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Prof. Ronny Berndtsson
Lund University

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Journal article
Published: 16 July 2021 in Journal of Environmental Management
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Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major groundwater aquifers are in a critical state. For this purpose, we introduce a novel approach using Artificial Intelligence (AI) and machine learning (ML). The methodology involves water budget variables that are easily accessible such as aquifer area, storage coefficient, groundwater use, return flow, discharge, and recharge. The GSL was calculated for 178 major aquifers of Iran using different combinations of input data. Out of 11 investigated variables, agricultural water consumption, aquifer area, river infiltration, and artificial drainage were highly associated to GSL with a correlation of 0.84, 0.79, 0.70, and 0.69, respectively. For the final model, 9 out of the totally 11 investigated variables were chosen for prediction of GSL. Results showed that ML methods are efficient in discriminating between different input variables for reliable GSL estimation. The Harris Hawks Optimization Adaptive Neuro-Fuzzy Inference System (HHO-ANFIS) and the Least-Squares Support Vector Machine (LS-SVM) gave best results. Overall, however, the HHO-ANFIS was most efficient to predict GSL. AI and ML methods can thus, save time and costs for these complex calculations and point at the most efficient data inputs. The suggested methodology is especially suited for data-scarce regions with a great deal of uncertainty and a lack of reliable observations of groundwater levels and pumping.

ACS Style

Zahra Kayhomayoon; Naser Arya Azar; Sami Ghordoyee Milan; Hamid Kardan Moghaddam; Ronny Berndtsson. Novel approach for predicting groundwater storage loss using machine learning. Journal of Environmental Management 2021, 296, 113237 .

AMA Style

Zahra Kayhomayoon, Naser Arya Azar, Sami Ghordoyee Milan, Hamid Kardan Moghaddam, Ronny Berndtsson. Novel approach for predicting groundwater storage loss using machine learning. Journal of Environmental Management. 2021; 296 ():113237.

Chicago/Turabian Style

Zahra Kayhomayoon; Naser Arya Azar; Sami Ghordoyee Milan; Hamid Kardan Moghaddam; Ronny Berndtsson. 2021. "Novel approach for predicting groundwater storage loss using machine learning." Journal of Environmental Management 296, no. : 113237.

Journal article
Published: 13 July 2021 in Sustainability
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Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance.

ACS Style

Sayed Afzali; Ali Khanamani; Ehsan Maskooni; Ronny Berndtsson. Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area. Sustainability 2021, 13, 7817 .

AMA Style

Sayed Afzali, Ali Khanamani, Ehsan Maskooni, Ronny Berndtsson. Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area. Sustainability. 2021; 13 (14):7817.

Chicago/Turabian Style

Sayed Afzali; Ali Khanamani; Ehsan Maskooni; Ronny Berndtsson. 2021. "Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area." Sustainability 13, no. 14: 7817.

Journal article
Published: 08 July 2021 in Water
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Root distribution during rice cultivation is a governing factor that considerably affects soil water content (SWC) and root water uptake (RWU). In this study, the effects of activating root growth (using growth function) and assigning a constant average root depth (no growth during simulation) on SWC and RWU for rice cultivation under four deficit drip irrigation treatments (T90, T80, T70, and T60) were compared in the HYDRUS-2D/3D model version 3.03. A secondary objective was to investigate the effect of applied deficit irrigation treatments on grain yield, irrigation water use efficiency (IWUE), and growth traits of rice. The simulated DI system was designed to reflect a representative field experiment implemented in El-Fayoum Governorate, Egypt, during two successive seasons during 2017 and 2018. The deficit treatments (T90, T80, T70, and T60) used in the current study represent scenarios at which the first irrigation event was applied when the pre-irrigation average SWC within the upper 60 cm of soil depth was equal to 90%, 80%, 70%, and 60% of plant-available water, respectively. Simulation results showed that as water deficiency increased, SWC in the simulation domain decreased, and thereby, RWU decreased. The average SWC within the root zone during rice-growing season under different deficit treatments was slightly higher when activating root growth function than when considering constant average root depth. Cumulative RWU fluxes for the case of no growth were slightly higher than for the case of root growth function for T90, T80, and T70 accounting for 1289.50, 1179.30, and 1073.10 cm2, respectively. Average SWC during the growth season (24 h after the first irrigation event, mid-season, and 24 h after the last irrigation event) between the two cases of root growth was strongly correlated for T90, T80, T70, and T60, where r2 equaled 0.918, 0.902, 0.892, and 0.876, respectively. ANOVA test showed that there was no significant difference for SWC between treatments for the case of assigning root growth function while the difference in SWC among treatments was significant for the case of the constant average root depth, where p-values equaled 0.0893 and 0.0433, respectively. Experimental results showed that as water deficiency decreased, IWUE increased. IWUE equaled 1.65, 1.58, 1.31, and 1.21 kg m−3 for T90, T80, T70, and T60, respectively. Moreover, higher grain yield and growth traits of rice (plant height, tillers number plant−1, panicles length, panicle weight, and grain number panicles−1) were obtained corresponding to T90 as compared with other treatments. Activating the root growth module in HYDRUS simulations can lead to more precise simulation results for specific dates within different growth stages. Therefore, the root growth module is a powerful tool for accurately investigating the change in SWC during simulation. Users of older versions of HYDRUS-2D/3D (version 2.05 and earlier) should consider the limitations of these versions for irrigation scheduling.

