Climate scale modelling of suspended sediment load in an Alpine catchment debris flow (Rio Cordon northeastern Italy)

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(1)Geomorphology 309 (2018) 20–28. Contents lists available at ScienceDirect. Geomorphology journal homepage: www.elsevier.com/locate/geomorph. Climate-scale modelling of suspended sediment load in an Alpine catchment debris flow (Rio Cordon-northeastern Italy) Nazzareno Diodato a, Luca Mao b, Pasquale Borrelli c, Panos Panagos d, Francesco Fiorillo e, Gianni Bellocchi a,f,⁎ a. Met European Research Observatory, 82100 Benevento, Italy Departamento de Ecosistemas y Medio Ambiente, Pontificia Universidad Católica de Chile, 6904411 Santiago, Chile Department of Environmental Sciences, University of Basel, Switzerland d European Commission, Joint Research Centre (JRC), Ispra, Italy e Department of Science and Technology, University of Sannio, 82100 Benevento, Italy f UCA, INRA, VetAgro Sup, UMR Ecosystème Prairial, 63000 Clermont-Ferrand, France b c. a r t i c l e. i n f o. Article history: Received 18 November 2017 Received in revised form 23 February 2018 Accepted 23 February 2018 Available online 27 February 2018 Keywords: Hydrological model Rainstorm Seasonality Sediment delivery ratio. a b s t r a c t Pulsing storms and prolonged rainfall can drive hydrological damaging events in mountain regions with soil erosion and debris flow in river catchments. The paper presents a parsimonious model for estimating climate forcing on sediment loads in an Alpine catchment (Rio Cordon, northeastern Italian Alps). Hydroclimatic forcing was interpreted by the novel CliSMSSL (Climate-Scale Modelling of Suspended Sediment Load) model to estimate annual sediment loads. We used annual data on suspended-solid loads monitored at an experimental station from 1987 to 2001 and on monthly precipitation data. The quality of sediment load data was critically examined, and one outlying year was identified and removed from further analyses. This outlier revealed that our model underestimates exceptionally high sediment loads in years characterized by a severe flood event. For all other years, the CliSMSSL performed well, with a determination coefficient (R2) equal to 0.67 and a mean absolute error (MAE) of 129 Mg y−1. The calibrated model for the period 1986–2010 was used to reconstruct sediment loads in the river catchment for historical times when detailed precipitation records are not available. For the period 1810–2010, the model results indicate that the past centuries have been characterized by large interannual to interdecadal fluctuations in the conditions affecting sediment loads. This paper argues that climate-induced erosion processes in Alpine areas and their impact on environment should be given more attention in discussions about climate-driven strategies. Future work should focus on delineating the extents of these findings (e.g., at other catchments of the European Alpine belt) as well as investigating the dynamics for the formation of sediment loads. © 2018 Elsevier B.V. All rights reserved.. 1. Introduction Mountain landscapes are highly variable and associated with climatic variability and surface processes, which operate over different spatial and temporal scales (Shroder and Bishop, 2004; Slack and Bell, 2006; Goonan et al., 2010; Diodato et al., 2015; Emberson et al., 2016). As such, they are a good indicator of the state of planet Earth with regard to natural climate variability (Pepin and Lundquist, 2008) and help to depict how climate variability can drive instability in the processes associated with hydroclimatic hazards (Wolock and McCabe, 1999; Pauli et al., 2001; Diodato, 2006; Fort, 2015). The Alps, one of the largest mountain range systems of Europe, are among the most vulnerable regions to hydroclimatic hazards such as flash floods, slides, overland flow, mass and debris flows (D'Agostino and Marchi, 2001; Petrucci ⁎ Corresponding author at: Met European Research Observatory, 82100 Benevento, Italy. E-mail address: gianni.bellocchi@inra.fr (G. Bellocchi).. https://doi.org/10.1016/j.geomorph.2018.02.026 0169-555X/© 2018 Elsevier B.V. All rights reserved.. and Polemio, 2003; Stoffel et al., 2011; Glur et al., 2013). This is mainly owing to the combination of high reliefs, heavy rain/snow events, severe erosion rates, and the nearly unlimited sediment supply from sources such as Quaternary moraine, scree deposits, debris fans, and landslides (Hinderer et al., 2013). Sediment yield from the Alps spans a wide range of values, depending especially on the percentage of glacial cover of the basin and also on the trapping effects of human infrastructures such as reservoirs (Hinderer et al., 2013). Also, past and current climatic conditions determine sediment yield (Syvitski et al., 2005; Bennett et al., 2013), especially in glacierized environments (Micheletti and Lane, 2016). Sediment yield from mountain basins determine the morphology, dynamics, and ecological diversity of rivers but is not usually assessed by direct sediment sampling in rivers. Indeed, sediment yield from Alpine basins is often estimated by quantifying the sedimentation in reservoirs overtime, as more detailed and short-term measurements of suspended sediment and bedload transport in mountain rivers are notoriously expensive and challenging to perform. In fact, only a few long-lasting sediment monitoring programmes are located in.

