Assessing spatially distributed infiltration capacity to evaluate storm runoff in forested catchments: Implications for hydrological connectivity

Miyata, Sumiko; Gomi, Takashi; Sidle, Roy C.; Hiraoka, M.; Onda, Yuichi; Yamamoto, Katsumi; Nonoda, Toshiro

doi:10.1016/j.scitotenv.2019.02.453

Cited by 31 publications

(19 citation statements)

References 71 publications

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“…The thematic maps (e.g., Figures 1-3) were prepared in ArcMap software of ESRI [47], a common tool in spatial analysis of hydrologic and environmental data widely used in many studies [42,44,[48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66]. The base information was compiled from various spatial databases, namely the maps published by the Brazilian Institute for Geography and Statistics (https://ww2.ibge.gov.br) on the 1: 100,000 scale, and the digital terrain model obtained from the ASTER GDEN V2 satellite image with a spatial resolution of 30 m.…”

Section: Thematic Mapsmentioning

confidence: 99%

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Pissarra

Valera

Costa

et al. 2019

Water

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Riparian vegetation represents a protective barrier between human activities installed in catchments and capable of generating and exporting large amounts of contaminants, and stream water that is expected to keep quality overtime. This study explored the combined effect of landscape composition and buffer strip width (L) on stream water quality. The landscape composition was assessed by the forest (F) to agriculture (A) ratio (F/A), and the water quality by an index (IWQ) expressed as a function of physico-chemical parameters. The combined effect (F/A × L) was quantified by a multiple regression model with an interaction term. The study was carried out in eight catchments of Uberaba River Basin Environmental Protection Area, located in the state of Minas Gerais, Brazil, and characterized by very different F/A and L values. The results related to improved water quality (larger IWQ values) with increasing values of F/A and L, which were not surprising given the abundant similar reports widespread in the scientific literature. But the effect of F/A × L on IWQ was enlightening. The interaction between F/A and L reduced the range of L values required to sustain IWQ at a fair level by some 40%, which is remarkable. The interaction was related to the spatial distribution of infiltration capacity within the studied catchments. The high F/A catchments should comprise a larger number of infiltration patches, allowing a dominance of subsurface flow widespread within the soil layer, a condition that improves the probability of soil water to cross and interact with a buffer strip before reaching the stream. Conversely, the low F/A catchments are prone to the generation of an overland flow network, because the absence of permanent vegetation substantially reduces the number of infiltration patches. The overland flow network channelizes runoff and conveys the surface water into specific confluence points within the stream, reducing or even hampering an interaction with a buffer strip. Notwithstanding the interaction, the calculated L ranges (45–175 m) are much larger than the maximum width imposed by the Brazilian Forest Code (30 m), a result that deserves reflection.

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Section: Thematic Mapsmentioning

confidence: 99%

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Pissarra

Valera

Costa

et al. 2019

Water

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“…Clearly, spatial and temporal scaling issues represent important challenges for hydrological modelling. New conceptualizations and approaches have been developed to assess the hydrologic dynamics in soils, runoff behaviour, streamflow response, and coupling atmospheric energy with water balances at various spatial and temporal scales (e.g., Mengelkamp et al 1999;Batelaan and De Smedt 2007;Sidle et al 2017;Miyata et al 2019). Recent developments in remote sensing and passive microwave sensors facilitate better assessment of changes in land cover, precipitation, surface temperatures, snow water equivalent, soil moisture, energy budgets, and demographic shifts, as well as near real-time land surface changes, thus improving our ability to better conceptualize how such changes affect hydrological processes (e.g., Schumann et al 2009;Quinton et al 2011;Wang et al 2012;Mohanty et al 2017;Singh 2018;Jiang and Wang 2019;Koci et al 2020).…”

Section: Advances In Hydrological Scaling and Process Conceptualizationmentioning

confidence: 99%

“…While most of the early applications were for surface processes (Hortonian and saturation overland flow), later developments using TAPES-C were used to model the temporal and spatial dynamics of shallow groundwater response in slope stability and sediment routing simulations (Wu and Sidle 1995;Dhakal and Sidle 2004). A later adaptation of this contour-based topographic approach (TOPOTUBE), which included spatial heterogeneity of infiltration capacity based on ground cover, simulated the distributed partitioning of Hortonian overland flow, saturation overland flow, and saturated soil matrix flow, providing reasonable predictions of storm runoff from a small forest catchment (Gomi et al 2013;Miyata et al 2019). Nevertheless, the main benefit of such contour-based models appears to be achieving more spatially and temporally explicit representation of internal catchment hydrological and associated material transport processes.…”

