Drought is a complicated problem, and it is difficult to provide a unique definition of its drivers and consequences. All types of droughts begin with a deficit in precipitation in time or space, and comprise a more complicated hydro-meteorological condition. Drought is one of the primary important challenges in the production, safety and resilience of foods and agricultural goods. Changing climate raises serious concerns about water scarcity and energy generation. In order to combat drought, sustainable water management practices are needed to achieve the Sustainable Development Goals (SDGs). This Special Issue, entitled “Drought and Sustainable Water Management”, focuses on drought analysis considering the Sustainable Development Goals. We welcome papers applying a variety of drought indices for examining the meteorological, agricultural, hydrological, socio-economical, and ecological aspects of drought. In addition to the implementation of drought indices, analyzing the trend of hydro-meteorological parameters such as precipitation and temperature can provide valuable insight to improve our understanding drought and climate change. Conducting trend analysis provides a projection for future events by using historical meteorological records. Together with analyzing drought in any region by applying a variety of drought indices and trend analysis methods, the implementation of advanced computational tools such as advanced evolutionary machine learning tools for monitoring the drought is welcome.

Dr. Mir Jafar Sadegh Safari
Dr. Gokmen Tayfur
Guest Editors

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Keywords

drought
drought indices
precipitation
temperature
sustainability
water management
climate change

Published Papers (8 papers)

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Research

27 pages, 4835 KiB

Open AccessArticle

Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada

by Amirhossein Salimi, Amir Noori, Isa Ebtehaj, Tadros Ghobrial and Hossein Bonakdari

Sustainability 2024, 16(8), 3461; https://doi.org/10.3390/su16083461 - 21 Apr 2024

Viewed by 309

Abstract

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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24 pages, 6687 KiB

Open AccessArticle

A Synergistic Optimization Algorithm with Attribute and Instance Weighting Approach for Effective Drought Prediction in Tamil Nadu

by Karpagam Sundararajan and Kathiravan Srinivasan

Sustainability 2024, 16(7), 2936; https://doi.org/10.3390/su16072936 - 01 Apr 2024

Viewed by 568

Abstract

The creation of frameworks for lowering natural hazards is a sustainable development goal specified by the United Nations. This study aims to predict drought occurrence in Tamil Nadu, India, using 26 years of data, with only 3 drought years. Since the drought-occurrence years are minimal, it is an imbalanced dataset, which gives a suboptimal classification performance. The accuracy metric has a tendency to produce misleadingly high results by focusing on the accuracy of forecasting the majority class while ignoring the minority class; hence, this work considers the metrics’ precision and recall. A novel strategy uses attribute (or instance) weighting, which allots weights to attributes (or instances) based on their importance, to improve precision and recall. These weights are found using a bio-inspired optimization algorithm, by designing its fitness function to improve precision and recall of the minority (drought) class. Since increasing precision and recall is a tug-of-war, multi-objective optimization helps to identify optimal attribute (or instance) weight balancing precision and recall while maximizing both. The newly introduced Synergistic Optimization Algorithm (SOA) is utilized for multi-objective optimization in order to ascertain weights for attributes (or instances). In SOA, to solve multi-objective optimization, each objective’s population was generated using three distinct algorithms, namely, the Genetic, Firefly, and Particle Swarm Optimization (PSO) algorithms. The experimental results demonstrated that the prediction performance for the minority drought class was superior when utilizing instance (or attribute) weighting compared to the approach not employing attribute/instance weighting. The Gradient Boosting classifier with an attribute-weighted dataset achieved precision and recall values of 0.92 and 0.79, whereas, with instance weighting, the values were 0.9 and 0.76 for the drought class. The attribute weighting shows that in addition to the default drought indices SPI and SPEI, pollution factors and mean sea level rise are valuable indicators in drought prediction. From instance weighting, it is inferred that the instances of the months of March, April, July, and August contribute most to drought prediction. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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24 pages, 38622 KiB

