Paolo Burlando

Scaling and muitiscaling models of depth-duration-frequency curves for storm precipitation

Abstract The scaling properties of temporal rainfall are shown to dictate the form of the depth-duration-frequency (DDF) curves of station precipitation, which are widely used in hydrological practice to predict design storms. Scale invariance of extreme storm probabilities is investigated, and the conservative (simple scaling) as well the dissipative (multiple scaling) nature of storm rainfall are considered, thus introducing a general distribution-free framework to derive DDF curves. A log-normal model is also introduced to represent either simple or multiple scaling DDF curves from extreme value storm data. This model allows for a parsimonious and efficient parametrisation of DDF curves, and its performance is shown to improve the accuracy and robustness of design storm predictions as compared with those achievable by interpolating the quantile predictions of extreme rainfall data for specified durations.

An enhanced temperature-index glacier melt model including the shortwave radiation balance : development and testing for Haut Glacier d'Arolla, Switzerland

An enhanced temperature-index glacier melt model, incorporating incoming shortwave radiation and albedo, is presented. The model is an attempt to combine the high temporal resolution and accuracy of physically based melt models with the lower data requirements and computational simplicity of empirical melt models, represented by the ‘degree-day’ method and its variants. The model is run with both measured and modelled radiation data, to test its applicability to glaciers with differing data availability. Five automatic weather stations were established on Haut Glacier d’Arolla, Switzerland, between May and September 2001. Reference surface melt rates were calculated using a physically based energy-balance melt model. The performance of the enhanced temperature-index model was tested at each of the four validation stations by comparing predicted hourly melt rates with reference melt rates. Predictions made with three other temperature-index models were evaluated in the same way for comparison. The enhanced temperature-index model offers significant improvements over the other temperature-index models, and accounts for 90–95% of the variation in the reference melt rate. The improvement is lower, but still significant, when the model is forced by modelled shortwave radiation data, thus offering a better alternative to existing models that require only temperature data input.

Forecasting of short-term rainfall using ARMA models

Abstract Flood forecasting depends essentially on forecasting of rainfall or snow melt. In this paper, rainfall forecasting is approached assuming that hourly rainfall follows an autoregressive moving average (ARMA) process. This assumption is based on the fact that the autocovariance structure of some point processes, such as hourly rainfall processes, is equivalent to the autocovariance structure of certain low-order ARMA processes. Two estimation and fitting procedures are investigated. The first takes all rainfall occurrences throughout the period of record as the basis for parameter estimation, and the second is an event-based adaptive procedure. These procedures are compared for rainfall data at a point and rainfall data averaged over a basin. Hourly rainfall from two gaging stations in Colorado, USA, and from several stations in Central Italy are used. Results show that the event-based estimation approach yields better forecasts.

A stochastic model for high‐resolution space‐time precipitation simulation

High-resolution space-time stochastic models for precipitation are crucial for hydrological applications related to flood risk and water resources management. In this study, we present a new stochastic space-time model, STREAP, which is capable of reproducing essential features of the statistical structure of precipitation in space and time for a wide range of scales, and at the same time can be used for continuous simulation. The model is based on a three-stage hierarchical structure that mimics the precipitation formation process. The stages describe the storm arrival process, the temporal evolution of areal mean precipitation intensity and wet area, and the evolution in time of the two-dimensional storm structure. Each stage of the model is based on appropriate stochastic modeling techniques spanning from point processes, multivariate stochastic simulation and random fields. Details of the calibration and simulation procedures in each stage are provided so that they can be easily reproduced. STREAP is applied to a case study in Switzerland using 7 years of high-resolution (2 × 2 km2; 5 min) data from weather radars. The model is also compared with a popular parsimonious space-time stochastic model based on point processes (space-time Neyman-Scott) which it outperforms mainly because of a better description of spatial precipitation. The model validation and comparison is based on an extensive evaluation of both areal and point scale statistics at hydrologically relevant temporal scales, focusing mainly on the reproduction of the probability distributions of rainfall intensities, correlation structure, and the reproduction of intermittency and wet spell duration statistics. The results shows that a more accurate description of the space-time structure of precipitation fields in stochastic models such as STREAP does indeed lead to a better performance for properties and at scales which are not used in model calibration.

