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Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections

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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 (10 November 2023) | Viewed by 9972

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Special Issue Editors

Dr. Yanping Li

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Guest Editor

College of Oceanography, Hohai University, Nanjing 210098, China
Interests: atmospheric dynamics; regional climate modeling; drought and precipitation extremes; atmosphere–land interaction

Dr. Ya Huang

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Guest Editor

College of Oceanography, Hohai University, Nanjing 210098, China
Interests: water resources; climate changes; precipitation extremes; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Omer Yetemen

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Guest Editor

Eurasia Institute of Earth Sciences, Istanbul Technical University, Maslak, Istanbul 34469, Turkey
Interests: landscape; ecohydrology; evapotranspiration; climate change; geology; hydrology; solar radiation; vegetation; geomorphology

Dr. Qing Yang

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: remote sensing; flood impact assessment; flood mapping

Special Issue Information

Dear Colleagues,

Regional climate models (RCMs) have been widely used at global and regional scales due to their high resolution, detailed regional climate characteristics and multiple configurable physical modules. RCMs have been widely used in studying the feedback of human activities on regional climate and their complex impacts on water, energy, food and ecology under climate change. However, the modeling and performance evaluation of RCM rely on high-quality observation for various climate variables (e.g., precipitation, evaporation, soil moisture, radiation, leaf area index, and crop yield). With the rapid development of remote sensing technology in recent decades, large-scale, long-time series, and multivariate high-resolution remote sensing datasets have become indispensable data sources for RCM modeling and application. The progress of remote sensing technology not only makes up for the deficiency of in-situ observation, but also effectively improves the simulation performance of RCM, which enables the study of the complex relationship among water, energy, food and ecology under the influence of climate change and human activities. Toward this end, this Special Issue aims to promote the latest advances in applying remote sensing to climate modeling at the regional scale. Major topics of interest include but are not limited to:

Detection and attribution of historical, current and future regional climate changes using remote sensing or combined with regional climate models.
Modeling and performance evaluation of regional climate and hydrological models based on remote sensing or site observation.
Application of remote sensing and regional climate models in solving water–energy–food–eco-environment problems, including the impacts of climate change on the hydrological cycle, clean energy, crop yield, eco-environment, etc.
Remote sensing and regional climate model applications in hydroclimatology, including assessing and predicting the impact of climate change on extreme hydroclimatic events such as flood, drought, and heavy precipitation.
Application of remote sensing and regional climate models to precipitation, evapotranspiration, soil moisture, groundwater and soil erosion.
Assessment of the impacts of human activities such as agricultural irrigation, water and soil conservation, inter-basin water diversion projects and afforestation on regional climate, water cycle and ecological environment using remote sensing and regional climate models.

Dr. Yanping Li
Dr. Ya Huang
Dr. Omer Yetemen
Dr. Qing Yang
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

remote sensing
climate projection
regional climate modeling
hydrological modeling
water resources
extreme hydrometeorological events
global water and energy cycles
climate change
dynamical downscaling
atmosphere–land interaction

Published Papers (9 papers)

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Research

32 pages, 11619 KiB

Open AccessArticle

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

by Bo Xie, Hui Guo, Fanhao Meng, Chula Sa and Min Luo

Remote Sens. 2023, 15(23), 5460; https://doi.org/10.3390/rs15235460 - 22 Nov 2023

Viewed by 832

Abstract

Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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19 pages, 5245 KiB

Open AccessArticle

Raindrop Size Distribution Characteristics of Heavy Precipitation Events Based on a PWS100 Disdrometer in the Alpine Mountains, Eastern Tianshan, China

by Puchen Chen, Puyu Wang, Zhongqin Li, Yefei Yang, Yufeng Jia, Min Yang, Jiajia Peng and Hongliang Li

Remote Sens. 2023, 15(20), 5068; https://doi.org/10.3390/rs15205068 - 23 Oct 2023

