Publications by Ronny Berndtsson
Environmental Pollution, 2024
Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that r... more Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.
Applied Soft Computing, 2024
Water scarcity poses a major obstacle to sustainable development, and precise discharge predictio... more Water scarcity poses a major obstacle to sustainable development, and precise discharge prediction plays a vital role in enabling effective water resource management. This study investigated improved prediction techniques for nonstationary time series. The study evaluated the effect of signal processing techniques and blending approaches on the performance of deep learning models for daily discharge prediction. It also compared the performance of cluster-based local modeling with hybrid signal processing-deep learning approaches. Two robust deep learning methods, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), along with a powerful signal processing approach called discrete wavelet transform, were utilized for prediction of daily discharge. Three blending approaches were assessed: 1) decomposing both inputs and target, 2) decomposing only the target, and 3) decomposing only the inputs and then blending them with deep learning models. Also, a new hybrid deep learning based model namely discrete wavelet transform-Temporal Convolutional Transformer (DWT-TCT) was developed. The results showed that a single-output wavelet transform-deep learning model (3rd blending approach) outperformed multi-output models, demonstrating a relative enhancement of up to 56% for the LSTM model and 51% for the CNN model. Furthermore, temporal cluster-based local modeling displayed promising performance, resulting in an improvement of up to 18% in NRMSE compared to the wavelet transform-deep learning model, while also requiring less computational cost. The successful results of the temporal cluster-based local modeling approach provide a beneficial alternative to hybrid signal processing-deep learning models. In addition the results showed that the proposed DWT-TCT model outperformed all other models with NRMSE ranges from 6.8% to 16.2% in the study areas. The results have implications for hydrology and water resources management, as they can be used to develop more precise and effective models for predicting discharge in view of nonstationarity.
Ecological Informatics, 2020
The aim of this research was to develop a method to produce a Dust Source Susceptibility Map (DSS... more The aim of this research was to develop a method to produce a Dust Source Susceptibility Map (DSSM). For this purpose, we applied remote sensing and statistical-based machine learning algorithms for experimental dust storm studies in the Khorasan Razavi Province, in northeastern Iran. We identified dust sources in the study area using MODIS satellite images during the 2005-2016 period. For dust source identification, four indices encompassing BTD3132, BTD2931, NDDI, and D variable for 23 MODIS satellite images were calculated. As a result, 65 dust source points were identified, which were categorized into dust source data points for training and validation of the machine learning algorithms. Three statistical-based machine learning algorithms were used including Weights of Evidence (WOE), Frequency Ratio (FR), and Random Forest (RF) to produce DSSM for the study region. We used land use, lithology, slope, soil, geomorphology, NDVI (Normalized Difference Vegetation Index), and distance from river as conditioning variables in the modelling. To check the performance of the models, we applied the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). As for the AUC success rate (training), the FR and WOE algorithms resulted in 82 and 83% accuracy, respectively, while the RF algorithm resulted in 91% accuracy. As for the AUC predictive rate (validation), the accuracy of all three models, FR, WOE, and RF, were 80, 81, and 88%, respectively. Although all three algorithms produced acceptable susceptibility maps of dust sources, the results indicated better performance of the RF algorithm.
Cogent Engineering, 2024
The geologically complex Kyzylzharma groundwater field is located in the south-central part of Ka... more The geologically complex Kyzylzharma groundwater field is located in the south-central part of Kazakhstan in the lower Syr Darya Basin. It supplies the 243,000 population of Kyzylorda City by drinking and agricultural water needs. Numerical modeling was used to predict the consequences of increased groundwater withdrawal for future water supply needs. The results displayed a mean squared error for the groundwater simulations of about 0.6 m and was thus acceptable. The validated transmissivity was between 2 x 10 −7 and 2 x 10 −5 m/d. These parameters showed that the planned groundwater withdrawal will result in a depression cone reaching 90-100 m below present levels in 2040. Maximum groundwater level drawdown may reach 130 m below present levels. This drawdown increases risks for brackish saltwater intrusion into the main groundwater aquifer. The results point at the depletion of groundwater resources in the Syr Daria Artesian Basin and the risks for groundwater quality, particularly increase in mineralization. The key outcome is the recognition that effective joint management of the Syr Daria Artesian Basin's groundwater resources, involving both Kazakhstan and Uzbekistan, necessitates development and operation of a joint numerical aquifer model. Building this model is a crucial tool for assessing the sustainable groundwater resources in the region.