Transmission of solar radiation through clouds on melting glaciers: a comparison of parameterizations and their impact on melt modelling

We explore the robustness and transferability of parameterizations of cloud radiative forcing used in glacier melt models at two sites in the Swiss Alps. We also look at the rationale behind some of the most commonly used approaches, and explore the relationship between cloud transmittance and several standard meteorological variables. The 2 m air-temperature diurnal range is the best predictor of variations in cloud transmittance. However, linear and exponential parameterizations can only explain 30-50% of the observed variance in computed cloud transmittance factors. We examine the impact of modelled cloud transmittance factors on both solar radiation and ablation rates computed with an enhanced temperature-index model. The melt model performance decreases when modelled radiation is used, the reduction being due to an underestimation of incoming solar radiation on clear-sky days. The model works well under overcast conditions. We also seek alternatives to the use of in situ ground data. However, outputs from an atmospheric model (2.2 km horizontal resolution) do not seem to provide an alternative to the parameterizations of cloud radiative forcing based on observations of air temperature at glacier automatic weather stations. Conversely, the correct definition of overcast conditions is important.

Modelling river and riparian vegetation interactions and related importance for sustainable ecosystem management

Abstract.We discuss the importance of modelling riparian vegetation and river flow interactions under differing hydrologic regimes. Modelling tools have notable implications with regard to the understanding of riverine ecosystem functioning and to promote sustainable management of water resources. We present both deterministic and stochastic approaches with different levels of simplification, and discuss their use in relation to river and vegetation dynamics at the related scale of interest. We apply such models to both meandering and braided rivers, in particular focusing on the floodplain dynamics of an alpine braided river affected by water impoundment. For this specific case we show what the expected changes in riparian vegetation may be in a ‘controlled release’ scenario for the postdam river Maggia, Switzerland. Finally, the use of these models is discussed in the context of current research efforts devoted to river restoration practice.

An observation‐based stochastic model for sediment and vegetation dynamics in the floodplain of an Alpine braided river

Riparian vegetation dynamics in Alpine rivers are to a large extent driven by the timing and magnitude of floods which inundate the floodplain, transport sediment, erode the river bed, and create and destroy suitable germination sites. Here we present a stochastic approach for studying sediment-vegetation dynamics lumped at the floodplain scale and driven by stochastic flood disturbances. The premise of the model is that floods erode riparian vegetation in the inundated part of the floodplain and expose bare sediment surfaces. In the absence of subsequent flooding these surfaces are gradually recolonized. The stochastic nature of the disturbance process and the deterministic rate of vegetation colonization are described by a Poisson arrival of floods and a process equation which treats the floodplain erosion and vegetation colonization processes, respectively. An analytical solution is developed to obtain the probability density function of the exposed sediment area. The model is applied to the Maggia River in Switzerland, where it reproduces the changes in riparian vegetation cover observed from aerial photographs with an absolute error less than 5%. The model has potential as a tool to study the impacts of changes in the disturbance regime on sediment and vegetation dynamics Copyright 2009 by the American Geophysical Union.

Integrated catchment assessment of riverine landscape dynamics

Abstract. The traditional approach to study riverine environments focuses on the river reach scale, with streamflow as a steady state driving force. Here, the accent is on the dynamic nature of streamflow. Impacts of the hydrological regime, of floods and streamflow variability, on riverine landscapes are reviewed. To evaluate such impacts, it is necessary to focus on the entire catchment in an integrated fashion, so that local changes in river morphology and river habitat can be evaluated in context with upstream catchment processes. A framework for an integrated physically-based catchment modelling system, based on models of hydrology, hydrodynamics, sedimentology and ecology, is presented. The hydrological element addresses runoff response in a catchment on a continuous basis in time and distributed in space, while the hydrodynamic, sedimentological and ecological elements address the interactions and feedbacks between water, sediment and the ecosystems at the scale of the river corridor. The models are arranged in a nested fashion, with long-term quantification of catchment and river system dynamics as the main objective. A long-term vision of catchment processes is important for the evaluation of potential anthropogenic influences and climate change effects, as well as for the evaluation of river conservation projects.

The role of local‐scale heterogeneities in terrestrial ecosystem modeling

The coarse-grained spatial representation of many terrestrial ecosystem models hampers the importance of local-scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund-Potsdam-Jena (LPJ), a well-established, area-based, dynamic global vegetation model (DGVM); (ii) LPJ-General Ecosystem Simulator, a hybrid, individual-based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space-LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation-induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global-scale modeling studies of carbon cycle and we illustrate alternative ways forward.

Bias Correction of Regional Climate Model Simulations in a Region of Complex Orography

AbstractThis study presents a method to correct regional climate model (RCM) outputs using observations from automatic weather stations. The correction applies a nonlinear procedure, which recently appeared in the literature, to both precipitation and temperature on a monthly basis in a region of complex orography. To assess the temporal stability of such a correction, the correcting parameters of each variable are investigated using different time periods within the observational record. The RCM simulations used in this study to evaluate the bias-correction method are the publicly available “Reg-CM3” experiments from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project. They provide daily precipitation and temperature time series on a raster with spatial resolution of 0.22°. The analysis is performed in the Rhone catchment, located in southwestern Switzerland and characterized by highly complex orography. The results show that the nonlinear bias correction increases dra...