Parametric uncertainty or hydrological changes?

The model calibration is the way of hydrologists for searching also a physical interpretation of complex interactions acting within a basin. Actually, it can be frequently noticed how model calibration performed on a given time-window may converge to a point in the parameter space that could be distant from another obtainable calibration of the model in the same basin but considering a different time window. Is that again parametric uncertainty or does the trajectory in the parametric space relate about to a slow hydrological basin change? This paper depicts a possible path for detecting changes’ signatures in a streamflow time series. In particular, the paper seeks to draw a way to discern the random variability over different time-windows of the calibrated model parameters set from that induced by the variation in time of some boundary conditions and external forcings. To this purpose, we will refer to a conceptual lumped model for simulating daily streamflow, the EHSM (EcoHydrological Streamflow Model), and to a hypothetical case study. The selected hydrological model requires a total of seven parameters, some of which can be easily related to land use, while others rely on climate variables. The calibration of the EHSM parameters with regard to different time-windows and the analysis of potential impacts of the anthropic variation in land use and/or climatic variability on the calibrated parameters set, will support our investigation

ModABa MODEL: ANNUAL FLOW DURATION CURVES ASSESSMENT IN EPHEMERAL BASINS

A representation of the streamflow regime for a river basin is required for a variety of hydrological analyses and engineering applications, from the water resource allocation and utilization to the environmental flow management. The flow duration curve (FDC)represents a comprehensive signature of temporal runoff variability often used to synthesize catchment rainfall-runoff responses. Several models aimed to the theoretical reconstruction of the FDC have been recently developed under different approaches, and a relevant scientific knowledge specific to this topic has been already acquired. In this work, a new model for the probabilistic characterization of the daily streamflows in perennial and ephemeral catchments is introduced. The ModABa model (MODel for Annual flow duration curves assessment in intermittent BAsins) can be thought as a wide mosaic whose tesserae are frameworks, models or conceptual schemes separately developed in different recent studies. Such tesserae are harmoniously placed and interconnected, concurring together towards a unique final aim that is the reproduction of the FDC of daily streamflows in a river basin. Two separated periods within the year are firstly identified: a non-zero period, typically characterized by significant streamflows, and a dry period, that, in the cases of ephemeral basins, is the period typically characterized by absence of streamflow. The proportion of time the river is dry, providing an estimation of the probability of zero flow occurring, is empirically estimated. Then, an analysis concerning the non-zero period is performed, considering the streamflow disaggregated into a slow subsuperficial component and a fast superficial component. A recent analytical model is adopted to derive the non zero FDC relative to the subsuperficial component; this last is considered to be generated by the soil water excess over the field capacity in the permeable portion of the basin. The non zero FDC relative to the fast streamflow component is directly derived from the precipitation duration curve through a simple filter model. The fast component of streamflow is considered to be formed by two contributions that are the entire amount of rainfall falling onto the impervious portion of the basin and the excess of rainfall over a fixed threshold, defining heavy rain events, falling onto the permeable portion. The two obtained FDCs are then overlapped, providing a unique non-zero FDC relative to the total streamflow. Finally, once the probability that the river is dry and the non zero FDC are known, the annual FDC of the daily total streamflow is derived applying the theory of total probability. The model is calibrated on a small catchment with ephemeral streamflows using a long period of daily precipitation, temperature and streamflow measurements, and it is successively validated in the same basin using two different time periods. The high model performances obtained in both the validation periods, demonstrate how the model, once calibrated,is able to accurately reproduce the empirical FDC starting from easily derivable parameters arising from a basic ecohydrological knowledge of the basin and commonly available climatic data such as daily precipitation and temperatures. In this sense, the model reveals itself as a valid tool for streamflow predictions in ungauged basins

Evaluating the performances of an ecohydrological model in semi-arid river basins

The EHSM (EcoHydrological Streamflow Model) is a conceptual lumped model aimed to daily streamflow simulation. The model, processing daily rainfall and reference evapotranspiration at the basin scale, reproduces surface and subsurface runoff, soil moisture dynamics and actual evapotranspiration fluxes. The key elements of this numerical model are the soil bucket, where rainfall, evapotranspiration and leakage drive soil moisture dynamics, and two linear reservoirs working in parallel with different characteristic response times. The surface reservoir, able to simulate the fast response of the basin, is fed by rain falling on impervious area and by runoff generated with excess of saturation mechanism while the deep reservoir, which simulates the slow response, is fed by instantaneous leakage pulses coming from the soil bucket. The model has seven parameters, which summarize soil, vegetation and hydrological catchment properties. Parameters can be assessed using simple basic ecohydrological knowledge or Monte Carlo simulations as well. The model has been here calibrated for three semi-arid river basins located in Sicily, Italy with area ranging from 10 up to 1780 Km2 with the aim of investigating how the spatial scale may influence model performances. At the same time, the link between knowledge driven parameters and the calibrated ones is explored, investigating the suitability of a lumped framework for the model as the basin size increases