(2) N. Diodato et al. / Geomorphology 309 (2018) 20–28. the Alps, that is, at the Erlenbach in Switzerland (Rickenmann et al., 2012), the Draix in France (Mathys et al., 2003), and the Cordon in Italy (Rainato et al., 2017). Mathematical models may simulate the combined effects of hydroclimatic forcing, such as the estimation of sediment budgets using—in the absence of distributed spatial and temporal data—the drainage catchment as a homogeneous landscape unit in which sediment fluxes and land surface changes can be calculated (Chiverrell et al., 2009). ‘When modelling involves both experience and intuition, it becomes an art to depict the interaction of environmental processes over extensive space and time, although modelling is not an alternative to observations’ (Mulligan and Wainwright 2004, p. 8). Mathematical approaches are useful in reconstructing historical suspended sediment load data in order to have a better understanding of land-atmosphere interactions over historical times (Harvey, 2001). They can also lead to an understanding of land surface sensitivity reflecting the magnitude and frequency of multiple storm events (Arnell, 2011), nested within patterns of longer-term climatic variability on different time scales and operating on different subcatchments (Wolman and Gerson, 1978; Thomas, 2001; Macklin and Lewin, 2008; Murray et al., 2009; Phillips, 2009). Land surface erosion and slope instability are scale-dependent and result from the complex interactions of climate, vegetation geology, geomorphic processes, and human activities (Werrity and Leys, 2001). These scale dependencies imply that landforms are nested-form resulting from the interactions of various processes in a hierarchical manner across a range of spatial and temporal scales (Rasemann et al., 2004; Murray et al., 2009). Over subannual time scales, the amount and distribution of rainfall events determine the degree of chemical and physical weathering of slope materials, the depth of weathered soils, and the vegetation type and cover across a landscape (Howard, 1994; Montgomery and Bolton, 2003). In turn, vegetation cover controls catchment slope through the susceptibility of soil erosion (Borrelli et al., 2016) and the resulting catchment morphological attributes and its sedimentary system dynamics. These natural processes are important factors in determining landscape evolution. The most important semiquantitative models developed for assessing erosion and suspended sediment load at the catchment scale. 21. use environmental factors to characterize drainage catchments in terms of sensitivity to erosion and sediment transport (Borrelli et al., 2014). The limited amount of data requirements and the fact that major erosion processes are considered to some extent make them suited for estimating on-site and off-site effects on soil erosion (de Vente and Poesen, 2005). Despite an increasing recognition of the relevant role that soil erosion plays in the carbon cycle of landscapes (Kuhn et al., 2009; Quinton et al., 2010; Lugato et al., 2016), to the best of the authors' knowledge no efforts have yet been devoted at discovering how climate variability affects soil erosion along multidecadal and century time scales. Indeed, water and especially sediment discharge data are hardly available over such long time scales for most rivers (Walling, 1999). Besides the lack of direct data, the main challenge is to separate the effects of climate change, human activity (e.g., land use changes), and the high natural variability of river basins and to consider the nonstationary sediment records (Walling and Fang, 2003). This also has implications for modelling sediment formation and transport to better understand the changes in past and contemporary suspended sediment loads transported by rivers before predicting future evolution. The aim of this study is to develop a hydrological model to reconstruct multidecadal variability of suspended sediment load data at catchment scale with relatively low data availability. This novel model, called CliSMSSL (Climate-Scale Model of Suspended Sediment Load), accounts for the effect of multiple sediment sources while considering system complexity and process interactions. CliSMSSL was applied to study the effect of time and space variability of rainstorms on suspended sediment transport in the Rio Cordon (northeastern Italian Alps). The relationships between catchment properties, dominant erosion processes, and sediment load were shown by combining spatial and temporal factors. 2. Environmental setting and modelling 2.1. Study area Research was conducted in an area among the most active in terms of fluvial sediment load (white box in Fig. 1A). The Rio Cordon is a. Fig. 1. (A) World transport suspended sediment in river flow (from Kabat et al., 2004). (B, C) Environmental setting of the study area (Rio Cordon catchment) in northeastern Italy (from Rainato et al., 2017)..