ACS Style

Mohamed Eltarabily; Ronny Berndtsson; Nasr Abdou; Mustafa El-Rawy; Tarek Selim. A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation. Water 2021, 13, 1892 .

AMA Style

Mohamed Eltarabily, Ronny Berndtsson, Nasr Abdou, Mustafa El-Rawy, Tarek Selim. A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation. Water. 2021; 13 (14):1892.

Chicago/Turabian Style

Mohamed Eltarabily; Ronny Berndtsson; Nasr Abdou; Mustafa El-Rawy; Tarek Selim. 2021. "A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation." Water 13, no. 14: 1892.

Journal article
Published: 07 July 2021 in Sustainability
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Current sustainability assessment (SA) tools to help deal with contaminated groundwater sites are inherently subjective and hardly applied. One reason may be lack of proper tools for addressing contaminant spread which are basically objective. To fill this gap, there is a need for contaminant transport models that provide site managers with needed room for applying their judgments and considerations about the efficiency of each remediation method based on their experiences in similar cases. INSIDE-T uses trend analysis and inverse modeling to estimate transport parameters. It then simulates contaminant transport both with and without the inclusion of remedial actions in a transparent way. The sustainability of each remedy measure can then be quantified based on the underlying SA tool (INSIDE). INSIDE-T was applied to a site in south Sweden, contaminated with pentachlorophenol. Simulation scenarios were developed to enable comparison between various remediation strategies and combinations of these. The application indicated that natural attenuation was not a viable option within the timeframe of interest. Although pump-and-treat combined with a permeable reactive barrier was found to be just as effective as bioremediation after five years, it received a much lower sustainability score overall. INSIDE-T outcomes enable site managers to test and evaluate different scenarios, a necessity in participatory decision-making practices such as remediation projects.

ACS Style

Mehran Naseri-Rad; Ronny Berndtsson; Ursula McKnight; Magnus Persson; Kenneth Persson. INSIDE-T: A Groundwater Contamination Transport Model for Sustainability Assessment in Remediation Practice. Sustainability 2021, 13, 7596 .

AMA Style

Mehran Naseri-Rad, Ronny Berndtsson, Ursula McKnight, Magnus Persson, Kenneth Persson. INSIDE-T: A Groundwater Contamination Transport Model for Sustainability Assessment in Remediation Practice. Sustainability. 2021; 13 (14):7596.

Chicago/Turabian Style

Mehran Naseri-Rad; Ronny Berndtsson; Ursula McKnight; Magnus Persson; Kenneth Persson. 2021. "INSIDE-T: A Groundwater Contamination Transport Model for Sustainability Assessment in Remediation Practice." Sustainability 13, no. 14: 7596.

Book chapter
Published: 15 June 2021 in Water Resources: Future Perspectives, Challenges, Concepts and Necessities
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Improper utilization of water resources has the potential to result in reduced availability of high-quality water and adverse effects on societal development. In fact, what appears to be a serious gap in comprehensive water resources studies is the lack of a coherent approach that can link different social, economic and environmental parts within the framework of the integrated water management paradigm to extract strategies and operational plans. Comprehensive water resources management (CWRM) is a process that intends to develop and manage water, land and other resources in a way that maximizes the social and economic well-being of human societies, without compromising the integrity and sustainability of vital ecosystems and future benefits. This chapter discusses the definitions of integrated and comprehensive water resources management describing the steps of using integrated management in practical examples.

ACS Style

Alireza Rezaee; Omid Bozorg-Haddad; Ronny Brendtsson; Vijay P. Singh. Comprehensive integrated water management. Water Resources: Future Perspectives, Challenges, Concepts and Necessities 2021, 119 -130.

