Article
Environmental Sciences
Pedram Darbandsari, Paulin Coulibaly
Summary: This study evaluates the impact of different hydrologic models on the performance of the hydrologic uncertainty processor (HUP) and proposes a multimodel Bayesian postprocessor (HUP-BMA). Results demonstrate the superiority of HUP-BMA in quantifying hydrologic uncertainty and forecasting compared to traditional HUP and BMA methods.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Sheen Mclean Cabaneros, Ben Hughes
Summary: The use of data-driven techniques, such as artificial neural network (ANN) models, for outdoor air pollution forecasting has been popular in the past two decades. However, research on the uncertainty surrounding the development of ANN models has been limited. This review outlines the approaches for addressing model uncertainty and reveals that input uncertainty has received the most attention, while structure, parameter, and output uncertainties have been less focused on. Ensemble approaches, particularly neuro-fuzzy networks, have been widely employed, but the direct measurement of uncertainty has received less attention. The study also suggests the need for development and application of approaches that can handle and quantify uncertainty in ANN model development.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Geochemistry & Geophysics
Xin Zhang, Andrew Curtis
Summary: This study introduces invertible neural networks (INNs) as an alternative to solving nonlinear and nonunique inverse problems in geophysics. By including data uncertainties as additional model parameters and training the network by maximizing the likelihood of the training data, INNs can provide comparable posterior probability density functions to Monte Carlo methods, including correlations between parameters.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Nuclear Science & Technology
Lesego E. Moloko, Pavel M. Bokov, Xu Wu, Kostadin N. Ivanov
Summary: This study uses Deep Neural Networks (DNNs) to predict assembly axial neutron flux profiles in the SAFARI-1 research reactor and quantifies the uncertainties in DNN predictions. Uncertainty Quantification is done using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by Variational Inference (BNN VI). The results show that regular DNNs, DNNs with MCD, and BNN VI all have good prediction and generalization capabilities, and the uncertainty bands produced by MCD and BNN VI accurately envelope the measurement data points.
ANNALS OF NUCLEAR ENERGY
(2023)
Article
Computer Science, Artificial Intelligence
Cristian Serpell, Carlos Valle, Hector Allende
Summary: This study proposes a deep model based on Monte Carlo dropout that handles both epistemic and aleatoric uncertainties in probabilistic forecasting. It can be applied for multi-step forecasting and allows changing the distribution assumption easily. The model is validated on wind speed, wind power, and electrical load forecasting tasks.
Article
Engineering, Civil
Hossien Riahi-Madvar, Majid Dehghani, Rasoul Memarzadeh, Bahram Gharabaghi
Summary: The study found that hybrid algorithms outperformed traditional models in streamflow prediction, with ANFIS-GWO1, ANFIS-GWO7, and ANFIS-GWO11 being the best performing models. Uncertainty analysis showed that hybrid models significantly reduced uncertainty levels compared to traditional models. The study also provided a simple explicit equation for streamflow forecasting based on hybrid ANFIS results, which is a major advantage over classical blackbox machine learning models.
WATER RESOURCES MANAGEMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Li-Li Bao, Jiang-She Zhang, Chun-Xia Zhang, Rui Guo, Xiao-Li Wei, Zi-Lu Jiang
Summary: In seismic exploration, reservoir prediction is important to reveal reservoir characteristics. A Bayesian neural network (BNN) model is proposed to predict reservoir thickness and quantify uncertainty. The BNN combines attribute data with spatial information and uses the Monte Carlo dropout (MC-dropout) approach to capture uncertainty.
COMPUTERS & GEOSCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Pieter Van Molle, Tim Verbelen, Bert Vankeirsbilck, Jonas De Vylder, Bart Diricx, Tom Kimpe, Pieter Simoens, Bart Dhoedt
Summary: The paper highlights the limitations of conventional neural networks in capturing uncertainty and introduces Bayesian techniques such as Monte Carlo dropout. The authors propose a novel method based on the overlap of output distributions of different classes to better approximate inter-class output confusion. They demonstrate the advantages of their approach using benchmark datasets and skin lesion classification.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Environmental
Jing-Cheng Han, Fangze Shang, Ping Li, Bing Li, Yang Zhou, Yuefei Huang
Summary: Phosphorus is essential for plants and healthy rivers, but an overload can degrade water quality. Substance flow analysis helps manage phosphorus flow, but has limitations in data uncertainty. The proposed BMC-SFA technique integrates Bayesian networks and Monte Carlo simulations to improve understanding of nutrient flows in surface waters, providing valuable insights for phosphorus management.