(3) 22. N. Diodato et al. / Geomorphology 309 (2018) 20–28. catchment (5 km2) located in the Dolomites, a section of the eastern Italian Alps centred around 46°27′ N latitude and 12°06′ E longitude (Fig. 1B). The elevation ranges between 1763 and 2748 m asl. The average slope is 27°. Soils are generally thin and belong to three main families (Lenzi et al., 2003): (i) skeletal soils, occurring on steep slopes with discontinuous vegetation cover; (ii) organic soils, with more continuous and dense vegetation cover than the previous group (mostly located in the lower part of the basin, Fig. 1C); and (iii) brown earth soils. The climatic conditions are typical of an Alpine environment. The mean annual precipitation over the period 1986–2010 is 1211 mm, mainly occurring as snowfall from November to April. Runoff is usually dominated by snowmelt in May and June, but in summer and early autumn floods also represent an important contribution to the flow regime. Usually late autumn, winter, and early spring lack noticeable runoff events (Rainato et al., 2017).. river to estimate yearly suspended sediment loads (Y = 0 being the current year) by the following equation: CliSMSSLðY¼0Þ ¼ A CGE RRTI þ SDη ðY¼−1Þ þ B. where CliSMSSL(Y=0) is the annual suspended sediment load at Y = 0 (Mg y−1); CGE (catchment gross erosion) is the principal driver of catchment gross erosion; RRTI (rainfall-runoff transport index) is an indicator of sediment transport at the outlet of catchment, and SD (standard deviation) is a proxy of the intermonthly climatic variability; Y indicates the year. The scale parameter A, the shape parameter η, and the shift parameter B were calibrated to match the criteria of Eq. (4) so as to accommodate the power of rainfall and the transport capacity of runoff to the climatic context upon which the model works. The sum of October–November precipitation amounts (mm) for a 9-year moving window drives the gross erosion produced within the catchment (CGE) as follows:. 2.2. Data source Annual values of total suspended sediment loads in the Rio Cordon catchment are available for the last decades (Rainato et al., 2017), and for this study, the dataset of the period 1986–2010 was used (Lenzi et al., 2003). To input the model, we referred to the precipitation series of the HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region, http://www.zamg.ac.at/histalp) database. The HISTALP database consists of monthly homogenised historical weather data for the European Alps and their wider surroundings (Efthymiadis et al., 2006; Auer et al., 2007). We obtained monthly values of precipitation for the period 1801–2010, relative to the grid point (1° × 1°) location of Rio Cordon. This study takes advantage of this kind of distributed climatic data to inform on sediment transport processes at the basin scale. Sediment load and precipitation data are measured as suspended solids and rainfall respectively (Table 1). An experimental station for the continuous measurement of bedload and suspended sediment transport rates has been operating since 1986 (Lenzi et al., 2003; Rainato et al., 2017). Suspended sediment is measured by two turbidimeters: a Partech SDM-10 light absorption instrument installed in the outlet channel that has been in operation since the early years of station operation, and a light-scatter turbidimeter (type Hach SS6) installed in 1994 in the inlet flume. Flow water