How spatiotemporal variation of soil moisture can explain hydrological connectivity of infiltration-excess dominated hillslope: Observations from lesser Himalayan landscape

Nanda, Aliva; Sen, Sumit; McNamara, J. P.

doi:10.1016/j.jhydrol.2019.124146

Cited by 35 publications

(14 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the warmer season, the soil moisture values are quite high. The runoff conversion of GA hillslope is less than the AgF hillslope which is also reflected in the spatial variability of soil moisture [29]. Thus, the GA hillslope shows more moisture content than the AgF hillslope.…”

Section: Hillslope-scale Soil Temperature and Moisture Variabilitymentioning

confidence: 98%

“…The hillslopes are dominated by the infiltration-excess runoff generation mechanism [28]. [29] with the copyright permission from Elsevier office).…”

Section: Site Descriptionsmentioning

confidence: 99%

“…Location of Aglar watershed in map of India; both aspects of watershed showing the location of grassed (GA) and agro-forested (AgF) hillslopes in Digital Elevation Model (DEM) (reproduced from Figure 1 of Nanda et al[29] with the copyright permission from Elsevier office).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Nanda

Sen

Sharma

et al. 2020

Water

Self Cite

View full text Add to dashboard Cite

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.

show abstract

Section: Hillslope-scale Soil Temperature and Moisture Variabilitymentioning

confidence: 98%

“…The hillslopes are dominated by the infiltration-excess runoff generation mechanism [28]. [29] with the copyright permission from Elsevier office).…”

Section: Site Descriptionsmentioning

confidence: 99%

See 1 more Smart Citation

Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Nanda

Sen

Sharma

et al. 2020

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, some of the variables selected (e.g. TWI, NDVI, ECa and slope) are known to be associated with soil moisture levels ( Costa et al, 2014 , Mohanty and Skaggs, 2001 , Nanda et al, 2019 ), which in turn determine water availability to the plants ( Prueger et al, 2019 ). NDVI is an indicator of plant vigor, that has been found to be associated with drought stress in crops ( Lee & Park, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

A multifunctional matching algorithm for sample design in agricultural plots

Ohana‐Levi¹,

Derumigny

Peeters³

et al. 2021

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Highlights An algorithm for determining optimal sampling locations was developed. It is based on multifunctional matching (MFM) using multiple known covariates. MFM was tested in a peach orchard and a vineyard using easily acquired variables. MFM performs well in representing the population distribution by a small sample. Better results were shown in the field with higher spatial variability.

show abstract

“…These methods can be categorized into two main groups: index‐based and process‐based approaches. Index‐based approaches study implicit evidence of connection or disconnection within the wetland system, focusing on the identification of critical thresholds in variables such as soil moisture and water level/depth that once transgressed provides evidence of hydrological connectivity (e.g., Ali & Roy, 2010; McLaughlin & Cohen, 2013; Nanda, Sen, & McNamara, 2019). For example, Karim, Kinsey‐Henderson, Wallace, Arthington, and Pearson (2012) identified a threshold of hydrologic connectivity during overbank flooding in north Queensland when water depth surpassed 30 cm.…”

Section: Introductionmentioning

confidence: 99%

Using InSAR to identify hydrological connectivity and barriers in a highly fragmented wetland

Liu

Wang

Aminjafari

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

Hydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high-resolution and large-scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)-based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016-2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long-time scales using remote sensing technique.

show abstract

How spatiotemporal variation of soil moisture can explain hydrological connectivity of infiltration-excess dominated hillslope: Observations from lesser Himalayan landscape

Cited by 35 publications

References 43 publications

Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

A multifunctional matching algorithm for sample design in agricultural plots

Using InSAR to identify hydrological connectivity and barriers in a highly fragmented wetland

Contact Info

Product

Resources

About