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GOWARE (transnational Guide toward an Optimal WAter REgime) represents a Decision Support Tool (DST) developed to support the implementation of innovative Best Management Practices (BMPs) for drinking water protection and flood/drought risk mitigation. The tool is one of the main outputs of the PROLINE-CE Project, an EU project funded within the Interreg Central Europe (CE) Programme (2014-2020). The aim of this paper is illustrating the design and the methodological approaches proposed for the operative development of the tool. Furthermore, the paper provides the results of a number of tests carried out to evaluate the understandability of the analysis processes and assessing the stakeholders acceptance. Specifically, GOWARE-DST has been developed for supporting single users or groups of users in the decision-making process. The tool has been provided with a catalogue of 92 BMPs to handle water issues in different land use contexts. The selection of practices suitable for addressing the specific users requirements is supported by the Analytic Hierarchy Process, a method that allows filtering a subset of BMPs by accounting for the relative importance that the user assigns to each characterizing criterion. GOWARE-DST represents an innovative tool for supporting users at different levels of planning (operational and strategic) by promoting sustainable land and water management and defining long-term governance activities.

This paper, developed under the framework of the RECCAP initiative, aims at providingimproved estimates of the carbon and GHG (CO2, CH4and N2O) balance of continentalAfrica. The various components and processes of the African carbon and GHG bud-get were considered, and new and available data derived by different methodologies (based on inventories, ecosystem fluxes, models, and atmospheric inversions) wereintegrated. The related uncertainties were quantified and current gaps and weaknessin knowledge and in the monitoring systems were also considered in order to provideindications on the future requirements. The vast majority of the results seem to agreethat Africa is probably a small sink of carbon on an annual scale, with an average value of−0.61±0.58 PgCyr−1. Nevertheless the emissions of CH4and N2O may turn Africainto a source in terms of CO2equivalents. At sub-regional level there is a significantspatial variability in both sources and sinks, mainly due to the biome’s differences andthe different anthropic impacts, with southern Africa as the main source and centralAfrica, with its evergreen tropical forests, as the main sink. Emissions from land use change in Africa are significant (around 0.32±0.05 PgCyr−1) and even higher than thefossil fuel ones; this is a unique feature among all the continents. In addition there canbe significant carbon losses from land even without changes in the land use (forest),as results from the impact of selective logging. Fires also play a significant role, with1.03±0.22 PgCyr−1of carbon emissions, mainly (90 %) originated by savanna and woodland burning. But whether fire carbon emissions are compensated by CO2up-take during the growing season, or are a non-reversible loss of CO2, remains unclear.Most of these figures are subjected to a significant interannual variability, on the orderof±0.5 PgCyr−1in standard deviation, accounting for around 25 % of the year-to-yearvariation in the global carbon budget. These results, even if still highly uncertain, show the important role that Africa playsin the carbon cycle at global level, both in terms of absolute values and variability, Biogeosciences Discussions, 10, ISSN:1810-6277, ISSN:1810-6285

Il modello afflussi - deflussi di tipo WFIUH (Width Function Instantaneous Unit Hydrograph) si basa sulla determinazione della Funzione d’Ampiezza o Width Function (WF), ovverosia la distribuzione dei punti aventi la stessa distanza idrologica dall’outlet. La Funzione d’Ampiezza (definita tramite supporto topografico digitale, DEM: Digital Elevation Model), riscalata rispetto alle velocità con cui il deflusso avviene sulle singole, fornisce la distribuzione dei tempi di arrivo all’outlet e quindi l’idrogramma unitario istantaneo del bacino (IUH). Per quanto il metodo sia stato ben sviluppato in letteratura ha incontrato difficoltà nella fase applicativa soprattutto a causa della determinazione delle velocità di deflusso. Tali velocità, infatti, sono assunte costanti nelle celle di tipo canale e nelle celle di tipo versante, non considerando quindi la variabilità spaziale del fenomeno. Inoltre si è verificato esse siano dei parametri di calibrazione del modello non desumibili da ipotesi di natura fisica. Scopo di questo lavoro è considerare la distribuzione spaziale delle velocità di deflusso sul terreno, proponendo e testando una metodologia speditiva per il calcolo delle velocità di deflusso per poter cosi calibrare il modello in bacini idrografici “poco strumentati” per i quali non sono disponibili misure contemporanee di precipitazione e portata.

