bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Spread F Detection and classification at Hainan Station of Chinese Meridian Project
Authors: Wang Zheng , Gao Pengdong , Wang Guojun , Shi Jiankui
Published: February 2023

The spread F phenomenon (SF), i.e., the spread characteristics on F trace in ionogram, is considered to be caused by ionospheric disturbances. The SF image features are considered to be corresponding to different physical mechanism. According to the URSI handbook of ionogram interpretation and reduction, depending on the shape of diffusion on ionogram, the SF all over the world could be divided into 4 types: FSF/RSF/MSF/BSF. However, we have found at low latitude, BSF is rare and strong RSF (SSF) is a major type. For the ionogram data obtained from Hainan Station (19.5°N, 109.1°E, magnetic 11°N), the Deep Learning technique is used for the image characteristics, making a model for automatic SF detection and classification at this station. No matter what the ionogram data formats or the ionosonde models, the model could automatic classify the SF as FSF/RSF/MSF/SSF for the first time.
the Paired Land Use Experiments (PLUE) theory in driver identification of regional vegetation change
Authors: Chen Tiexi
Published: February 2023

Vegetation change is one of the essential indices of global change. In the past 30 years, based on the remote sensing records, the whole world has shown an overall greening phenomenon, accompanied by regional browning. How to identify the drivers of regional-scale vegetation change, especially to distinguish between climate change and human activities remains a great challenge. Modeling studies show that the CO2 fertilization effect plays a dominant role, but the significant greening contribution of farmland areas at the global scale seems to indicate that human land management (LMC) activities have a huge impact. Methods can be divided into two categories: model and observation statistics. Models are easy to quantify contributions but lack descriptions of LMC processes, and regional-scale statistical methods are difficult to identify driving factors. This study proposes the theory of Paired Land Use Experiment (PLUE), which selects areas with large differences in land management and consistent climate change to achieve "control" of climate change and attribute the difference in vegetation change to on the LMC. The PLUE theory was applied in two selected regions around the world. First, the Khabur River plain on the border between Syria and Turkey was selected. The two countries occupy roughly the same area of the plain. The climate conditions on both sides are consistent with the changes, and the interannual time series correlation coefficient of the Enhanced Vegetation Index (EVI) after detrending exceeds 0.8 (p <0.01). For multi-year trends, this difference can be attributed to LMC. Combined with relevant reports on Syrian social development, social unrest has caused serious degradation of land management capabilities. Therefore, social unrest and occasional severe natural disasters have led to a continuous decline in land management capabilities in the region, further contributing to the "browning" of Syrian vegetation. Secondly, the Sanjiang Plain was selected. China and Russia roughly divided the plain into two, with farmland and temperate savannah as the main vegetation types on both sides. The temperature and precipitation changes in the two places were basically the same, and the leaf area index (LAI) showed a significant growth trend with the same magnitude. However, the seasonal characteristics of the LAI trend in the two regions are significantly different. Using the PLUE method, it can be seen that this difference is caused by land management, including the expansion of paddy fields and the increase in farmland management intensity (mechanization, pesticide and fertilizer application). At the same time, it is found that the climate residual method will give false conclusions in the attribution of interannual changes. In summary, the PLUE method can directly identify land management activities other than climate elements from observations at the regional scale, which is helpful for further research on the driving forces of long-term vegetation change trends.
Influence of Hydraulic Factors on Landslide Susceptibility of Riverbank in the Chenyulan watershed
Authors: Chan Hsun-Chuan
Published: February 2023

The riverbank landslide is considered as the major sediment supply in the watershed. It mostly due to the river flows erode the foot of the riverbank, which makes the slope unstable. This study focused on the watershed susceptibility analysis of the riverbank landslide in the Chenyulan watershed. The Logistic regression method was used to establish the landslide susceptibility model not only with the topography, geological and anthropic factors, but also with the hydraulic factors including the hydraulic Sinuosity index, channel gradient, and concave-or-convex bank. The study areas were classified into four regions, according to the river-bed slope and confluence of rivers. The effects of the hydraulic factors on the model results were investigated. In the upstream region with mild topographic slope, the landslides were found to be dominated by the topography factors. The area under the curve (AUC) value of the model was 74.2%. In the upstream region with steep topographic slope, the steep hillslopes and the channel erosion of the concave bank produced a high weight of concave-or-convex bank in the model. The developed model exhibited an increased AUC value of 76.8%. In the downstream region, the lateral erosion of the channel increased the weights of hydraulic sinuosity index and channel gradient in the model. The developed model exhibited high area under the curve (AUC) value of 88.1%. The hydraulic factors increased the predictive performance of the model considerably.
Application cases of remote sensing-integrated crop model to simulate and predict crop yield with satellite images
Authors: Jeong Seungtaek , Yeom Jong-min , Ko Jonghan , Chung Daewon , Lee Sun-Gu
Published: February 2023

