Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach

2018 ◽  
Vol 564 ◽  
pp. 944-966 ◽  
Author(s):  
Urooj Khan ◽  
Hoori Ajami ◽  
Narendra Kumar Tuteja ◽  
Ashish Sharma ◽  
Seokhyeon Kim
2015 ◽  
Vol 2 (6) ◽  
pp. 635-647 ◽  
Author(s):  
Heye R. Bogena ◽  
Johan A. Huisman ◽  
Andreas Güntner ◽  
Christof Hübner ◽  
Jürgen Kusche ◽  
...  

2020 ◽  
Author(s):  
María Valiente ◽  
Ane Zabaleta ◽  
Maite Meaurio ◽  
Jesus A. Uriarte ◽  
Iñaki Antigüedad

<p>The Pyrenees mountain range is the main source of water resources for a large surrounding region, extending from the Atlantic to the Mediterranean. This area is particularly vulnerable to the consequences of climate change. The PIRAGUA project (Interreg-POCTEFA) evaluates the components of the hydrological cycle in the Pyrenees, with the central objective of improving the adaptation of territories to climate change. One of its tasks focuses on the analysis of the effect that land cover and associated soil properties have on different hydrological services. Indeed, land use and its management directly affect soil hydrology, which is a key factor in streamflow temporal distribution. A better understanding of the water-soil-vegetation system is essential for a reliable hydrological modelling which results should be considered in adaptation strategies to climate change.</p><p>To this aim, chemical and physical characterization of soil properties is being conducted at the 681 km<sup>2</sup> humid Bidasoa catchment (Pyrenees). In order to understand the soil-moisture dynamics, a monitoring network was established in July 2019 in a 0.4 km<sup>2</sup> experimental site within the catchment. Four soil-moisture stations and a meteorological one were installed within the same geological setting, same rainfall conditions and similar soil texture characteristics (silt-loamy texture and about one meter deep), but different land covers (pine forest, oak forest, grassland and fernery). Continuous soil-moisture data obtained to date show that upper soil layers (0-20 cm) are deeply influenced by top vegetation cover. Grassland has the highest soil-moisture variations, ranging from 16.2 to 36.6 %, as they closely mirror precipitation patterns. Pine and oak forests present similar variation trend, varying from 33.9 to 42.8 % and from 35.3 to 41.9 %, respectively. Soil-moisture at fernery goes from 30.5 to 36 %. Minimum soil-moisture values coincide in all plots with the end of the dry period (end of September). Maximum values, occurring during very heavy and continuous precipitation in November (647 mm registered from 1 to 24 November), are considered as a proxy for saturated soil conditions. In all the plots, fluctuations in soil-moisture diminish significantly with increasing soil depth. However, considerable differences are found in the vertical soil-moisture profile across land covers. In both forest plots, a decreasing trend of soil-moisture within the profile is observed, while grassland and fernery show an increasing trend. Preliminary results show that soil water infiltration is different among different land covers, which give some insight into the hydrological functionality of soil under different vegetation types. Longer records of soil-moisture dynamics in the area would contribute to better assess the linkages between water, soil and vegetation and, in turn, to improve hydrological modelling in humid mountainous areas. This knowledge is necessary for a better consideration of the adaptation measures that should be taken from the territory.</p>


Author(s):  
Ryoko Araki ◽  
Flora Branger ◽  
Inge Wiekenkamp ◽  
Hilary McMillan

Soil moisture signatures provide a promising solution to overcome the difficulty of evaluating soil moisture dynamics in hydrologic models. Soil moisture signatures are metrics that quantify the dynamic aspects of soil moisture timeseries and enable process-based model evaluations. To date, soil moisture signatures have been tested only under limited land-use types. In this study, we explore soil moisture signatures’ ability to discriminate different dynamics among contrasting land-uses. We applied a set of nine soil moisture signatures to datasets from six in-situ soil moisture networks worldwide. The dataset covered a range of land-use types, including forested and deforested areas, shallow groundwater areas, wetlands, urban areas, grazed areas, and cropland areas. Our set of signatures characterized soil moisture dynamics at three temporal scales: event, season, and a complete timeseries. Statistical assessment of extracted signatures showed that (1) event-based signatures can distinguish different dynamics for all the land-uses, (2) season-based signatures can distinguish different dynamics for some types of land-uses (deforested vs. forested, urban vs. greenspace, and cropped vs. grazed vs. grassland contrasts), (3) timeseries-based signatures can distinguish different dynamics for some types of land-uses (deforested vs. forested, urban vs. greenspace, shallow vs. deep groundwater, wetland vs. non-wetland, and cropped vs. grazed vs. grassland contrasts). Further, we compared signature-based process interpretations against literature knowledge; event-based and timeseries-based signatures generally matched well with previous process understandings from literature, but season-based signatures did not. This study will be a useful guideline for understanding how catchment-scale soil moisture dynamics in various land-uses can be described using a standardized set of hydrologically relevant metrics.


2009 ◽  
Vol 17 (2) ◽  
pp. 256-260 ◽  
Author(s):  
Feng WANG ◽  
Shu-Qi WANG ◽  
Xiao-Zeng HAN ◽  
Feng-Xian WANG ◽  
Ke-Qiang ZHANG

2016 ◽  
Vol 75 (2) ◽  
Author(s):  
Muhammad Ajmal ◽  
Muhammad Waseem ◽  
Waqas Ahmad ◽  
Tae-Woong Kim

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.


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