RESOURCES CONSERVATION AND RECYCLING
(2021)
Article
Computer Science, Interdisciplinary Applications
B. S. Kronheim, M. P. Kuchera, H. B. Prosper
Summary: TensorBNN is a new package that implements Bayesian inference for modern neural network models based on TensorFlow, sampling the posterior density of model parameters using Hamiltonian Monte Carlo. It leverages TensorFlow's architecture and GPU utilization in both training and prediction stages.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Stephen Whitelam, Viktor Selin, Sang-Won Park, Isaac Tamblyn
Summary: Authors derive an analytic equivalence between neural network training under conditioned stochastic mutations and under gradient descent, showing that in the presence of small mutations, training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent to gradient descent on the loss function with Gaussian white noise. Neuroevolution is found to be equivalent to gradient descent on the loss function when averaged over independent realizations of the learning process, which is demonstrated through numerical simulations across finite mutations and various neural network architectures. This provides a connection between two families of neural-network training methods that are usually considered to be fundamentally different.
NATURE COMMUNICATIONS
(2021)
Article
Environmental Sciences
Fatemeh Ghobadi, Doosun Kang
Summary: This study developed a probabilistic forecasting model using the Bayesian deep learning approach and Long short-term memory (LSTM) neural network for uncertainty estimation in water resources management. The results showed that the proposed model outperformed other models in terms of forecasting reliability, sharpness, and overall performance, and it was able to handle data with higher variation and peak.
Article
Multidisciplinary Sciences
Magnus Roding, Victor Wahlstrand Skarstrom, Niklas Loren
Summary: In this study, the effective diffusivity of spinodal decomposition-like structures with tunable anisotropy is predicted using a convolutional neural network. By incorporating the predictions into an approximate Bayesian computation framework for inverse problems, computationally efficient design of microstructures with prescribed diffusivity in all three directions is achieved.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Kristian Miok, Blaz Skrlj, Daniela Zaharie, Marko Robnik-Sikonja
Summary: Bayesian method using Monte Carlo dropout within the attention layers of transformer models is proposed to provide well-calibrated reliability estimates, enhancing the performance of the BERT model in hate speech detection across multiple languages. Affective dimensions are tested to see their impact on classification.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Information Systems
Wilson Tsakane Mongwe, Rendani Mbuvha, Tshilidzi Marwala
Summary: Sampling with integrator-dependent shadow Hamiltonians has shown improved properties compared to Hamiltonian Monte Carlo. The proposed algorithm, PS2HMC, combines the benefits of S2HMC with partial momentum refreshment, outperforming existing methods in various target distributions. Numerical experiments on different distributions and models demonstrate the effectiveness of the PS2HMC algorithm.
Article
Environmental Sciences
Anthony D. Campbell, Temilola Fatoyinbo, Sean P. Charles, Laura L. Bourgeau-Chavez, Joaquim Goes, Helga Gomes, Meghan Halabisky, James Holmquist, Steven Lohrenz, Catherine Mitchell, L. Monika Moskal, Benjamin Poulter, Han Qiu, Celio H. Resende De Sousa, Michael Sayers, Marc Simard, Anthony J. Stewart, Debjani Singh, Carl Trettin, Jinghui Wu, Xuesong Zhang, David Lagomasino
Summary: This study reviewed and analyzed the monitoring of wet carbon systems using remote sensing technology, and found variations in the application of remote sensing in different systems. While carbon distribution in mangroves and oceans has been extensively mapped globally, more accurate and comprehensive global maps are needed for seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost.