AMA Style

Alireza Rezaee, Omid Bozorg-Haddad, Ronny Brendtsson, Vijay P. Singh. Comprehensive integrated water management. Water Resources: Future Perspectives, Challenges, Concepts and Necessities. 2021; ():119-130.

Chicago/Turabian Style

Alireza Rezaee; Omid Bozorg-Haddad; Ronny Brendtsson; Vijay P. Singh. 2021. "Comprehensive integrated water management." Water Resources: Future Perspectives, Challenges, Concepts and Necessities , no. : 119-130.

Original paper
Published: 05 June 2021 in Irrigation Science
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The efficiency of irrigation, as well as optimization of nutrients, affect spinach yield in all growth stages. In this study, the sensitivity of spinach (early and mature yields) to shallow saline groundwater and the effect of fertigation treatments on mature yield were experimentally investigated. The sprinkler irrigation experiments were conducted on 0.47 ha of silty clay soil at the University of California Desert Research and Extension Center (DREC) in Imperial Valley, California. Twelve beds in the experimental field were chosen randomly to investigate the effect of three fertigation treatments on spinach yield. Three rates of urea ammonium nitrate (UAN-32) fertilizer; T1: 200 kg ha−1 (150%), T2: 133.3 kg ha−1 (100%), and T3: 66.7 kg ha−1 (50%) in four replicates were applied. Soil samples to depths of up to 120 cm were collected at baby leaves and mature harvesting dates (17th October and 19th November 2019, respectively) for salinity measurements. Additionally, soil matric potential through the 120 cm soil depth was measured and groundwater levels in five observation wells were recorded during the growing season. Results showed that average soil salinity at baby leaves harvesting stage through the top 60 cm active root zone depth ranged from 0.61 to 1.48 dS m−1, which is lower than the spinach salinity threshold limit (2 dS m−1), while the average groundwater depth was 1.90 m with salinity ranging from 6.35 to 10.60 dS m−1. Correlation analysis showed that the baby spinach leaves yield was weakly correlated (r = 0.40) to the average soil salinity in the top 60 cm soil depth. Although groundwater and top 60 cm soil salinity showed an increase at the mature yield harvesting stage, the mature yield was weakly correlated to soil salinity (p = 0.116). As the UAN-32 rate increased, the mature spinach yield increased. The mature spinach yields were 17.31, 14.00, and 12.54 ton ha−1 for T1, T2, and T3 fertigation treatments, respectively. However, only a 10% reduction in yield occurred in T3 treatment corresponding to a 50% reduction in UAN-32 rate by 66.7 kg ha−1. Based on the results of this study, shallow saline groundwater has little impact on spinach yield. In addition, the 50% increase in UAN-32 rate had a significant impact on mature spinach yield. The 150% UAN-32 rate resulted in an increase in spinach yield and could be used in arid and semiarid regions with similar conditions to the Imperial Valley but additional measures to minimize the leaching of nitrate from the root zone and to reduce the load of nitrogen in drainage water are needed to minimize the potential negative impact of over-fertigation on the environment.

ACS Style

Khaled M. Bali; Mohamed G. Eltarabily; Ronny Berndtsson; Tarek Selim. Nutrient and salinity management for spinach production under sprinkler irrigation in the low desert region of California. Irrigation Science 2021, 1 -15.

AMA Style

Khaled M. Bali, Mohamed G. Eltarabily, Ronny Berndtsson, Tarek Selim. Nutrient and salinity management for spinach production under sprinkler irrigation in the low desert region of California. Irrigation Science. 2021; ():1-15.

Chicago/Turabian Style

Khaled M. Bali; Mohamed G. Eltarabily; Ronny Berndtsson; Tarek Selim. 2021. "Nutrient and salinity management for spinach production under sprinkler irrigation in the low desert region of California." Irrigation Science , no. : 1-15.

Journal article
Published: 05 February 2021 in Water
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Nitrate pollution in groundwater is a serious problem in Shimabara Peninsula, Nagasaki, Japan. A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is important for health and environment. In this study, groundwater samples were collected at 12 residential and 57 municipal water supply wells and springs in July and August 2018. Nitrate (NO3−N) concentration at eight sampling sites (12%) exceeded Japanese drinking water standard for NO3 + NO2−N (10 mg L−1). The highest nitrate concentration was 19.9 mg L−1. Polluted groundwater is distributed in northeastern, northwestern, and southwestern areas, where land is used for intensive agriculture. Correlation analysis suggests that nitrate sources are agricultural fertilizers and livestock waste. Dominant groundwater chemistry is (Ca+Mg)−HCO3 or (Ca+Mg)−(SO4+NO3) type. Groundwater with higher nitrate concentration is of (Ca+Mg)−(SO4+NO3) type, indicating nitrate pollution affecting water chemistry. Principal component analysis extracted two important factors controlling water chemistry. The first principal component explained dissolved ions through water–rock interaction and agricultural activities. The second principal component explained cation exchange and dominant agricultural effects from fertilizers. Hierarchical cluster analysis classified groundwater into four groups. One of these is related to the dissolution of major ions. The other three represent nitrate pollution.