Int. J. Environ. Res. Public Health, 2024
Infant mortality in Kazakhstan is six times higher compared with the EU. There are several reason... more Infant mortality in Kazakhstan is six times higher compared with the EU. There are several reasons for this, but a partial reason might be that less than 30% of Kazakhstan’s population has access to safe water and sanitation and more than 57% uses polluted groundwater from wells that do not comply with international standards. For example, nitrate pollution in surface and groundwater continues to increase due to intensified agriculture and the discharge of untreated
wastewater, causing concerns regarding environmental and human health. For this reason, drinking water samples were collected from the water supply distribution network in eight districts of Almaty,
Kazakhstan, and water quality constituents, including nitrate, were analyzed. In several districts, the nitrate concentration was above the WHO and Kazakhstan’s maximum permissible limits for drinking water. The spatial distribution of high nitrate concentration in drinking water was shown to be strongly correlated with areas that are supplied with groundwater, whereas areas with lower nitrate levels are supplied with surface water sources. Based on source identification, it was shown that groundwater is likely polluted by mainly domestic wastewater. The health risk for infants, children, teenagers, and adults was assessed based on chronic daily intake, and the hazard quotient
(HQ) of nitrate intake from drinking water was determined. The non-carcinogenic risks increased in the following manner: adult < teenager < child < infant. For infants and children, the HQ was greater than the acceptable level and higher than that of other age groups, thus pointing to infants and children as the most vulnerable age group due to drinking water intake in the study area. Different water management options are suggested to improve the health situation of the population now drinking nitrate-polluted groundwater.
Nanomaterials, 2024
Inexpensive and efficient desalination is becoming increasingly important due to dwindling freshw... more Inexpensive and efficient desalination is becoming increasingly important due to dwindling freshwater resources in view of climate change and population increase. Improving desalination techniques of brackish water using graphene-based materials has the possibility to revolutionize freshwater production and treatment. At the same time, graphene matter can be cheaply massproduced from biowaste materials. In view of this, graphene material was obtained from a four-step production approach starting from rice husk (RH), including pre-carbonation, desilication, chemical activation, and exfoliation. The results showed that the produced samples contained a mixture of
graphene layers and amorphous carbon. The activation ratio of 1:5 for carbonized RH and potassium hydroxide (KOH), respectively, provided higher graphene content than the 1:4 ratio of the same components, while the number of active layers remained unaffected. Further treatment with H2O2 did not affect the graphene content and exfoliation of the amorphous carbon. Preparation of the graphene material by the NIPS technique and vacuum filtration displayed different physicochemical characteristics of the obtained membranes. However, the membranes’ main desalination function might be related more to adsorption rather than size exclusion. In any case, the desalination properties of the different graphene material types were tested on 35 g/L saltwater samples containing NaCl, KCl, MgCl2, CaSO4, and MgSO4. The produced graphene materials efficiently reduced the salt content by up to 95%. Especially for the major constituent NaCl, the removal efficiency was high.
Study region: A densely populated urban area located in the 13th municipality of Tehran metropoli... more Study region: A densely populated urban area located in the 13th municipality of Tehran metropolis, Iran. Study focus: Bioretention cell is one of the low-impact development methods that aims to restore the hydrological cycle in city areas before urban development. However, the bioretention cell's hydrological performance can vary in urban environments. As a result, this research investigated the effectiveness of a bioretention cell in reducing runoff and recharging groundwater in a densely populated metropolitan area located in eastern Tehran, Iran. Groundwater and surface water modeling were conducted separately. The SWMM model was used for surface water modeling, while a novel approach that utilized the SWMM groundwater module was implemented to assess the bioretention cell's impact on groundwater recharge quantitatively. New hydrological insights for the region: The study found that implementing bioretention cells can significantly reduce total runoff volume, ranging from 75.6% to 60.7% for rainfall with a return period of 2-100 years. This reduction is due to increased infiltration from the bioretention cells, which can lead to a maximum monthly increase of 12.2-44.0 millimeters of groundwater table for the same rainfall events. The study highlights the effectiveness of retaining runoff through bioretention cells in mitigating flooding, restoring the hydrological cycle, and reviving aquifers in urban areas.
Bihar State, located in India's eastern region, displays significant spatial and temporal variati... more Bihar State, located in India's eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focus: Recent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the region: The results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.
