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- Synoptic weather types (1)
Soil degradation by water is a serious environmental problem worldwide, with specific climatic factors being the major causes. We investigated the relationships between synoptic atmospheric patterns (i.e. weather types, WTs) and runoff, erosion and sediment yield throughout the Mediterranean basin by analyzing a large database of natural rainfall events at 68 research sites in 9 countries. Principal Component Analysis (PCA) was used to identify spatial relationships of the different WTs including three hydro-sedimentary variables: rainfall, runoff, and sediment yield (SY, used to refer to both soil erosion measured at plot scale and sediment yield registered at catchment scale). The results indicated 4 spatial classes of rainfall and runoff: (a) northern sites dependent on North (N) and North West (NW) flows; (b) eastern sites dependent on E and NE flows; (c) southern sites dependent on S and SE flows; and, finally, (d) western sites dependent on W and SW flows. Conversely, three spatial classes are identified for SY characterized by: (a) N and NE flows in northern sites (b) E flows in eastern sites, and (c) W and SW flows in western sites. Most of the rainfall, runoff and SY occurred during a small number of daily events, and just a few WTs accounted for large percentages of the total. Our results confirm that characterization by WT improves understanding of the general conditions under which runoff and SY occur, and provides useful information for understanding the spatial variability of runoff, and SY throughout the Mediterranean basin. The approach used here could be useful to aid of the design of regional water management and soil conservation measures.
Random multi-hopper model
(2018)
We develop a mathematical model considering a random walker with long-range hops on arbitrary graphs. The random multi-hopper can jump to any node of the graph from an initial position, with a probability that decays as a function of the shortest-path distance between the two nodes in the graph. We consider here two decaying functions in the form of Laplace and Mellin transforms of the shortest-path distances. We prove that when the parameters of these transforms approach zero asymptotically, the hitting time in the multi-hopper approaches the minimum possible value for a normal random walker. We show by computational experiments that the multi-hopper explores a graph with clusters or skewed degree distributions more efficiently than a normal random walker. We provide computational evidences of the advantages of the random multi-hopper model with respect to the normal random walk by studying deterministic, random and real-world networks.