The remote sensing-integrated crop model (RSCM) was designed to simulate crop growth processes and yield using remote sensing data. The RSCM is based on the radiation use efficiency (RUE) model and employs a within-season calibration procedure recalibrating the daily crop leaf area index (LAI) using satellite images. And it has functions to calculate daily biomass, evapotranspiration (ET), gross primary productivity (GPP), and net primary productivity from the LAI in addition to crop yield. In previous studies, the essential crop growth parameters required in the model, such as RUE, light extinction coefficient, specific leaf area, base temperature, etc., were determined through field experiments. And its performances were validated using various remote sensing data, including proximity sensing data, drone images, and satellite images. Among them, this study presented the application results with satellite images in the RSCM. The target crop is rice (Oryza Sativa), one of the world's major crops, and the study areas range from South Korea to Northeast Asia. Satellite images and meteorological data were used differently depending on the study sites. The types of satellite images used in this study are the RapidEye, the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra/Aqua satellite, and the Geostationary Ocean Color Imager (GOCI) and the Meteorological Imager (MI) of Communication, Ocean and the Meteorological (COMS) satellite. And gridded data for air temperature and solar radiation was acquired from the Korea Local Analysis and Prediction System (KLAPS) and the European Centre for Medium-Range Weather Forecasts (ECMFW). The primary application of the RSMC is to simulate rice yield, but some results showed crop growth factors such as biomass, LAI, GPP, and ET. In addition, the most recent study performed the early prediction of crop yield by combining deep learning with crop models. Through this study, it is possible to know the future utilization of the RSCM model in the agriculture and satellite application fields.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2022-00165154, "Development of Application Support System for Satellite Information Big Data").
Lessons from Adapting Applied Hydrology Instruction to Open Source Software Tools
Authors: Kovacek Daniel , Weijs Steven
Published: February 2023

Resource use decisions in the extractive resource industry in Canada are supported in various ways by collection and analysis of water resources data.  Data quality assurance and analysis are influenced by the methods taught in academic training, the tools used to teach and practice, and industry standards and culture.  The growth in popularity of computer programming languages such as Matlab, Python, and R, and new web-based collaboration and publishing tools from Project Jupyter (Notebook, Book) have created opportunities for teaching applied hydrology in new ways that can support the evolving nature of data in hydrology practice, namely in treating open-ended problems more typical to industry practice.The abrupt shift to web-based instruction at the undergraduate level in 2019 spurred development of interactive instructional content in an applied hydrology course at the University of British Columbia, in Vancouver, Canada.  Using the open-source Jupyter Book software framework, we developed open-access course material to complement the hydrology theory curriculum.  The new course content consists of a set of tutorials designed to give students a practical introduction to important components of engineering practice such as data quality assurance, and uncertainty in hydrological models.  The content is provided as an open-access online textbook with an embedded Python code interpreter.   With each successive cohort, the material has adapted to student feedback, namely in treating the types of open problems common in industry, and in the amount of programming experience required. 
Atmospheric observation and source analysis of CFCs, HCFCs and HFCs at the Medog background site on the southern slope of the Himalayas, China
Authors: Wu Jing , Liu Zehua , Yao Bo , An Minde , Niu Yueyuan , Zhao Wanglin , Yu Haibo , Wang Tong , Dong Bingqi , Peng Lin
Published: February 2023

The ozone-depleting substances chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs) and their major substitutes hydrofluorocarbons (HFCs) are all potent greenhouse gases, and their atmospheric concentrations and emission sources have received international attention. The relevant studies have been carried out at multiple background sites around the world, but there is no report on the southern slope of the Himalayas. For the first time, this study set up the Medog background observation site 30 km away from Medog County on the southern slope of the Himalayas (95.5262ºE, 29.5314ºN, 1298.8 m above sea level) in China. From August 10 to September 19 and November 3 to November 30 in 2021, 114 atmospheric samples were collected instantaneously using evacuated electro-polished stainless-steel canisters and manual pressurization equipment and 1-3 samples were collected per day. Nine substances (CFC-11, 12, 113; HCFC-22, 141b, 142b; HFC-23, 125, 134a) were analyzed using a high-precision measurement system of halogenated gases (ODS5-pro). For each substance, all the atmospheric samples were divided into two categories: background samples and polluted samples. The background samples are those whose concentrations were within the range of the background concentrations (measured at the Ragged Point site at the same latitude in 2021) ± 3σ (instrument precision), and the others were classified as polluted samples. Our results showed that the number of the polluted samples of each substance accounted for about 40%-90%. In addition, we found that the mixing ratios of CFC-113, HFC-23, and HCFC-22 in the polluted samples were 93, 44 and 311 pptv, respectively, with higher mixing ratios (31.6%, 24.5%, and 22.5%) above the background levels than other substances (4.3%~10.2%). Furthermore, CFC-113 and HFC-23 were significantly correlated (R = 0.429, P < 0.01), suggesting that they may have similar sources. Both Backward Trajectory and Potential Source Contribution Function of the polluted samples of CFC-113, HFC-23, and HCFC-22 found that the polluted air masses mainly came from the northeast of India and other regions southwest of the sampling site. In contrast, the background air masses mainly came from the local areas. The concentrations of CFC-113, HFC-23, and HCFC-22 in the polluted samples observed in this study, likely from the northeast of India, were about 20-30% higher than the results of aircraft samples conducted by Say et al. (2019) over Northern and Southern India in 2016. In the future, we will continue to carry out atmospheric observations at the Medog background site and try to use the suitable atmospheric transport model to inverse the emissions in the surrounding areas.
Simulating tsunami flooding and seawater infiltration using coupled surface-subsurface flow models
Authors: Liu Jiaqi , Brunner Philip , Tokunaga Tomochika
Published: February 2023

