Pub Date : 2023-10-20DOI: 10.5194/gmd-16-5895-2023
Daehyeon Han, Jungho Im, Yeji Shin, Juhyun Lee
Abstract. Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 h), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 h. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (≤ 5 mm h−1), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 h) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.
摘要定量降水临近预报(QPN)有助于减少极端天气造成的巨大社会经济损失。由于大气的快速变化，QPN一直是一个具有挑战性的课题。最近的QPN研究提出了使用深度学习(DL)和地面气象雷达的数据驱动模型。以往的研究主要集中在开发DL- qpn模型上，但DL- qpn的其他因素尚未得到深入研究。本研究考察了DL-QPN中的四个关键因素，重点研究了它们对预测性能的影响。这些因素是深度学习模型(U-Net，以及卷积长短期记忆，或ConvLSTM)，输入过去的序列长度(1,2,3小时)，损失函数(均方误差，MSE，或平衡MSE, BMSE)和集成聚合。利用韩国气象雷达数据，共设计了24个方案来测量每个因素的影响，最大提前时间为2小时。从业务角度对2020-2022年夏季进行了长期评估，并分析了一次强降雨事件以检查极端情况。在这两项评估中，U-Net在总体精度指标上优于ConvLSTM。对于临界成功指数(CSI)，两种模式在弱强度范围内(≤5 mm h−1)均能获得较好的结果，而在较强降水条件下，BMSE损失更为有效。有一个小的趋势，即较长的输入时间(3小时)在MSE和BMSE方面的结果更好，但这种影响不如其他因素显著。通过平均使用MSE和BMSE损失的结果来集成，在所有方面提供了平衡的性能，这表明当为每个成员实现最佳权重时，可以提高技能分数的潜在策略。所有DL-QPN方案在分别用MSE和BMSE损失训练时都表现出低估和高估的问题。随着提前期的增加，所有深度学习模型的预测结果都很模糊，而非深度学习模型在预测中保留了细节。通过对这些关键因素的综合比较，本研究为未来使用气象雷达数据的DL-QPN工作提供了一种建模策略。
{"title":"Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning","authors":"Daehyeon Han, Jungho Im, Yeji Shin, Juhyun Lee","doi":"10.5194/gmd-16-5895-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5895-2023","url":null,"abstract":"Abstract. Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 h), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 h. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (≤ 5 mm h−1), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 h) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.5194/gmd-16-5865-2023
Jianghui Du
Abstract. Trace elements and isotopes (TEIs) are important tools in studying ocean biogeochemistry. Understanding their modern ocean budgets and using their sedimentary records to reconstruct paleoceanographic conditions require a mechanistic understanding of the diagenesis of TEIs, yet the lack of appropriate modeling tools has limited our ability to perform such research. Here I introduce SedTrace, a modeling framework that can be used to generate reactive-transport code for modeling marine sediment diagenesis and assist model simulation using advanced numerical tools in Julia. SedTrace enables mechanistic TEI modeling by providing flexible tools for pH and speciation modeling, which are essential in studying TEI diagenesis. SedTrace is designed to solve one particular challenge facing users of diagenetic models: existing models are usually case-specific and not easily adaptable for new problems such that the user has to choose between modifying published code and writing their own code, both of which demand strong coding skills. To lower this barrier, SedTrace can generate diagenetic models only requiring the user to supply Excel spreadsheets containing necessary model information. The resulting code is clearly structured and readable, and it is integrated with Julia's differential equation solving ecosystems, utilizing tools such as automatic differentiation, sparse numerical methods, Newton–Krylov solvers and preconditioners. This allows efficient solution of large systems of stiff diagenetic equations. I demonstrate the capacity of SedTrace using case studies of modeling the diagenesis of pH as well as radiogenic and stable isotopes of TEIs.
