Hillslope scale soil moisture variability in a steep alpine terrain

Penna, Daniele; Borga, Marco; Norbiato, Daniele; Fontana, Giancarlo Dalla

doi:10.1016/j.jhydrol.2008.11.009

Cited by 192 publications

(155 citation statements)

References 48 publications

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“…The value of NRS varied between 2 and 30 per 100 km 2 in the Pósa Valley when AE was changed to values between 0.5 and 2% (Figure 8). Similar to the results of [2], the number of representative stations required for optimal spatial soil moisture detection significantly decreased with decreasing measurement error of the employed soil moisture sensor (1% for the Spectrum TDR-300). Our findings indicate that at topographical conditions typical for the Mecsek Hills and the low mountain regions of Hungary, a higher sensor density is required than indicated by [2].…”

Section: Discussionsupporting

confidence: 78%

“…Similar to the results of [2], the number of representative stations required for optimal spatial soil moisture detection significantly decreased with decreasing measurement error of the employed soil moisture sensor (1% for the Spectrum TDR-300). Our findings indicate that at topographical conditions typical for the Mecsek Hills and the low mountain regions of Hungary, a higher sensor density is required than indicated by [2]. According to [39], topography does not profoundly influence soil spatial patterns.…”

Section: Discussionsupporting

confidence: 78%

“…where CV s,k is the spatial coefficient of variation for all monitoring stations on measurement day k, σs is the mean spatial deviation on measurement day k and, and θ t is the mean temporal soil moisture content for the same measurement day k. The determination of the Number of Representative Stations (NRS for a unit area of 100 km 2 ) was based on prior studies [2,19,20]:…”

Section: Calculation Of Standard Errors Significance Levels and Optimentioning

confidence: 99%

“…Flooding in mountainous headwater catchments is governed by rainfall intensities and antecedent catchment states including land use, soil moisture, groundwater levels, and snow depth [1][2][3][4]. One way to estimate the magnitude of runoff and peak discharge values is the use of rainfall-runoff numeric models [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Extreme soil moisture values are also affected by soil depth [29,30]. Penna and co-authors found that temporal stability of soil moisture was greater over dry periods than humid periods [2]. Due to natural variations, such as the decreasing return period of extreme hydrologic events such as catastrophic floods and flash floods, there is an increasing need to define catchment-scale soil moisture accurately and to reduce the efforts and the costs related to the deployment of several soil moisture probes.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

et al. 2015

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Between September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SW Hungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density. According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data. Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionsupporting

confidence: 78%

Section: Calculation Of Standard Errors Significance Levels and Optimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

et al. 2015

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A field and modeling study of nonlinear storage-discharge dynamics for an Alpine headwater catchment

et al. 2014

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A process-based coupled model of surface-subsurface flow is applied to the simulation of nonlinear hydrological dynamics for an experimental mountain headwater catchment in northeastern Italy. The comparison between measured and simulated responses, both distributed (water table and soil moisture) and integrated (streamflow at the outlet), shows that the model satisfactorily reproduces various nonlinear processes, in particular threshold behavior and hysteresis in the catchment storage-discharge relationship. We typically observe a clockwise loop in this relationship, i.e., streamflow response is faster than groundwater and soil moisture response, due to larger time scales for subsurface processes and to soil moisture persistence and redistribution. The model is based on a standard Richards equation representation of integrated saturated-unsaturated-runoff dynamics and needs no ad hoc parameterization (e.g., for macropores, pipe flow, or retention curve hysteresis) to capture observed hysteretic relationships between storage and discharge. Additional numerical experiments are carried out to investigate how heterogeneity (bedrock permeability and the distinction between riparian and hillslope areas) and aquifer thickness and topography affect this nonlinear dynamics. The results show that catchment topography and soil depth exert the main control on the hysteresis and threshold patterns. This is evident from a spatial analysis of streamflow and water table response times to storm events, where the threshold points correspond to changes in terrain slope. These findings are confirmed by a further set of analyses carried out on an idealized v-shaped catchment.

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An entropy‐based measure of hydrologic complexity and its applications

Castillo

Castelli

Entekhabi

2015

Water Resources Research

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Basin response and hydrologic fluxes are functions of hydrologic states, most notably of soil moisture. However, characterization of hillslope-scale soil moisture is challenging since it is both spatially heterogeneous and dynamic. This paper introduces an entropy-based and discretization-invariant dimensionless index of hydrologic complexity H that measures the distance of a given distribution of soil moisture from a Dirac delta (most organization) and a uniform distribution (widest distribution). Applying the distributed hydrologic model MOBIDIC to seven test basins with areas ranging 10 0 210 3 km 2 and representing semiarid and temperate climates, H is shown to capture distributional characteristics of soil moisture fields. It can also track the temporal evolution of the distributional features. Furthermore, this paper explores how basin attributes affect the characteristic H, and how H can be used to explain interbasin variability in hydrologic response. Relationships are found only by grouping basins with the same climate or size. For the semiarid basins, H scales with catchment area, topographic wetness, infiltration ratio, and base flow index; while H is inversely related to relief ratio.

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Hillslope scale soil moisture variability in a steep alpine terrain

Cited by 192 publications

References 48 publications

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

A field and modeling study of nonlinear storage-discharge dynamics for an Alpine headwater catchment

An entropy‐based measure of hydrologic complexity and its applications

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