samples were automatically gathered using a Sigma pumping sampler installed at a fixed position in the inlet channel. The sampler is set to pick up flow samples automatically at fixed time intervals when a discharge threshold is exceeded. In addition, samples are manually collected during floods at selected verticals using a USDH 48-bottle sampler (Lenzi, 2000, 2001; Lenzi and Marchi, 2000). Annual budgets of the suspended solid transport for the period 1986–2010—divided into snowmelt, summer, and autumn seasons—have been calculated (Lenzi et al., 2003; Rainato et al., 2017). Over the study period, suspended load accounted for 76% of total load that occurred during two flood events: an extreme summer flash flood in September 1994 (27% of the 15-year total suspended load) and a snowmelt-induced event in May 2001 accompanied by a mud flow that fed the stream with sediments (Lenzi et al., 2003). In the absence of distributed rainfall data, the catchment was taken as a homogeneous unit, yet subcatchments and the watershed itself play different roles in determining the sediment load. 2.3. CliSMSSL model development CliSMSSL is developed as a suite of major hydrogeomorphic events that contribute to the sediment storage at subcatchment (as landslides, debris cones, and hillsope colluvium) and catchment scales (as alluvial fans and floodplains) on decadal and subdecadal time scales. The model input is a number of sediment-transporting events along the. ð1Þ. CGE ¼. X. P ðOct−Nov Þ. −8 Y¼0. ð2Þ. The factor dominating sediment transport dynamics is defined as the rainfall-runoff transport index (RRTI), which is maximum during snowmelt and late spring precipitation (monthly precipitation in mm for the current and prior two years during winter and spring), followed by flashfloods during summertime (maximum monthly rainfall in mm over July–October): X −2 RRTI ¼ maxP Jul−Oct Y¼0 P Jan−May Y¼0. ð3Þ. The inter-monthly standard deviation SDη ðY¼−1Þ is computed from 12 monthly rainfall totals at Y = −1. 2.4. Model evaluation For the period upon which a time series of annual suspended sediment load (SSL) data was available (1986–2010), a recursive procedure was performed to obtain the best fit of a regression equation y = a + b·x, where x = CliSMSSL estimates and y = actual SSL data, according to the following criteria: 8 < MAE ¼ min R2 ¼ max : jb−1j ¼ min. ð4Þ. The first condition is to minimize the distance between modelled and observed data, by minimizing the mean absolute error (0 ≤ MAE b ∞, Mg y−1; Willmott and Matsuura, 2005): MAE ¼. n X jOi −Ei j i¼1. n. ð5Þ. where O is the observed value, E is the estimated value, i is the ith O/E pair, and n is the number of O/E pairs. The second condition is to maximize the goodness-of-fit (0 ≤ R2 b 1) that is the variance explained by the model (also supported by an ANOVA test of the relationship between observed and predicted data). The third condition approximates the unit slope (b) of the straight line that would minimize the bias of the linear function estimates versus observations. Poor models have high MAE, low R2 and b far from 1. In addition, the Nash-Sutcliffe efficiency (−∞ b EF ≤ 1, optimum; Nash and Sutcliffe, 1970) value is reported. The Durbin-Watson test (Durbin and Watson, 1950, 1951) was also performed to test for autocorrelated residuals because strong temporal dependence may induce spurious correlations (Granger et al., 2001)..