Introduction: Runoff measurement and monitoring is a laborious, timeconsuming, and costly task. A... more Introduction: Runoff measurement and monitoring is a laborious, timeconsuming, and costly task. Additionally, common runoff monitoring usually primarily provide water level, requiring information on the stage-discharge relation. Automatic equipment such as flow meter tipping bucket (TB) is a potential option to simplify and provide continuous runoff monitoring in small catchments. However, a proper description of how to size and adapt the design under different flow conditions is still lacking. Methodology: In this paper we present a novel standardized framework for the design of TB that can be used for low-cost and real-time runoff monitoring under many different conditions. The framework consists of an estimation of the runoff peak rate using the rational equation and a volumetric capacity estimate of the cavity based on runoff rate, operation speed, and inclination angle of TB when at resting position. The proposed framework was implemented in a case study where four TBs were designed for continuous runoff monitoring from experimental plots (100 m 2) with different land use (sugarcane, soybean, and bare soil). Results: During field tests (five months), the designed TBs had a recovery rate of actual runoff ranging from 61% to 81% and were able to capture features poorly studied (starting/ending time and peak flow) that have potential importance in hydrological models. Discussion: The proposed framework is flexible and can be used for different environmental conditions to provide continuous runoff data records.
The present study used results from a 'paleosimulation' covering the Baltic Sea basin and the sur... more The present study used results from a 'paleosimulation' covering the Baltic Sea basin and the surrounding areas to investigate chaotic properties of temperature, precipitation, and runoff. Three periods between years 1000 and 1929 (1000-1199, 1551-1749, and 1751-1929), representing a warm, a cool, and an intermediate climate episode, respectively, were studied. Time series of annual temperature, precipitation, and runoff were analyzed using phase space reconstruction (both univariate and multivariate) to investigate their dynamic and chaotic nature. The phase spaces for these variables display more or less clear attractors, suggesting possible nonlinear determinism in the underlying dynamics. This property may be exploited for pattern recognition and more accurate short-term predictions, which would contribute to a better understanding of regional runoff dynamics due to climate effects.
Plantation forests (PF) and natural secondary forests (NSF) are the primary reforestation approac... more Plantation forests (PF) and natural secondary forests (NSF) are the primary reforestation approaches. The establishment of PF can affect forest hydrological processes by changing soil structure. To date, few studies have focused on these changes and the effects on hydrological processes for the paleo-periglacial landform. To reveal reforestation approaches effects on water infiltration, including soil water infiltration capacity, retention capacity, and waterflow path pattern, we conducted field dye-tracer investigations with rainfall and laboratory infiltration experiments for the paleo-periglacial landform of eastern Liaoning mountains, China. The results showed that (1) Soil physical properties (including total porosity (TP), capillary porosity (CP), non-capillary porosity (NCP), initial soil water content (IWC), field water capacity (FWC)) and root abundance (RA) decreased with soil depth in both PF and NSF, while the soil bulk density (BD) and distribution of gravel content showed opposite changes. (2) Establishment of PF reduced the infiltration capacity and water retention capacity in the 0-20 cm layer, but enhanced the water retention capacity in 20-30 cm layer. Low IWC was conducive to increase soil water content (SWC) after infiltration. (3) Infiltration capacity parameters (including saturated hydraulic conductivity (Ks), SWC, difference between SWC and IWC (SWC-IWC), dye coverage ratio (DC)) were significantly correlated with BD, TP, CP, NCP, FWC, and fine roots RA (P < 0.05). Better connectivity gravels were more conducive to water infiltration. (4) Preferential flow was the main infiltration type, but with different waterflow paths pattern, with the 'funnel', 'finger' shape for PF, NSF, respectively. Increasing infiltration could increase flow path connectivity. Our findings show that soil physical properties, roots, and gravel occurrence affected soil infiltration, and different reforestation approaches had varying impacts on soil infiltration, water redistribution, transportation, and storage of surface and groundwater, improving the understanding of ecohydrological processes and effects of water resources management in forest ecosystems of paleo-periglacial landform.
Natural Hazards, 2023
Flooding is a natural but unavoidable disaster that occurs over time. Flooding threatens human li... more Flooding is a natural but unavoidable disaster that occurs over time. Flooding threatens human life, property, and resources and affects regional and national economies. Through frequency ratio and MaxEnt modeling, flood sensitivity was determined in the Amu Darya River Basin in Badakhshan Province, Afghanistan. Slope, plan curvature, distance to river, rainfall, aspect, land use, elevation, Normalized Difference Vegetation Index (NDVI), soil type, lithology, Topographic Humidity Index (TWI), and drainage density were used to quantify flood susceptibility. In total, 88 flood points collected from Google Earth were used to train the frequency ratio model to predict flood susceptibility, and 34 GPS-recorded points of the flooded area were used to evaluate the model's performance. The frequency ratio model displayed a success rate of above 86%. However, using a jackknife entropy test, the MaxEnt model yielded a 97% success rate. The results showed that rainfall, land use, distance to river, and soil type were the most important parameters for evaluating flood sensitivity. The developed models can help planners and decision-makers perform flood susceptibility mapping in the region by determining locations of flooding sensitivity.