Open AccessArticle

Hydrological Implications of Recent Droughts (2004–2022): A SWAT-Based Study in an Ancient Lowland Irrigation Area in Lombardy, Northern Italy

by Alice Bernini, Rike Becker, Odunayo David Adeniyi, Giorgio Pilla, Seyed Hamidreza Sadeghi and Michael Maerker

Sustainability 2023, 15(24), 16771; https://doi.org/10.3390/su152416771 - 12 Dec 2023

Viewed by 799

Abstract

This study examines the hydrological dynamics of the Ticino irrigation cascade in northern Italy from 2004 to 2022. The region, which is shaped by human activity, is characterized by its flat topography and complex management of water resources, featuring a unique historic irrigation cascade. Utilizing the Soil and Water Assessment Tool (SWAT), we investigated the water availability during recent severe droughts in this complex agricultural environment, which lacks natural drainage. This area faces risks due to increasing temperatures and increased rainless days. Therefore, understanding the soil water dynamics is essential for maintaining the system’s sustainability. Calibrating and validating the SWAT model with runoff data was challenging due to the absence of natural drainage. Thus, we utilized MOD16 evapotranspiration (AET) data for calibration. Generally, the calibration and validation of the SWAT model yielded satisfactory results in terms of the Kling–Gupta efficiency (KGE). Despite some discrepancies, which were mainly related to the data sources and resolution, the calibrated model’s outputs showed increased actual evapotranspiration that was influenced by climate and irrigation, leading to water deficits and droughts. The soil water content (SWC) decreased by 7% over 15 years, impacting crop productivity and environmental sustainability. This also resulted in rising water stress for crops and the ecosystem in general, highlighting the direct impact of adverse climate conditions on soil hydrology and agriculture. Our research contributes to the understanding of soil–water dynamics, as it specifically addresses recent droughts in the Lombardy lowlands. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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17 pages, 2018 KiB

Open AccessArticle

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

by Babak Vaheddoost, Babak Mohammadi and Mir Jafar Sadegh Safari

Sustainability 2023, 15(21), 15675; https://doi.org/10.3390/su152115675 - 06 Nov 2023

Cited by 1 | Viewed by 727

Abstract

This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash–Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR²), and the Kling–Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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19 pages, 3401 KiB

Open AccessArticle

Hydrological Properties of Rill Erosion on a Soil from a Drought-Prone Area during Successive Rainfalls as a Result of Microorganism Inoculation

by Masumeh Ashgevar Heydari, Seyed Hamidreza Sadeghi and Atefeh Jafarpoor

Sustainability 2023, 15(19), 14379; https://doi.org/10.3390/su151914379 - 29 Sep 2023

Cited by 3 | Viewed by 824

Abstract

Soil and water loss is one of the most severe kinds of land degradation, particularly in drought-vulnerable regions. It diminishes fertility and increases natural catastrophes, such as floods, landslides, sedimentation, drought, and economic, social, and political issues. The current study explores the efficacy of individual and combination cyanobacteria and bacteria inoculation on runoff production from plots generated by rill erosion on soil from the Marzanabad drought-prone region, northern Iran, and exposed to five successive rainfalls with three days intervals. Experiments were conducted on mid-sized plots with dimensions of 6 × 1 m, three replications, and a 30% slope during simulated rains at the lab with an intensity of 50 mm h⁻¹ and a duration of 30 min. Also, excess runoff of about 2.180 L min⁻¹ was introduced to the plots to promote rill formation. Because none of the treated plots created runoff during the design rainfall, the expected circumstances were subject to continuous rainfall until runoff was generated. Compared to the control plots, statistical analysis indicated that the study treatments had a significant (p < 0.01) lower influence on hydrological components during the initial rainfall event. The highest performance was obtained in the combination inoculation of cyanobacteria and bacteria in successive rainfalls (i.e., first to the fourth event), which reduced runoff volume and coefficient by 35.41, 45.34, 26.35, and 36.43%, respectively. During subsequent rainfalls, the bacteria and combination treatment of cyanobacteria and bacteria did not vary substantially (p = 0.94) on the study components. As a result, after consecutive rainfall events, runoff volume dropped by 20.79, 22.15, 12.83, and 15.87%, and the runoff coefficient reduced by 20.80, 22.15, 12.84, and 15.88%. The cyanobacteria treatment diminished the study components only after the initial rainstorm event. The current study’s findings underscored the need to minimize water loss in the early phases of erosion in drought-sensitive regions where soil and water conservation is a vital task. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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17 pages, 3855 KiB