(4) 1326 336 1587 229. 1322 515. 2008. 1134 130 898 184. 2006. 2007. 2009. 2010. N. Diodato et al. / Geomorphology 309 (2018) 20–28. All analyses were performed in a spreadsheet with the graphical support of STATGRAPHICS (Nau, 2005) and using a Wessa statistical routine (Wessa, 2009). To further evaluate the CliSMSSL, we compared its performance with three well-known approaches that also use, with a similar level of detail, precipitation characteristics as the main explanatory variable for sediment yield in the catchment, SSLFI, SSLEP, and SSLDI: – Fournier index-based model (Fournier, 1960): SSLFIðY¼0Þ ¼ aFI . 12 2 X p i. 2005. 1097 233. 2004. 841 59. 2003. 930 10 1358 551. – Effective precipitation-sediment model (Langbein and Schumm, 1958): SSLEPðY¼0Þ ¼ aEP EP þ bEP. ð7Þ. – Douglas-based index (Douglas, 1967): SSLDIðY¼0Þ ¼aP . 1:631 ð0:03937 P Þ2:3 1 þ 0:0007 ð0:03937 P Þ3:3. þ bP. ð8Þ. 3. Results and discussion. 1998. 1232 745 1057 110. 1997 1996. 1377 436 1118 111. 1995. 1147 87 1146 302. 1224 105. 1991 1990. 1242 252 1081 313 1257 897 Precipitation (mm) SSL (Mg y−1). 1402 792. 1986 Year. 1987. 1988. 1989. 1992. 1291 73. P is the precipitation total (mm) for the year Y = 0, p is the precipitation total (mm) for the month i, a (scale coefficient) and b (shift coefficient) are empirical parameters used for calibration to optimize prediction of suspended sediment load (SSL, Mg y−1). The subscripts FI, EP, and DI stands for Fourier index, effective precipitation, and Douglas index, respectively. In Eq. (7), the effective precipitation (EP) measured in mm is the difference between the total precipitation (P, mm) and the losses owing to actual evapotranspiration (AET, mm)—that is an estimate of the rainfall that actually contributes to runoff. In this study, AET annual data were estimated using the relation of Zhang et al. (2004), which is appropriate to assess the annual actual evapotranspiration of a catchment (Diodato et al., 2010). The appropriateness of the above models for comparative assessment of suspended sediment load of mountainous river systems is supported by adequate evidence (e.g., Gericke and Venohr, 2012; Diodato et al., 2012, 2015).. 1999. 1141 2470. 1198 1569. ð6Þ. 1994. 2001. þ bFI. 1191 175. 1675 642. P. 1993. 2000. 2002. i¼1. Table 1 Suspended sediment load (SSL) and precipitation at outlet of Rio Cordon catchment during 1986–2010.. 23. In this section, we first present the comparison between measured suspended sediment load (SSL) and CliSMSSL. Second, the concept and the assumptions of CliSMSSL are discussed along with its applicability beyond the study area. Third, we show the SSL reconstruction obtained with CliSMSSL for the period 1810–2010. 3.1. Model parameterization and evaluation Eq. (1) was parameterized with A = 0.000353, η = 3 and B = −838 Mg y−1, as obtained by applying the criteria of Eq. (4). According to the ANOVA analysis, a highly significant relationship (p~0.00) exists between observed and predicted SSL (Y = 126.627 + 0.697951·X). One data point (shaded in Fig. 2A) falls outside of the upper 95% confidence limit. This is likely owing to the occurrence of an exceptional flood in the middle of the study period (September 1994). This highmagnitude/low-recurrence flood featuring a peak water discharge of 10.4 m3 s−1 and a peak bedload transport rate of about 25 kg s−1 m−1, represented a geomorphic threshold for the Rio Cordon catchment, as it has altered the streambed geometry (Lenzi, 2001) and the sediment supply characteristics of the catchment for a few years afterward (Lenzi et al., 2004). The 1994 event changed the availability of sediment to be transported, such that the events occurred immediately after transport of more sediments than comparable floods that occurred.