Env Res Lett, 2023
Peatlands are unique ecosystems that contain massive amounts of carbon. These ecosystems are incr... more Peatlands are unique ecosystems that contain massive amounts of carbon. These ecosystems are incredibly vulnerable to human disturbance and climate change. This may cause the peatland carbon sink to shift to a carbon source. A change in the carbon storage of peatlands may result in surface deformation. This research uses the interferometric synthetic aperture radar (InSAR) technique to measure the deformation of the peatland's surface in south Sweden in response to the seasonal and extreme weather conditions in recent years, including the unprecedented severe drought in the summer of 2018. The deformation map of the study area is generated through a time-series analysis of InSAR from June 2017 to November 2020. Monitoring the peatland areas in this region is very important as agricultural and human activities have already caused many peatlands to disappear. This further emphasizes the importance of preserving the remaining peat sites in this region. Based on the InSAR results, a method for calculating the carbon flux of the peat areas is proposed, which can be utilized as a regular monitoring approach for other remote areas. Despite the severe drought in the summer of 2018, our findings reveal a significant uplift in most of the investigated peat areas during the study period. Based on our estimations, 86% of the peatlands in the study area experienced an uplift corresponding to about 47 000 tons of carbon uptake per year. In comparison, the remaining 14% showed either subsidence or stable conditions corresponding to about 2300 tons of carbon emission per year during the study period. This emphasizes the importance of InSAR as an efficient and accurate technique to monitor the deformation rate of peatlands, which have a vital role in the global carbon cycle.
Water, 2023
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Applied Water Science, 2023
Hydropower is a clean and efficient technology for producing renewable energy. Assessment and for... more Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor's diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.
Theoretical and Applied Climatology, 2019
GCMs (general circulation models) are main tools for generating climate projections for climate c... more GCMs (general circulation models) are main tools for generating climate projections for climate change research in hydrology and water resources. Accordingly, evaluating the performance of these models in simulating future climate is very important for choice of proper models. In this study, performance of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) model series was assessed using a technique for order performance by similarity to ideal solution (TOPSIS)based approach together with normalized root mean square error (NRMSE), the Taylor skill score (S Taylor), and two probability density function (PDF) skill scores. Precipitation and temperature data during 1976 to 2005 from three river basins including Zard River (ZR), Bakhtegan (BKH), and Ghareso (GH) in west and southwest Iran were used to select the best model. In general, models showed superiority in simulating temperature over precipitation. Based on the GCM ranking results for the ZR Basin, MIROC-ESM and IPSL-CM5A-LR were selected as the best and the weakest model, respectively. For the BKH Basin, the best model was BCC-CSM1.1 and the weakest IPSL-CM5A-MR and CCSM4. In other words, BCC-CSM1.1 had the maximum relative closeness to ideal solution. Based on the TOPSIS results, BCC-CSM1.1 and CanESM2 were the best models and IPSL-CM5A-MR the weakest model with a minimum relative closeness to the ideal solution in simulating temperature and precipitation for the GH basin. The approach presented in this study can be utilized to select appropriate climate models in other regions for future studies of climate change.
Sensors, 2023
Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment a... more Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow for the determination of crops’ biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0
from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d−1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d −1).
Sensors, 2012
Automated soil moisture systems are commonly used in precision agriculture. Using lowcost sensors... more Automated soil moisture systems are commonly used in precision agriculture. Using lowcost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.
Water, 2023
Assessing the status of water resources is essential for long-term planning related to water and ... more Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all
parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface
water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the
integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data
prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.
Journal of Hydrology, 2023
Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in... more Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in Scandinavia for sub-km and minute scale rainfall observations. This study develops a method for merging data from two X-WRs in Dalby and Helsingborg, southern Sweden (operated at five and one elevation angle levels, respectively) to improve the accuracy of rainfall observations. In total, 87 rainfall events from May-September 2021, observed by 38 tipping bucket gauges in the overlapping coverage of the X-WRs, were used for ground truth. The gauges were classified into four zones. An artificial neural network using doppler and dual-polarization variables (ANN) and a regression-based hybrid of RATEs (single-level rainfall products built-in to the X-WRs) based on the Marshall-Palmer equation (RMP) were calibrated for each zone. The calibrated models at 5-min scale significantly outperformed RATEs for all zones verified by Gilbert skill score (GSS), relative bias (rBIAS), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) not using the calibration data. Quantile-quantile plots confirmed a considerable improvement of the statistical distribution of the merged rainfall estimates for Zone I (closest to Dalby), II (mid-way between Dalby and Helsingborg), and IV (similar range as II for Dalby but farthest to Helsingborg) especially using ANN. Zone III (farthest to Dalby and closest to Helsingborg) was problematic for all RATEs, ANN, and RMP. The lowest-level elevation angle for both X-WRs showed the most erroneous RATEs. Consequently, the problems with Zone III can be solved if multiple levels of Helsingborg X-WR at higher levels are available.