Open AccessArticle

Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

by Enes Gul, Efthymia Staiou, Mir Jafar Sadegh Safari and Babak Vaheddoost

Sustainability 2023, 15(15), 11568; https://doi.org/10.3390/su151511568 - 26 Jul 2023

Cited by 2 | Viewed by 864

Abstract

The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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23 pages, 6601 KiB

Open AccessArticle

Meteorological Drought Assessment and Trend Analysis in Puntland Region of Somalia

by Nur Mohamed Muse, Gokmen Tayfur and Mir Jafar Sadegh Safari

Sustainability 2023, 15(13), 10652; https://doi.org/10.3390/su151310652 - 06 Jul 2023

Cited by 1 | Viewed by 1484

Abstract

Drought assessment and trend analysis of precipitation and temperature time series are essential in the planning and management of water resources. Long-term precipitation and temperature historical records (monthly for 41 years, from 1980 to 2020) are used to investigate annual drought characteristics and trend analysis in Somalia’s northern region. Six drought indices of the normal Standardized Precipitation Index (normal-SPI), the log normal Standardized Precipitation Index (log-SPI), the Standardized Precipitation Index using the gamma distribution (Gamma-SPI), the Percent of Normal Index (PNI), the Discrepancy Precipitation Index (DPI), and the Deciles Index (DI) are used in this study for the annual drought assessment. The log-SPI, the gamma-SPI, the PNI, and the DPI could capture historical extreme and severe droughts that occurred in the early 1980s and over the last two decades. The results indicate that Somalia has gone through extended drought periods over the past quarter century, exacerbating the existing humanitarian situation. The normal-SPI, gamma-SPI, and PNI indicate less and moderate drought conditions, whereas log-SPI, DPI, and DI accurately capture historical extreme and severe drought periods; thus, these methods are recommended as annual drought assessment tools in the studied region. Not only are the PNI and DPI less correlated to each other, but their correlation coefficient (CC) with SPI-based drought indices are not as high as SPI-based indices which are close to unity. For the purpose of the trend analysis, the Mann Kendall (MK) test, the Spearman’s rho (SR) test, and the Şen test are used. Furthermore, the Pettitt test is implemented to detect the change points and the Thiel-Sen approach is used to estimate the magnitude of trend in the precipitation and temperature time series. The results indicate that there is overall warming in the region which has experienced a significant shift in trend direction since 2000. The trend analysis of annual precipitation data time series shows that Bossaso and Garowe stations have significant positive trends, while the Qardho station has no trend. In 1997 and 1998, respectively, abrupt changes in annual precipitation are detected at Qardho and Garowe stations. Due to the civil war of more than three decades in Somalia and the non-institutionalized governance to inform historical drought conditions in the country, determining the most appropriate meteorological drought index would help to develop a drought monitoring system for states and the entire country. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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17 pages, 14427 KiB

Open AccessArticle

Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

by Ali Danandeh Mehr, Rifat Tur, Mohammed Mustafa Alee, Enes Gul, Vahid Nourani, Shahrokh Shoaei and Babak Mohammadi

Sustainability 2023, 15(5), 3923; https://doi.org/10.3390/su15053923 - 21 Feb 2023

Cited by 6 | Viewed by 1603

Abstract

Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively. Full article

(This article belongs to the Special Issue Drought and Sustainable Water Management)

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