{"title":"SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes","authors":"Jianghui Du","doi":"10.5194/gmd-16-5865-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5865-2023","url":null,"abstract":"Abstract. Trace elements and isotopes (TEIs) are important tools in studying ocean biogeochemistry. Understanding their modern ocean budgets and using their sedimentary records to reconstruct paleoceanographic conditions require a mechanistic understanding of the diagenesis of TEIs, yet the lack of appropriate modeling tools has limited our ability to perform such research. Here I introduce SedTrace, a modeling framework that can be used to generate reactive-transport code for modeling marine sediment diagenesis and assist model simulation using advanced numerical tools in Julia. SedTrace enables mechanistic TEI modeling by providing flexible tools for pH and speciation modeling, which are essential in studying TEI diagenesis. SedTrace is designed to solve one particular challenge facing users of diagenetic models: existing models are usually case-specific and not easily adaptable for new problems such that the user has to choose between modifying published code and writing their own code, both of which demand strong coding skills. To lower this barrier, SedTrace can generate diagenetic models only requiring the user to supply Excel spreadsheets containing necessary model information. The resulting code is clearly structured and readable, and it is integrated with Julia's differential equation solving ecosystems, utilizing tools such as automatic differentiation, sparse numerical methods, Newton–Krylov solvers and preconditioners. This allows efficient solution of large systems of stiff diagenetic equations. I demonstrate the capacity of SedTrace using case studies of modeling the diagenesis of pH as well as radiogenic and stable isotopes of TEIs.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"8 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135617129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.
摘要在不同气候条件下，准确的全球尺度土壤表层水分信息对水文和气候学应用具有重要意义。基于机器学习的现场水文测量、复杂的环境和气候数据以及卫星观测的系统集成，有助于生成可靠的数据产品，以适当的时空分辨率监测和分析地球系统中水、能量和碳的交换。本研究使用8种优化的机器学习(ML)算法和10个集成模型(通过模型自举聚合技术和五倍交叉验证构建)来研究每日SSM的估计。使用分布在世界各地的1722个站点收集的国际土壤湿度网络(ISMN)数据对算法实施进行了培训和测试。结果表明，k -邻居回归器(KNR)在“test_random”集(用于测试训练期间随机分割数据的性能)上具有最低的均方根误差(0.0379 cm3 cm - 3)，随机森林回归器(RFR)在“test_temporal”集(用于测试未用于训练的时间段的性能)上具有最低的RMSE (0.0599 cm3 cm - 3)。AdaBoost (AB)在“test_independence -stations”集(用于测试未用于训练的工作站的性能)上的RMSE最低(0.0786 cm3 cm - 3)。对不同气候带的新站进行了独立评价。对于优化的ML算法，中位数RMSE值低于0.1 cm3 cm - 3。在15个气候带中，梯度增强(GB)、多层感知器回归(MLPR)、随机梯度下降回归(SGDR)和RFR分别在12个、11个、9个和9个气候带中值r值为0.6。集合模式的性能显著提高，所有气候带的RMSE中值均低于0.075 cm3 cm - 3。13个气候带的投票回归因子r值均在0.6以上;由于训练站分布稀疏，BSh(炎热半干旱气候)和BWh(炎热沙漠气候)是例外。度量评价表明，集成模型可以提高单一机器学习算法的性能，并获得更稳定的结果。基于三个不同测试集的计算结果，具有KNR、RFR和极端梯度增强(Extreme Gradient Boosting, XB)的集成模型表现最好。总体而言，我们的研究表明，与优化或基本ML算法相比，集成机器学习算法在预测SSM方面具有更大的能力;这表明它们在估算水循环预算、管理灌溉和预测作物产量方面具有巨大的潜在适用性。
{"title":"Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale","authors":"Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, Bob Su","doi":"10.5194/gmd-16-5825-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5825-2023","url":null,"abstract":"Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.5194/gmd-16-5803-2023
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, Xavier Fettweis
Abstract. The Greenland Ice Sheet (GrIS) is losing mass as the climate warms through both increased meltwater runoff and ice discharge at marine-terminating sectors. At the ice sheet surface, meltwater runoff forms a dynamic supraglacial hydrological system which includes stream and river networks and large supraglacial lakes (SGLs). Streams and rivers can route water into crevasses or into supraglacial lakes with crevasses underneath, both of which can then hydrofracture to the ice sheet base, providing a mechanism for the surface meltwater to access the bed. Understanding where, when, and how much meltwater is transferred to the bed is important because variability in meltwater supply to the bed can increase ice flow speeds, potentially impacting the hypsometry of the ice sheet in grounded sectors, and iceberg discharge to the ocean. Here we present a new, physically based, supraglacial hydrology model for the GrIS that is able to simulate (a) surface meltwater routing and SGL filling; (b) rapid meltwater drainage to the ice sheet bed via the hydrofracture of surface crevasses both in and outside of SGLs; (c) slow SGL drainage via overflow in supraglacial meltwater channels; and, by offline coupling with a second model, (d) the freezing and unfreezing of SGLs from autumn to spring. We call the model the