ACS Style

Kei Nakagawa; Hiroki Amano; Ronny Berndtsson. Spatial Characteristics of Groundwater Chemistry in Unzen, Nagasaki, Japan. Water 2021, 13, 426 .

AMA Style

Kei Nakagawa, Hiroki Amano, Ronny Berndtsson. Spatial Characteristics of Groundwater Chemistry in Unzen, Nagasaki, Japan. Water. 2021; 13 (4):426.

Chicago/Turabian Style

Kei Nakagawa; Hiroki Amano; Ronny Berndtsson. 2021. "Spatial Characteristics of Groundwater Chemistry in Unzen, Nagasaki, Japan." Water 13, no. 4: 426.

Journal article
Published: 31 December 2020 in Minerals
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The 2016 Kumamoto earthquake had a significant impact on groundwater levels and quality. In some areas, the groundwater level increased significantly due to the release of groundwater from upstream mountainous regions. Conversely, the groundwater level in other areas greatly decreased due to the creation of new fracture networks by the earthquake. There were also significant changes in certain groundwater quality variables. In this study, we used clustering based SOM (self-organizing maps) analysis to improve the understanding of earthquake effects on groundwater quality. We were especially interested in effects on groundwater used for drinking purposes and in nitrate concentration. For this purpose, we studied groundwater nitrate (NO3− + NO2−–N) concentrations for the period 2012–2017. Nitrate concentration changes were classified into seven typical SOM clusters. The clusters were distributed in three representative geographical regions: a high concentration region (>4 mg/L), a low concentration region (<1.6 mg/L) with minimal anthropogenic loading area, and an intermediate concentration region (2–4 mg/L). Depending on these regions, the nitrate concentration changes just before and after the earthquake had both increasing and decreasing trends between 2015–2017. This points to complex physiographical relationships for release of stored upstream groundwater, promotion of infiltration of shallow soil water/groundwater, and nitrate concentration as affected by earthquakes. We present an analysis of these complex relationships and a discussion of causes of nitrate concentration changes due to earthquakes.

ACS Style

Kei Nakagawa; Jun Shimada; Zhi-Qiang Yu; Kiyoshi Ide; Ronny Berndtsson. Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply. Minerals 2020, 11, 43 .

AMA Style

Kei Nakagawa, Jun Shimada, Zhi-Qiang Yu, Kiyoshi Ide, Ronny Berndtsson. Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply. Minerals. 2020; 11 (1):43.

Chicago/Turabian Style

Kei Nakagawa; Jun Shimada; Zhi-Qiang Yu; Kiyoshi Ide; Ronny Berndtsson. 2020. "Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply." Minerals 11, no. 1: 43.

Journal article
Published: 17 December 2020 in Remote Sensing
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Vegetation is an important component of the terrestrial ecosystem that plays an essential role in the exchange of water and energy in climate and biogeochemical cycles. This study investigated the spatiotemporal variation of normalized difference vegetation index (NDVI) in northern China using the GIMMS-MODIS NDVI during 1982–2018. We explored the dominant drivers of NDVI change using regression analyses. Results show that the regional average NDVI for northern China increased at a rate of 0.001 year−1. NDVI improved and degraded area corresponded to 36.1% and 9.7% of the total investigated area, respectively. Climate drivers were responsible for NDVI change in 46.2% of the study area, and the regional average NDVI trend in the region where the dominant drivers were temperature (T), precipitation (P), and the combination of precipitation and temperature (P&T), increased at a rate of 0.0028, 0.0027, and 0.0056 year−1, respectively. We conclude that P has positive dominant effects on NDVI in the subregion VIAiia, VIAiic, VIAiib, VIAib of temperate grassland region, and VIIBiia of temperate desert region in northern China. T has positive dominant effects on NDVI in the alpine vegetation region of Qinghai Tibet Plateau. NDVI is negatively dominated by T in the subregion VIIBiib, VIIBib, VIIAi, and VIIBi of temperate desert regions. Human activities affect NDVI directly by reforestation, especially in Shaanxi, Shanxi, and Hebei provinces.

ACS Style

Xingna Lin; Jianzhi Niu; Ronny Berndtsson; Xinxiao Yu; Linus Zhang; Xiongwen Chen. NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China. Remote Sensing 2020, 12, 4138 .

AMA Style

Xingna Lin, Jianzhi Niu, Ronny Berndtsson, Xinxiao Yu, Linus Zhang, Xiongwen Chen. NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China. Remote Sensing. 2020; 12 (24):4138.

Chicago/Turabian Style

Xingna Lin; Jianzhi Niu; Ronny Berndtsson; Xinxiao Yu; Linus Zhang; Xiongwen Chen. 2020. "NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China." Remote Sensing 12, no. 24: 4138.

Journal article
Published: 11 November 2020 in Water
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Urban flooding is a growing concern in Northern Europe. While all countries in this region invest substantial resources into urban flood risk management, all property owners have unequal opportunity to have their flood risk managed. This paper presents the notion of equity in the urban flood risk management, focusing on urban flood exposure and compensation after sewage surcharge on the 31 August 2014 cloudburst over Malmö in Sweden. All damage claims and the procedures assessing them were analysed based on the type of sewer system (combined or separated) that affected the properties. The results demonstrated considerable systemic inequity in both flood exposure and damage compensation. The owners of properties connected to combined systems were four times more likely to make a damage claim, while having their damage claims approved just over half as often, compared with owners of properties connected to separated sewage systems. Considering the multifaceted nature of both inequities and their possible resolution, current praxis is not sufficient for the management of future urban drainage systems; not only concerning changing climatic conditions but also concerning social conditions.

ACS Style

Shifteh Mobini; Per Becker; Rolf Larsson; Ronny Berndtsson. Systemic Inequity in Urban Flood Exposure and Damage Compensation. Water 2020, 12, 3152 .

AMA Style

Shifteh Mobini, Per Becker, Rolf Larsson, Ronny Berndtsson. Systemic Inequity in Urban Flood Exposure and Damage Compensation. Water. 2020; 12 (11):3152.

Chicago/Turabian Style

Shifteh Mobini; Per Becker; Rolf Larsson; Ronny Berndtsson. 2020. "Systemic Inequity in Urban Flood Exposure and Damage Compensation." Water 12, no. 11: 3152.

Original paper
Published: 08 September 2020 in Arabian Journal of Geosciences
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We investigated fluoride (F−) concentration and physicochemical features of groundwater in the Urmia coastal aquifer (northwest, Iran). Groundwater samples were collected during both dry (58 wells) and wet seasons (84 wells). Approximately 15 and 23% of the groundwater samples in the dry and wet seasons, respectively, exceeded the recommended F− value by WHO for drinking water (1.5 mg/L). High F− concentration in groundwater is mainly found in shallow wells. The cause of high F− concentration appears to be mainly caused by human activities. Agricultural fertilizers and industrial waste can result in rapid release of F− into the groundwater. Release of F− into the groundwater can, however, also be triggered by the interaction between rock and water. In the experimental area, high F− concentrations were found close to volcanic rocks. Health risks from exposure to F− in groundwater were analyzed for adults and children. Utilizing sensitivity analysis and Monte Carlo simulation, the uncertainties in the risk estimates were calculated. Sensitivity analyses showed that the most pertinent variables are F− concentration in drinking water, averaging time, exposure time, and ingestion rate of water. Children are more susceptible to the noncarcinogenic risk of F− in groundwater.

ACS Style

Vahab Amiri; Ronny Berndtsson. Fluoride occurrence and human health risk from groundwater use at the west coast of Urmia Lake, Iran. Arabian Journal of Geosciences 2020, 13, 1 -23.

AMA Style

Vahab Amiri, Ronny Berndtsson. Fluoride occurrence and human health risk from groundwater use at the west coast of Urmia Lake, Iran. Arabian Journal of Geosciences. 2020; 13 (18):1-23.

Chicago/Turabian Style

Vahab Amiri; Ronny Berndtsson. 2020. "Fluoride occurrence and human health risk from groundwater use at the west coast of Urmia Lake, Iran." Arabian Journal of Geosciences 13, no. 18: 1-23.

Articles
Published: 02 September 2020 in International Journal of Digital Earth
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Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993–2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM+, and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.

ACS Style

Ehsan Kamali Maskooni; Hossein Hashemi; Ronny Berndtsson; Peyman Daneshkar Arasteh; Mohammad Kazemi. Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data. International Journal of Digital Earth 2020, 14, 250 -270.

AMA Style

Ehsan Kamali Maskooni, Hossein Hashemi, Ronny Berndtsson, Peyman Daneshkar Arasteh, Mohammad Kazemi. Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data. International Journal of Digital Earth. 2020; 14 (2):250-270.