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Publications by Ronny Berndtsson
wastewater, causing concerns regarding environmental and human health. For this reason, drinking water samples were collected from the water supply distribution network in eight districts of Almaty,
Kazakhstan, and water quality constituents, including nitrate, were analyzed. In several districts, the nitrate concentration was above the WHO and Kazakhstan’s maximum permissible limits for drinking water. The spatial distribution of high nitrate concentration in drinking water was shown to be strongly correlated with areas that are supplied with groundwater, whereas areas with lower nitrate levels are supplied with surface water sources. Based on source identification, it was shown that groundwater is likely polluted by mainly domestic wastewater. The health risk for infants, children, teenagers, and adults was assessed based on chronic daily intake, and the hazard quotient
(HQ) of nitrate intake from drinking water was determined. The non-carcinogenic risks increased in the following manner: adult < teenager < child < infant. For infants and children, the HQ was greater than the acceptable level and higher than that of other age groups, thus pointing to infants and children as the most vulnerable age group due to drinking water intake in the study area. Different water management options are suggested to improve the health situation of the population now drinking nitrate-polluted groundwater.
graphene layers and amorphous carbon. The activation ratio of 1:5 for carbonized RH and potassium hydroxide (KOH), respectively, provided higher graphene content than the 1:4 ratio of the same components, while the number of active layers remained unaffected. Further treatment with H2O2 did not affect the graphene content and exfoliation of the amorphous carbon. Preparation of the graphene material by the NIPS technique and vacuum filtration displayed different physicochemical characteristics of the obtained membranes. However, the membranes’ main desalination function might be related more to adsorption rather than size exclusion. In any case, the desalination properties of the different graphene material types were tested on 35 g/L saltwater samples containing NaCl, KCl, MgCl2, CaSO4, and MgSO4. The produced graphene materials efficiently reduced the salt content by up to 95%. Especially for the major constituent NaCl, the removal efficiency was high.
from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d−1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d −1).
parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface
water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the
integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data
prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.
wastewater, causing concerns regarding environmental and human health. For this reason, drinking water samples were collected from the water supply distribution network in eight districts of Almaty,
Kazakhstan, and water quality constituents, including nitrate, were analyzed. In several districts, the nitrate concentration was above the WHO and Kazakhstan’s maximum permissible limits for drinking water. The spatial distribution of high nitrate concentration in drinking water was shown to be strongly correlated with areas that are supplied with groundwater, whereas areas with lower nitrate levels are supplied with surface water sources. Based on source identification, it was shown that groundwater is likely polluted by mainly domestic wastewater. The health risk for infants, children, teenagers, and adults was assessed based on chronic daily intake, and the hazard quotient
(HQ) of nitrate intake from drinking water was determined. The non-carcinogenic risks increased in the following manner: adult < teenager < child < infant. For infants and children, the HQ was greater than the acceptable level and higher than that of other age groups, thus pointing to infants and children as the most vulnerable age group due to drinking water intake in the study area. Different water management options are suggested to improve the health situation of the population now drinking nitrate-polluted groundwater.
graphene layers and amorphous carbon. The activation ratio of 1:5 for carbonized RH and potassium hydroxide (KOH), respectively, provided higher graphene content than the 1:4 ratio of the same components, while the number of active layers remained unaffected. Further treatment with H2O2 did not affect the graphene content and exfoliation of the amorphous carbon. Preparation of the graphene material by the NIPS technique and vacuum filtration displayed different physicochemical characteristics of the obtained membranes. However, the membranes’ main desalination function might be related more to adsorption rather than size exclusion. In any case, the desalination properties of the different graphene material types were tested on 35 g/L saltwater samples containing NaCl, KCl, MgCl2, CaSO4, and MgSO4. The produced graphene materials efficiently reduced the salt content by up to 95%. Especially for the major constituent NaCl, the removal efficiency was high.
from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d−1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d −1).
parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface
water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the
integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data
prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.