Supraglacial Hydrology Evolution and Drainage (or SHED) model. We apply the model to three study regions in southwest Greenland between 2015 and 2019 (inclusive) and evaluate its performance with respect to observed supraglacial lake extents and proglacial discharge measurements. We show that the model reproduces 80 % of observed lake locations and provides good agreement with observations in terms of the temporal evolution of lake extent. Modelled moulin density values are in keeping with those previously published, and seasonal and inter-annual variability in proglacial discharge agrees well with that which is observed, though the observations lag the model by a few days since they include transit time through the subglacial system, while the model does not. Our simulations suggest that lake drainage behaviours may be more complex than traditional models suggest, with lakes in our model draining through a combination of both overflow and hydrofracture and with some lakes draining only partially and then refreezing. This suggests that, in order to simulate the evolution of Greenland's surface hydrological system with fidelity, a model that includes all of these processes needs to be used. In future work, we will couple our model to a subglacial model and an ice flow model and thus use our estimates of where, when, and how much meltwater gets to the bed to understand the consequences for ice flow.
{"title":"A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)","authors":"Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, Xavier Fettweis","doi":"10.5194/gmd-16-5803-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5803-2023","url":null,"abstract":"Abstract. The Greenland Ice Sheet (GrIS) is losing mass as the climate warms through both increased meltwater runoff and ice discharge at marine-terminating sectors. At the ice sheet surface, meltwater runoff forms a dynamic supraglacial hydrological system which includes stream and river networks and large supraglacial lakes (SGLs). Streams and rivers can route water into crevasses or into supraglacial lakes with crevasses underneath, both of which can then hydrofracture to the ice sheet base, providing a mechanism for the surface meltwater to access the bed. Understanding where, when, and how much meltwater is transferred to the bed is important because variability in meltwater supply to the bed can increase ice flow speeds, potentially impacting the hypsometry of the ice sheet in grounded sectors, and iceberg discharge to the ocean. Here we present a new, physically based, supraglacial hydrology model for the GrIS that is able to simulate (a) surface meltwater routing and SGL filling; (b) rapid meltwater drainage to the ice sheet bed via the hydrofracture of surface crevasses both in and outside of SGLs; (c) slow SGL drainage via overflow in supraglacial meltwater channels; and, by offline coupling with a second model, (d) the freezing and unfreezing of SGLs from autumn to spring. We call the model the Supraglacial Hydrology Evolution and Drainage (or SHED) model. We apply the model to three study regions in southwest Greenland between 2015 and 2019 (inclusive) and evaluate its performance with respect to observed supraglacial lake extents and proglacial discharge measurements. We show that the model reproduces 80 % of observed lake locations and provides good agreement with observations in terms of the temporal evolution of lake extent. Modelled moulin density values are in keeping with those previously published, and seasonal and inter-annual variability in proglacial discharge agrees well with that which is observed, though the observations lag the model by a few days since they include transit time through the subglacial system, while the model does not. Our simulations suggest that lake drainage behaviours may be more complex than traditional models suggest, with lakes in our model draining through a combination of both overflow and hydrofracture and with some lakes draining only partially and then refreezing. This suggests that, in order to simulate the evolution of Greenland's surface hydrological system with fidelity, a model that includes all of these processes needs to be used. In future work, we will couple our model to a subglacial model and an ice flow model and thus use our estimates of where, when, and how much meltwater gets to the bed to understand the consequences for ice flow.