Viewed by 912

Abstract

As a key component of the hydrological cycle, knowledge and comprehension of precipitation formation and evolution are of leading significance. This study investigates the statistical characteristics of raindrop size distribution for heavy precipitation events with observations collected by a Present Weather Sensor (PWS100) disdrometer located in the alpine area of eastern Tianshan, China. The characteristics are quantified based on heavy rain, heavy snow, and hail precipitation events classified using the rainfall intensity and the precipitation-related weather codes (US National Weather Service). On average, the heavy precipitation events in the headwaters of the Urumqi River are dominated by medium-sized (2–4 mm) raindrops. As well, we investigate mass-weighted mean diameter–normalized intercept parameter scatterplots, which demonstrate that the heavy precipitation events in alpine regions of the Tianshan Mountains can be identified as maritime-like clusters. The concentration of raindrops in heavy precipitation is the highest overall, while the concentration of raindrops in heavy snow is the lowest when the diameter is lower than 1.3 mm. The power–law relationships of radar reflectivity (Z) and rain rate (R) [Z = AR^b] for the heavy rain, heavy snow, and hail precipitation events are also calculated. The Z–R relationship of heavy rain and heavy snow in this work has a lower coefficient value of A (10 and 228.7, respectively) and a higher index value of b (2.6 and 2.1, respectively), and the hail events are the opposite (A = 551.5, b = 1.3), compared to the empirical relation (Z = 300R^1.4). Furthermore, the possible thermodynamics and general atmospheric circulation that cause the distinctions in the raindrop size distribution characteristics between alpine areas and other parts of the Tianshan Mountains are also debated in this work. The headwaters of the Urumqi River in alpine areas have relatively colder and wetter surroundings in the near-surface layer than the foothills of the Tianshan Mountains during the precipitation process. Meanwhile, a lower temperature, a higher relative humidity, a more efficient collision coalescence mechanism, and glacier local microclimate effects (temperature jump, inverse glacier temperature, glacier wind) at the headwaters of the Urumqi River during the precipitation process are probably partly responsible for more medium- and large-size drops in the mountains. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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21 pages, 5751 KiB

Open AccessArticle

Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model

by Tingan Zhu, Wei Zhang, Jun Wang, Yuanpeng Chen, Shuhao Xin and Jinxin Zhu

Remote Sens. 2023, 15(19), 4745; https://doi.org/10.3390/rs15194745 - 28 Sep 2023

Cited by 1 | Viewed by 742

Abstract

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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19 pages, 7990 KiB

Open AccessArticle

Projected Changes in Precipitation Based on the CMIP6 Optimal Multi-Model Ensemble in the Pearl River Basin, China

by Mengfei He, Yangbo Chen, Huaizhang Sun and Jun Liu

Remote Sens. 2023, 15(18), 4608; https://doi.org/10.3390/rs15184608 - 19 Sep 2023

Viewed by 959

Abstract

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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17 pages, 13912 KiB

Open AccessArticle

The Role of Changbai Mountain in an Extreme Precipitation Event in Liaoning Province, China

by Jing Yang, Ya Huang, Liping Luo and Yanping Li

Remote Sens. 2023, 15(18), 4381; https://doi.org/10.3390/rs15184381 - 06 Sep 2023

Cited by 1 | Viewed by 911

Abstract

Based on the half-hourly Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data product (0.1°), the fifth-generation European Center for Medium-Range Weather Forecasting atmospheric reanalysis dataset (ERA5), sounding data, and the Weather Research and Forecasting Model (WRF-ARW), this study explored the developmental process of a typical extreme precipitation event in Liaoning Province on 2 June 2021. This study focused on the impact of Changbai Mountain on this precipitation process and its corresponding physical mechanisms. The research findings revealed that Changbai Mountain significantly affected the precipitation event in three main aspects: blocking drag, forcing uplift, and leeside convergence. The blocking drag caused by the mountain topography led to an extension in the duration of heavy rainfall. The dynamic lifting and leeside convergence associated with the mountainous terrain also substantially increased the amount of precipitation. Furthermore, the topography hindered the movement of the Bohai Bay cold pool and enhanced the intensity of the cold pool, contributing to the sustained extreme precipitation in Liaoning Province. Lastly, the terrain sensitivity experiment demonstrated that when the height of Changbai Mountain was reduced, the convergence uplift, moisture condensation, and cold pool intensity were weakened, leading to significant changes in precipitation intensity and spatial distribution. These findings further confirm the crucial role of Changbai Mountain in the occurrence and development of local precipitation in Liaoning Province. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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14 pages, 4927 KiB