Desertification risk assessment has become a prior goal for environmental protection, recently even in the Mediterranean area. Assuming this phenomenon as interaction among predisposing (i.e. geographic location), triggering (i.e. extreme climatic events) and quickening (i.e. human activities) factors, a new model-based methodology was developed, combining synergically a wide range of desertification indicators developed by previous projects in Mediterranean area and included within the DPSIR (Driving force, Pressure, State, Impact, Response) scheme. The presented approach was applied for desertification risk evaluation in Sardinia (Italy). A large multi-thematic dataset has been acquired, processed and re-arranged in a GIS environment in order to supply the input to five models regarding several degradation processes typical in the study area: soil erosion, soil degradation, vegetation degradation, coastal aquifer salinization and overgrazing. The quantities resulted by modelling were normalized as indices varying from 0 (no degradation) to 1 (irreversible degradation) and they supplied a map of risk for each degradation process. These procedures were applied for two different periods according to the largest availability of data: the early nineties and the present. A total of ten single degradation indices were then integrated into a final one, combining in this way different factors of desertification, their magnitude, their changes, their development rate and giving them different weights according to the significance of each process and to the quality and detail of its input data. The final Integrated Desertification Index (IDI) has the same range of values and has been classified into 5 levels of increasing severity. The desertification risk map obtained using this methodology well reflects conditions observed on the ground. Further spatial statistic analyses allowed a comprehensive knowledge of features in most critical areas. This methodology allowed to highlight the spatial and temporal variability of desertification phenomenon, besides to estimate the land vulnerability in respects to each single degradation process. Starting from environmental vulnerability estimation and jointly with socio-economic aspects regarding probable land use scenarios, we will identify areas at different risk for the future, again in a GIS-oriented approach, supplying an useful instrument for prevention, adaptation and mitigation countermeasures. La valutazione del rischio di desertificazione è diventato un obiettivo primario per la tutela ambientale, recentemente anche nell’area Mediterranea. Assumendo il fenomeno di desertificazione come l’interazione tra fattori predisponenti (es: localizzazione geografica), scatenanti (es: eventi climatici estremi) ed acceleranti (es: le attività umane), è stata una sviluppata una nuova metodologia che combina insieme, nella struttura di un modello integrato, numerosi indicatori di desertificazione, precedentemente sviluppati da vari progetti relativi all’area Mediterranea, integrandoli nello schema DPSIR (Driving forces, Pressure, State, Impact, Response). Il presente approccio è stato applicato per valutare il rischio di desertificazione in Sardegna. E’ stato realizzato un database multitematico, che in seguito è stato processato ed organizzato in ambiente GIS allo scopo di fornire gli input per cinque modelli riguardanti diversi processi di degrado tipici dell’area di studio: erosione idrica, erosione eolica, degrado biologico del suolo, perdita della produttività della vegetazione, salinizzazione degli acquiferi costieri e sovrapascolamento. Le quantità risultanti dalla modellazione di tali processi sono state in seguito normalizzate in indici variabili da 0 (nessun degrado) a 1 (degrado irreversibile: desertificazione) che hanno fornito una mappa di rischio per ciascun processo di degrado. L’intera procedura è stata applicata per due differenti periodi secondo la massima disponibilità di dati: l’inizio degli anni novanta ed i nostri giorni. I dieci indici di degrado così ottenuti sono stati successivamente integrati in un indice finale, combinando in questo modo differenti fattori di desertificazione, la loro intensità, e la loro rapidità di variazione, e dando pesi diversi sia secondo l’importanza di un singolo processo sia secondo la qualità dei dati input. L’indice integrato di desertificazione finale (IDI – Integrated Desertification Index) ha un range di variazione da 0 (nessun degrado) a 1 (degrado irreversibile). Tale metodologia ha permesso di evidenziare la variabilità spaziale e temporale del fenomeno di desertificazione, oltre a stimare la vulnerabilità del territorio rispetto a ciascun singolo processo di degrado. Dalla stima della vulnerabilità ambientale ed unitamente ad aspetti più strettamente socio-economici riguardanti possibili scenari di cambiamenti di uso del suolo, sarà possibile identificare aree a differente rischio per il futuro, di nuovo in un approccio GIS-orientato, fornendo un utile strumento per predisporre misure di prevenzione, adattamento o mitigazione contro la desertificazione. Dottorato di ricerca in Ecologia forestale

I modelli digitali del terreno (DEM) sono affetti da errori di varia natura. I più comuni, dal punto di vista idrologico, sono gli avvallamenti (o pit) e le aree a pendenza nulla poiché impediscono la propagazione dei deflussi superficiali. L’origine di tali imperfezioni deriva sia dalle tecniche di rilievo o interpolazione utilizzate per costruire il DEM, sia dai metodi di trattamento preliminare applicati per ottenere DEM idrologicamente corretti. Un’impropria analisi di tali punti può influenzare significativamente la planimetria ed il relativo profilo altimetrico dei percorsi di drenaggio, nonché la stima di indici idrologici e geomorfologici che dipendono in genere dalla distribuzione delle pendenze e delle aree contribuenti. Recentemente è stato introdotto in letteratura un metodo di correzione del DEM fisicamente basato PEM4PIT (Physically Erosion Model for PIT removal), che sembra fornire risultati più realistici rispetto ai metodi tradizionali. In questa memoria si presenta una procedura per stimare i tre parametri del PEM4PIT e si mostrano alcune applicazioni sia per validare tale procedura di stima, sia per evidenziare le caratteristiche peculiari di tale metodo di correzione.

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (similar to biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH(4) yr(-1), range 540-568. About 60% of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH(4) yr(-1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (similar to 64% of the global budget, amp;lt;30 degrees N) as compared to mid (similar to 32 %, 30-60 degrees N) and high northern latitudes (similar to 4 %, 60-90 degrees N). Top-down inversions consistently infer lower emissions in China (similar to 58 TgCH(4) yr(-1), range 51-72, -14 %) and higher emissions in Africa (86 TgCH(4) yr(-1), range 73-108, + 19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project. Funding Agencies|Swiss National Science Foundation; NASA [NNX14AF93G, NNX14AO73G]; National Environmental Science Program - Earth Systems and Climate Change Hub; European Commission [283576, 633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; US Department of Energy, BER [DE-AC02-05CH11231]; FAO member countries; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1502]; ERC [322998]; NERC [NE/J00748X/1]; Swedish Research Council VR; Research Council of Norway [244074]; NSF [1243232, 1243220]; National Science and Engineering Research Council of Canada (NSERC); Chinas QianRen Program; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organization (CSIRO Australia); Bureau of Meteorology (Australia); Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]