Weekly Literature Review

Week 02 · January 6–12, 2020

31 relevant papers found across 5 themes

Executive Summary

A landmark period for Earth system model development saw the documentation of five major CMIP6-class models (CESM2, NorESM2, GFDL-ESM 4.1, ACCESS-ESM1.5, and MIROC-ES2L). Drought research surged with multiple CMIP6-based projection studies identifying future hot spots globally, while deep learning methods—particularly LSTM variants—demonstrated growing capability for runoff prediction and flood forecasting. Milly and Dunne’s Science paper on Colorado River flow decline underscored the urgency of understanding warming impacts on water resources.

Table of Contents

Executive Summary
Earth System Model Development
Drought Projections and Assessment
Flood Research and Monitoring
Machine Learning for Hydrological Prediction
River Systems and Water Resources
Statistics
1. Papers by journal
Filtering Criteria

Earth System Model Development

2020 was a landmark year for Earth system model development, with the documentation and release of several major CMIP6-class ESMs. Danabasoglu et al. presented the Community Earth System Model Version 2 (CESM2) in a comprehensive overview, while Emmons et al. detailed its chemistry mechanism. The Norwegian (NorESM2, Seland et al.), American (GFDL-ESM 4.1, Dunne et al.), Australian (ACCESS-ESM1.5, Ziehn et al.), and Japanese (MIROC-ES2L, Hajima et al.) modeling centers all published descriptions of their CMIP6 contributions. Deser et al. provided important insights from ESM initial-condition large ensembles, demonstrating the value of ensemble approaches for separating forced responses from internal variability.

The Community Earth System Model Version 2 (CESM2)

Authors: G. Danabasoglu, J. Lamarque, J. Bacmeister, D. A. Bailey, A. K. DuVivier, J. Edwards et al.

Journal: Journal of Advances in Modeling Earth Systems · DOI: 10.1029/2019MS001916 · Citations: 1901

Matched topics: earth system model

An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low‐top” (40 km, with limited chemistry) and “high‐top” (130 km,…

Insights from Earth system model initial-condition large ensembles and future prospects

Authors: C. Deser, F. Lehner, K. Rodgers, T. Ault, T. Delworth, P. DiNezio et al.

Journal: Nature Climate Change · DOI: 10.1038/s41558-020-0731-2 · Citations: 738

Matched topics: earth system model

No abstract available.

Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations

Authors: Ø. Seland, M. Bentsen, D. Oliviè, T. Toniazzo, A. Gjermundsen, L. S. Graff et al.

Journal: Geoscientific Model Development · DOI: 10.5194/gmd-13-6165-2020 · Citations: 651

Matched topics: earth system model

Abstract. The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2) and shares with CESM2 the computer code infrastructure and many Earth system model components. However, NorESM2 employs entirely different ocean and ocean biogeochemistry models. The atmosphere component of NorESM2 (CAM-Nor) includes a different module for aerosol physics and chemistry, including inter…

The GFDL Earth System Model Version 4.1 (GFDL‐ESM 4.1): Overall Coupled Model Description and Simulation Characteristics

Authors: J. Dunne, L. Horowitz, A. Adcroft, P. Ginoux, I. Held, J. John et al.

Journal: Journal of Advances in Modeling Earth Systems · DOI: 10.1029/2019MS002015 · Citations: 547

Matched topics: earth system model

We describe the baseline coupled model configuration and simulation characteristics of GFDL’s Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL’s CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system …

The Australian Earth System Model: ACCESS-ESM1.5

Authors: T. Ziehn, M. Chamberlain, R. Law, A. Lenton, R. Bodman, M. Dix et al.

Journal: Journal of Southern Hemisphere Earth Systems Science · DOI: 10.1071/es19035 · Citations: 465

Matched topics: earth system model

The Australian Community Climate and Earth System Simulator (ACCESS) has been extended to include land and ocean carbon cycle components to form an Earth System Model (ESM). The current version, ACCESS-ESM1.5, has been mainly developed to enable Australia to participate in the Coupled Model Intercomparison Project Phase 6 (CMIP6) with an ESM version. Here we describe the model components and changes to the previous version, ACCESS-ESM1. We use the 500-year pre-industrial control run to highli…

Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks

Authors: T. Hajima, Michio Watanabe, A. Yamamoto, H. Tatebe, Maki A. Noguchi, M. Abe et al.