Tsunami disasters cause not only human casualties and economic losses by short-term seawater flooding but also long-term salinization issues to groundwater resources due to seawater infiltration into coastal unconfined aquifers. The two processes, seawater flooding and infiltration, have been commonly simulated in a separate manner using tsunami models and groundwater models, respectively. Thanks to many recent advances in fully integrated hydrological modeling techniques, simulating surface and subsurface flow processes across various time scales within one conceptual framework is now feasible. Here, we present numerical simulations of the coupled processes of seawater flooding and infiltration based on a coastal urban area of Niijima Island, Japan, under the future Nankai Trough earthquake and tsunami scenarios. The HydroGeoSphere code was used to solve 2-D surface flow by St Venant equation and 3-D subsurface flow by Richard’s equation. The baseline simulation showed that road networks acted as fast paths for seawater flooding, while bare lands and building areas were the primary locations for seawater infiltration into the subsurface. The occurrence of seawater ponding was found to be controlled by both topographic variations at the land surface and the saturation condition of the soil medium in the subsurface. Moreover, the type of topographic data used in the model (DEM or DSM) and the equivalent hydraulic conductivity applied to building restructures showed significant effects on the simulated intensity of surface flow and the amount of seawater infiltration. These findings indicated that surface-subsurface interactions and the properties of both surface and subsurface domains are important factors to be considered for improving infrastructure safety evaluation and water resources management in tsunami-prone areas.
Offshore wind power in China contributing to regional power system upgrade and carbon neutrality
Authors: Deng Xi , Qin Zhangcai
Published: February 2023

The whole world is urgently looking for alternative renewable energy resources to power its future economy with less carbon footprint. As one of the world’s largest energy consumers and CO2 emitters, China is making all efforts to decarbonize its power systems (‘carbon neutrality by 2060’), with special attention to offshore wind power. However, the potential of offshore wind power generation and emissions mitigation is largely unknown, and the contribution to regional carbon neutrality needs to be further clarified. Here, we estimate the offshore wind resource, its generation potential, and the reduction of CO2 emissions from offshore wind power to replace coal-fired power generation. We find that the abundant offshore wind energy resources in China can potentially generate enough electricity to fully power the country. However, current utilization of offshore wind energy is relatively limited, supplying less than 1% of local electricity needs. With the development of offshore wind farms, this share could be over 20 times higher in 2050 than that at present. The total emissions reduction would increase from 11.9 Tg CO2-eq yr–1 in 2019 to 294.3 Tg CO2-eq yr–1 in 2050 because of reduced coal use, significantly contributing to emissions mitigation along the coastal provinces. Our results highlight the important role of offshore wind power in upgrading the regional energy system and achieving carbon neutrality of China, and it will also give imputes to a cleaner electricity system worldwide. Future studies are encouraged to further explore the feasibility of offshore wind farm construction.
Evaluation of the OCO-2 and OCO-3 ACOS data products against TCCON
Authors: Kiel Matthäus , Das Saswati , Osterman Gregory , Laughner Joshua , Payne Vivienne , Chatterjee Abhishek
Published: February 2023