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Coastal wetland vegetation modulates water flow by exerting drag, which has important implications for sediment transport and geomorphic dynamics. This vegetation effect on flow is commonly represented in hydrodynamic models by approximating the vegetation as an array of vertical cylinders or increased bed roughness. However, this simple approximation may not be valid in the case of Rhizophora mangroves that have complicated three-dimensional root structures. Here, we present a new model to represent the impact of Rhizophora mangroves on flow in hydrodynamic models. The model explicitly accounts for the effects of the three-dimensional root structures on mean flow and turbulence as well as for the effects of two different length scales of vegetation-generated turbulence characterized by stem diameter and root diameter. The model employs an empirical model for the Rhizophora root structures that can be applied using basic vegetation parameters (mean stem diameter and tree density) without rigorous measurements of the root structures. We tested the model against the flows measured by previous studies in a model mangrove forest in the laboratory and an actual mangrove forest in the field, respectively. We show that, compared with the conventional approximation using an array of cylinders or increased bed roughness, the new model significantly improves the predictability of velocity, turbulent kinetic energy, and bed shear stress in Rhizophora mangrove forests. Overall, the presented new model offers a more realistic but feasible framework for simulating flows in Rhizophora mangrove forests with complex root structures using hydrodynamic models.
{"title":"Representing the impact of <i>Rhizophora</i> mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures","authors":"Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, Kazuo Nadaoka","doi":"10.5194/gmd-16-5847-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5847-2023","url":null,"abstract":"Abstract. Coastal wetland vegetation modulates water flow by exerting drag, which has important implications for sediment transport and geomorphic dynamics. This vegetation effect on flow is commonly represented in hydrodynamic models by approximating the vegetation as an array of vertical cylinders or increased bed roughness. However, this simple approximation may not be valid in the case of Rhizophora mangroves that have complicated three-dimensional root structures. Here, we present a new model to represent the impact of Rhizophora mangroves on flow in hydrodynamic models. The model explicitly accounts for the effects of the three-dimensional root structures on mean flow and turbulence as well as for the effects of two different length scales of vegetation-generated turbulence characterized by stem diameter and root diameter. The model employs an empirical model for the Rhizophora root structures that can be applied using basic vegetation parameters (mean stem diameter and tree density) without rigorous measurements of the root structures. We tested the model against the flows measured by previous studies in a model mangrove forest in the laboratory and an actual mangrove forest in the field, respectively. We show that, compared with the conventional approximation using an array of cylinders or increased bed roughness, the new model significantly improves the predictability of velocity, turbulent kinetic energy, and bed shear stress in Rhizophora mangrove forests. Overall, the presented new model offers a more realistic but feasible framework for simulating flows in Rhizophora mangrove forests with complex root structures using hydrodynamic models.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.5194/gmd-16-5783-2023
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla T. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, David J. Beerling
Abstract. Surficial enhanced rock weathering (ERW) is a land-based carbon dioxide removal (CDR) strategy that involves applying crushed silicate rock (e.g., basalt) to agricultural soils. However, unintended biogeochemical interactions with the nitrogen cycle may arise through ERW increasing soil pH as basalt grains undergo dissolution that may reinforce, counteract, or even offset the climate benefits from carbon sequestration. Increases in soil pH could drive changes in the soil emissions of key non-CO2 greenhouse gases, e.g., nitrous oxide (N2O), and trace gases, e.g., nitric oxide (NO) and ammonia (NH3), that affect air quality and crop and human health. We present the development and implementation of a new improved nitrogen cycling scheme for the Community Land Model v5 (CLM5), the land component of the Community Earth System Model, allowing evaluation of ERW effects on soil gas emissions. We base the new parameterizations on datasets derived from soil pH responses of N2O, NO, and NH3 in ERW field trial and mesocosm experiments