Journal: Geoscientific Model Development · DOI: 10.5194/gmd-13-2197-2020 · Citations: 460

Matched topics: earth system model

Abstract. This article describes the new Earth system model (ESM), the Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations (MIROC-ES2L), using a state-of-the-art climate model as the physical core. This model embeds a terrestrial biogeochemical component with explicit carbon–nitrogen interaction to account for soil nutrient control on plant growth and the land carbon sink. The model’s ocean biogeochemical component is largely updated to simulate t…

The Chemistry Mechanism in the Community Earth System Model Version 2 (CESM2)

Authors: L. Emmons, R. Schwantes, J. Orlando, G. Tyndall, D. Kinnison, J. Lamarque et al.

Journal: Journal of Advances in Modeling Earth Systems · DOI: 10.1029/2019MS001882 · Citations: 333

Matched topics: earth system model

The Community Earth System Model version 2 (CESM2) includes a detailed representation of chemistry throughout the atmosphere in the Community Atmosphere Model with chemistry and Whole Atmosphere Community Climate Model configurations. These model configurations use the Model for Ozone and Related chemical Tracers (MOZART) family of chemical mechanisms, covering the troposphere, stratosphere, mesosphere, and lower thermosphere. The new MOZART tropospheric chemistry scheme (T1) has a number of …

Drought Projections and Assessment

A surge of studies examined future drought risk using the latest CMIP6 projections. Cook et al. analyzed 21st-century drought projections across the hydrologic cycle, while Ukkola et al. demonstrated robust meteorological drought changes despite precipitation uncertainty. Regional studies focused on China (Su et al.), the Mediterranean (Tramblay et al.), and Central Europe (Hari et al.), with the latter showing the exceptional 2018–2019 drought will become more frequent under warming. Spinoni et al. identified future global drought hot spots using CORDEX data. Ault provided a comprehensive Science review synthesizing the physics and statistics of drought in a changing climate.

Twenty‐First Century Drought Projections in the CMIP6 Forcing Scenarios

Authors: B. Cook, J. Mankin, K. Marvel, A. P. Williams, J. Smerdon, K. Anchukaitis

Journal: Earth’s Future · DOI: 10.1029/2019EF001461 · Citations: 626

Matched topics: drought

There is strong evidence that climate change will increase drought risk and severity, but these conclusions depend on the regions, seasons, and drought metrics being considered. We analyze changes in drought across the hydrologic cycle (precipitation, soil moisture, and runoff) in projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6). The multimodel ensemble shows robust drying in the mean state across many regions and metrics by the end of the 21st century, even fol…

On the essentials of drought in a changing climate

Authors: T. Ault

Journal: Science · DOI: 10.1126/science.aaz5492 · Citations: 471

Matched topics: drought

Droughts of the future are likely to be more frequent, severe, and longer lasting than they have been in recent decades, but drought risks will be lower if greenhouse gas emissions are cut aggressively. This review presents a synopsis of the tools required for understanding the statistics, physics, and dynamics of drought and its causes in a historical context. Although these tools have been applied most extensively in the United States, Europe, and the Amazon region, they have not been as wi…

Challenges for drought assessment in the Mediterranean region under future climate scenarios

Authors: Y. Tramblay, A. Koutroulis, L. Samaniego, S. Vicente‐Serrano, F. Volaire, A. Boone et al.

Journal: Earth-Science Reviews · DOI: 10.1016/j.earscirev.2020.103348 · Citations: 455

Matched topics: drought

Droughts can have strong environmental and socio-economic impacts in the Mediterranean region, in particular for countries relying on rain-fed agricultural production, but also in areas in which irrigation plays an important role and in which natural vegetation has been modified or is subject to water stress. The purpose of this review is to provide an assessment of the complexity of the drought phenomenon in the Mediterranean region and present various perspectives on drought in the present …

Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation

Authors: A. Ukkola, M. D. De Kauwe, M. Roderick, G. Abramowitz, A. Pitman

Journal: Geophysical Research Letters · DOI: 10.1029/2020GL087820 · Citations: 362

Matched topics: drought

Quantifying how climate change drives drought is a priority to inform policy and adaptation planning. We show that the latest Coupled Model Intercomparison Project (CMIP6) simulations project coherent regional patterns in meteorological drought for two emissions scenarios to 2100. We find robust projected changes in seasonal drought duration and frequency (robust over >45% of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of th…

Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China

Authors: Buda Su, Jinlong Huang, Sanjit Kumar Mondal, J. Zhai, Yanjun Wang, Shanshan Wen et al.