The Orbiting Carbon Observatory-2 (OCO-2), launched in 2014, is NASA’s first satellite dedicated to measure sources and sinks of carbon dioxide (CO2) in Earth’s atmosphere on regional scales. Since 2019, measurements from the Orbiting Carbon Observatory-3 (OCO-3) have complemented OCO-2’s data record. In addition, OCO-3’s Snapshot Area Mapping (SAM) mode observations over emission hotspots like cities, power plants, and volcanoes provide a novel data set for carbon cycle studies on local scales. Data from both instruments are analyzed with the Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm to estimate column-average dry-air mole fractions of carbon dioxide (XCO2) in Earth’s atmosphere. Evaluating these space-based estimates of XCO2 against independent validation data sets provides information about the quality, potential biases, and errors in the OCO-2/3 data record. Here, we present comparisons of the ACOS V10 XCO2 from OCO-3 and the new and improved ACOS V11 XCO2 from OCO-2 against ground-based measurements from the Total Carbon Column Observing Network (TCCON).For both instruments, the root-mean-square error (RMSE) is below 1 ppm for all observational modes when compared to collocated TCCON observations. The OCO-3 V10.4 data version, an improvement over the initial vEarly data version, reduces an XCO2 time-dependent bias that was present in the previous OCO-3 data record. Consequently, data from both instruments does not indicate any significant time-dependent bias over the span of several years. Further, we evaluate differences between OCO-3 and TCCON related to different local overpass times. On average, OCO-3’s equator crossing time occurs about 20 minutes earlier every day. Comparisons against TCCON indicate no significant local time of day bias in the OCO-3 XCO2 data, however, comparisons over individual TCCON sites indicate a dependence which provides insight into potential airmass and viewing geometry related biases. The improved OCO-2 V11 data version reduces the mean bias against TCCON to ~0.15ppm from previously ~0.4ppm in V10. We find the largest reduction in RMSE over ocean due to an improved ocean glint surface treatment in V11. Both OCO data products are of comparable quality and are an improvement over earlier OCO data versions. Finally, we analyze how well OCO-2 captures the mean seasonal cycle amplitudes and growth rates over selected TCCON sites.
Impact of climate change on the fire weather in Indian forests
Authors: Barik Anasuya , Baidya Roy Somnath
Published: February 2023

Forest fires strongly depend on local weather conditions. Weather conditions conducive for occurrence and growth of fires is known as “fire weather”. This work investigates how climate change can affect the future fire weather in Indian forests using the Canadian Forest Fire Danger Rating System – Fire Weather Index (CFFDRS-FWI), a well-known fire danger assessment system. To drive this model, we used a high-resolution dynamically downscaled climate projection DSCESM for a baseline (2006-2015) and an end-century (2091-2100) period to compute the metric ‘Fire Weather Index (FWI)’. We divided the forest areas of the country into 5 zones based on climate and forest types viz., Himalayan (HIM), Northeast (NE), Central India (CEN), Deccan (DEC) and Western Ghats (WG) zones. Then, we developed thresholds for five fire weather danger classes using the baseline FWI in conjunction with observed fire count from MODIS active fire data. The baseline and future FWI, fire weather danger, Seasonal Severity Ratings (SSR) and characteristics of the fire weather season were compared to estimate the effect of climate change on forest fire danger.Results show that there is considerable heterogeneity in the baseline as well as future fire weather danger across, and even within, the different forest zones of India. Climate change is likely to have a strong effect on fire weather. Days exceeding the Very High FWI threshold are likely to increase by about 30-40 days by the end-century despite a modest increase of about 5% in annual FWI values. SSR analysis suggests a maximum increase in fire disturbances during the pre-monsoon months of March-April-May.  About 55% of forest area over India will experience increased fire danger in this season. The least effect will be in the post monsoon season in September-October-November. The fire season is also expected to lengthen up to 59 days depending upon the forest type. The forests which are most likely to be affected by fire disturbances by end-century are moist deciduous and evergreen forests in the northern WG, mixed dry deciduous forests in central and southern CEN, Pine and Sal forests in the HIM and the scrub forests in the DEC zone. Climate change is unlikely to affect the fire weather danger in the NE.This study is one of the first attempts to quantify the effects of climate change on forest fire hazard in India. It has significant policy implications and can be valuable for fire management authorities for designing appropriate fire suppression and mitigating measures. 
On the plausibility of climate variable trends
Authors: Rateb Ashraf , R. Scanlon Bridget , Y. Sun Alexander
Published: February 2023

 

Studying long-term changes in physical systems (e.g., ocean, ice, and land water) helps to understand the impact of climate change, natural variability, and human interventions on these systems. Most of the time, our ability to conduct deductive reasoning on climate variable trends (CVTs) is compromised by insufficient data and limitation of deterministic statistical methods, and our reasoning is reduced to inductive (i.e., plausible). Nevertheless, quantifying such plausibility is rarely addressed—instead, traditional binary beliefs (i.e., significant versus insignificant) are held for CVTs.

Here, we derive the plausibility of three CVTs (i.e., sea level rise, ice melting, and total water storage change) using satellite data (e.g., altimetry and Gravity Recovery Climate Experiments (GRACE)) between 1993 and 2021. We regressed the satellite data using a non-parametric probabilistic approach (e.g., Gaussian process regression) and derived two probabilistic indices for the trend changes. We probabilistically evaluated the CVTs magnitude; trendiness (i.e., change in trend strength) and stability (i.e., maintaining the progression in the same direction) over a 29-yr period.

Our results show the global sea level (GSL) is increasing with 85% plausibility at an average magnitude of 3.1 mm/yr over 29 yr. Pauses and hiatus in the GSL rise were quantified and attributed to El Niño Southern Oscillation. We find acceleration in the regional sea level in six hot spots, where the trend plausibility exceeds 60% beyond the modulation of interannual and decadal climate variability. Results were also obtained for the ice melting in ice sheets and glaciers, and the total water storage over 21 yr.

