Generazione del deflusso (Runoff Generation)

Questa parte include l'analisi della formazione dei deflussi superficiali e, sebbene da me non amati, vari metodi più o meno empirici per calcolare l'infiltrazione.

1 - La generazione del deflusso. Audio 2014 (27.9 Mb). Audio 2015 (36.5 Mb).
2 - Il Topmodel. Audio 2015 (14.8 Mb). YouTube2017.
3 - La generazione del deflusso nel modello ARNO/XINJANG. YouTube2017 (Part I, Part II)
4 - Il modello del Soil Conservation Service

Ecco alcuni modelli semplificati di infiltrazione. Ma perchè usarli, quando si possono usare semplificazioni dell'equazione di Richards (see point 10) ?

5 - The good-old- Horton model of Infiltration
6 -  The Green-Ampt model of infiltration

Altri Vecchi Audio (2014)
 2b-5b - Audio metodi semplificati per il calcolo del deflusso superficiale o dell'infiltrazione (escluso SCS - 23.4 Mb)

Simulated effect of soil depth and bedrock topography on near-surface hydrologic response and shallow landslide triggering by Lanni, McDonnell, Hopp and Rigon

We have just submitted a paper  that, looked from a certain perspective, can be thought on the evolution of soil moisture content in presence of variable soil depth. In fact, variable soil depth, jointly with the fact that increases in hydraulic conductivity follows the increase in water pressure, and that hydraulic conductivity itself can often be considered negligible when the soil is unsaturated, delays the formation of a widespread water table in a hillslope. Therefore the effective contributing area above a point of a catchment is usually  not the total upstream area but just a part of it.  This obviously has consequences on the propagation of instabilities along a slope.

The Abstract of the paper:

This paper explores the effect of hillslope hydrological behavior on slope stability in the context of transient subsurface saturation development and landslide triggering. We perform a series of virtual experiments to address how subsurface topography affects the location and spatial pattern of slip surface development and pore pressure dynamics. We use a 3D Darcy-Richards equation solver (Hydrus 3-D) combined with a cellular automata slope stability model to simulate the spatial propagation of the destabilized area. Our results showed that the soil-bedrock interface and in particular, bedrock depressions, played a key role on pore pressure dynamics, acting as an impedance for the downslope drainage of perched water. Filling and spilling of depressions in the bedrock surface microtopography induced localized zones of increased pressure head such that the development of pore-pressure fields—not predictable by surface topography—lead to rapid landslide propagation. Our work suggests that landslide models should consider the subsurface topography in order to include a connectivity component in the mathematical description of hydrological processes operating at the hillslope scale. Quantitative soil- landscape methods combined with physically-based landslide models may improve our ability to predict shallow landslide potential. 

Among the original stuff presented in this paper, there is a tentativ to move away from the concept of instable points to the one of instable regions.  The traditional (simplified) approach based on the infinite slope stability (Ning Lu is discussing it here) is in fact used in modern GIS based program like SHALSTAB (here on ARCGIS or here on opensource GIS) or SINMAP to determine the instabilities of single points, which we try here to generalize a little. 

The paper draft is available here, if you are interested in. A related discussion can also be found in the previous papers by Lanni et Al., 2011 and in the draft by Cordano and Rigon, 2012.

On the relative role of upslope and downslope topography for describing water flowpath and storage dynamics: a theoretical analysis