Preliminary analysis of high-resolution precipitation in Friuli Venezia Giulia region, Italy

The northeastern area of Italy, and specifically of Friuli Venezia Giulia region (FVG), is characterized by the heaviest precipitation annual totals in the country. Effects of both prolonged and extreme precipitation can be particularly damaging in this area, causing debris flow, flash floods, avalanches. Due to the very short times of concentration and hydrological response of the mountain watersheds of the analyzed area, extreme and short events are of particular interest. The region has a dense ground-station network which is managed by the regional Civil Protection Agency, constituted by 2 main rain-gauges networks, based on CAE and Micros-SIAP technology, respectively; this last is co-managed by the OSMER-ARPA (OSservatorio MEteorologico Regionale-Agenzia Regionale per la Protezione dell’Ambiente) FVG. The networks count a total of about 200 rain-gauges; for some stations, data at 5-minute resolution are available since the 1996 (CAE network), whereas Micros-SIAP works continuously and at high resolution since the early 2000s. Over the last two decades, the temporal resolution of stations has been progressively increased up to 1-minute step. This work presents a comprehensive analysis of the available dataset at high temporal resolution (i.e. 30 min, 5 min and 1 min) to verify whether trends in very short rainfall duration are underway. The continuous time series of data recorded by a sample of rain-gauges by the two networks are first analyzed. A preliminary analysis aims at verifying the consistency of the dataset at the higher resolutions. Statistical trends are then assessed by comparing two methods, i.e., the classical Mann-Kendall and the quantile regression at different thresholds and durations. Differently than the traditional methods that require a subset of data (e.g., the rainfall annual maxima), the quantile regression method allows to detect changes in the tails of the rainfall distributions and to screen the whole rainfall time series

High-resolution rain analysis in FVG, Northeastern Italy

The Julian Alps, located in the region of Friuli Venezia Giulia (FVG, Northeastern Italy), record the heaviest precipitation annual totals in the country. Due to the complex orography and several other prone factors, effects of both prolonged and extreme precipitation can be particularly damaging in this area, causing debris flow, flash floods, avalanches. A proper planning of protection against natural hazards then requires the understanding of possible modification in rainfall characteristics. Since the mountain watersheds of the Alpine area are characterized by a very short time of concentration and hydrological response, extreme events are of particular interest, and rainfall analyses at sub-daily scale could not be appropriate. The region counts on a dense ground-station network which is managed by the regional Civil Protection Agency, constituted by 2 main rain-gauges networks, based on CAE and Micros-SIAP technology, respectively; this last is co-managed by the OSMER-ARPA (OSservatorio MEteorologico Regionale-Agenzia Regionale per la Protezione dell’Ambiente) FVG. The networks count a total of about 200 rain-gauges; for some stations, data at 5-minute resolution are available since the 1996 (CAE network), whereas Micros-SIAP works continuously and at high resolution since the early 2000s. Over the last two decades, the temporal resolution of stations has been progressively increased up to 1-minute step. In this work, we propose a comprehensive analysis of the available dataset at high temporal resolution (i.e. 30 min, 5 min and 1 min) in order to verify whether trends in very short rainfall duration are underway. At this aim, we first analyzed the continuous time series of data recorded by a sample of rain-gauges by the two networks. A preliminary analysis aims at verifying the consistency of the dataset at the higher resolutions. Statistical trends are then assessed by comparing two methods, i.e., the classical Mann-Kendall and the quantile regression at different thresholds and durations. The quantile regression method, which is increasingly used in hydrology, allows to detect changes in the tails of the rainfall distributions and to screen the whole rainfall time series, differently than the traditional methods that require a subset of data (e.g., the rainfall annual maxima)

