Possible changes of the brown trout habitat suitability

Possible changes of the brown trout habitat suitability in the upper Po River basin due to global change. A. Lombardi 1, M. Verdecchia 1, 2, B. Tomassetti 1, V. Colaiuda 1 , A. Di Sabatino 3 [1]{CETEMPS, Department of Physic and Chimestry, University of L’Aquila, Italy} [2]{Department of Physic and Chimestry, University of L’Aquila, Italy} [3]{Department of Life, Health and Environmental Sciences, University of L’Aquila, Italy}

Overview Climatic Model Grid Scale RCM General Circulation Model (GCM) 100 -200 km CHy. M Regional Climatic Model (RCM) 50 -25 km Downscaling GCM Bio-Climatic Model Hydrological Model < 1 km

RCM: Reg. CM Simulation ü Reg. CM has been driven by the general circulation model ECHAM 5 (Roeckner et al. , 1996) ü Reg. CM has been forced with A 1 B (Nakicenovic et al. , 2000) scenario simulation, a Medium. High scenario ü It simulated a period of 140 years, from 1960 to 2100. ü The simulation covered Europe domain at 25 km grid spacing. ü The simulation had a time resolution of 3 hours

CHy. M – CETEMPS Hydrological Model q q q q Distributed grid-based deterministic hydrological model; It includes an explicit parameterization of different physical processes contributing to hydrological cycle, for each cell of the selected domain, contribute of: o Runoff o Melting Soil moisture o Evapotranspiration o Interception storage o Infiltration o Rainfall o Return flow are explicitly calculated. It rebuilds precipitation field and drainage network using cellular automata technique. It runs in any geographical domain with any resolution up to DEM resolution, drainage network is extracted by a native algorithm implemented in CHy. M code; Different sets of precipitation data can be assimilated and merged in a hierarchical way at each hourly time step; It runs in any Unix platforms; It reads, in the current implementation, precipitation and temperature fields from: o o Reg. CM model output, MM 5 model output, WRF model output, ERA-interim reanalysis

CHy. M Applications Tacina River Basin 25 Sept 2009 00: 00 – 12: 00 AM Prediction Effects and of Cmonitoring limate Change on hydrological cycle

Climatic Change on upper Po basin According to many climate change projections produced with global and regional climate models, the Italian peninsula will experience pronounced changes in temperature and precipitation (Coppola and Giorgi, 2010 and Giorgi, 2006). The hydro-climatic simulations show • a lengthening of the dry season, which would increase the water stress for the area. • the largest signal is in winter, when the discharge increases everywhere in the basin, and particularly in high elevation areas, as a result of increased magnitude and a liquid fraction of precipitation (Coppola and Giorgi, 2010). The Po River water resources are indeed vulnerable to future climate changes, which should be taken into account in the development of suitable adaptation options in terms of water management for the basin (Coppola et al. , 2014).

From GCM to Bio-Climatic Model Could expected changes in hydrometeorological condition on the Po basin affect a specific specie? GCM RCM CHy. M The first aim of this preliminary study is represented by development of detailed numerical approach to estimate how the fitness of our target specie is linked to hydroclimatic condition. Bio-Climatic Model

Bio-Climatic Model The target specie used is adult brown trout. Brown trout is among the world’s top invasive species, displacing other fish species through both competition and predation (Lowe et al. , 2000; Mc. Intosh et al. , 2011) Its presence is based on three parameters (Jorde, 1996; . Capra et al, 1995; Heggenes et al, 1996 a, b; Peviani et al. , 1996; Bartsch et al. , 1996; Boudreau et al. , 1996; Bovee, 1986): • Water Velocity • Water depth • Water temperature. The Bio-Climatic Model uses hydrological model outputs and USGS HSC (Habitat Suitability Criteria) curve interpolation to associate to each grid point of the domain a value beetween 0 and 1.

How are these parameters calculated? • • •

Water Temperature • Hourly long time series of Air temperature (10 years X 10 sensor stations). • Daily long time series of water temperature (almost 3 years X 10 sensor stations). Hourly obserbed air temperature Daily observed water temperature Daily estimated water temperature

Overall Suitability Index

salmo trutta, Linnaeus, 1758 salmo [trutta] trutta (Zerunian, 2004) Areal distribution in Italy salmo [trutta] trutta(Ruffo and Stoch, 2006) Presence signs in Italy

salmo trutta, Linnaeus, 1758 in upper river Po basin salmo [trutta] trutta(Ruffo and Stoch, 2006)

Overall Suitability Index Trend • Brown trout is among the world’s top invasive species, displacing other fish species through both competition and predation (Lowe et al. , 2000; Mc. Intosh et al. , 2011). • The sexually mature fishes move to upstream of rivers and smaller tributaries. • It is both migratory and territorial. Usually fry, after only one year of life, moves down to the valley, where, as adults, it prefers to remain, seeking out those areas with higher food availability, directly connected to a sufficient flow discharge, a mild water temperature and minimum flow velocity that guarantee good oxygenation of the water and less pollution. • Renata E Hari, David M Livingstone, Rosi Siber, PATRICIA BURKHARDT-HOLM, and Herbert Guettinger. Consequences of climatic change for water temperature and brown trout populations in alpine rivers and streams. Global Change Biology, 12(1): 10– 26, 2006. • William J Matthews and Earl G Zimmerman. Potential effects of global warming on native fishes of the southern great plains and the southwest. Fisheries, 15(6): 26– 32, 1990.

Conclusion Climatic simulation models predict an increase in temperature and extreme events occurrence. These changes are expected to lead a sensible modification of the hydrological cycle with significant impacts on the ecological integrity of aquatic ecosystems. Changes in temperature regime and instream habitat/microhabitat characteristics will also affect the natural distribution of many aquatic species. To this aim we carried out a simulation based on a chain of models to predict the distribution of the brown trout in the upper Po River basin (North Italy). A 140 -years long simulation, carried out with a Regional climate model, is used to force a hydrological model simulating the hydrological cycle. The results of hydrological simulation, in particular variations in temperature and discharge regimes, are then used to evidence the areas where the target species is expected to occur. The results show the complex proposed approach can reproduce, with a good confidence, the habitat suitability of the brown trout. The projection for future years indicates a shift of the distribution toward locations of the upper part of the basin, with a sensible decrease of the areas where the brown trout can survive, reproduce and grow. It appears strategic to predict the effects of global change on freshwater biodiversity and species distribution in order to propose adequate measures aimed at mitigating the impacts of climate modification on natural systems. The work also focuses on the potential application of the proposed approach to evaluate the effects of climate changes on more complex ecological systems.

CHy. M (Cetemps Hydrological Model) – Parametrization of physical processes contributing to hydrological cycle. Based on the kinematic wave approximation (Lighthill and Whitam, 1955) of the shallow water wave the equations used by CHYM model to simulate the surface routing overland for channel flow are the continuity and momentum conservation equations: S is longitudinal bed slope of the flow element , n the Manning’s roughness coefficient while R is the hydraulic radius that can be written as a linear function of the drained area DA as: