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Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 17391

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Special Issue Editors

Prof. Qiang Zhang

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Guest Editor

Institute of Arid Meteorology, CMA, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Key Laboratory of Arid Climatic Change and Disaster Reduction of CMA, Lanzhou 730020, China
Interests: land–atmosphere interaction; atmospheric boundary; the oasis microclimate; drought monitoring; warning and risk management; cloud water resources development; hail monitoring and warning
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Prof. Dr. Yu Zhang

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Guest Editor

College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: land surface processes and land–atmosphere interactions; atmospheric boundary layer physics
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Prof. Dr. Ping Yue

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Guest Editor

Institute of Arid Meteorology, CMA, Lanzhou 730020, China
Interests: land–atmosphere interaction; drought-induced disaster mechanism
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Dr. Jun Wen

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Guest Editor

College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: remote sensing; modeling; hydrology; meteorology
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Dr. Zesu Yang

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Guest Editor

College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: land–atmosphere interaction; boundary layer meteorology; evapotranspiration
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Dr. Yongli He

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Guest Editor

Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Interests: climate feedbacks in dryland ecosystems; dryland climate change and mechanisms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precipitation and evapotranspiration, as the two main components of the hydrological cycle, are essential for water resources, agriculture, and ecosystem management. Drylands, covering about 45% of the Earth’s land surface, are home to more than 38% of the world’s population and are one of the most sensitive areas to climate change and human activities. Over drylands, precipitation and evapotranspiration are scarce and highly variable. An accurate characterization of precipitation and evapotranspiration properties is lacking due to the limited ground monitoring systems typical of these regions. Precipitation and evapotranspiration mechanisms are complex and different from other regions. Precipitation is also strongly coupled with evapotranspiration in these regions due to strong land–atmosphere interactions. Improving our understanding of the mechanisms in precipitation and evapotranspiration and their simulation over dryland is a top priority for weather and climate research. Satellites have been providing vital information from multispectral, hyperspectral, thermal, and microwave remote sensing data to estimate precipitation. ET is a multifaceted variable and is controlled by a combination of radiative, atmospheric, and vegetation drivers, which could be obtained from remote sensing. Estimation from satellite observations provides the opportunity to improve our knowledge of precipitation and evapotranspiration in these regions.

To promote wide communication on the subject, we convened a session AS31 at the AOGS2022 19th Annual Meeting with a similar theme: "Precipitation Mechanisms in Drylands and Their Simulation". This Special Issue builds on the session and expands and enriches the research themes for a wider research scope.

This Special Issue will showcase recent efforts in applying remote sensing data in precipitation and evapotranspiration research, including remote sensing inversion methods on precipitation and evapotranspiration, precipitation mechanisms and evapotranspiration regulation mechanisms, and numerical simulation studies based on remote sensing combined with other data. This subject involves the multidisciplinary intersection of atmospheric and hydrometeorological sciences with remote sensing. It fits well with the research scope of this journal.

This Special Issue invites contributions dealing with the retrieval of precipitation and evapotranspiration data on different spatial and temporal scales, monitoring their dynamics, exploring the mechanisms of precipitation and evapotranspiration, and improving simulation accuracy based on the integrated use of remotely sensed data and in situ measurements. Articles may address but are not limited to the following topics:

Retrieval of precipitation;
Estimation of evapotranspiration;
Evapotranspiration regulation mechanisms;
Validation of precipitation and evapotranspiration models;
Characterization of precipitation properties;
The impact of climate change on precipitation and evapotranspiration;
Spatial and temporal characteristics of evapotranspiration;
Precipitation mechanism;
Numerical simulation of precipitation and evapotranspiration;
Land–atmosphere interaction;
Drought and flood assessment and monitoring.

Prof. Qiang Zhang
Prof. Dr. Yu Zhang
Prof. Dr. Ping Yue
Dr. Jun Wen
Dr. Zesu Yang
Dr. Yongli He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

precipitation
evapotranspiration
environmental regulations
sensible heat fluxes
soil moisture
precipitation recycling process
evapotranspiration partitioning
evapotranspiration–precipitation coupling
Asia summer monsoon
westerly wind
vegetation dynamics
numerical simulation
hydrological extremes
drylands

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Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation II in Remote Sensing

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26 pages, 5205 KiB

Open AccessArticle

Remote Sensing Monitoring of Drought in Southwest China Using Random Forest and eXtreme Gradient Boosting Methods

by Xiehui Li, Hejia Jia and Lei Wang

Remote Sens. 2023, 15(19), 4840; https://doi.org/10.3390/rs15194840 - 06 Oct 2023

Viewed by 1190

Abstract

A drought results from the combined action of several factors. The continuous progress of remote sensing technology and the rapid development of artificial intelligence technology have enabled the use of multisource remote sensing data and data-driven machine learning (ML) methods to mine drought features from different perspectives. This method improves the generalization ability and accuracy of drought monitoring and prediction models. The present study focused on drought monitoring in southwest China, where drought disasters occur frequently and with a high intensity, especially in areas with limited meteorological station coverage. Several drought indices were calculated based on multisource satellite remote sensing data and weather station observation data. Remote sensing data from multiple sources were combined to build a reconstructed land surface temperature (LST) and drought monitoring method using the two different ML methods of random forest (RF) and eXtreme Gradient Boosting (XGBoost 1.5.1), respectively. A 5-fold cross-validation (CV) method was used for the model’s hyperparameter optimization and accuracy evaluation. The performance of the model was also assessed and validated using several accuracy assessment indicators. The model monitored the results of the spatial and temporal distributions of the drought, drought grades, and influence scope of the drought. These results from the model were compared against historical drought situations and those based on the standardized precipitation evapotranspiration index (SPEI) and the meteorological drought composite index (MCI) values estimated using weather station observation data in southwest China. The results show that the average score of the 5-fold CV for the RF and XGBoost was 0.955 and 0.931, respectively. The root-mean-square error (RMSE) of the LST values reconstructed using the RF model on the training and test sets was 1.172 and 2.236, the mean absolute error (MAE) was 0.847 and 1.719, and the explained variance score (EVS) was 0.901 and 0.858, respectively. Furthermore, the correlation coefficients (CCs) were all greater than 0.9. The RMSE of the monitoring values using the XGBoost model on the training and test sets was 0.135 and 0.435, the MAE was 0.095 and 0.328, the EVS was 0.976 and 0.782, and the CC was 0.982 and 0.868, respectively. The consistency rate between the drought grades identified using SPEI1 (the SPEI values of the 1-month scale) based on the observed data from the 144 meteorological stations and the monitoring values from the XGBoost model was more than 85%. The overall consistency rate between the drought grades identified using the monitoring and MCI values was 67.88%. The aforementioned two different ML methods achieved a high comprehensive performance, accuracy, and applicability. The constructed model can improve the level of dynamic drought monitoring and prediction for regions with complex terrain and topography and formative factors of climate as well as where weather stations are sparsely distributed. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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16 pages, 4296 KiB

Open AccessArticle

Quantitative Precipitation Estimation in the Tianshan Mountains Based on Machine Learning

by Xinyu Lu, Jing Li, Yan Liu, Yang Li and Hong Huo

Remote Sens. 2023, 15(16), 3962; https://doi.org/10.3390/rs15163962 - 10 Aug 2023

Cited by 2 | Viewed by 968

Abstract

Precipitation in the Tianshan Mountains is abundant, and the quantitative estimation of precipitation in mountainous areas is important to the application and evaluation of regional water resources. With remote sensing technology, satellite inversion of precipitation can estimate precipitation in mountainous areas. However, the Tianshan Mountain terrain is complex, and the spatiotemporal variation in precipitation is large, so the accuracy of satellite precipitation inversion is not high. Here, precipitation data from around 1000 automatic weather stations in the Tianshan Mountains are used to study the correction technology of the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (GPM) mission’s (IMERG) monthly precipitation products using stepwise regression (STEP), geographically weighted regression (GWR), and random forest (RF). First, geographic information system technology was used to extract topographic variables from a digital elevation model, and vegetation indexes, which are important precipitation indicators, were introduced as explanatory factors to correct satellite precipitation data. Second, GPM IMERG precipitation was corrected by establishing the stepwise regression, the geographically weighted regression model, and the random forest model. The three correction methods can improve the GPM IMERG in terms of relative bias, root mean square error, correlation coefficient, and Nash–Sutcliffe efficiency, while the random forest method shows better corrections than the two traditional methods. For dense rainfall stations, the geographically weighted regression method is as effective as random forest. For different altitudes, the results show that RF has the best correction effect in the first three zones, but the correction effect in the last zone (over 3000 m) is worse than STEP. This study provides a practical reference method for estimating precipitation data in the non-rainfall observation area, which helps to deepen the scientific understanding of the water resource distribution in the Tianshan Mountains and provide scientific data support for regional hydrological and meteorological research. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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18 pages, 8497 KiB

Open AccessArticle

Eddy Covariance CO₂ Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition

by Dexiang Gao, Jingyu Yao, Shuting Yu, Yulong Ma, Lei Li and Zhongming Gao

Remote Sens. 2023, 15(10), 2695; https://doi.org/10.3390/rs15102695 - 22 May 2023

Cited by 1 | Viewed by 1958

Abstract

Continuous long-term eddy covariance (EC) measurements of CO₂ fluxes (NEE) in a variety of terrestrial ecosystems are critical for investigating the impacts of climate change on ecosystem carbon cycling. However, due to a number of issues, approximately 30–60% of annual flux data obtained at EC flux sites around the world are reported as gaps. Given that the annual total NEE is mostly determined by variations in the NEE data with time scales longer than one day, we propose a novel framework to perform gap filling in NEE data based on machine learning (ML) and time series decomposition (TSD). The novel framework combines the advantages of ML models in predicting NEE with meteorological and environmental inputs and TSD methods in extracting the dominant varying trends in NEE time series. Using the NEE data from 25 AmeriFlux sites, the performance of the proposed framework is evaluated under four different artificial scenarios with gap lengths ranging in length from one hour to two months. The combined approach incorporating random forest and moving average (MA-RF) is observed to exhibit better performance than other approaches at filling NEE gaps in scenarios with different gap lengths. For the scenario with a gap length of seven days, the MA-RF improves the R² by 34% and reduces the root mean square error (RMSE) by 55%, respectively, compared to a traditional RF-based model. The improved performance of MA-RF is most likely due to the reduction in data variability and complexity of the variations in the extracted low-frequency NEE data. Our results indicate that the proposed MA-RF framework can provide improved gap filling for NEE time series. Such improved continuous NEE data can enhance the accuracy of estimations regarding the ecosystem carbon budget. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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29 pages, 9153 KiB

Open AccessArticle

Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data

by Lei Wang, Jinjie Wang, Jianli Ding and Xiang Li

Remote Sens. 2023, 15(10), 2565; https://doi.org/10.3390/rs15102565 - 14 May 2023

Cited by 4 | Viewed by 1858

Abstract

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced a 1 km monthly ETa dataset for Turpan and Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, and the ETMonitor model from 1980 to 2021. We analyzed the spatiotemporal variation of ETa using various statistical approaches and discussed the impact of climate and land use and cover changes (LUCC) on ETa. The results show the following: (1) the estimation results correlate well with ETa products on monthly scales (coefficient of determination (R²) > 0.85, root mean square error (RMSE) < 15 mm/month) with high reliability. (2) The ETa values were spatially distributed similarly to precipitation and LUCC, with the multi-year (1980–2021) average of 66.31 mm and a slightly fluctuating downward trend (−0.19 mm/a). (3) During the 42-year period, 63.16% of the study area exhibited an insignificant decrease in ETa, while 86.85% experienced pronounced fluctuations (coefficient of variation (CV) > 0.20), and 78.83% will show an upward trend in the future. (4) ETa was significantly positively correlated with precipitation (94.17%) and insignificantly positively correlated with temperature (55.81%). The impact of human activities showed an insignificant decreasing trend (85.41%). Additionally, the intensity of ETa varied considerably among land types, with the largest for cropland (424.12 mm/a). The results of the study have implications for promoting the rational allocation of regional water resources and improving water use efficiency in arid zones. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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14 pages, 7420 KiB

Open AccessArticle

Interannual Variability of Extreme Precipitation during the Boreal Summer over Northwest China

by Qianrong Ma, Zhongwai Li, Hongjia Lei, Zhiheng Chen, Jiang Liu, Shuting Wang, Tao Su and Guolin Feng

Remote Sens. 2023, 15(3), 785; https://doi.org/10.3390/rs15030785 - 30 Jan 2023

Cited by 2 | Viewed by 1462

Abstract

Herein, we investigated the characteristics and mechanisms of interannual variability of extreme summer precipitation in northwest China (NWC). The four high-resolution precipitation predicting products under assessment indicated that both the accumulation of summer daily precipitation ≥95th percentile, and the summer maxima of daily precipitation generally decreased in a southeast—northwest direction, while relatively high values were observed in the Tienshan and Qilian Mountain areas. In turn, the Tropical Rainfall Measuring Mission (TRMM) satellite dataset underestimated extreme precipitation in mountainous areas, while Asian precipitation highly—resolved observational data integration towards evaluation (APHRODITE) and Climate Prediction Center (CPC) captured the characteristics of extreme precipitation in NWC. AMIP-type simulations of the interannual variability of extreme summer precipitation in NWC were quite unsuccessful. However, all of them can capture the increasing trends. Therefore, we further found that the interannual increase in extreme precipitation in NWC is strongly correlated with the weakened South Asian high, strengthened Western Pacific Subtropical high, the enhanced westerly jet, and the mid- to high-latitude Rossby wave trains, whose formation and sustenance can be traced back to sea surface temperature-anomalies in the western Pacific in May, June, and July. An increased sea surface temperature promotes convection and induces diabatic heating, which stimulates anticyclonic anomalies that disturb the enhanced westerly jet, resulting in a barotropic Rossby wave train via the Gill-type response. Additionally, it guides more water vapor convergence to NWC and enhances upward motion via anticyclonic anomalies over western Europe and Eastern Asia, and cyclonic anomalies over Central Asia. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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23 pages, 12293 KiB

Open AccessArticle

Regulation of Evapotranspiration in Different Precipitation Zones and Its Application in High-Temperature and Drought Monitoring

by Lijuan Wang, Ni Guo, Ping Yue, Die Hu, Sha Sha and Xiaoping Wang

Remote Sens. 2022, 14(24), 6190; https://doi.org/10.3390/rs14246190 - 07 Dec 2022

Cited by 2 | Viewed by 1039

Abstract

When drought occurs in different regions, evapotranspiration (ET) changes differently with the process of drought. To achieve an accurate monitoring of large-scale drought using remote sensing, it is particularly necessary to clarify the temporal and spatial characteristics of ET changes with soil water content (SWC). Firstly, based on the measured data, combined with the artificial intelligence particle swarm optimization (PSO) algorithm, an empirical model of ET retrieval by FY–4A satellite data was established and the spatial–temporal characteristics of ET changes with SWC were further analyzed. Lastly, different ET regulation regions were distinguished to achieve the remote sensing monitoring of large-scale drought based on SWC. The main results are as follows: (1) The correlation coefficient between the ET estimated by the empirical model and the measured value was 0.48 and the root mean square error was 24 W·m⁻². (2) In the areas with extreme water shortage, water limits the conversion rate of net radiation (Rn) to ET (ECR) and surpasses Rn to become the determinative factor of ET. (3) In extreme arid areas, ET has a significant positive correlation with WVP and SWC. In other precipitation areas, ET has a significant linear correlation with WVP, but the slope of the linear fitting line is different for precipitation. The relationship between ET and SWC is more complex. In areas with precipitation exceeding 800 mm, the correlation between SWC and ET is not significant. In areas with precipitation between 200 mm and 800 mm or in alpine regions, SWC and ET have a quadratic relationship. (4) ECR has quadratic correlations with WVP and SWC, and ECR reaches the maximum when WVP = 0.182 kPa and SWC = 0.217 m³∙m⁻³. ET may be inhibited for water shortage or water supersaturation. (5) In areas where SWC determines ET, the ET stress index (ESI) is inversely proportional to SWC, and in areas where heat affects ET, the ESI is directly proportional to SWC. Therefore, for the accurate monitoring of large-scale drought, various drought monitoring criteria should be determined in different areas and periods, considering information on precipitation, the underlying surface type, and digital elevation. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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16 pages, 3792 KiB

Open AccessArticle

Analysis of the Spatial and Temporal Distribution of Potential Evapotranspiration in Akmola Oblast, Kazakhstan, and the Driving Factors

by Yusen Chen, Shihang Zhang and Yongdong Wang

Remote Sens. 2022, 14(21), 5311; https://doi.org/10.3390/rs14215311 - 24 Oct 2022