Chicago/Turabian Style

Ehsan Kamali Maskooni; Hossein Hashemi; Ronny Berndtsson; Peyman Daneshkar Arasteh; Mohammad Kazemi. 2020. "Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data." International Journal of Digital Earth 14, no. 2: 250-270.

Journal article
Published: 24 August 2020 in Remote Sensing
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Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

ACS Style

Ehsan Kamali Maskooni; Seyed Naghibi; Hossein Hashemi; Ronny Berndtsson. Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. Remote Sensing 2020, 12, 2742 .

AMA Style

Ehsan Kamali Maskooni, Seyed Naghibi, Hossein Hashemi, Ronny Berndtsson. Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. Remote Sensing. 2020; 12 (17):2742.

Chicago/Turabian Style

Ehsan Kamali Maskooni; Seyed Naghibi; Hossein Hashemi; Ronny Berndtsson. 2020. "Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data." Remote Sensing 12, no. 17: 2742.

Editorial
Published: 09 August 2020 in Water
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Central Asia is an increasingly important strategic geopolitical region. During the latest decades, the region has often been identified as close to potential conflict regarding water usage. This includes the sharing of water from the Syr Darya and the Amu Darya in the Aral Sea Basin. The Aral Sea disaster has exposed a complex picture of water needs and potential political conflict. Rapid population increase together with climate change impacts are likely to further aggravate the short- and long-term future precarious situation for water management in the region. This Special Issue focuses on present and future water management issues in Central Asia in view of future climate changes and how these will affect socioeconomic development. Central Asia is, in general, water rich; however, exercising efficient and fair water management will be important in view of future population increase and climate change. At the same time, water and natural resource development is a cornerstone in all the Central Asian republics. Especially, water resources are, to a great extent, shared between all five republics. A common ground for water-sharing is, therefore, of utmost importance.

ACS Style

Ronny Berndtsson; Kamshat Tussupova. The Future of Water Management in Central Asia. Water 2020, 12, 2241 .

AMA Style

Ronny Berndtsson, Kamshat Tussupova. The Future of Water Management in Central Asia. Water. 2020; 12 (8):2241.

Chicago/Turabian Style

Ronny Berndtsson; Kamshat Tussupova. 2020. "The Future of Water Management in Central Asia." Water 12, no. 8: 2241.

Journal article
Published: 05 August 2020 in International Journal of Environmental Research and Public Health
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Contamination of the water and sediment with per- and polyfluoroalkyl substances (PFAS) was studied for the lake impacted by the release of PFAS-containing aqueous film forming foam (AFFF). PFAS concentrations were analyzed in lake water and sediment core samples. ΣPFAS concentrations were in the range of 95–100 ng L−1 in the lake water and 3.0–61 µg kg−1 dry weight (dw) in sediment core samples, both dominated by perfluorohexane sulfonate, perfluorooctane sulfonate; 6:2 fluortelomer sulfonate was inconsistently present in water and sediment core samples. The sediment–water partitioning coefficients (log Kd) were estimated and ranged 0.6–2.3 L kg−1 for individual perfluoroalkyl carboxylates (PFCAs) and 0.9–5.6 L kg−1 for individual perfluoroalkane sulfonates (PFSAs). The influence of the sediment inorganic content and organic matter on PFAS distribution was investigated. In studied sediments, the mineral content (corresponding to <5% of the bulk media mass) was mainly represented by sulfur, iron and calcium. The PFAS distribution was found strongly connected to the sediment mineral content (i.e., Fe, Pb, Rb and As), whereas the sediment organic carbon content did not to have a direct influence on the PFAS distribution. The aim of this study was to improve our understanding of the PFAS distribution in the natural heterogeneous media.

ACS Style

Dauren Mussabek; Kenneth M. Persson; Ronny Berndtsson; Lutz Ahrens; Kei Nakagawa; Tomomi Imura. Impact of the Sediment Organic vs. Mineral Content on Distribution of the Per- and Polyfluoroalkyl Substances (PFAS) in Lake Sediment. International Journal of Environmental Research and Public Health 2020, 17, 5642 .

AMA Style

Dauren Mussabek, Kenneth M. Persson, Ronny Berndtsson, Lutz Ahrens, Kei Nakagawa, Tomomi Imura. Impact of the Sediment Organic vs. Mineral Content on Distribution of the Per- and Polyfluoroalkyl Substances (PFAS) in Lake Sediment. International Journal of Environmental Research and Public Health. 2020; 17 (16):5642.