Hydrological studies have shown, for many years now, that catchments organize themselves. The signals that go into a basin (in our case rainfall) look different to those that come out of it (i.e.river discharge), due to hydrodynamics, flow path geometry, and topology effects (e.g. Rinaldo et al., 1991, 1995; D’Odorico and Rigon, 2003; Botter and Rinaldo, 2003). However, tracer campaigns (e.g.,isotope studies) and their interpretation have shown that the whole dynamic is more complex than first naively expected (e.g., Soulsby et al., 2009), and that “the total catchment storage is likely to be much greater than the dynamic storage inferred by hydrometric data alone, and needs to be invoked to explain some nonlinearity in rainfall-runoff responses in relation to antecedent conditions” (Birkel et al., 2011). For instance, in many catchment settings, dynamically expanding and contracting riparian saturation zones can play a major role in producing the real (proper) travel time of water (Fiori and Russo, 2008, Russo and Fiori, 2008, Tetzlaff et al., 2007). At the same time, the small-scale topographic variations in the bedrock and the filling and spilling of water into depressions and over the bedrock micro-topography (Tromp-van Meerveld and McDonnell, 2006; Hopp and McDonnell, 2009) can control the subsurface flow routing. Several researchers have also reported the role played by geological landscape features. The lack of confining layers in jointed and fractured bedrock and the local variations in its hydraulic conductivity may strongly influence water storage dynamics in the overlying soil layer (Pierson, 1977; Wilson and Dietrich, 1987; Montgomery et al., 2002).
The overall model of the spatial structure that leads to flow and storage organization (something that is crucial to prioritizing what to do and where to do it in river catchments) brings, therefore, to a system of reservoirs which, uphill, can be defined on the basis of bedrock geometry and permeability, and, close to the riparian zones, on the basis of various storage areas that interact dynamically with the stream network.

In this paper, we analyze the case where topography (i.e., lateral flow) is recognized as the predominant control for subsurface flow mechanisms. This is generally the case in mountain regions with moderate to steep topography (Tetzlaff et al., 2009a) where a shallow (highly conductive) soil layer lies on an impervious bedrock substrate (Western et al., 2004). Under these conditions, the availability of storage for water is limited almost exclusively to soil drainable porosity (e.g., Hilberts et al., 2005; Cordano and Rigon, 2008), complex riparian dynamics are less important, and can, as a first approximation, be neglected. Moreover, elevation potential dominates total hydraulic potential, and thus topography represents a good proxy (in theory) for water flow paths (e.g., Seibert et al., 2007; McNamara et al., 2005) and spatial patterns of soil moisture (e.g., Schmidt and Persson, 2003).
The fact that elevation potential dominates total hydraulic potential led to assume that local topography could represent a good way for describing hydrological processes at the hillslope/catchment scale.
Upon this belief, topographic indices have been developed and used as proxies to represent the role of topography on subsurface flow paths and soil-water storage dynamics. However, over the years these indices proved to be insufficient to explain an increasing number of case studies (e.g. Burt and Butcher, 1986; Western et al., 1999; Seibert et al., 1997) and brought to several reconsiderations of the matter, of which we briefly report.

The paper is available on Hydrological Processes Preview, and is the same paper presented in a previous post when submitted.

References

Birkel C, Tetzlaff D, Dunn SM, Soulsby C. 2011. Using time domain and geographic source tracers to conceptualise streamflow generation processes in lumped rainfall-runoff models. Water Resources Research. 47. W02515. Doi:10.1029/2010WR009547.

Botter, G, Rinaldo, A. 2003. Scale effect on geomorphologic and kinematic dispersion. Water Resour. Res. 39(10): 1286. Doi:10.1029/2003WR002154.
Burt TP, Butcher DP. 1985. Topographic controls of soil moisture distributions. J. Soil Sci. 36: 469 – 486.

Cordano E, Rigon R. 2008. A perturbative view on the subsurface water pressure response at hillslope scale, Water Resour. Res. 44. W05407. Doi:10.1029/2006WR005740.

D’Odorico P, Rigon R. 2003. Hillslope and channel contributions to the hydrologic response, Water Resour. Res. 39(5): 1113–1121. Doi:10.1029/2002WR001708.

Fiori A, Russo D. 2008. Travel Time Distribution in a Hillslope: Insight from Numerical Simulations. Water Resour. Res. 44. W12426. Doi:10.1029/2008WR007135.