with crushed basalt. These new capabilities involve the direct implementation of routines within the CLM5 N cycle framework, along with asynchronous coupling of soil pH changes estimated through an ERW model. We successfully validated simulated “control” (i.e., no ERW) seasonal cycles of soil N2O, NO, and NH3 emissions against a wide range of global emission inventories. We benchmark simulated mitigation of soil N2O fluxes in response to ERW against a subset of data from ERW field trials in the US Corn Belt. Using the new scheme, we provide a specific example of the effect of large-scale ERW deployment with croplands on soil nitrogen fluxes across five key regions with high potential for CDR with ERW (North America, Brazil, Europe, India, and China). Across these regions, ERW implementation led to marked reductions in N2O and NO (both 18 %), with moderate increases in NH3 (2 %). While further developments are still required in our implementations when additional ERW data become available, our improved N cycle scheme within CLM5 has utility for investigating the potential of ERW point-source and regional effects of soil N2O, NO, and NH3 fluxes in response to current and future climates. This framework also provides the basis for assessing the implications of ERW for air quality given the role of NO in tropospheric ozone formation, as well as both NO and NH3 in inorganic aerosol formation.
{"title":"Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N<sub>2</sub>O, NO, and NH<sub>3</sub> emissions from enhanced rock weathering with croplands","authors":"Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla T. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, David J. Beerling","doi":"10.5194/gmd-16-5783-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5783-2023","url":null,"abstract":"Abstract. Surficial enhanced rock weathering (ERW) is a land-based carbon dioxide removal (CDR) strategy that involves applying crushed silicate rock (e.g., basalt) to agricultural soils. However, unintended biogeochemical interactions with the nitrogen cycle may arise through ERW increasing soil pH as basalt grains undergo dissolution that may reinforce, counteract, or even offset the climate benefits from carbon sequestration. Increases in soil pH could drive changes in the soil emissions of key non-CO2 greenhouse gases, e.g., nitrous oxide (N2O), and trace gases, e.g., nitric oxide (NO) and ammonia (NH3), that affect air quality and crop and human health. We present the development and implementation of a new improved nitrogen cycling scheme for the Community Land Model v5 (CLM5), the land component of the Community Earth System Model, allowing evaluation of ERW effects on soil gas emissions. We base the new parameterizations on datasets derived from soil pH responses of N2O, NO, and NH3 in ERW field trial and mesocosm experiments with crushed basalt. These new capabilities involve the direct implementation of routines within the CLM5 N cycle framework, along with asynchronous coupling of soil pH changes estimated through an ERW model. We successfully validated simulated “control” (i.e., no ERW) seasonal cycles of soil N2O, NO, and NH3 emissions against a wide range of global emission inventories. We benchmark simulated mitigation of soil N2O fluxes in response to ERW against a subset of data from ERW field trials in the US Corn Belt. Using the new scheme, we provide a specific example of the effect of large-scale ERW deployment with croplands on soil nitrogen fluxes across five key regions with high potential for CDR with ERW (North America, Brazil, Europe, India, and China). Across these regions, ERW implementation led to marked reductions in N2O and NO (both 18 %), with moderate increases in NH3 (2 %). While further developments are still required in our implementations when additional ERW data become available, our improved N cycle scheme within CLM5 has utility for investigating the potential of ERW point-source and regional effects of soil N2O, NO, and NH3 fluxes in response to current and future climates. This framework also provides the basis for assessing the implications of ERW for air quality given the role of NO in tropospheric ozone formation, as well as both NO and NH3 in inorganic aerosol formation.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.5194/gmd-16-5755-2023
Anthony Schrapffer, Jan Polcher, Anna Sörensson, Lluís Fita