Assessing the trend strength and change goes beyond the simple linear and quadratic regressions and the binary beliefs. Trends evolve over short and long-time scales as a response to the driving processes. Understanding and quantifying how plausible these changes over certain periods improves the physical attribution and overcomes the limitations of data and methods. It is also practical for policymaking and helps to communicate science effectively.
A new indicator reveals frequent flash droughts with a common physical evolution in different agricultural regions worldwide
Authors: Lovino Miguel A. , Berbery Ernesto H. , Pierrestegui M. Josefina , Müller Omar V. , Müller Gabriela V.
Published: February 2023

Flash droughts negatively impact agriculture and natural ecosystems worldwide. However, there is still no flash drought indicator that can couple rapid soil moisture depletion and its impacts on vegetation health. Moreover, the literature describes several indicators that may not reliably represent flash drought evolution or its drivers in varying climatic regimes. Our study introduces a robust and straightforward approach to identify flash droughts based on ERA5 root-zone soil moisture along with soil properties (field capacity and wilting point) to reflect the soil moisture deficit and plant water stress conditions across different climate regions. Using this approach, we identify the regions in the world prone to flash droughts, and their seasonal frequency. This study also analyzes the processes involving atmospheric and surface drivers during the drought lifecycle.The proposed indicator captures the rapid soil moisture depletion by assessing the decline in the soil water deficit index (SWDI) from an upper threshold to a lower threshold over a period of 20 days (4 pentads). The upper threshold (SWDI=-3) ensures an increase in evaporative stress as it is close to or at the critical soil moisture value, which differentiates energy- and soil moisture-limited evapotranspiration regimes. The lower threshold (SWDI=-5) is the readily available soil water limit for vegetation growth. Below this point, plants begin to experience water stress. Although soil water is theoretically available for plants before reaching the wilting point, the plant water uptake is reduced well before that value.Our findings show that the main flash drought hotspots are found to be located in southeastern South America, southern China, India, central-eastern Europe and southern Russia, and the central-eastern United States. Most flash drought occurrence hotspots are found in agricultural regions. Additionally, the analysis of the seasonal flash drought frequency indicates that most flash drought events impact the critical growth periods of crops.Our results reveal that all flash drought hotspots exhibit similar evolution of key atmospheric and surface variables regardless of the location or climatic regime. Thus, the physical processes involved in flash droughts appear to be similar worldwide. As expected, a precipitation deficit is the main driver for rapid soil moisture depletion. Temperature also plays an important role in the persistence of flash drought events. The evolution of evapotranspiration during flash droughts modulates the precipitation effects: evapotranspiration increases until the onset of the flash drought (energy-limited regime) and then decreases during the intensification period due to water stress (soil moisture-limited regime).
An End-to-end Deep Learning Approach for Analyzing Tropical Cyclone 2-D Surface Winds Utilizing Satellite Data
Authors: Cheng Yung-Yun , Chen Buo-Fu
Published: February 2023

Although tropical cyclone (TC) forecasts can fairly well capture the TC track and primary rainfall distribution, limited skills are found in forecasting TC structural changes and asymmetric gusty winds. The barrier to further understanding TC structural change is due mainly to the lack of observation, and it is difficult to have systematic 2-D wind analyses. Here, we developed a deep learning model — Deep Learning 2-D Structure Analysis Model for Tropical Cyclones (DSAT-2D) — to produce TC wind analysis in high-temporal-spatial resolutions based on generative adversarial networks (GAN). We use IR1 satellite observation and ERA5 reanalysis data as the model input for the DSAT-2D. The ASCAT surface wind data were collected and used as the label data. Note, however, that the ASACT analysis tends to underestimate winds greater than 15 m/s. Thus, we proposed several methods to fix this issue before training the model. Furthermore, other innovative designs in the DSAT-2D model include: (i) we regrid all data in a polar coordinate to better handle the TC tangential and radial features, and (ii) we also set the target of the DSAT-2D model as the TC radial wind and tangential wind.Experiment results demonstrate that the DSAT-2D model can capture the TC asymmetric wind structure while possessing the capability of increasing the maximum estimation frequency from approximately 12 hours (e.g., ASCAT data) to less than one hour. The DSAT-2D model may help understand the TC asymmetric wind evolution and improve TC forecasts. Future applications of assimilating this value-added information into the numerical weather prediction model will also be discussed.
Moisture export by shallow convective mixing during EUREC4A
Authors: Bailey Adriana , Noone David , Henze Dean
Published: February 2023