Open AccessCommunication

Simulation Study on the Effect of Elevated CO₂ on Regional Temperature Change on the Loess Plateau

by Zhifang Shi, Yaoping Cui, Liyang Wu, Yan Zhou, Mengdi Li and Shenghui Zhou

Remote Sens. 2023, 15(10), 2607; https://doi.org/10.3390/rs15102607 - 17 May 2023

Cited by 1 | Viewed by 973

Abstract

CO₂ undisputedly affects global temperature change, but the specific impact of change in atmospheric CO₂ concentration on regional warming remains to be quantified, especially in different climatic backgrounds. Taking the Loess Plateau as the research area, this study quantified the effect of CO₂ elevation on regional temperature change based on a single-factor sensitivity experiment of the regional Weather Research and Forecasting (WRF) climatic model, and the results revealed the following: (i) The correlation coefficient between monthly mean values of temperature simulated by the WRF model and the observed values reached 0.96 (p < 0.01), and the overall spatial trends of simulated and observed temperatures increased from the northwest to the southeast. (ii) CO₂ concentration increased from 370.70 ppm in 2000 to 414.54 ppm in 2020, and the Loess Plateau region warmed by 0.04 and 0.06 °C under the MODIS land cover of 2000 and 2020, respectively. This indicates that increase in CO₂ concentration over the Loess Plateau has greater impact than land cover change on regional temperature change. (iii) As CO₂ concentration increased, the maximum fluctuation of temperature in summer exceeded 2.0 °C, while the fluctuations in spring (0.72 °C), autumn (0.77 °C), and winter (0.15 °C) were relatively small, indicating that summer temperature is most sensitive to CO₂ concentration change. By emphasizing the marked temperature difference associated with the same CO₂ change in different seasons, this study provides an important basis for extending the understanding of the differences in the effect of CO₂ on regional temperatures. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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26 pages, 16741 KiB

Open AccessArticle

High-Resolution Precipitation Modeling in Complex Terrains Using Hybrid Interpolation Techniques: Incorporating Physiographic and MODIS Cloud Cover Influences

by Karam Alsafadi, Shuoben Bi, Bashar Bashir, Ehsan Sharifi, Abdullah Alsalman, Amit Kumar and Shamsuddin Shahid

Remote Sens. 2023, 15(9), 2435; https://doi.org/10.3390/rs15092435 - 05 May 2023

Cited by 2 | Viewed by 1347

Abstract

The inclusion of physiographic and atmospheric influences is critical for spatial modeling of orographic precipitation in complex terrains. However, attempts to incorporate cloud cover frequency (CCF) data when interpolating precipitation are limited. CCF considers the rain shadow effect during interpolation to avoid an overly strong relationship between elevation and precipitation in areas at equivalent altitudes across rain shadows. Conventional multivariate regression or geostatistical methods assume the precipitation–explanatory variable relationship to be steady, even though this relation is often non-stationarity in complex terrains. This study proposed a novel spatial mapping approach for precipitation that combines regression-kriging (RK) to leverage its advantages over conventional multivariate regression and the spatial autocorrelation structure of residuals via kriging. The proposed hybrid model, RK (GT + CCF), utilized CCF and other physiographic factors to enhance the accuracy of precipitation interpolation. The implementation of this approach was examined in a mountainous region of southern Syria using in situ monthly precipitation data from 57 rain gauges. The RK model’s performance was compared with conventional multivariate regression models (CMRMs) that used geographical and topographical (GT) factors and CCF as predictors. The results indicated that the RK model outperformed the CMRMs with a root mean square error of <8 mm, a mean absolute percentage error range of 5–15%, and an R² range of 0.75–0.96. The findings of this study showed that the incorporation of MODIS–CCF with physiographic variables as covariates significantly improved the interpolation accuracy by 5–20%, with the largest improvement in modeling precipitation in March. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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19 pages, 5541 KiB