Abstract. Adapting and improving the hydrological processes in land surface models are crucial given the increase in the resolution of the climate models to correctly represent the hydrological cycle. The present paper introduces a floodplain scheme adapted to the higher-resolution river routing of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model. The scheme is based on a sub-tile parameterisation of the hydrological units – a hydrological transfer unit (HTU) concept – based on high-resolution hydrologically coherent digital elevation models, which can be used for all types of resolutions and projections. The floodplain scheme was developed and evaluated for different atmospheric forcings and resolutions (0.5∘ and 25 km) over one of the world's largest floodplains: the Pantanal, located in central South America. The floodplain scheme is validated based on the river discharge at the outflow of the Pantanal which represents the hydrological cycle over the basin, the temporal evolution of the water mass over the region assessed by the anomaly of total water storage in the Gravity Recovery And Climate Experiment (GRACE), and the temporal evaluation of the flooded areas compared to the Global Inundation Extent from Multi-Satellites version 2 (GIEMS-2) dataset. The hydrological cycle is satisfactorily simulated; however, the base flow may be underestimated. The temporal evolution of the flooded area is coherent with the observations, although the size of the area is underestimated in comparison to GIEMS-2. The presence of floodplains increases the soil moisture up to 50 % and decreases average temperature by 3 ∘C and by 6 ∘C during the dry season. The higher soil moisture increases the vegetation density, and, along with the presence of open-water surfaces due to the floodplains, it affects the surface energy budget by increasing the latent flux at the expense of the sensible flux. This is linked to the increase in the evapotranspiration related to the increased water availability. The effect of the floodplain scheme on the land surface conditions highlights that coupled simulations using the floodplain scheme may influence local and regional precipitation and regional circulation.
{"title":"Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands","authors":"Anthony Schrapffer, Jan Polcher, Anna Sörensson, Lluís Fita","doi":"10.5194/gmd-16-5755-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5755-2023","url":null,"abstract":"Abstract. Adapting and improving the hydrological processes in land surface models are crucial given the increase in the resolution of the climate models to correctly represent the hydrological cycle. The present paper introduces a floodplain scheme adapted to the higher-resolution river routing of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model. The scheme is based on a sub-tile parameterisation of the hydrological units – a hydrological transfer unit (HTU) concept – based on high-resolution hydrologically coherent digital elevation models, which can be used for all types of resolutions and projections. The floodplain scheme was developed and evaluated for different atmospheric forcings and resolutions (0.5∘ and 25 km) over one of the world's largest floodplains: the Pantanal, located in central South America. The floodplain scheme is validated based on the river discharge at the outflow of the Pantanal which represents the hydrological cycle over the basin, the temporal evolution of the water mass over the region assessed by the anomaly of total water storage in the Gravity Recovery And Climate Experiment (GRACE), and the temporal evaluation of the flooded areas compared to the Global Inundation Extent from Multi-Satellites version 2 (GIEMS-2) dataset. The hydrological cycle is satisfactorily simulated; however, the base flow may be underestimated. The temporal evolution of the flooded area is coherent with the observations, although the size of the area is underestimated in comparison to GIEMS-2. The presence of floodplains increases the soil moisture up to 50 % and decreases average temperature by 3 ∘C and by 6 ∘C during the dry season. The higher soil moisture increases the vegetation density, and, along with the presence of open-water surfaces due to the floodplains, it affects the surface energy budget by increasing the latent flux at the expense of the sensible flux. This is linked to the increase in the evapotranspiration related to the increased water availability. The effect of the floodplain scheme on the land surface conditions highlights that coupled simulations using the floodplain scheme may influence local and regional precipitation and regional circulation.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136033649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-17DOI: 10.5194/gmd-16-5729-2023
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, Rob Stoll
Abstract. Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.