In 2020, the EUREC4A (ElUcidating the RolE of Clouds-Circulation Coupling in ClimAte) field mission set out to investigate the role of shallow convective mixing in regulating trade cumulus and their influence on global climate. Recent results from this mission refute the idea that shallow convective mixing reduces cloudiness, as previous studies had argued. Instead, they suggest that shallow convective mixing is positively correlated with cloudiness when both are modulated by mesoscale circulations. Here, we provide independent evidence that further substantiates these findings. Using the unprecedented collection of water isotopic data sampled during EUREC4A, we derive estimates of total moisture exported from the sub-cloud layer by shallow convective mixing. We also derive vertical profiles of exported sub-cloud layer moisture, which allow us to investigate how shallow convective mixing alters the vertical structure of thermodynamic quantities and clouds. We show a strong association between the amount of moisture exported, the top-heaviness of the exported-moisture profile, the trade wind inversion height, and the average cloud top altitude. All increase when large cloud decks are present, indicating a role for mesoscale convective organization. We extend the analysis with remotely sensed isotope ratios in order to investigate the associations between mixing, moisture export, and cloudiness on larger scales (in both time and space) and to examine the conditions that favor convective organization at the mesoscale.
Secure Consistency of COMS/MI and GK-2A/AMI Shortwave Radiation product based on machine learning
Authors: Woo Jongho , Han Kyung-soo , Choi Sungwon , Seong Noh-hun , Jung Daeseong , Sim Suyoung , Kim Nayeon , Sohn Eunha
Published: February 2023

 Satellite-based solar radiation data is widely used to monitor global climate and environmental changes and is also actively used to analyze weather data and predict particulate matter. Korea can continuously retrieval solar radiation in the observation area due to the generational shift of COMS (Communication, Ocean and Meteorological Satellite)/MI (Meteorological Imager sensor) and GK-2A (GEO-KOMPSAT-2A)/AMI(Advanced Meteorological Imager sensor). However, The quality of each solar radiation output is different due to difference between the algorithms, input data and resolution. Therefore, it is possible to produce a climate resource map for the Korean Peninsula for continuous climate change monitoring by analyzing the error characteristics between the solar radiation of COMS/MI and GK-2A/AMI and expanding the retrieval period through correction between the two products. In this study. We analyzed the error characteristics of the two satellites compare to the meteorological observation data of Korea and the satellite CERES solar radiation data in overlapping periods. As a result of error analysis, the RMSE of COMS/MI was 85.6 (W/m2), lower than the RMSE of GK-2A/AMI, 95.6 (W/m2). Considering the solar radiation data error characteristics of these satellites, a correction model based on machine learning techniques was created to secure the consistency of solar radiation data. When this was verified with in situ data for a period of 10 years, RMSE was 89.21 (W/m2) and Bias was 17.39 (W/m2), which was stable in the temporal consistency test, and the annual increase in solar radiation on the Korean Peninsula was confirmed. ※ This work was supported by the "Graduate school of Particulate matter specialization." of Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment, Republic of Korea.
Impact of Model Parameters on Runoff Sensitivities in the Community Land Model: A Study on the Upper Colorado River Basin
Authors: Pokhrel Yadu , Elkouk Ahmed , Luo Lifeng , Payton Liz , Livneh Ben , Cheng Yifan
Published: February 2023

Understanding how land surface models (LSMs) partition precipitation into evapotranspiration and runoff under changing climate is key to improved future hydrologic predictions. This sensitivity is rarely tuned in land models, as evidenced by prevalent biases in the sensitivity of simulated runoff to precipitation and temperature change compared to observational estimates. Here, using the Community Land Model (CLM5) over the Colorado River basin (CRB), we investigate what the informative model parameters for runoff sensitivities are and how their choices affect the sensitivities under changing temperature and precipitation. We focus on the headwater region of the CRB, motivated by inconsistent model estimates of runoff sensitivities in the region and the critical need to better understand runoff changes to address the ongoing water crises in the CRB. In each headwater basin, a set of informative parameters were identified through parameter perturbations using “one at a time” method within an adaptive surrogate-based model optimization scheme (ASMO). Results of perturbations highlight that different parameter sets with similar performance (with respect to water-year discharge) provide very different runoff sensitivities to temperature and precipitation during the 1951-2010 period. Additionally, both precipitation and temperature sensitivities of runoff show sensitivity to similar parameters across the region. The most sensitive parameters control the conductance-photosynthesis relationship, soil surface resistance for direct evaporation, the partitioning of runoff into the surface and the subsurface component, and soil hydraulic properties. We show how the importance of each parameter varies through the parameter space and derive parameter estimates by maximizing the “fit to observed sensitivities” within the ASMO scheme. Our results provide key insights regarding parameters optimization to improve long-term hydrologic sensitivities in LSMs.
Comparison of outflow hydrographs following dam breach arising from overtopping, wide-width overtopping, and geotechnical seepage failure mechanisms.
Authors: McKellar Megan , McDougall Scott , Evans Steve , Take Andy
Published: February 2023