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Agronomy
Sangchul Lee, Junyu Qi, Gregory W. McCarty, Martha Anderson, Yun Yang, Xuesong Zhang, Glenn E. Moglen, Dooahn Kwak, Hyunglok Kim, Venkataraman Lakshmi, Seongyun Kim
Summary: Water cycling within agricultural watersheds is uncertain due to natural and anthropogenic factors. Remotely sensed evapotranspiration products have been used as additional constraints in watershed modeling to improve accuracy and reduce uncertainty. This study assesses the predictive uncertainty of the Soil and Water Assessment Tool (SWAT) with or without annual crop yield as a constraint for an agricultural watershed.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Computer Science, Interdisciplinary Applications
Nicholas Majeske, Xuesong Zhang, McKailey Sabaj, Lei Gong, Chen Zhu, Ariful Azad
Summary: This study presents machine learning methods to predict hydrologic features such as streamflow and soil moisture based on hydrological and meteorological data. Temporal reduction technique and Long Short-Term Memory (LSTM) network are utilized to reduce computation and memory requirements and improve accuracy. The research demonstrates the effectiveness of LSTM in predicting hydrologic features with minimal prior knowledge of the watershed. The methodologies are shared as an end-to-end software pipeline for rapid prototyping of hydrologic learners.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Environmental Sciences
Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, Fernando Miralles-Wilhelm
Summary: Groundwater use for irrigation has a significant impact on agricultural productivity and local water resources. This study evaluated different groundwater irrigation schemes and found that auto-irrigation scheduling based on plant water stress generally outperformed prescribed irrigation based on well pumping rates in simulating irrigation volume and groundwater levels. The study also highlighted the effects of groundwater irrigation on the water cycle and crop water productivity.
Article
Engineering, Civil
Shouzhi Chen, Yongshuo H. Fu, Zhaofei Wu, Fanghua Hao, Zengchao Hao, Yahui Guo, Xiaojun Geng, Xiaoyan Li, Xuan Zhang, Jing Tang, Vijay P. Singh, Xuesong Zhang
Summary: The Soil and Water Assessment Tool (SWAT) model is widely used for simulating the water cycle and quantifying the impacts of climate change and human activities on hydrological processes. However, its representation of vegetation dynamics has been a major source of uncertainty. This study improves the SWAT model by incorporating dynamic growth start dates and heat requirements for vegetation growth based on long-term remote sensing data. The improved model shows significant improvements in simulating leaf area index (LAI) and evapotranspiration, indicating the importance of accurately representing phenological dates in vegetation growth modules.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Jiye Lee, Ather Abbas, Gregory W. McCarty, Xuesong Zhang, Sangchul Lee, Kyung Hwa Cho
JOURNAL OF HYDROLOGY
(2023)
Article
Agronomy
Tongxi Hu, Xuesong Zhang, Gil Bohrer, Yanlan Liu, Yuyu Zhou, Jay Martin, Yang Li, Kaiguang Zhao
Summary: Statistical crop modeling is crucial for understanding the impact of climate on crop yields. The choice of models is important, as linear regression is interpretable but lacks predictive power, while machine learning is highly predictive but often lacks interpretability. In this study, a Bayesian ensemble model (BM) was developed to explore historical crop yield data and predict future yields, providing both interpretability and high predictive power. BM incorporates many models via Bayesian model averaging, fits complex functions, and quantifies model uncertainty.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
R. Karki, J. Qi, C. A. Gonzalez-Benecke, X. Zhang, T. A. Martin, J. G. Arnold
Summary: This study developed a new forest module (SWAT-3PG) for the Soil and Water Assessment Tool (SWAT) based on the Physiological Process in Predicting Growth (3-PG) model. The new model improves biomass assimilation, partitioning, and losses in forests. Evaluation at field-scale demonstrated the model's capability to replicate forest biomass components accurately. Additionally, the model can simulate key forest variables such as leaf area index, net primary productivity, and actual evapotranspiration, making it useful for forest managers.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Environmental Sciences
Kang Liang, Xuesong Zhang, Xin-Zhong Liang, Virginia L. Jin, Girma Birru, Marty R. Schmer, G. Philip Robertson, Gregory W. McCarty, Glenn E. Moglen