Journal: Atmospheric Research · DOI: 10.1016/j.atmosres.2020.105375 · Citations: 338

Matched topics: drought

Abstract In this paper, future drought characteristics (frequency, duration and intensity) over China are analysed by using four climate models from CMIP6 under the seven SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, SSP370, and SSP585) for three defined periods of 2021–2040 (near-term), 2041–2060 (mid-term) and 2081–2100 (long-term). The corresponding four climate models output from CMIP5 are also used to conduc…

Future Global Meteorological Drought Hot Spots: A Study Based on CORDEX Data

Authors: J. Spinoni, P. Barbosa, E. Bucchignani, J. Cassano, Tereza Cavazos, J. Christensen et al.

Journal: Journal of Climate · DOI: 10.1175/jcli-d-19-0084.1 · Citations: 336

Matched topics: drought

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation …

Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming

Authors: Vittal Hari, O. Rakovec, Y. Markonis, M. Hanel, Rohini Kumar

Journal: Scientific Reports · DOI: 10.1038/s41598-020-68872-9 · Citations: 331

Matched topics: drought

Since the spring 2018, a large part of Europe has been in the midst of a record-setting drought. Using long-term observations, we demonstrate that the occurrence of the 2018–2019 (consecutive) summer drought is unprecedented in the last 250 years, and its combined impact on the growing season vegetation activities is stronger compared to the 2003 European drought. Using a suite of climate model simulation outputs, we underpin the role of anthropogenic warming on exacerbating the future risk o…

Flood Research and Monitoring

Flood research during this period spanned hazard assessment, remote sensing, and machine learning. Tabari demonstrated that climate change impacts on flood and extreme precipitation increase with water availability. Several studies advanced ML-based flood susceptibility mapping using ensemble methods (Islam et al., Gudiyangada Nachappa et al., Shahabi et al.). Remote sensing advances included Google Earth Engine-based flood monitoring with Sentinel-1 and Landsat (DeVries et al.). Kao et al. proposed an LSTM-based encoder-decoder for multi-step-ahead flood forecasting, and Menéndez et al. quantified the global flood protection benefits of mangroves.

Climate change impact on flood and extreme precipitation increases with water availability

Authors: H. Tabari

Journal: Scientific Reports · DOI: 10.1038/s41598-020-70816-2 · Citations: 1228

Matched topics: flood, climate change

The hydrological cycle is expected to intensify with global warming, which likely increases the intensity of extreme precipitation events and the risk of flooding. The changes, however, often differ from the theorized expectation of increases in water‐holding capacity of the atmosphere in the warmer conditions, especially when water availability is limited. Here, the relationships of changes in extreme precipitation and flood intensities for the end of the twenty-first century with spatial an…

Flood susceptibility modelling using advanced ensemble machine learning models

Authors: A. Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, K. Eibek, Q. Pham et al.

Journal: Geoscience Frontiers · DOI: 10.1016/j.gsf.2020.09.006 · Citations: 451

Matched topics: flood

Abstract Because of the tremendous damage to properties, infrastructures, and human casualties, floods are one of the greatest devastating disasters from nature. Due to the dynamic and complex nature of the flash flood, it is challenging to predict the sites which are vulnerable to flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new …

The Global Flood Protection Benefits of Mangroves

Authors: Pelayo Menéndez, I. Losada, Saúl Torres-Ortega, S. Narayan, M. Beck

Journal: Scientific Reports · DOI: 10.1038/s41598-020-61136-6 · Citations: 390

Matched topics: flood

Coastal flood risks are rising rapidly. We provide high resolution estimates of the economic value of mangroves forests for flood risk reduction every 20 km worldwide. We develop a probabilistic, process-based valuation of the effects of mangroves on averting damages to people and property. We couple spatially-explicit 2-D hydrodynamic analyses with economic models, and find that mangroves provide flood protection benefits exceeding $US 65 billion per year. If mangroves were lost, 15 million …

Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine

Authors: B. DeVries, Chengquan Huang, J. Armston, Wenli Huang, John W. Jones, M. Lang

Journal: Remote Sensing of Environment · DOI: 10.1016/j.rse.2020.111664 · Citations: 379