Dam breach is a large-scale, highly unsteady, and complex water-sediment flow. Dam breach events span scenarios involving natural dams (created following valley blocking landslides) to scenarios involving anthropogenic structures such as water-retaining dams and dams designed specifically to store mine waste (e.g. tailings). Management of the risk posed by a potential breach of a dam structure requires a careful analysis of the consequences of failure. These analyses, often called dam breach studies, aim to improve safety and risk management through the prediction of travel time, flow velocity, and spatial extent of the hazard (e.g. map of depth of inundation) in the event of a breach. These factors in turn define the consequence classification of a dam and guide the development of emergency preparedness plans. The key boundary condition required for flood routing numerical simulations often conducted for dam breach studies is the outflow hydrograph which describes the relationship between outflow of the retained volume with time. In this study we explore the effect of failure mechanism on the characteristics of the outflow hydrograph of otherwise identical physical model dams. Physical model dams of 1 m in height were constructed of fine sand near the midspan of the 36 m long, 2.1 m wide, and 1.2 m high large landslide flume facility at Queen’s University Coastal Engineering Laboratory. Three failure mechanisms are explored; a) notch overtopping, initiated by incising a v-notch into the dam crest and allowing the impounded reservoir to intersect this local low point; b) wide-width overtopping, where a retaining wall placed along the crest of the dam allows for an additional reservoir capacity above the height of the crest that when rapidly removed a full width sheet of water cascades over the dam; and c) geotechnical seepage failure, where closing the toe drain allows a seepage face to develop that causes a failure in the downstream face of the dam. The reservoir surface elevation during breach was monitored with a series of five Akamina AWP-24 wave capacitance height gauges distributed centerline in the upstream reservoir. The evolution of breach shape is captured every 3 s using five Canon EOS Rebel T5 Digital Single-lens Reflex (DSLR) that capture a plan view area of the entire 2.1 m width of the dam and a combined upstream and downstream length of 4.3 m. To further capture the evolution of failure a Blickfeld LiDAR sensor was positioned oblique to the downstream slope to capture a point cloud scan ever 1.4 s. These data sets are then used to compare the physical characteristics of each breach process and the resulting implications for the observed outflow hydrographs for each failure mechanism.
Be dating of middle-late Pleistocene uplifted marine terraces in northern Pacific coast of Northeastern Japan
Authors: Wakasa Sachi , Ishiyama Tatsuya , Hirouchi Daisuke , Matta Nobuhisa , Fujita Natsuko , Echigo Tomoo
Published: February 2023

Surface exposure ages of marine and fluvial terraces based on in situ 10Be dating were determined to estimate formation ages and long-term rates of coastal uplift along the northern Pacific coast of Northeastern Japan. Surface rock samples were collected along the northern and southern coasts of Sanriku based on a reinterpretation of dispersed marine and fluvial terraces using DEM and aerial photographs. We collected three samples at the Samuraihama site from outcrops of pairs of marine and fluvial terraces distributed over the east-facing flank of the Kitakami Mountains. At the Yoshihama site, in contrast, where bedrock surfaces could be better exposed, we took vertical samples from weathered granite rocks on small trench walls dug on the middle marine terrace at Yoshihama Bay. Surface exposure ages from 10Be concentrations in quartz calculated from the measured 10Be/9Be ratios commonly suggest slow in both sites, whereas steep (~ 10°) dip domains on the marine terraces along the northern Sanriku coast may imply localized permanent strain accumulation.
Contributions from climate variation and human activities to ﬂow regime change of Tonle Sap Lake and Mekong River from 2001 to 202
Authors: Morovati Khosro , Shi Lidi , Zhang Keer , Tian Fuqiang , Tudaji Mahmut , Nakhaei Pouria
Published: February 2023

The ﬂow regime of the largest lake in Southeast Asia, Tonle Sap Lake, is driven by the reverse ﬂow phenomenon caused by its link with the Mekong River. This reverse ﬂow makes the lake one of the most productive aquatic ecosystems globally and thus provides important economic opportunities for local communities. The recent human activities in the upstream, as well as climate variations, have resulted in unforeseen alterations in the flow regime of the Mekong River and Tonle Sap Lake. However, little is known about the explicit attribution of different parts of the upstream basin to these variations, which would be essential for transboundary water management. To unveil these attributions, we developed a novel modeling setup consisting of hydrodynamic, hydrological, and machine learning models. This modeling setup allowed us to separate the impacts of a) climate variation, b) human activities in the Chinese part of the basin, and b) the lower part of the basin (i.e., Laos, Thailand, and Vietnam). During the 2001–2009 baseline, when human modifications to the ﬂow were still minimal, we found that Tonle Sap Lake received, on average, 42.4 km3/yr water from the Mekong, 48.2 % of the total inﬂow to the lake. During the period of increased human activities, 2010–2020, this decreased due to climate variation to 40.1 km3/yr (a 5.7 % drop), which was further exacerbated by the increased human activities in the upstream parts of the basin (China ~ 7.3%, Laos, Thailand, and Vietnam ~ 9 %). Additionally, during the ﬂow period when water ﬂows from the lake towards the Mekong, on average, 31 % of the total inﬂow into the Mekong Delta originated from the lake during the baseline period. Climate variation decreased this by 4 percentage points (pp), i.e., to 27 %, while the human activities in China and lower parts of the basin decreased this by 1.6 pp (25.4 %) and 1.9 pp (25.1 %), respectively. Our findings unveiled the attributions of different drivers on Tonle Sap Lake’s hydrology and will facilitate transboundary water management in the basin. The impacts of future plans on different parts of the basin should be carefully evaluated together with existing anthropogenic impacts, as well as climate change, to minimize the further impacts on the lake and Mekong River.
Sensitivity of water vapor vertical shape in GNSS atmospheric observation
Authors: Fujita Mikiko
Published: February 2023

