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Until recently, the development of a global geography of floods was challenged by the fragmentation and heterogeneity of in situ data and the high costs of processing large amounts of remote sensing data. Such geography would facilitate the exploration of large-scale drivers of flood extent and timing including wide latitudinal, climate, and topographic effects. Here we used a monthly dataset spanning 30 years (Global Surface Water Extent) to develop a worldwide geographical characterization of slow floods (1-degree grid), weighting the relative contribution of seasonal, interannual, and long-term fluctuations on overall variability, and quantifying precipitation-flooding delays where seasonality dominated. We explored the dominance of different flooding timings across five Köppen-Geiger main climates and seven topography classes derived from modeled water table depths (i.e., hydro-topography) to contribute top-down insight about the outstanding, cross-regional flooding patterns and their likely large-scale drivers. Our results showed that, globally, the mean extent of floods averaged 0.48% of the global land area, predominantly associated with hydro-topography (>2x more extensive in flatter landscapes). Climate drove flood timings, with predictable, seasonally-dominated fluctuations in cold regions, interannual and mixed patterns in temperate climates, and more irregular (higher variability) and unpredictable (less seasonal) patterns in arid regions. Net gains of flooded area dominated temporal variability in 9% of the cells including boreal clusters likely affected by warming trends. We propose that this new geographical perspective of floods can aid different avenues of hydrological research in the upscaling and extrapolation of field studies and the parsimonious representation of floods in hydroclimatic models.

Faingerch, Melina. Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina. Faingerch, Melina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales. Mar del Plata, Argentina. Vallejos, María. Instituto Nacional de Investigación Agropecuaria (INIA). La Estanzuela, Colonia, Uruguay. Vallejos, María. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina. Texeira, Marcos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina. Texeira, Marcos. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART). Buenos Aires, Argentina. Texeira, Marcos. CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART) Buenos Aires, Argentina. Texeira, Marcos. Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina. Mastrángelo, Matías Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina. Mastrángelo, Matías Enrique. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Grupo de Estudio de Agroecosistemas y Paisajes Rurales. Mar del Plata, Argentina. Conservation policies often promote land privatization to reduce incentives for deforestation. However, empirical evidence on the relationship between landtenure form and forest conservation outcomes is inconclusive. We combined key informant mapping and geospatial analyses to test the association between the area under tenure and the area deforested by extra-local private and local nonprivate agents in the Argentine Dry Chaco over four decades (1976–2016). The study area is a typical commodity production frontier within a global deforestation hotspot. We found a strong spatial and temporal coupling between the area under tenure and the area deforested by extra-local private agents from 1987 to 2006, when a 59% increase in the former was accompanied by a 508% increase in the latter. Local private agents maintained high levels of forest cover, similarly to local nonprivate agents. Our findings have implications for the adaptive design of the Forest Law in the Argentine Dry Chaco. grafs., tbls., mapas

At micro-site scale, the spatial pattern of a plant species depends on several factors including interac- tions with neighbours. It has been seen that unfavourable effects generate a negative association between plants, while beneficial effects generate a positive association. In grasslands, the presence of shrubby species promotes a particular microenvironment beneath their canopy that could affect differently the spatial distribution of plants with different tolerance to abiotic conditions. We measured photosynthetic active radiation, air temperature and wind speed under shrub canopies and in adjacent open sites and analysed the spatial distribution of four grass species (two C3 and two C4) in relation to shrub canopy in a grazed sub-humid natural grassland in southern Uruguay. Radiation, air temperature and wind speed were lower under shrubs than in adjacent open sites. The spatial distribution of grasses relative to the shrub canopy varied depending on the photosynthetic metabolism of grasses. C4 grasses showed a negative association or no correlation with the shrubs, whereas C3 grasses showed a positive association. Our results highlight the importance of the photosynthetic metabolism of the grasses in the final outcome of interactions between grasses and shrubs. Micro-environmental conditions generated underneath shrubs create a more suitable site for the establishment of C3 than for C4 grasses.These results show that facilitation could be more important than previously thought in sub-humid grasslands.

Questions: How do fragment-level characteristics affect remnant grassland functioning in a highly transformed landscape? Are artificial neural networks (ANNs) a better statistical tool to model variations in grassland functioning compared to linear regression models (LRMs)? Location: Tandilia Range, Southern Pampa, Buenos Aires Province, Argentina. Methods: We characterized the dynamics of the vegetation functioning in 60 remnant grasslands using enhanced vegetation index (EVI) data provided by MODIS/Terra images from July 2000 to June 2005. First, we performed a principal components analysis (PCA) on the fragment mean monthly values of EVI in order to obtain synthetic measures (i.e. the PCA axes) of grassland functioning. Grassland fragments were also characterized by size, vegetation structure (abundance of the tall-tussock grass Paspalum quadrifarium) and physical environment (soil type – abundance of litholitic soils – elevation, aspect and slope). The relationship between grassland functioning and these explanatory variables was explored using linear regression models (LRMs) and artificial neural networks (ANNs). Results: The first and second PCA axes were related to the annual integral of EVI (EVI-I) and EVI seasonality (EVI-S), respectively; these explained jointly ca. 80% of total variability in mean EVI values. ANNs captured better than regression models the relationships among the proposed controls and the spatial variability of grassland functioning in Southern Pampa. Results showed that EVI-I variability was related to all independent variables except aspect. While fragment size, litholitic soils and slope were negatively related to EVI-I, the abundance of P. quadrifarium had a positive effect on the spectral index. Grasslands with high seasonality were large and had high slope and aspect, low abundance ofP. quadrifarium and increased abundance of litholitic soils. Conclusions: Our results showed that grassland functioning in Southern Pampa, as estimated by EVI, depends on fragment size, vegetation structure and physical factors (soil type, aspect and slope). Paspalum quadrifarium may have an important functional role in this grassland system.

Questions: (1) Can above-ground net primary production (ANPP) of Patagonian meadows be estimated from remote sensing? (2) How does ANPP of Patagonian meadows change in space and time? Location: Northwestern Patagonia, meadows embedded in a steppe matrix (39–43°S, 70–72oW; area: 85 000 km 2 ). Methods: For the first question, we contrasted field ANPP measurements with MODIS high-spatial resolution (pixel size: 0.0625 km 2 ) data and developed a model that estimates radiation use efficiency. For the second question, we applied the model to a 6-year MODIS record for 14 meadows whose physiognomic heterogeneity was known from previous work. Results: Up to 77% of the field-based ANPP variation was accounted for by the absorbed photosynthetic radiation, based on a linear transformation of the normalized difference vegetation index derived from MODIS data. Mean radiation use efficiency was 0.54 g dry matter MJ � 1 . ANPP ranged between 610 and 1060 g m � 2 year � 1 , which represents three to 5.3 times the ANPP of the surrounding arid and semi-arid steppes. The inter-annual coefficient of variation of ANPP was 10%, which is higher than other systems of similar productivity, but much lower than the surrounding steppes (33%). At the level of management units (paddock), ANPP spatial variations were mainly related to the proportion of Prairies, a proxy for low topographic position in the landscape, and longitude, a proxy for precipitation. ANPP inter-annual variation was most related to latitude, a proxy for temperature. Conclusion: The model developed and tested can be used to infer ANPP from remote sensing data at a spatial resolution that allows one to detect variability within meadows and management units. Variations at both the physiognomic unit and paddock level were associated with geographic patterns and topography. Meadows were three to five times more productive and less fluctuating than nearby steppes. When compared with other ecosystems, their productivity was high, but more variable inter-annually, likely due to exceptionally high variability of precipitation in Patagonia.