(5) 24. N. Diodato et al. / Geomorphology 309 (2018) 20–28. Fig. 2. Performance of the CliSMSSL model (Eq. (1)) for the Rio Cordon catchment for the period 1986–2010. (A) Scatterplot of observed and predicted SSL (Mg y−1), with their respective 1:1 line shown with the 95% prediction limits for new observations given by the grey curves. The value falling outside the 95% limits is shaded grey; (B) Autocorrelation function of the residuals (observed minus predicted); and (C) Q-Q plot of SSL data, giving visual support for the performance of the model.. before. After 1994, the progressive self-consolidation of the step-pool geometry (Lenzi, 2001), along with reestablishment of sediment armoring and depletion of the material supplied by the 1994 source areas reestablished sediment supply conditions similar to pre-1994 flood (Rainato et al., 2017). There was another substantial event on 11 May 2001. In comparison to September 1994, the 2001 event was much smaller in terms of recurrence interval, magnitude, and volume of transported sediments. The event was not exceptional (Mao and Lenzi, 2007) and also the sediment transported during the event came mainly from a single debris flow event from a slope, and sediment availability conditions at the basin scale were not altered by the event (Rainato et al., 2017). Taking out the 1994 outlier, goodness-offit R2 and modelling efficiency EF, both equal to 0.67, and MAE equal to 129 Mg y−1 indicate that a linear relationship exists between observations and predictions, with little error. The MAE was also lower than the standard deviation of the residuals, equal to 171 Mg y−1. The Durbin-Watson (DW) statistic (2.05) indicated that the residuals were not autocorrelated (p = 0.53), as also displayed in Fig. 2B, in which aurocorrelations of residuals at different time lags are all within the 95% confidence limits. The quantile-quantile plot (Q-Q plot) also shows a satisfactory performance, since predicted SSL data deviate only for extreme values (Fig. 2C). This indicates that the model is biased in that part of the distribution for which SSL values are not quantitatively important. This is also reflected in the plot of the Studentized residuals vs. predicted values, in which the variability of the residuals changes as the values of the dependent variable change (data not shown). The comparison to alternative prediction equations—Eqs. (6), (7) and (8)—revealed that CliSMSSL performed better (Fig. 3) as the residuals. of each alternative model were larger and the explained variability considerably lower than with CliSMSSL (R2 ≤ 0.1). 3.2. Discussion of CliSMSSL In relatively small and high relief mountain catchments, hydrogeomorphological processes are strongly characterized by nonlinear interactions between climate forcing, land surface processes, and fluvial responses over different spatial and temporal scales (Wolman and Gerson, 1978; Huss et al., 2008). As a result, down-scaled climate models and geomorphological process models are difficult to apply to such environments with high spatial and temporal variability (e.g., Calanca et al., 2006). Strong antecedent effects are also not captured by many models (such as those used in this study for the comparative evaluation). For example, sediment transport dynamics is nonlinear, as it can be strongly influenced by increased sediment availability after major flood events and by complex dynamics of activation of sediment sources with different degrees of connectivity with the river network at the basin scale (Lenzi et al., 2004; Rainato et al., 2017). Also, in some years in the Alps, catchment-wide rains of long duration may produce large volumes of runoff even though the intensity may be mild but greater than infiltration capacity. Autumn rains of long (lasting 24 h or longer) are generally associated with large-scale weather phenomena (Frei and Schär, 1998). Our model, in applying a smaller than decadal moving window (Fig. 4), can account for such antecedent effects (e.g., Crozier, 1999; Diodato et al., 2013b). The rationale behind CliSMSSL (Eq. (1)) is that two main processes are relevant for yielding suspended sediments (Diodato et al., 2017): power of rainfall by erosive-storm events and transport capacity of. Fig. 3. Scatterplots between observed and modelled suspended sediment loads (SSL, Mg y−1). (A) Fournier index-based model (Eq. (6)), SSLFI ðYÞ ¼ 1:93 precipitation-sediment based model (Eq. 7), SSLEP(Y) = 0.05 ⋅ EP + 371. (C) Douglas-based index (Eq. 8), SSLFI ðYÞ ¼ −5:30. . 2:3. 1:631ð0:03937PÞ ð1þ0:0007ð0:03937PÞ3:3 Þ. . 12 2 X p i. i¼1. þ 644.. P. þ 111. (B) Effective.