Matched topics: flood

Abstract Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth’s surface nearly independently of weather conditions and time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time global, operational SAR data have been made freely available. Combined with the emergence of cloud computin…

Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory

Authors: Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilou, Khalil Gholamnia, O. Ghorbanzadeh, Omid Rahmati, T. Blaschke

Journal: Journal of Hydrology · DOI: 10.1016/j.jhydrol.2020.125275 · Citations: 330

Matched topics: flood

Abstract Floods are one of the most widespread natural hazards occurring across the globe. The main objective of this study was to produce flood susceptibility maps for the province of Salzburg, Austria, using two multi-criteria decision analysis (MCDA) models including analytical hierarchical process (AHP) and analytical network process (ANP) and two machine learning (ML) models including random forest (RF) and support vector machine (SVM). Additionally, we compare which of the MCDA and ML m…

Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting

Authors: I-Feng Kao, Yanlai Zhou, Li-Chiu Chang, F. Chang

Journal: Journal of Hydrology · DOI: 10.1016/j.jhydrol.2020.124631 · Citations: 328

Matched topics: flood

Abstract Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training,…

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Authors: H. Shahabi, A. Shirzadi, K. Ghaderi, E. Omidvar, N. Al‐Ansari, J. Clague et al.

Journal: Remote Sensing · DOI: 10.3390/rs12020266 · Citations: 319

Matched topics: flood

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict …

Machine Learning for Hydrological Prediction

Deep learning applications in hydrology advanced rapidly during this period. Xiang et al. published a study in Water Resources Research on LSTM-based sequence-to-sequence learning for rainfall–runoff modeling, while Gao et al. addressed time step optimization in GRU and LSTM runoff prediction. Ding et al. introduced an interpretable spatio-temporal attention LSTM for flood forecasting, balancing accuracy with explainability. Sit et al. provided a comprehensive review of deep learning across the water resources domain, covering precipitation, streamflow, water quality, and groundwater applications.

Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation

Authors: Shuai Gao, Yuefei Huang, Shuo Zhang, Jingcheng Han, Guangqian Wang, Meixin Zhang et al.

Journal: Journal of Hydrology · DOI: 10.1016/j.jhydrol.2020.125188 · Citations: 553

Matched topics: runoff

Abstract Runoff forecasting is an important approach for flood mitigation. Many machine learning models have been proposed for runoff forecasting in recent years. To reconstruct the time series of runoff data into a standard machine learning dataset, a sliding window method is usually used to pre-process the data, with the size of the window as a variable parameter which is commonly referred to as the time step. Conventional machine learning methods, such as artificial neural network models (…

A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Authors: Z. Xiang, June Yan, I. Demir

Journal: Water Resources Research · DOI: 10.1029/2019WR025326 · Citations: 551

Matched topics: runoff

Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still room for improvement, researchers have been developing physical and machine learning models for decades to predict runoff using rainfall data sets. With the advancement of computational hardware resources and algorithms, deep learning methods such as the long short‐term memory (LSTM) model and sequence‐to‐sequence (seq2seq) modeling have shown a good deal of promise in dealing with time series problems b…

A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

Authors: M. Sit, B. Demiray, Z. Xiang, Gregory Ewing, Y. Sermet, I. Demir

Journal: Water Science and Technology · DOI: 10.31223/osf.io/xs36g · Citations: 450

Matched topics: hydrology

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. I…

Interpretable spatio-temporal attention LSTM model for flood forecasting

Authors: Yukai Ding, Yuelong Zhu, Jun Feng, Pengcheng Zhang, Zirun Cheng

Journal: Neurocomputing · DOI: 10.1016/j.neucom.2020.04.110 · Citations: 354

Matched topics: flood

Abstract Modeling interpretable artificial intelligence (AI) for flood forecasting represents a serious challenge: both accuracy and interpretability are indispensable. Because of the uncertainty and nonlinearity of flood, existing hydrological solutions always achieve low prediction robustness while machine learning (ML) approaches neglect the physical interpretability of models. In this paper, we focus on the need for flood forecasting and propose an interpretable Spatio-Temporal Attention …

River Systems and Water Resources

Several important studies addressed river systems and broader water resources challenges. Milly and Dunne published a landmark Science paper showing Colorado River flow is declining as warming-driven snow loss energizes evaporation. Maavara et al. reviewed river dam impacts on biogeochemical cycling, while Tickner et al. proposed an emergency recovery plan for global freshwater biodiversity. Mariotti et al. examined windows of opportunity for skillful subseasonal-to-seasonal forecasts relevant to water management. Minhas et al. reviewed strategies for coping with salinity in irrigated agriculture, and Asadollah et al. compared ML models for river water quality prediction.

Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan

Authors: David Tickner, Jeffrey J. Opperman, Robin Abell, Mike Acreman, Angela H. Arthington, Stuart E. Bunn et al.

Journal: BioScience · DOI: 10.1093/biosci/biaa002 · Citations: 1110

Matched topics: hydrology, land surface model, hydropower, irrigation

Despite their limited spatial extent, freshwater ecosystems host remarkable biodiversity, including one-third of all vertebrate species. This biodiversity is declining dramatically: Globally, wetlands are vanishing three times faster than forests, and freshwater vertebrate populations have fallen more than twice as steeply as terrestrial or marine populations. Threats to freshwater biodiversity are well documented but coordinated action to reverse the decline is lacking. We present an Emergen…

River dam impacts on biogeochemical cycling

Authors: T. Maavara, Qiuwen Chen, K. V. Van Meter, L. Brown, Jianyun Zhang, J. Ni et al.

Journal: Nature Reviews Earth & Environment · DOI: 10.1038/s43017-019-0019-0 · Citations: 642

Matched topics: river

No abstract available.

Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation

Authors: P. Milly, K. Dunne

Journal: Science · DOI: 10.1126/science.aay9187 · Citations: 329

Matched topics: river

Evaporating futures Drought and warming have been shrinking Colorado River flow for many years. Milly and Dunne used a hydrologic model and historical observations to show that this decrease is due mainly to increased evapotranspiration caused by a reduction of albedo from snow loss and the associated rise in the absorption of solar radiation (see the Perspective by Hobbins and Barsugli). This drying will be greater than the projected precipitation increases expected from climate warming, inc…

Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues

Authors: P. Minhas, T. Ramos, Alon Ben-Gal, L. S. Pereira

Journal: Agricultural Water Management · DOI: 10.1016/j.agwat.2019.105832 · Citations: 322

Matched topics: water management

Abstract Soil and water salinity and associated problems are a major challenge for global food production. Strategies to cope with salinity include a better understanding of the impacts of temporal and spatial dynamics of salinity on soil water balances vis-a-vis evapotranspiration (ET) and devising optimal irrigation schedules and efficient methods. Both steady state and transient models are now available for predicting salinity effects on reduction of crop growth and means for its optimizat…

Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

Authors: Annarita Mariotti, Cory Baggett, Elizabeth A. Barnes, Emily Becker, Amy H. Butler, Dan C. Collins et al.

Journal: Bulletin of the American Meteorological Society · DOI: 10.1175/bams-d-18-0326.1 · Citations: 318

Matched topics: water management, seasonal, land surface model, earth system model

Abstract There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the …

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

Authors: S. Asadollah, A. Sharafati, D. Motta, Z. Yaseen

Journal: Journal of Environmental Chemical Engineering · DOI: 10.1016/j.jece.2020.104599 · Citations: 307

Matched topics: river

Abstract The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (…

Statistics

Metric	Count
Databases searched	2
Topics searched	16
Total papers fetched	1828
After deduplication	1509
After LLM relevance filtering	31
Rejected (not relevant)	1478

Papers by journal

Journal	Papers
Journal of Advances in Modeling Earth Systems	3
Scientific Reports	3
Journal of Hydrology	3
Geoscientific Model Development	2
Science	2
Nature Climate Change	1
Journal of Southern Hemisphere Earth Systems Science	1
Earth’s Future	1
Earth-Science Reviews	1
Geophysical Research Letters	1
Atmospheric Research	1
Journal of Climate	1
Geoscience Frontiers	1
Remote Sensing of Environment	1
Remote Sensing	1
Water Resources Research	1
Water Science and Technology	1
Neurocomputing	1
BioScience	1
Nature Reviews Earth & Environment	1
Agricultural Water Management	1
Bulletin of the American Meteorological Society	1
Journal of Environmental Chemical Engineering	1

Filtering Criteria

Topics: hydrology, hydrologic model, river, runoff, streamflow, reservoir, water management, flood, drought, seasonal, land surface model, climate change, hydropower, surface water, irrigation, earth system model

Databases: Semantic Scholar, OpenAlex