Article
Meteorology & Atmospheric Sciences
Haohao Sun, Lili Lei, Zhengyu Liu, Liang Ning, Zhe-Min Tan
Summary: An analog offline ensemble Kalman filter (AOEnKF) is proposed, which constructs ensemble priors from a control climate simulation for each assimilation time based on an analog criterion using proxy observations. AOEnKF generates smaller posterior errors and requires much less computational cost compared to the online cycling EnKF (CEnKF). It has the advantages of having a more accurate prior ensemble mean and flow-dependent background error covariances compared to the commonly applied offline EnKF (OEnKF).
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Jeffrey S. Whitaker, Anna Shlyaeva, Stephen G. Penny
Summary: This study compares two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system: the hybrid-covariance approach and the hybrid-gain approach. The results show that the simpler and less expensive hybrid-gain approach can achieve similar performance if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Meteorology & Atmospheric Sciences
Lars Nerger
Summary: The study introduces a hybrid filter combining LETKF and NETF with the performance improved by adjusting the hybrid weight. Results show that a hybrid variant applying NETF followed by LETKF yields the best results in complex nonlinear models. Calculating the hybrid weight based on skewness, kurtosis, and effective sample size reduces estimation errors and enhances stability of the hybrid filter.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Cardiac & Cardiovascular Systems
Dario De Marinis, Dominik Obrist
Summary: The proposed data assimilation methodology aims to enhance the spatial and temporal resolution of voxel-based data obtained from biomedical imaging modalities, specifically focusing on turbulent blood flow assessment in large vessels. The methodology, utilizing a Stochastic Ensemble Kalman Filter approach, combines observed flow fields with numerical simulations to improve the accuracy of flow field predictions. Validation against canonical flows and application to a clinically relevant scenario demonstrate the potential of the method to enhance 4D flow MRI data for future use.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Meteorology & Atmospheric Sciences
Troy Arcomano, Istvan Szunyogh, Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Edward Ott
Summary: This paper describes the implementation of a combined hybrid-parallel prediction approach on a low-resolution atmospheric global circulation model. The hybrid model, which combines a physics-based numerical model with a machine learning component, produces more accurate forecasts for various atmospheric variables compared to the host model. Furthermore, the hybrid model exhibits smaller systematic errors and more realistic temporal variability in simulating the climate.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2022)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Geochemistry & Geophysics
I Kovalets, K. O. Kim, O. Shrubkovsky, V Maderich
Summary: The study evaluated the abilities of ensemble Kalman filter data assimilation method in reducing errors in concentration distribution simulations after contaminant release in the ocean. Results showed that data assimilation gradually improved the accuracy of simulated concentration fields, bringing them closer to the true distribution over time.
PURE AND APPLIED GEOPHYSICS
(2022)
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Chuan-An Xia, Xiaodong Luo, Bill X. Hu, Monica Riva, Alberto Guadagnini
Summary: In this study, we used the MEs-EnKF approach to investigate the relationship between conductivity estimates and the type of available hydraulic head information in a heterogeneous groundwater field. Our results show that monitoring wells of Type A provide the best quality estimates, while Type B and C wells yield similar quality estimates. Additionally, inflating the measurement-error covariance matrix can improve conductivity estimates in simplified flow models.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Engineering, Geological
Muhammad Mohsan, Femke C. Vossepoel, Philip J. Vardon
Summary: Different data assimilation schemes were implemented in a hydro-mechanical slope stability analysis. Results showed that the ensemble Kalman filter (EnKF) provided the most accurate estimation of the factor of safety (FoS) with the smallest standard deviation. The ensemble smoother (ES) and ensemble smoother with multiple data assimilation (ESMDA) underestimated the FoS with lower confidence. Assimilating measurements over a longer period improved the accuracy of the estimation. The ES had the best computational performance, followed by ESMDA, and EnKF had a computational performance around 50% worse than ES, mainly due to the non-linearity of the underlying problem.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2023)
Article
Environmental Sciences
Mehrad Bayat, Hosein Alizadeh, Barat Mojaradi
Summary: This paper introduces the application of multivariate data assimilation (DA) to the SWAT model (DA-SWAT) and discusses the limitations of existing integrated approaches. A new approach is proposed that allows the perfect integration of SWAT with any desired DA algorithm. The results show that multivariate assimilation improves the accuracy of SCF (Streamflow) estimation and mitigates the equifinality problem.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Lingzhong Kong, Ruibin Chen, Hongwu Tang, Saiyu Yuan, Qian Yang, Qingfeng Ji
Summary: Accurate water level forecasts are crucial for river management. The proposed joint ensemble Kalman filter (EnKF) framework allows concurrent estimation of flow disturbances and water levels, improving prediction accuracy. Even when the location of the disturbance point is unknown, the introduction of a fictitious lateral outflow enables estimation of the flow disturbance process. Synthetic case study results demonstrate superior water level estimations compared to scenarios without considering flow disturbances.