{"title":"QES-Plume v1.0: a Lagrangian dispersion model","authors":"Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, Rob Stoll","doi":"10.5194/gmd-16-5729-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5729-2023","url":null,"abstract":"Abstract. Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.5194/gmd-16-5703-2023
Jérémy Bernard, Fredrik Lindberg, Sandro Oswald
Abstract. URock 2023a is an open-source diagnostic model dedicated to wind field calculation in urban settings. It is based on a quick method initially proposed by Röckle (1990) and already implemented in the proprietary software QUIC-URB. First, the model method is described as well as its implementation in the free and open-source geographic information system called QGIS. Then it is evaluated against wind tunnel measurements and QUIC-URB simulations for four different building layouts plus one case with an isolated tree. The correlation between URock and QUIC-URB is high, and URock reproduces the spatial variation of the wind speed observed in the wind tunnel experiments quite well, even in complex settings. However, sources of improvements, which are applicable for both URock and QUIC-URB, are highlighted. URock and QUIC-URB overestimate the wind speed downstream of the upwind edges of wide buildings and also downstream of isolated tree crowns. URock 2023a is available via the Urban Multiscale Environment Predictor (UMEP), a city-based climate service tool designed for researchers and service providers presented as a plug-in for QGIS. The model, data, and scripts used to write this paper can be freely accessed at https://doi.org/10.5281/zenodo.7681245 (Bernard, 2023).
{"title":"URock 2023a: an open-source GIS-based wind model for complex urban settings","authors":"Jérémy Bernard, Fredrik Lindberg, Sandro Oswald","doi":"10.5194/gmd-16-5703-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5703-2023","url":null,"abstract":"Abstract. URock 2023a is an open-source diagnostic model dedicated to wind field calculation in urban settings. It is based on a quick method initially proposed by Röckle (1990) and already implemented in the proprietary software QUIC-URB. First, the model method is described as well as its implementation in the free and open-source geographic information system called QGIS. Then it is evaluated against wind tunnel measurements and QUIC-URB simulations for four different building layouts plus one case with an isolated tree. The correlation between URock and QUIC-URB is high, and URock reproduces the spatial variation of the wind speed observed in the wind tunnel experiments quite well, even in complex settings. However, sources of improvements, which are applicable for both URock and QUIC-URB, are highlighted. URock and QUIC-URB overestimate the wind speed downstream of the upwind edges of wide buildings and also downstream of isolated tree crowns. URock 2023a is available via the Urban Multiscale Environment Predictor (UMEP), a city-based climate service tool designed for researchers and service providers presented as a plug-in for QGIS. The model, data, and scripts used to write this paper can be freely accessed at https://doi.org/10.5281/zenodo.7681245 (Bernard, 2023).","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.5194/gmd-16-5685-2023
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, Xi Chen
Abstract. Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature.
{"title":"Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification","authors":"Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, Xi Chen","doi":"10.5194/gmd-16-5685-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5685-2023","url":null,"abstract":"Abstract. Despite recent developments in geoscientific (e.g., physics- or data-driven) models, effectively assembling multiple models for approaching a benchmark solution remains challenging in many sub-disciplines of geoscientific fields. Here, we proposed an automated machine-learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Details of the methodology and workflow of AutoML-Ens were provided, and a prototype model was realized with the key strategy of mapping between the probabilities derived from the machine learning classifier and the dynamic weights assigned to the candidate ensemble members. Based on the newly proposed framework, its applications for two real-world examples (i.e., mapping global soil water retention parameters and estimating remotely sensed cropland evapotranspiration) were investigated and discussed. Results showed that compared to conventional ensemble approaches, AutoML-Ens was superior across the datasets (the training, testing, and overall datasets) and environmental gradients with improved performance metrics (e.g., coefficient of determination, Kling–Gupta efficiency, and root-mean-squared error). The better performance suggested the great potential of AutoML-Ens for improving quantification and reducing uncertainty in estimates due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow. In addition to the representative results, we also discussed the interpretational aspects of the used framework and its possible extensions. More importantly, we emphasized the benefits of combining data-driven approaches with physics constraints for geoscientific model ensemble problems with high dimensionality in space and nonlinear behaviors in nature.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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