(6) N. Diodato et al. / Geomorphology 309 (2018) 20–28. 25. Fig. 4. (A) Example of the spreadsheet application of the CliSMSSL model for a sample of years (1993–2001). The grouping and intersection of monthly matrices that detect rainstorm events based on their scale are hierarchically organised into three-year groups (blue box), quasidecadal time scales (orange box), and pulsing storms (red box) for each element of the spatiotemporal integration process. Intermonthly variability is also considered within standard deviation upon the precedent year (green box).. eroded material. A third component, computed from the intermonthly standard deviation, is a proxy of erosion risk factors based on a concept developed by Aronica and Ferro (1997). The first process is essentially reflected by autumn precipitation, while the second process is interpreted by the interaction of two terms: snowmelt and late spring precipitation (monthly precipitation in mm for the current and prior two years during winter and spring), followed by flashfloods during summertime (maximum monthly rainfall in mm over July–October). Autumn precipitation is the most erosive one in this region (Ballabio et al., 2017). The CGE factor operates in any subcatchment from raindrop splash erosive forces driven by autumn showers. At this scale rainfall cannot always convey the sediment to the outlet of catchment. The autumn storms (October–November) act to redistribute sediment across the drainage catchments (GCE⋅RRMI, Eq. (2)). Maintaining the concept of rain events sequence (mainly responsible for the erosion, transport, and storage of sediment across the catchment over quasidecadal time scales) the sum of the precipitation in October and November (Eq. (2)) is sufficient for explaining the sediment outputs. However, to better explain the annual cumulative SSL data, the monthly maximum rainfall over July–October was multiplied by the sum of the precedent 5-year precipitation during January–May (Eq. (3)). Although winter precipitation is not explicitly accounted for in the model, we capture the relatively large flows during the spring period. After Diodato et al. (2013b), snowmelt from precipitation falls into the drainage ditches in the lowlands and ultimately feeds the larger drainage system. Indeed, wet springs not only induce runoff but also provide favourable (typical of local-scale storms) conditions for summer floods. The last component of the model (B, Mg y−1) in Eq. (1) is a sink term representing the fraction of the gross erosion (GE, Mg y−1) stored in the catchment during the resedimentation process. Based on Diodato and Grauso (2009), term B derives from the sediment delivery ratio (SDR), also known as connectivity ratio (Walling and Zhang, 2004), which describes the extent to which eroded sediment is stored within the basin: B ¼ ð1−SDRÞ GE. ð9Þ. In this way, the amount of sediment reaching a stream over the amount of sediment eroded characterizes the efficiency of slopechannel transfer, depending on the transport capacity, in turn modified by the effects of slope shape and drainage pattern (e.g., Quinton et al., 2006; Cavalli et al., 2012). In Alpine catchments, however, given the occurrence of landslides and other processes, the long-term constant B cannot be easily calculated. The B value estimated for the 5-km2 catchment area of Rio Cordon (−838 Mg y−1 ) corresponds to ~168 Mg km−2 y−1. This estimate is intermediate between that reported for the Glonn River basin (−44 Mg km−2 y−1, Diodato et al., 2015) and the upper Lech River Basin (−235 Mg km−2 y−1, Diodato. et al., 2012), located in the German/Austrian Alps. This range of values likely depends on the altitudinal and slope gradients of mountain landscapes. The Glonn basin elevation ranges from 450 to 559 m asl, with moderate slope gradients, which make negligible the discharges typical for snowmelt in spring. The Lech is a typical Alpine catchment characterized by high altitude (on average, 1615 m asl) and slope (on average, 46%), with substantial snow flow regime (Meier, 2004). The Rio Cordon catchment originates at a higher altitude (2763 m asl) but its slope (on average, 27%) only locally exceeds 45° (Cavalli et al., 2008), and its annual precipitation (1211 mm) is lower than that of the two other catchments (N1500 mm). The estimated average gross erosion for each of the three basin is about 60 (Glonn), 456 (Lech), and 258 (Rio Cordon) Mg km−2 y−1, which give SDR values equal to 0.27, 0.48, 0.35 respectively. Overall, Lech and Rio Cordon have a similar environmental setting, with high altitudes and slopes, which finally indicate that high amounts of soil are mobilized by erosion (gross erosion) but only a relatively low fraction is retained in the catchment area. More particularly, in the Rio Cordon basin sediment transport is affected by a complex geology (Mao et al., 2009) and a relatively low hillslope-channel connectivity (Rainato et al., 2017). The estimation of B and the other parameters of CliSMSSL obtained in more steps make their confidence bounds not easily quantifiable (Diodato et al., 2013a). The model error (mismatch between the observed and the estimated values) is, however, an indication of total model uncertainty (e.g., Shrestha and Solomatine, 2008); and NashSutcliffe efficiency values of 0.6 can discriminate between unsatisfactory and satisfactory performances (e.g., Lim et al., 2006). The efficiency value obtained (EF = 0.67) indicates limited model uncertainty, likely associated with narrow parameter uncertainty.. 3.3. Suspended sediment load historical reconstruction The SSL was reconstructed over the time spanning 1810–2010. It includes three periods, from the cool nineteenth century to the most recent warming period extending over the twentieth and the early twenty-first centuries (Fig. 5A). The whole period is characterized by the emergence of similar patterns in interdecadal variability. They are reflected in return periods (Fig. 5B) that are, however, to be taken with caution considering that yearly SSL data may not be completely independent. Overall, the reconstructed losses of SSL from the catchment showed a stationary trend during the end of the Little Ice Age (LIA, 1800–1900; Fagan, 2001) and characterized by greater time variability than the twentieth century. In the late LIA, some years were estimated with sediment loads for which return periods approach 50 years. A second period is evident between about 1900 and 1990, in which SSL undergoes a marked and continuous decrease until reaching a phase with return period below 5 years. The final and shorter phase coincides with the.