Olive yield and future climate forcings

The rainfall reduction and the temperature increase forecasted for Mediterranean regions would likely increase the vegetation water stress and decrease productivity in rainfed agriculture. Olive trees, which have traditionally been grown under rainfed conditions, are one of the most characteristic tree crops from the Mediterranean not only for economical importance but also for minimizing erosion and desertification and for improving the carbon balance of these areas. In order to simulate how climatic change could alter soil moisture dynamics, biomass growth and fruit productivity, a water driven crop model is used in this study. The model quantitatively links olive yield to climate and soil moisture dynamics using an ecohydrological model, which simulates soil moisture, evapotranspiration and assimilation dynamics of olive orchards. The model is able to explicitly reproduce two different hydrological and climatic phases in Mediterranean areas: the well-watered conditions and the actual conditions, where the limitations induced by soil moisture availability are taken into account. Annual olive yield is obtained by integrating the carbon assimilation during the growing season, including the effects of vegetation water stress on biomass allocation. The numerical model, previously calibrated on an olive orchard located in Sicily (Italy) with a satisfactory reproduction of historical olive yield data, has been forced with future climate scenarios generated using a stochastic weather generator which allows for the downscaling of an ensemble of climate model outputs. The stochastic downscaling is carried out using simulations of some General Circulation Models adopted in the IPCC 4AR for future scenarios. In particular, 2010, 2050, 2090 and 2130 scenarios have been analyzed

A coupled stability and eco-hydrological model to predict shallow landslides

Knowledge of spatio-temporal dynamics of soil water content, groundwater and infiltration processes is of considerable importance for the understanding and prediction of landslides. Rainfall and consequent water infiltration affect slope stability in various ways, mainly acting on the pore pressure distribution whose increase causes a decrease of the shearing resistance of the soil. For such reasons rainfall and transient changes in the hydrological systems are considered the most common triggers of landslides. So far, the difficulty to monitor groundwater levels or soil moisture contents in unstable terrain have made modeling of landslide a complex issue. At the present, the availability of sophisticated hydrological and physically based models, able to simulate the main hydrological processes, has allowed the development of coupled hydrologicalstability models able to predict when and where a failure could occur. In this study, a slope-failure module, with capability to predict shallow landslides, implemented into an ecohydrological model, tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator with VEGetation Generator for Interactive Evolution), is presented. The model evaluates the stability dynamics in term of factor of safety consequent to the soil moisture dynamics, strictly depending on the textural soil characteristics and hillslope geometry. Failure criterion used to derive factor of safety equation accounts for the stabilizing effect of matric suction arising in unsaturated soils. The eco-hydrological framework allows also to take into account the effect of vegetation with its cohesive effect as well as its weight load. The Mameyes basin, located in the Luquillo Experimental Forest in Puerto Rico, has been selected for modeling based on the availability of soil, vegetation, topographical, meteorological and historic landslide data. A static analysis based on susceptibility mapping approach was also carried out on the same area at a larger spatial scale, providing the hot spot of landsliding area. Application of the model yields a temporal and spatial distribution of predicted rainfall-induced landslides. Moreover, stability dynamics have been assessed for different meteorological forcing and soil types, to better evaluate the influence of hydrological dynamics on slope stability

Using a physically-based model, tRIBS-Erosion, for investigating the effects of climate change in semi-arid headwater basins.

Soil erosion due to rainfall detachment and flow entrainment of soil particles is a physical process responsible for a continuous evolution of landscapes. The rate and spatial distribution of this phenomenon depend on several factors such as climate, hydrologic regime, geomorphic characteristics, and vegetation of a basin. Many studies have demonstrated that climate-erosion linkage in particular influences basin sediment yield and landscape morphology. Although soil erosion rates are expected to change in response to climate, these changes can be highly non-linear and thus require mechanistic understanding of underlying causes. In this study, an integrated geomorphic component of the physically-based, spatially distributed hydrological model, tRIBS, the TIN-based Real-time Integrated Basin Simulator, is used to analyze the sensitivity of semi-arid headwater basins to climate change. Downscaled outputs of global circulation models are used to inform a stochastic weather generator that produces an ensemble of climate scenarios for an area in the Southwest U.S. The ensemble is used as input to the integrated model that is applied to different headwater basins of the Walnut Gulch Experimental Watershed to understand basin response to climate change in terms of runoff and sediment yield. Through a model application to multiple catchments, a scaling relationship between specific sediment yield and drainage basin area is also addressed and probabilistic inferences on future changes in catchment runoff and yield are drawn. Geomorphological differences among catchments do not influence specific changes in runoff and sediment transport that are mostly determined by precipitation changes. Despite a large uncertainty dictated by climate change projections and stochastic variability, sediment transport is predicted to decrease despite a non-negligible possibility of larger runoff rates