Key Hydrological Processes and Its Controlling Factors in Terrestrial Ecosystems

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2816

Special Issue Editors


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Guest Editor
School of Ecology and Environment, Hainan University, Haikou 570228, China
Interests: soil water retention; soil infiltration; land degradation; plant water use strategies

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Guest Editor
Qinghai Provincial Key Laboratory of Restoration Ecology for Cold Region, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
Interests: grassland degradation; hydrologic process; water cycle; water conservation function
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Guest Editor
Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: hydrological processes in landscapes; vegetation and land interaction with the atmosphere; biospheric fluxes in deserts, shrub land, and forests

Special Issue Information

Dear Colleagues,

Terrestrial ecosystems are among the most complex and critical ecosystems on Earth, harboring abundant biodiversity and providing essential ecosystem services. Hydrological processes play an important role in terrestrial ecosystems, affecting water cycling, nutrient cycling, and energy flow; therefore, understanding and studying the hydrological processes inherent to terrestrial ecosystems is crucial for the conservation and management of these ecosystems, improving water resource utilization, and maintaining ecological balance.

This Special Issue aims to explore the key hydrological processes occurring in terrestrial ecosystems and how they influence ecosystem dynamics, water availability, and ecosystem services. We seek to cover various aspects of hydrology, including water infiltration, runoff, evapotranspiration, groundwater recharge, and their interactions with ecological processes and land management. The specific research content can include, but is not limited to:

  1. Land-use or land-cover change impacts on hydrological processes: Investigating the effects of land-use or land-cover change on hydrological processes in terrestrial ecosystems, such as precipitation, evapotranspiration, infiltration, and runoff, to enhance understanding of terrestrial water cycling.
  2. Soil–sediment–hydro-ecological processes at multiple scale: Studying the interaction between soils and sediments and hydro-ecological processes at multiple scales in terrestrial ecosystems.
  3. Forest disturbance impacts on hydrological processes: Investigating the effects of forest disturbance, such land degradation or forest fires, on hydrological processes related to ecosystem services, providing scientific evidence for land management and conservation.
  4. Hydrological processes and climate change adaptation: Exploring the impact of climate change on hydrological processes in terrestrial ecosystems, studying the response and potential of hydrological processes in adapting to and mitigating climate change.

Dr. Licong Dai
Dr. Xiaowei Guo
Prof. Dr. Charles Bourque
Guest Editors

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Keywords

  • hydrological processes
  • water infiltration
  • runoff
  • evapotranspiration
  • groundwater recharge
  • soil water retention
  • terrestrial ecosystems
  • land cover change

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Published Papers (4 papers)