Hilberts A, Troch P, Paniconi C. 2005. Storage-dependent drainable porosity for complex hillslopes. Water Resour. Res. 41. W06001. Doi:10.1029/2004WR003725.

Hopp L, McDonnell JJ. 2009. Connectivity at the hillslope scale: Identifying interactions between storm size, bedrock permeability, slope angle and soil depth. Journal of Hydrology 376(3-4): 378-391.DOI: 10.1016/j.jhydrol.2009.07.047

McNamara P, Chandler D, Seyfried M, Achet S. 2005. Soil moisture states, lateral flow, and streamflow generation in a semi-arid, snowmelt-driven catchmen. Hydrol. Process. 19: 4023– 4038.

Montgomery DR, Dietrich WE, Heffner JT. 2002. Piezometric response in shallow bedrock at CB1: Implications for runoff generation and landsliding. Water Resour. Res. 38(12): 1274. Doi:10.1029/2002WR001429.

Pierson TC. 1977. Factors controlling debris-flow initiation on forested hillslopes in the Oregon Coast Range, Ph.D. dissertation, 166 pp., Univ. of Wash., Seattle.

Rinaldo A, Marani A, Rigon R. 1991. Geomorphological dispersion. Water Resour. Res 27(4): 513–525.

Rinaldo A, Vogel GK, Rigon R, Rodriguez-Iturbe I. 1995. Can one gauge the shape of a basin?. Water Resour. Res. 31(4):1119–1127.

Russo D, Fiori A. 2008. Equivalent Vadose Zone Steady-State Flow: An Assessment its Capability to Predict Transport in a Realistic Combined Vadose Zone - Groundwater Flow System. Water Resour. Res. 44. W09436. Doi:10.1029/ 2007WR006170.

Seibert J, Bishop KH, Nyberg L. 1997. A test of TOPMODEL's ability to predict spatially distributed groundwater levels. Hydrological Processes 11: 1131–1144.

Seibert J, McGlynn BL. 2007. A new triangular multiple flow-direction algorithm for computing upslope areas from gridded digital elevation models. Water Resour. Res. 43. W04501, Doi:10.1029/2006WR005128. 

Schmidt F, Persson A. 2003. Comparison of DEM Data Capture and Topographic Wetness Indices. Precision Agricolture 4: 179-192.

Soulsby C, Tetzlaff D, Hrachowitz M. 2009. Tracers and transit times: Windows for viewing catchment scale storage?. Hydrological Processes 23: 3503-3507.

Tetzlaff D, Soulsby C, Bacon PJ, Youngson AF, Gibbins CN, Malcolm IA. 2007. Connectivity between landscapes and riverscapes—A unifying theme in integrating hydrology and ecology in catchment science?. Hydrological Processes 21: 1385–1389

Tetzlaff D, Seibert J, McGuire KJ, Laudon H, Burns DA, Dunn SM, Soulsby C. 2009a. How does landscape structure influence catchment transit times across different geomorphic provinces?. Hydrological Processes 23: 945 – 953.

Tromp-van Meerveld HJ, McDonnell JJ. 2006. Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis. Water Resour. Res. 42(2). DOI: 10.1029/2004WR003800.

Western AW, Grayson RB, Blöschl G, Willgoose GR, McMahon TA. 1999. Observed spatial organization of soil moisture and its relation to terrain indices. Water Resour. Res. 35 (3). DOI: 10.1029/1998WR900065.

Western AW, Zhou SL, Grayson RB, McMahon TA, Bloschl G, Wilson DJ, 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology 286 (1-4): 113-134. Doi: 10.1016/j.jhydrol.2003.09.014.

Wilson CJ, Dietrich WE. 1987. The contribution of bedrock groundwater flow to storm runoff and high pore pressure development in hollows, in Erosion and Sedimentation in the Pacific Rim, IAHS Publ., vol. 165, edited by R. L. Beschta et al.: 49–60, Int. Assoc. of Hydrol Sci., Wallingford, UK