(7) 26. N. Diodato et al. / Geomorphology 309 (2018) 20–28. Fig. 5. (A) Suspended-sediment load values from 1810 to 2010 for the Rio Cordon catchment. (B) The return periods (grey lines) from Gumbel-GEV distribution.. recent warming period. However, in this phase peaks occur again with return periods near to 50 years. The observed pattern suggests that the peaks reached during the nineteenth century were indeed characterized by more snow in winter-spring time and cooler and wetter summer (Luterbacher et al., 2001), which may have accelerated the slope denudation with sediment mass yield (Blass et al., 2007), and consequently increased SSL fluxes within the catchment. The SSL fluctuations in a narrow range are observed over the most part of the twentieth century (quiet period)—with more regular but with decreasing trend—typically the time when a transition to warm conditions is underway. More erratic fluctuations of SSL are noticed from the end of twentieth century until recent decades when the period is characterized by a pronounced change of extremely wet and dry conditions, with important remobilization of sediments.. 4. Concluding remarks This study demonstrates the importance of rainfall seasonality for the prediction of catchment sediment yields, based on the satisfactory performance of the newly developed CliSMSSL for the Rio Cordon catchment in northern Italy. This parsimonious hydroclimatological model was used for investigating dominant pathways and processes in an Alpine catchment (Rio Cordon) and for offering insights on the conveyance of suspended-sediment across the catchment. With limited precipitation inputs, the model was developed and tested successfully to estimate the annual sediment loads over 25 years (with one outlying year owing to the occurrence of a high-magnitude flood event) from monthly rainfall data. The model outperformed three well-known sediment load prediction equations that also use rainfall factors for prediction but do not account for antecedent precipitation effects on the soil surface. In this study, the attempt was to conceptualize the effect of precipitation in determining the soil wetness conditions, which in turn is important for the calculation of catchment sediment loads. This lays the foundation for applications in other river catchments and for the reconstruction and prediction of sediment loads in past and future scenarios based on monthly inputs. However, the present study also showed limitations. The lack of long-term sediment yield records has hampered a thorough evaluation of CliSMSSL. A more detailed pluviometric series can also ensure a higher reliability of CliSMSSL estimates. Moreover, for a wider applicability to more heterogeneous catchments (topography, climate, vegetation, and lithology) and different land cover conditions, model parameters should be recalibrated. Environments with a larger contribution of snowmelt toward runoff may also require modifications in the equation. Furthermore, in future applications we have to scrutinize the significance. and interaction of nonclimatological factors, important for the river catchment response to climate (in particular, the role of land use, land cover, topography, and lithology). The seasonality (e.g., windows of months) over which erosion processes dominate and are relevant to sediment load rates also remains critical. For the period 1810 to 2010, the model results indicated a high variability in sediment load, without an obvious trend. Sediment loads over this time period rather seem to be related to a complex interaction of climatic change as moving from the Little Ice Age to recent warming. The high interannual variability of sediment loads certainly demands for a better understanding of the mechanisms responsible for it and for the role of rainfall amount, frequency, intensity, and seasonality. The interannual variability observed in sediment load does not correspond to the interannual variability in rainfall totals and distribution, and we showed that the model is of limited use to capture extreme values occurring during severe floods. In spite of its limitation, the approach used in CliSMSSL based on monthly precipitation data may be useful for other erosion models that consider rainfall erosivity only, often represented by the Universal Soil Loss Equation's R factor. 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