Cited by 1 | Viewed by 1543

Abstract

Potential evapotranspiration (PET) is the capacity of the sub-surface evapotranspiration process, which is determined by weather and climate conditions. As an important component of the surface energy balance and hydrological cycle, PET determines hydrothermal transport in surface ecosystems and is an important factor in regional water resource evaluation, water use efficiency, and drought prediction. Most of the existing studies have focused on the impact of PET on the ecological environment and regional climate, providing limited information on the characteristics of the regional distribution of potential evapotranspiration itself and the associated drivers. In this study, we use the Penman-Monteith (P–M) model to calculate the PET in Akmola Oblast, combined with relevant climate data, partial correlation analysis, and structural equation modelling (SEM) to investigate the spatial and temporal distribution characteristics of PET in the study area and its driving factors, as well as the influence of meteorological activity on PET after the implementation of the Green Ring Project in the capital area of Kazakhstan. The results of the study show that: (1) The PET in Akmola State presented a decreasing trend from 1991 to 2021, with a multi-year average value of 835.87 mm. There is large heterogeneity in the spatial distribution of PET, being significantly higher in the southwestern and northeastern regions of the study area than in the central region. (2) Simple and partial correlation analyses indicate that most of the correlations between meteorological and PET were significant, with strong spatial heterogeneity in the number of biased relationships between different meteorological activity and PET. The spatial characteristics of the correlations between PET and Srad (Solar radiation), VS (wind speed), and MAT (Mean annual temperature) were similar, with the strongest correlations observed in the southwestern part of Akmola State. Furthermore, the spatial distribution of the correlations between PET and SWC (soil water content) and ST (soil temperature) was similar, with stronger correlations in the central part of the study area than elsewhere. (3) The SEM demonstrated that the main drivers of PET change across the study area are Srad (0.59) and VS (0.37). In the metropolitan area, MAP (mean annual precipitation) is also a major driver of PET change, due to the implementation of the Green Ring Project, which has increased vegetation cover and improved the local environment. The results of this study highlight the impact of climate change on PET in Akmola Oblast, Kazakhstan, contributing to a better understanding of PET evolution and providing guidance for water management planning. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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19 pages, 12736 KiB

Open AccessArticle

Simulation Performance and Case Study of Extreme Events in Northwest China Using the BCC-CSM2 Model

by Minhong Song, Yufei Pei, Shaobo Zhang and Tongwen Wu

Remote Sens. 2022, 14(19), 4922; https://doi.org/10.3390/rs14194922 - 01 Oct 2022

Cited by 3 | Viewed by 1273

Abstract

The BCC-CSM2 model is the second generation of the Beijing Climate Center Climate System Model developed by the National Center of China Meteorological Administration. Using the outputs of two versions of the BCC-CSM2 model with different resolutions, namely: BCC-CSM2-MR and BCC-CSM2-HR, their performance in simulating the climate characteristics of Northwest China was compared. The BCC-CSM2-HR had a better ability to simulate the detailed distribution of the average temperature and precipitation in Northwest China, and could delineate the influence of the topography in detail. The extreme events in Northwest China were evaluated further using the BCC-CSM2-HR and the observation data from China Meteorological Data Center. The BCC-CSM2-HR provided a good simulation of the spatial distribution of extreme climate events in Northwest China, and the spatial distribution of TXx, TNx, TXn, and TNn in Northwest China show closer proximity to the observation than that of TX90p, TN90p, TX10p, and TN10p, even in the case of extreme heavy precipitation. This case study of the extreme weather events showed that the BCC-CSM2-HR model had the best simulation performance for extreme high temperature events in Northwest China, followed by extreme low temperature events, and had the worst simulation ability for extreme precipitation events. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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20 pages, 8129 KiB

Open AccessArticle

Spatiotemporal Variation of Actual Evapotranspiration and Its Relationship with Precipitation in Northern China under Global Warming

by Tao Su, Siyuan Sun, Shuting Wang, Dexiao Xie, Shuping Li, Bicheng Huang, Qianrong Ma, Zhonghua Qian, Guolin Feng and Taichen Feng

Remote Sens. 2022, 14(18), 4554; https://doi.org/10.3390/rs14184554 - 12 Sep 2022