JOURNAL OF HYDROLOGY
(2023)
Article
Biology
Ralf Engbert, Maximilian M. Rabe, Reinhold Kliegl, Sebastian Reich
Summary: The study suggests that applying sequential data assimilation to the stochastic SEIR epidemic model can capture the dynamic behavior of outbreaks and achieve short-term predictions on a regional level. Regional modeling, considering spatial heterogeneity and stochasticity, offers a more realistic approach for epidemic prediction and control.
BULLETIN OF MATHEMATICAL BIOLOGY
(2021)
Article
Environmental Sciences
Ching Pui Hung, Bernd Schalge, Gabriele Baroni, Harry Vereecken, Harrie-Jan Hendricks Franssen
Summary: Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Data assimilation can reduce uncertainties by improving the representation of soil moisture, groundwater level, and other variables in the model. This study used a virtual reality simulation to mimic a river catchment in Germany and assimilated soil moisture and groundwater level data to improve the model's performance.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Wanxin Li, Harrie-Jan Hendricks Franssen, Philip Brunner, Zhi Li, Zhoufeng Wang, Yike Wang, Wenke Wang
Summary: Calculating actual bare soil evaporation based on potential evaporation is a widely used method in various disciplines. However, the impact of soil texture on potential evaporation is often overlooked. This study assessed the seasonal and diurnal variations of potential evaporation over different soil textures and found clear differences in evaporation rates. The differences can be explained by factors such as surface energy balance and thermal properties, which are influenced by soil texture.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Environmental
Alessandro Pansa, Ilaria Butera, J. Jaime Gomez-Hernandez, Bartolomeo Vigna
Summary: The ensemble smoother with multiple data assimilation can be used to predict discharge in an Alpine karst aquifer, specifically the Bossea aquifer. This method effectively fits a unit hydrograph along with other parameters in a hydrologic model, using observed discharge flow rates, daily precipitation, and temperatures. By analyzing multiple events, average models are defined for predicting flow discharge during spring and autumn, with acceptable results for the fall rainfall events. However, further exploration is needed for refining the snow melting approximation and the parameterization of the infiltration coefficient. Overall, the study concludes that the ensemble smoother can be used to characterize a karst aquifer for forecast analyses.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Engineering, Civil
Nikhil Ghodichore, C. T. Dhanya, Harrie-Jan Hendricks Franssen
Summary: This study aims to assess the impacts of land use land cover change (LULCC) and inter-decadal climate variations (CV) on water and energy cycles in India. The results show that LULCC causes a decrease in evapotranspiration and soil moisture, while CV leads to an increase in evapotranspiration, soil moisture, and net radiation. The combined effect of LULCC and CV results in an overall increase in evapotranspiration.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Zi Chen, Teng Xu, J. Jaim Gomez-Hernandez, Andrea Zanini, Quanping Zhou
Summary: This study employs the ensemble smoother with multiple data assimilation (ES-MDA) method to tackle groundwater contamination issues. Results show that ES-MDA performs well in recovering the release history, especially with higher observation data frequency. However, more detailed uncertainties and parameterization of the time functions are needed to move towards field cases.
JOURNAL OF CONTAMINANT HYDROLOGY
(2023)
Article
Chemistry, Analytical
Cosimo Brogi, Vassilios Pisinaras, Markus Koehli, Olga Dombrowski, Harrie-Jan Hendricks Franssen, Konstantinos Babakos, Anna Chatzi, Andreas Panagopoulos, Heye Reemt Bogena
Summary: Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have the potential to monitor and inform irrigation management, optimizing water resource usage in agriculture. However, current practical methods for monitoring small, irrigated fields with CRNSs are unavailable, and the challenges of targeting areas smaller than the CRNS sensing volume remain unaddressed. This study uses CRNSs to continuously monitor soil moisture dynamics in two similar-sized irrigated apple orchards in Greece, and compares the CRNS-derived soil moisture with a reference obtained from a dense sensor network. An ad hoc calibration improved the accuracy of the CRNS-derived soil moisture before irrigation, but a correction based on neutron transport simulations and measurements from a non-irrigated location proved to be more effective in enhancing the CRNS-derived soil moisture, allowing for the monitoring of irrigation-induced soil moisture dynamics.