Section: Incorporating Hydrological Connectivity Into Modelsmentioning

confidence: 99%

“…The spatial variability of K s has been recognized and assessed across landscapes with respect to geomorphology and impacts of land management (e.g., Mohanty and Mousli 2000;Ziegler et al 2006Ziegler et al , 2007Bevington et al 2016). Variability in K s plays an important role in routing subsurface water through hillslopes and catchments and controlling where and to what extent infiltration-excess overland flow propagates during storms (Sidle et al 2007;Gomi et al 2008;Miyata et al 2019). While the dependency of K s measurements on scale is known (Pachepsky and Hill 2017), it is usually not applied in catchment models.…”

Section: Introductionmentioning

confidence: 99%

“…Other examples of nonlinear hydrologic responses include timing and spatial organization of preferential flow (Tsuboyama et al 1994;Sidle et al 2001), infiltration/runoff domains (Gomi et al 2008;Miyata et al 2019), discharge from zero-order basins (hollows) (Sidle et al 2000;Tsuboyama et al 2000), rapid subsurface flow generation (Scaife et al 2020), areas of saturation overland flow (Dunne and Black 1970;Tanaka et al 1988), exfiltration from fractured bedrock (Montgomery et al 1997;Kosugi et al 2006), and baseflow/groundwater storage relationships (Wittenberg 1999). These natural hydrologic nonlinearities are exacerbated by both extensive (e.g., agriculture, forest management, grazing) and intensive (e.g., roads, trails, building sites) management practices.…”

Section: Introductionmentioning

confidence: 99%

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Strategies for smarter catchment hydrology models: incorporating scaling and better process representation

Sidle

2021

Geosci. Lett.

Self Cite

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Hydrological models have proliferated in the past several decades prompting debates on the virtues and shortcomings of various modelling approaches. Rather than critiquing individual models or modelling approaches, the objective here is to address the critical issues of scaling and hydrological process representation in various types of models with suggestions for improving these attributes in a parsimonious manner that captures and explains their functionality as simply as possible. This discussion focuses mostly on conceptual and physical/process-based models where understanding the internal catchment processes and hydrologic pathways is important. Such hydrological models can be improved by using data from advanced remote sensing (both spatial and temporal) and derivatives, applications of machine learning, flexible structures, and informing models through nested catchment studies in which internal catchment processes are elucidated. Incorporating concepts of hydrological connectivity into flexible model structures is a promising approach for improving flow path representation. Also important is consideration of the scale dependency of hydrological parameters to avoid scale mismatch between measured and modelled parameters. Examples are presented from remote high-elevation regions where water sources and pathways differ from temperate and tropical environments where more attention has been focused. The challenge of incorporating spatially and temporally variable water inputs, hydrologically pathways, climate, and land use into hydrological models requires modellers to collaborate with catchment hydrologists to include important processes at relevant scales—i.e. develop smarter hydrological models.

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Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results

Follette

Hahm

Rempe

et al. 2022

Hydrological Processes

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Although shallow (<1.5 m) soil water storage has been extensively studied, the significance of deeper unsaturated zone water storage to flow generation is poorly documented. However, a limited but growing body of empirical work shows that the weathered bedrock vadose zone, not soil, stores the majority of plant available water in many seasonally dry and semi‐arid landscapes. Moreover, this storage dynamic mediates recharge to hillslope groundwater systems that generate stream discharge and support ecologically significant baseflows. Explicit representations of bedrock vadose zone processes are rarely incorporated into runoff models, due in part to a paucity of observations that can constrain simulations. Here, we develop a simple representation of the weathered bedrock vadose zone that is guided by in situ field observations. We incorporate this representation into a rainfall‐runoff model, and calibrate it on streamflow alone, on rock moisture (i.e., weathered bedrock vadose zone moisture) alone, and on both using the concept of Pareto optimality. We find that the model is capable of accurately simultaneously simulating dynamics in rock moisture and streamflow, in terms of Kling‐Gupta Efficiency, when using Pareto optimal parameter sets. Calibration on streamflow alone, however, is insufficient to accurately simulate rock moisture dynamics. We further find that the posterior distributions of some model parameters are sensitive to choice of calibration scenario. The posterior distribution of high‐performing model parameters resulting from the streamflow only calibration scenario include physically unrealistic values that are not yielded by the rock moisture only or Pareto calibration strategies. These results suggest that the accuracy of some model results can be increased and parameter uncertainty decreased via incorporation of rock moisture data in calibration, without sacrificing streamflow simulation quality. Emerging recognition of the global significance of weathered bedrock water storage in seasonally dry and semi‐arid regions motivates more observations of weathered bedrock moisture and integration of this variable into earth system models.

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Assessing spatially distributed infiltration capacity to evaluate storm runoff in forested catchments: Implications for hydrological connectivity

Cited by 31 publications

References 71 publications

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

A Regression Model of Stream Water Quality Based on Interactions between Landscape Composition and Riparian Buffer Width in Small Catchments

Strategies for smarter catchment hydrology models: incorporating scaling and better process representation

Multicriteria analysis on rock moisture and streamflow in a rainfall‐runoff model improves accuracy of model results

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