Cited by 3 | Viewed by 1669

Abstract

The analysis of actual evapotranspiration (ETa) changes is of great significance for the utilization and allocation of water resources. In this study, ETa variability in northern China (aridity index < 0.65) is investigated based on the average of seven datasets (GLEAM, GLASS, a complementary relationship-based dataset, CRA-40, MERRA2, JRA-55, and ERA5-Land). The results show that ETa increases significantly from 1982 to 2017. Limited by water supply, ETa is significantly correlated with precipitation (R = 0.682), whereas the increase in precipitation is insignificant (p = 0.151). Spatially, the long-term trend of ETa is also not completely consistent with that of precipitation. According to a singular value decomposition (SVD) analysis, the trend of ETa is mainly related to the first four leading SVD modes. Homogeneous correlation patterns indicate that more precipitation generally leads to high ETa; however, this relationship is modulated by other factors. Overall, positive potential evapotranspiration anomalies convert more surface water into ETa, resulting in a higher increase in ETa than in precipitation. Specifically, ETa in the northern Tibetan Plateau is associated with meltwater generated by rising temperatures, and ETa in the Badain Jaran Desert is highly dependent on the wet-day frequency. Under global warming, the inconsistency between ETa and precipitation changes has a great impact on water resources in northern China. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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18 pages, 11728 KiB

Open AccessArticle

The Validation of Soil Moisture from Various Sources and Its Influence Factors in the Tibetan Plateau

by Na Li, Changyan Zhou and Ping Zhao

Remote Sens. 2022, 14(16), 4109; https://doi.org/10.3390/rs14164109 - 22 Aug 2022

Cited by 6 | Viewed by 1652

Abstract

The tempo-spatial continuous soil moisture (SM) datasets of satellite remote sensing, land surface models, and reanalysis products are very important for correlational research in the Tibetan Plateau (TP) meteorology. Based on the in situ observed SM, AMSR2, SMAP, GLDAS-Noah, and ERA5 SM are assessed at regional and site scales in the TP during the non-frozen period from 2015 to 2016. The results indicate that SMAP and ERA5 SM (AMSR2 and GLDAS-Noah SM) present an overestimation (underestimation) of the TP regional average. Specifically, SMAP (ERA5) SM performs best in Maqu and south-central TP (Naqu, Pali, and southeast TP), with a Spearman’s rank correlation (

ρ

) greater than 0.57 and an unbiased root mean square error (ubRMSE) less than 0.05 m³/m³. In Shiquanhe, GLDAS-Noah SM performs best among the four SM products. At the site scale, SMAP SM has relatively high

ρ

and low ubRMSE values at the most sites, except the sites at the Karakoram Mountains and Himalayan Mountains. The four SM products show underestimation in different degrees at Shiquanhe. The

ρ

values between AMSR2 SM and rainfall are the highest in most study subregions, especially in Naqu and Pali. For the other SM products, they have the highest positive correlations with a normalized difference vegetation index (NDVI). Besides, land surface temperature (LST) has significant negative (positive) correlations with SM products in the summer (other seasons). Through the multiple linear stepwise regression analysis, NDVI has negative (positive) impacts on SM products in the spring (other seasons), while LST shows the opposite conditions. NDVI (rainfall) is identified as the main influencing factor on the in situ observed, SMAP, GLDAS-Noah, and ERA5 (AMSR2) SM in this study. Compared to previous studies, these results comprehensively present the applicability of SM products in the TP and further reveal their main influencing factors. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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14 pages, 3683 KiB

Open AccessTechnical Note

The Influence of Horizontal Thermal Advection on Near-Surface Energy Budget Closure over the Zoige Alpine Wetland, China

by Xuancheng Lu, Jun Wen, Dongxiao Wang, Wenhui Liu, Yue Yang, Hui Tian, Yueyue Wu and Yuqin Jiang

Remote Sens. 2023, 15(1), 220; https://doi.org/10.3390/rs15010220 - 30 Dec 2022

Viewed by 1506

Abstract

Near-surface energy budget closure has been a trending topic in land surface processes research, especially on the underlying surfaces of heterogeneous wetlands. In this investigation, the horizontal thermal advection caused by thermal inhomogeneity over the alpine wetland is calculated based on the eddy covariance data observed at the Flower Lake observation field and WRF modelling data over the Zoige alpine wetland, China. The contribution of horizontal thermal advection to the near-surface energy closure is analysed. The results show that the mean horizontal heat advection of the Zoige wetland is 20.2 W·m⁻², and the maximum value reached 55.0 W·m⁻² in the summer of 2017. After introducing thermal advection into the near-surface energy balance equation, the near-surface energy closure ratio increased from 72.3% to 81.0%. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation)

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