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Research

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24 pages, 9587 KiB  
Article
Hydrological Response to Predominant Land Use and Land Cover in the Colombian Andes at the Micro-Watershed Scale
by Henry Garzón Sánchez, Juan Carlos Loaiza Usuga and Jaime Ignacio Vélez Upégui
Land 2024, 13(8), 1140; https://doi.org/10.3390/land13081140 - 25 Jul 2024
Viewed by 341
Abstract
The hydrological response (HR), generally defined as the relationship between rainfall and runoff, should be understood holistically within the processes of the conversion of rainfall to evapotranspiration, surface and subsurface runoff, groundwater flow, and streamflow. The objective of this study was to evaluate [...] Read more.
The hydrological response (HR), generally defined as the relationship between rainfall and runoff, should be understood holistically within the processes of the conversion of rainfall to evapotranspiration, surface and subsurface runoff, groundwater flow, and streamflow. The objective of this study was to evaluate the HR of three predominant land use and land cover (LULC) types in the Colombian Andes at the micro-watershed scale. Experimental micro-watersheds were established to replicate LU (pasture, and a coffee agroforestry system) and LC (natural forest). The TETIS model was applied, calibrated, and verified, and the similarity between observed flows (using level sensors and volumetric gauges) and flows simulated by the model was evaluated, relating the HR to each type of LULC. The HR included an analysis of the Water Retention and Regulation Index—IRH and Base Flow Index—IFB. The best model fit and HR were found for the agroforestry system, with a moderate NSE (0.48), R2 (0.7), RMSE (0.2), and BE (20.8%). On the other hand, a forest cover was found to guarantee the permanence of subsurface inputs and base flows to the river, as evidenced by high IRH, IFB, and water balance values. Natural forest land uses present high volumetric moisture content in the soil, corresponding to a high IFB. Full article
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16 pages, 2815 KiB  
Article
Drought Sensitivity and Vulnerability of Rubber Plantation GPP—Insights from Flux Site-Based Simulation
by Runqing Zhang, Xiaoyu E, Zhencheng Ma, Yinghe An, Qinggele Bao, Zhixiang Wu, Lan Wu and Zhongyi Sun
Land 2024, 13(6), 745; https://doi.org/10.3390/land13060745 - 26 May 2024
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Abstract
Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. [...] Read more.
Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapotranspiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries × 3 drought initiation seasons × 4 drought intensities × 12 drought durations × 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughts in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughts on GPP is most pronounced. This research lays the foundation for estimating the impact of droughts on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection. Full article
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22 pages, 4786 KiB  
Article
Performance of a Set of Soil Water Retention Models for Fitting Soil Water Retention Data Covering All Textural Classes
by Ali Rasoulzadeh, Javad Bezaatpour, Javanshir Azizi Mobaser and Jesús Fernández-Gálvez
Land 2024, 13(4), 487; https://doi.org/10.3390/land13040487 - 9 Apr 2024
Viewed by 817
Abstract
A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a [...] Read more.
A clean environment is an essential component of sustainable development, which is based on a comprehensive understanding of the behavior of water, soil, and air. The soil water retention (SWR) curve is a crucial function that describes how soil retains water, playing a fundamental role in irrigation and drainage, soil conservation, as well as water and contaminant transport in the vadose zone. This study evaluates the accuracy, performance, and prediction capabilities of 15 SWR models. A total of 140 soil samples were collected from different sites, covering all textural classes. Standard suction tests, using both hanging column and ceramic pressure plate extractors, were conducted to compile the SWR databank. 15 SWR models were selected and fitted to the SWR data points. Soil texture, bulk density, and organic matter were used to determine their effect on the performance of the SWR models. The results indicate that the Tani and Russo models exhibit the lowest levels of accuracy and performance among the selected models. Based on the Akaike and Bayesian information criteria analysis, the van Genuchten model exhibits the lowest values among the selected models, with poor prediction capabilities in estimating the SWR curve. The significance test at the 0.05 level (95% confidence interval) shows that according to the calculated p-values for the Pearson correlation coefficient between RMSE and texture, the Brooks-Corey and van Genuchten models are poorly influenced by soil properties. The performance of the models is not significantly affected by the soil organic matter. Similarly, bulk density does not significantly affect model performance except for the Brooks–Corey, van Genuchten, Tani, and Russo models. Among the SWR models considered, the double exponential, Groenevelt and Grant, and Khlosi et al. models demonstrate superior accuracy and performance in predicting the SWR curve. This is supported by lower values for RMSE, Akaike, and Bayesian information criteria. Full article
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Review

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21 pages, 4994 KiB  
Review
An Examination of Stream Water Quality Data from Monitoring of Forest Harvesting in the Eastern Highlands of Victoria
by Leon Bren and Michael Ryan
Land 2024, 13(8), 1217; https://doi.org/10.3390/land13081217 - 6 Aug 2024
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Abstract
A large data set measuring surface stream turbidity, dissolved oxygen levels, and water temperature was developed by sampling 32 rivers and streams with forested catchments at weekly intervals for between two and three years. This was in response to allegations of possible water [...] Read more.
A large data set measuring surface stream turbidity, dissolved oxygen levels, and water temperature was developed by sampling 32 rivers and streams with forested catchments at weekly intervals for between two and three years. This was in response to allegations of possible water quality impairment by forest harvesting (“logging”). Additionally, nine rivers or streams external to the forests were sampled to form a “reference set”; concern was expressed that the water quality of these may be impaired by upstream forest harvesting. An unlogged forested control catchment was selected from the data set and used as a comparator to help reduce seasonal variation. Division of the data into “logged forested catchments” and “unlogged forested catchments” allowed for us to test the null hypothesis. The null hypothesis was that there was no difference between the means of the logged and unlogged sets by a “Student’s t test of difference between means”. The null hypothesis was supported for the three parameters. There was no discernible deterioration in water quality associated with the presence of logging in the stream catchments. It was concluded that logging in this environment was not a determinant of water quality, and that the presence or absence of logging in these catchments did not affect the measured water quality. Spearman rank correlation analysis was unable to detect any statistically significant correlations between the water quality parameters, suggesting they are substantially independent measures of water quality. The monitoring showed that the small upland streams had generally good-to-excellent water quality. The water quality in these was generally better than the “reference set”—this probably reflected agriculture and cultivation in the proximity of their sampling points. Full article
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