Summary: This study improved and evaluated the SWAT-C model for simulating long-term dynamics of soil inorganic nitrogen (SIN) in different cropping systems. By adding new nitrification and denitrification algorithms, the model achieved better performance in SIN simulation. The revised SWAT-C model's performance was comparable to or better than other agroecosystem models tested in previous studies. Sensitivity analysis showed that parameters controlling soil organic matter decomposition, nitrification, and denitrification were most important for SIN simulation. The improved prediction of plant-available SIN using SWAT-C can inform agroecosystem management decisions to enhance crop productivity and minimize negative environmental impacts.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, Martha Anderson, Wade Crow, Sangchul Lee, Glenn E. Moglen, Gregory W. McCarty
Summary: This study calibrated the Soil and Water Assessment Tool (SWAT) model using streamflow data and remotely sensed hydrologic variables. The results show that adding remotely sensed ET and soil moisture to streamflow for calibration can impact the sensitive parameters of the model, but it does not necessarily improve its performance. Using remote sensing data alone leads to a deterioration in model performance. Different choices of remote sensing data for calibration also result in noticeable differences in simulated hydrologic processes. The comparison between SWAT and SWAT-Carbon models under different calibration setups reveals significant differences in their performance.
Article
Multidisciplinary Sciences
Han Qiu, Xuesong Zhang, Anni Yang, Kimberly P. Wickland, Edward G. Stets, Min Chen
Summary: River networks are crucial for the global carbon cycle. This study provides important data on the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) across the Conterminous United States (CONUS) and estimates net gain or net loss of POC and DOC in watersheds using river network connectivity information.
Article
Environmental Sciences
Molly E. Brown, Catherine Mitchell, Meghan Halabisky, Benjamin Gustafson, Helga do Rosario Gomes, Joaquim Goes, Xuesong Zhang, Anthony D. Campbell, Benjamin Poulter
Summary: This article investigates stakeholders of wet carbon (WC) ecosystems and analyzes the gaps between scientific understanding and information needs. The study reveals that stakeholder interest in WC systems is primarily determined by its significance for local policy, economics, or ecology. To bridge the gap between stakeholders and available WC data, improved communication of data availability and uncertainty, capacity building, engagement between stakeholder groups, and data continuity are needed.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Engineering, Civil
Rajith Mukundan, Rakesh K. Gelda, Mahrokh Moknatian, Xuesong Zhang, Tammo S. Steenhuis
Summary: This study transformed the SWAT-Carbon (C) model to simulate dissolved organic carbon (DOC) from variable source runoff areas in a humid forested watershed in the northeastern United States. The calibrated model accurately simulated streamflow and DOC flux, and showed sensitivity to soil properties and precipitation. The good performance of the model makes it a valuable tool for understanding the influence of climate and watershed management on DOC and developing mitigation strategies.
JOURNAL OF HYDROLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Sijal Dangol, Xuesong Zhang, Xin-Zhong Liang, Elena Blanc-Betes
Summary: In this study, the SWAT model was enhanced by integrating grass growth algorithms from the DAYCENT model, resulting in the SWAT-GRASSM model which showed improved simulation of switchgrass biomass yield and LAI seasonal development. SWAT-GRASSM also provided a more realistic representation of root development, crucial for biomass and nutrient allocation between aboveground and belowground pools, enhancing the credibility of environmental impact assessments for perennial grasses grown for bioenergy production.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Environmental Sciences
Yiming Wang, Yuyu Zhou, Kristie J. Franz, Xuesong Zhang, Junyu Qi, Gensuo Jia, Yun Yang
Summary: Agriculture is a major water user, and excessive groundwater extraction has led to a drop in water levels and changes in the hydrological cycle. This study compares the impacts of irrigation on hydrological processes in the Jing-Jin-Ji region in China and northern Texas in the US. The results show that deficit irrigation is more common in Jing-Jin-Ji and can significantly reduce irrigation water use. The intensity and timing of irrigation also affect hydrological processes, and the adoption of water conservation techniques can help reduce groundwater extraction.
SCIENCE OF THE TOTAL ENVIRONMENT
(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)