This study examined the impact of the vertical shapes of water vapour on GNSS atmospheric observations. The standard model used in GNSS measurements to represent the vertical distribution of water vapour is a natural logarithm function based on the water vapour scale height. This model is effective in describing the variation of vertical structure of water vapour below the water vapour scale height (similar to the atmospheric boundary layer). However, in very humid conditions, it is important to measure water vapour above the water vapour scale height to predict whether a heavy rain system will persist. The presentation will discuss the characteristics of the vertical structure and an improved model for these structures.
Comparative biodiversity analysis of Kurichiya heritage rice-based farming system with other farming systems
Authors: Lopus Merlin , Jaiswal Deepak
Published: February 2023

The Wayanad district of Kerala, India resides on the crest of the Western Ghats, one of the 36 Biodiversity hotspots in the world and known for its rich abundance of flora and ethnic cultures. Switching of farm practices from traditional to modern and rapid urban developmental activities is seen as a trend in the district. In this scenario, analysis of biodiversity associated with rice-based farms under various farming systems is important in this district. The adjacent upland agriculture area of rice fields of 9 rice-based agroecosystems was selected for the current study. Out of the 9 sites, 3 sites were traditional farms maintained by Kurichiya tribal communities, 3 were organic farms, and the other 3 farms were modern. A total of 45 families, 99 genera, 129 species of tree, and 101 bird species which belonged to 48 families, and 17 orders were identified from the study sites. This study recorded 7302, and 2072 tree and bird individuals respectively. The Normalized Difference Vegetation Index (NDVI) time series data was also derived for each site. The principal component analysis portrayed that there is a compositional relationship among native tree diversity indices, mean NDVI for May, June, August, and October, and bird diversity indices.  Further, Pearson Correlation proved their significant correlation. This study also exhibits the possibility of an increased abundance of Granivorous bird species in less native tree-abundant farming sites, which are considered a pest in rice farms. All the traditional farms were found to be abundant in native tree species and they are reported to have sustainable production in rice fields.  The culture and religious beliefs are the reason for the native tree abundance in their farming sites. Increasing native tree abundance can attract many species of birds which can act as natural enemies for the pests in the farmland.
Optimization of Green Infrastructure Networks to Maximize Stormwater-Related Benefits and Minimize Life Cycle Costs Using a Noisy Genetic Algorithm and Machine Learning
Authors: Minsker Barbara , Heidari Haratmeh Bardia
Published: February 2023

Green infrastructure (GI) has become a common solution to mitigate stormwater-related problems such as water quality and flooding hazards. Despite  widespread acknowledgement of GI benefits, there is a lack of decision support methods that allow practitioners to identify optimal locations and evaluate the costs and benefits of numerous spatially distributed small GI practices at larger scales (subwatershed to entire watershed) under uncertainty. To address these needs, an online Cloud-based interactive tool coupling SWMM (Storm Water Management Model) and the Water Research Foundation LID life cycle model, , called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI), is optimized using a noisy genetic algorithm (GA) with life cycle costs and stormwater volume reduction as the primary objectives. To overcome the computational challenge of probabilistic sampling with the noisy GA and to identify significant features for preferable locations, the GA  is merged with an artificial neural network, which acts as a meta-model (surrogate) for the numerical simulation model (SWMM). Post-optimization, machine learning decision trees are also generated that classify the numerous potential solutions generated by the noisy GA into GI coverage classes based on sub-watershed parameters. This framework is applied to a watershed in Baltimore, Maryland, U.S., under multiple budgetary scenarios. The results suggest that the greatest GI investments under the highest and lowest budgetary scenarios should be allocated to subwatersheds closest to the watershed outlet. For the lowest scenario, GI practices should be installed only in subwatersheds closest to the watershed outlet. When the budgetary scenario is highest, GI is sited across the watershed but highest priority is still given to subwatersheds closest to the watershed outlet. On the other hand, the importance of total distance to the watershed outlet is lower for the medium budgetary scenario. In fact, the impacts of different features for preferable GI coverage for these solutions are more complex, don’t follow a consistent pattern, and require more depth to capture the patterns in their corresponding classifier decision trees. In addition to these GI findings, the results showed that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%.