Article
Geosciences, Multidisciplinary
Valeria Todaro, Marco D'Oria, Andrea Zanini, J. Jaime Gomez-Hernandez, Maria Giovanna Tanda
Summary: Estimating aquifer properties and their spatial variability is a challenging task in groundwater flow and transport simulations. This study applies the ensemble smoother with multiple data assimilation method to infer the characteristics of a binary field using tracer test data. Two different approaches are compared, with the second one performing better by coupling the ensemble smoother with a truncated Gaussian model. Synthetic experiments are conducted to find the optimal configurations for real cases, and the results show that both the fully parameterized approach and the pilot point approach yield comparable solutions.
HYDROGEOLOGY JOURNAL
(2023)
Article
Geosciences, Multidisciplinary
Joao Lino Pereira, J. Jaime Gomez-Hernandez, Andrea Zanini, Emmanouil A. Varouchakis, Leonardo Azevedo
Summary: Electrical resistivity tomography (ERT) is a geophysical method used to create an image of the subsurface. This study proposes an iterative geostatistical resistivity inversion method using stochastic sequential simulation and co-simulation to generate electrical resistivity models and predict subsurface properties. The method is validated using synthetic and real ERT data sets, showing its ability to model small-scale variability and assess spatial uncertainty.
HYDROGEOLOGY JOURNAL
(2023)
Editorial Material
Geosciences, Multidisciplinary
Philippe Renard, J. Jaime Gomez-Hernandez, Maria-Theresia Schafmeister, Emmanouil A. Varouchakis
HYDROGEOLOGY JOURNAL
(2023)
Article
Geosciences, Multidisciplinary
J. Jaime Gomez-Hernandez, Daniele Secci
Summary: The use of spreadsheets in numerical groundwater flow modeling has not been fully utilized in educational settings. This article introduces a teaching aid that expands the scope of a previous publication, covering various types of groundwater flow situations and incorporating new features, making the spreadsheet model a versatile tool. Students can use this user-friendly platform for experimentation and research to gain a better understanding of groundwater flow modeling and related numerical codes.
MATHEMATICAL GEOSCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Cosimo Brogi, Heye Reemt Bogena, Markus Koehli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, Olga Dombrowski
Summary: This study investigates the feasibility of monitoring irrigation with cosmic-ray neutron sensors (CRNSs) and finds challenges with contributions from outside irrigated fields. Thin high-density polyethylene (HDPE) moderators result in smaller footprints while thick HDPE moderators with gadolinium shielding improve monitoring in irrigated fields. Retrieving soil moisture (SM) data from surrounding areas is important to obtain meaningful information for irrigation management.
GEOSCIENTIFIC INSTRUMENTATION METHODS AND DATA SYSTEMS
(2022)
Review
Water Resources
Gabrielle J. M. De Lannoy, Michel Bechtold, Clement Albergel, Luca Brocca, Jean Christophe Calvet, Alberto Carrassi, Wade T. Crow, Patricia de Rosnay, Michael Durand, Barton Forman, Gernot Geppert, Manuela Girotto, Harrie-Jan Hendricks Franssen, Tobias Jonas, Sujay Kumar, Hans Lievens, Yang Lu, Christian Massari, Valentijn R. N. Pauwels, Rolf H. Reichle, Susan Steele-Dunne
Summary: The rapid growth of land surface satellite data and model sophistication in the 21st century has opened up new opportunities for estimating multiple components of the water cycle through satellite-based land data assimilation. However, the increased level of detail in models and data also presents challenges in terms of dimensionality and the volume of observations to assimilate. Advanced data assimilation methods and efficient solutions are needed to address these challenges.
FRONTIERS IN WATER
(2022)
Article
Geosciences, Multidisciplinary
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, Heye Bogena
Summary: The study developed a new submodel, CLM5-FruitTree, to study land surface processes in fruit orchards. The model showed good performance in representing biomass growth and partitioning, as well as seasonal carbon, energy, and water fluxes, although some inconsistencies and simplifications were identified.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Mechanics
Pablo Moreno-Navarro, Jose L. Perez-Aparicio, J. J. Gomez-Hernandez
Summary: The current article proposes closed-form and semianalytical solutions to improve the temperature distribution of Bi2Te3 thermoelements, aiming to maximize cooling. By studying different geometries of thermoelements, optimal electric intensity is determined and the accuracy of the analytical solutions is validated through numerical simulations.
COUPLED SYSTEMS MECHANICS
(2022)
Article
Geosciences, Multidisciplinary
Lukas Strebel, Heye R. Bogena, Harry Vereecken, Harrie-Jan Hendricks Franssen
Summary: Land surface models are crucial for understanding the Earth system, and data assimilation techniques can optimize the combination of models and observational data. This study presents the development of a new interface between PDAF and CLM5, and demonstrates the application of the coupled CLM5-PDAF system using soil water content observations.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)