Open AccessArticle

Analysis of the Characteristics and Causes of Land Degradation and Development in Coastal China (1982–2015)

by Ya Huang, Guiping Li, Yong Zhao, Jing Yang and Yanping Li

Remote Sens. 2023, 15(9), 2249; https://doi.org/10.3390/rs15092249 - 24 Apr 2023

Viewed by 1046

Abstract

Land degradation and development (LDD) is one of the important ecological issues in coastal China. This study analyzed the temporal and spatial changes of the LDD process in coastal China from 1982 to 2015 using the LDD index constructed from normalized NDVI and GPP data. The study also quantitatively evaluated the relative contributions of climate and human factors to LDD and explored their impact on LDD. The study’s findings indicate that coastal regions in China experienced a marked increase in land development during the study period, with 62.47% of the regions displaying a growth trend and only 7.03% exhibiting signs of land degradation. The impact of climate change on the change in LDD processes was limited, while human activities were the main driving force, with their impact becoming increasingly apparent over time. Human activities were the dominant contributor to the change in LDD in most regions, accounting for over 60% of the change. Fast urbanization led to a notable decrease in cropland, wetland, shrub, and grassland, with a substantial proportion of the affected cropland transformed into impervious surfaces, accounting for 91.31% of the total cropland conversion. These findings deepen our understanding of the LDD process and its driving factors in coastal China, providing valuable insights for developing effective policy interventions and implementing successful land restoration plans in the region. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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24 pages, 8347 KiB

Open AccessArticle

Influence of Precipitation Effects Induced by Large-Scale Irrigation in Northwest China on Soil Erosion in the Yellow River Basin

by Ya Huang, Yong Zhao, Guiping Li, Jing Yang and Yanping Li

Remote Sens. 2023, 15(7), 1736; https://doi.org/10.3390/rs15071736 - 23 Mar 2023

Viewed by 1241

Abstract

Large-scale irrigation can alter the regional water cycle process, which changes the structure and spatiotemporal distribution of local and downwind precipitation, impacting soil erosion in both the irrigated areas and the surrounding regions. However, the effects of large-scale irrigation on soil erosion in downwind vulnerable areas have not been investigated. The study used the high-resolution regional climate model (RegCM4) and the revised universal soil loss equation (RUSLE) to examine the effects of irrigation-induced precipitation in Northwest China on the frequency, distribution, and intensity of precipitation in the Yellow River Basin (YRB) under different Representative Concentration Pathways (RCPs). The response characteristics of soil erosion to the irrigation-induced precipitation effects and its relationship with slope, elevation, and land use type were analyzed as well. The results indicate that soil erosion in most regions of the YRB is below moderate, covering 84.57% of the basin. Irrigation leads to a 10% increase in summer precipitation indices (e.g., total wet-day precipitation, consecutive wet days, number of wet days with precipitation ≥ 1 mm, and number of heavy precipitation days with precipitation ≥ 12 mm) in the northwest of the basin. Irrigation also leads to a change in local circulation, resulting in reduced precipitation in the southeast of the basin, particularly under the RCP8.5 scenario. The transformation of erosion intensity between low-grade and high-grade erosion is relatively stable and small under the influence of precipitation. However, soil erosion changes display strong spatial heterogeneity, inter-annual and intra-annual fluctuations, and uncertainties. The findings of this study can be helpful for policymakers and water resource managers to better understand the impacts of large-scale irrigation on the environment and to develop sustainable water management strategies. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)

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