Chicago/Turabian Style

Dauren Mussabek; Kenneth M. Persson; Ronny Berndtsson; Lutz Ahrens; Kei Nakagawa; Tomomi Imura. 2020. "Impact of the Sediment Organic vs. Mineral Content on Distribution of the Per- and Polyfluoroalkyl Substances (PFAS) in Lake Sediment." International Journal of Environmental Research and Public Health 17, no. 16: 5642.

Journal article
Published: 14 April 2020 in Water
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Groundwater is a major source of drinking and agricultural water supply in arid and semiarid regions. Poor groundwater quality can be a threat to human health especially when it is combined with hazardous pollutants like heavy metals. In this study, an innovative method involving entropy weighted groundwater quality index for both physicochemical and heavy metal content was used for a semiarid region. The entropy weighted index was used to assess the groundwater’s suitability for drinking and irrigation purposes. Thus, groundwater from 19 sampling sites was used for analyses of physicochemical properties (electrical conductivity—EC, pH, K+, Ca2+, Na+, SO42−, Cl−, HCO3−, TDS, NO3−, F−, biochemical oxygen demand—BOD, dissolved oxygen—DO, and chemical oxygen demand—COD) and heavy metal content (As, Ca, Sb, Se, Zn, Cu, Ba, Mn, and Cr). To evaluate the overall pollution status in the region, heavy metal indices such as the modified heavy metal pollution index (m-HPI), heavy metal evaluation index (HEI), Nemerow index (NeI), and ecological risks of heavy metals (ERI) were calculated and compared. The results showed that Cd concentration plays a significant role in negatively affecting the groundwater quality. Thus, three wells were classified as poor water quality and not acceptable for drinking water supply. The maximum concentration of heavy metals such as Cd, Se, and Sb was higher than permissible limits by the World Health Organization (WHO) standards. However, all wells except one were suitable for agricultural purposes. The advantage of the innovative entropy weighted groundwater quality index for both physicochemical and heavy metal content, is that it permits objectivity when selecting the weights and reduces the error that may be caused by subjectivity. Thus, the new index can be used by groundwater managers and policymakers to better decide the water’s suitability for consumption.

ACS Style

Ehsan Kamali Maskooni; Mehran Naseri-Rad; Ronny Berndtsson; Kei Nakagawa. Use of Heavy Metal Content and Modified Water Quality Index to Assess Groundwater Quality in a Semiarid Area. Water 2020, 12, 1115 .

AMA Style

Ehsan Kamali Maskooni, Mehran Naseri-Rad, Ronny Berndtsson, Kei Nakagawa. Use of Heavy Metal Content and Modified Water Quality Index to Assess Groundwater Quality in a Semiarid Area. Water. 2020; 12 (4):1115.

Chicago/Turabian Style

Ehsan Kamali Maskooni; Mehran Naseri-Rad; Ronny Berndtsson; Kei Nakagawa. 2020. "Use of Heavy Metal Content and Modified Water Quality Index to Assess Groundwater Quality in a Semiarid Area." Water 12, no. 4: 1115.

Journal article
Published: 30 March 2020 in Water
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Precipitation extremes and their underlying causes are important processes to understand to plan appropriate adaptation measures. This paper presents an analysis of the spatiotemporal variability and trend of precipitation extremes in the important source region of the Yellow River and explores the connection to global teleconnection patterns and the 850-mb vector wind. Six indices for precipitation extremes were computed and analyzed for assessment of a changing regional climate. Results showed that these indices have a strong gradient from the northwest to the southeast part for the period 1961–2015, due to the great influence from the south-easterly summer monsoon flow. However, no statistically significant trends were found for the defined indices at the majority of stations, and their spatial distribution are noticed by irregularly mixed positive and negative changes except for the maximum number of consecutive wet days (CWD). Singular value decomposition analysis revealed that the precipitation extreme indices—including annual total precipitation when daily precipitation >95th percentile (R95p), annual count of days with daily precipitation ≥10 mm (R10mm), annual maximum consecutive 5-day precipitation (R5d), total precipitation divided by the number of wet days (SDII), and CWD—are negatively related to the El Nino-Southern Oscillation (NINO 3.4) in the first mode, and the maximum number of consecutive dry days (CDD) is positively related to the Scandinavian pattern in the second mode at 0.05 significance level. The 850-mb vector wind analysis showed that the southwestern monsoon originating from the Indian Ocean brings sufficient moisture to this region. Furthermore, the anti-cyclone in the western part of the North Pacific plays a significant role in the transport of moisture to the source region of the Yellow River. The links between precipitation extremes and teleconnection patterns explored in this study are important for better prediction and preparedness of climatic extremes.

ACS Style

Feifei Yuan; Jiahong Liu; Ronny Berndtsson; Zhenchun Hao; Qing Cao; Huimin Wang; Yiheng Du; Dong An. Changes in Precipitation Extremes over the Source Region of the Yellow River and Its Relationship with Teleconnection Patterns. Water 2020, 12, 978 .

AMA Style

Feifei Yuan, Jiahong Liu, Ronny Berndtsson, Zhenchun Hao, Qing Cao, Huimin Wang, Yiheng Du, Dong An. Changes in Precipitation Extremes over the Source Region of the Yellow River and Its Relationship with Teleconnection Patterns. Water. 2020; 12 (4):978.

Chicago/Turabian Style

Feifei Yuan; Jiahong Liu; Ronny Berndtsson; Zhenchun Hao; Qing Cao; Huimin Wang; Yiheng Du; Dong An. 2020. "Changes in Precipitation Extremes over the Source Region of the Yellow River and Its Relationship with Teleconnection Patterns." Water 12, no. 4: 978.

Journal article
Published: 01 January 2020 in Cogent Engineering
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We assess the sustainability of rural electrification in Manica Province, Mozambique, focusing on different alternatives for mini-grid and off-grid power supply. The qualitative assessment considers four dimensions of sustainability, namely environmental, socio-cultural, economic, and institutional. We argue that small-scale hydropower is the most sustainable alternative for off-grid or mini-grid solutions in rural Manica Province with good possibilities to scale up this to the major parts of rural Mozambique. The investigation shows that social acceptance for small-scale hydropower is high. Environmental sustainability of small-scale hydropower is higher than for PV systems. To speed up the electrification process, efficient rural electrification has to connect policy to local scale and institutional strengthening. The legislation needs to be improved, and there is a need for better institutional coordination for hydropower mini-grids’ regulation. Along this line, a national framework to support small and independent power producers is necessary.

ACS Style

Miguel M. Uamusse; Kamshat Tussupova; Kenneth M. Persson; Lars Bengtsson; Ronny Berndtsson; Ahmed Zobaa. Access to sustainable electrification: Possibilities for rural Mozambique. Cogent Engineering 2020, 7 .

AMA Style

Miguel M. Uamusse, Kamshat Tussupova, Kenneth M. Persson, Lars Bengtsson, Ronny Berndtsson, Ahmed Zobaa. Access to sustainable electrification: Possibilities for rural Mozambique. Cogent Engineering. 2020; 7 (1):.

Chicago/Turabian Style

Miguel M. Uamusse; Kamshat Tussupova; Kenneth M. Persson; Lars Bengtsson; Ronny Berndtsson; Ahmed Zobaa. 2020. "Access to sustainable electrification: Possibilities for rural Mozambique." Cogent Engineering 7, no. 1: .

Journal article
Published: 20 December 2019 in Remote Sensing
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Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014–2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product’s performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to –Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product’s performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.

ACS Style

Fatemeh Fadia Maghsood; Hossein Hashemi; Seyyed Hasan Hosseini; Ronny Berndtsson. Ground Validation of GPM IMERG Precipitation Products over Iran. Remote Sensing 2019, 12, 48 .

AMA Style

Fatemeh Fadia Maghsood, Hossein Hashemi, Seyyed Hasan Hosseini, Ronny Berndtsson. Ground Validation of GPM IMERG Precipitation Products over Iran. Remote Sensing. 2019; 12 (1):48.

Chicago/Turabian Style

Fatemeh Fadia Maghsood; Hossein Hashemi; Seyyed Hasan Hosseini; Ronny Berndtsson. 2019. "Ground Validation of GPM IMERG Precipitation Products over Iran." Remote Sensing 12, no. 1: 48.

Journal article
Published: 29 November 2019 in Sensors
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Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m3 m−3) as compared to laboratory experiments (RMSE = 0.06 m3 m−3). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K’ reading, failed to improve the Hilhorst model for ECp > 3 dS m−1 for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K0, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

ACS Style

Nessrine Zemni; Fethi Bouksila; Magnus Persson; Fairouz Slama; Ronny Berndtsson; Rachida Bouhlila. Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor. Sensors 2019, 19, 5272 .

AMA Style

Nessrine Zemni, Fethi Bouksila, Magnus Persson, Fairouz Slama, Ronny Berndtsson, Rachida Bouhlila. Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor. Sensors. 2019; 19 (23):5272.

Chicago/Turabian Style

Nessrine Zemni; Fethi Bouksila; Magnus Persson; Fairouz Slama; Ronny Berndtsson; Rachida Bouhlila. 2019. "Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor." Sensors 19, no. 23: 5272.