Definition of ecological flow using iha and iari as an operative procedure for water management

It is widely recognized that the hydrological regime of natural flow plays a primary and crucial role in influencing the physical condition of habitats, which, in turn, determines the biotic composition and sustainability of aquatic ecosystems. The current hydro‐ecological understanding states that all flow components might be considered as operational targets for water management, starting from base flows (including low flows) to high and flood regimes in terms of magnitude, frequency, duration, timing, and rate of change. Several codes have been developed and applied on different case studies in order to define common tools to be implemented for Eflow assessment. This work deals with the definition of an operative procedure for the evaluation of the Eflow monthly distribution to be adopted in a generic watercourse cross‐section for sustainable surface water resource management and exploitation. The methodology proposes the application of the Indicators of Hydrologic Alteration methodology (IHA by TNC) coupled to the valuation of the Index of Hydrological Regime Alteration (IARI by ISPRA) as an operative tool to define the ecological flow in each monitoring cross‐section to support sustainable water resource management and planning. The case study of the Agri River in Basilicata (Southern Italy) is presented. The analyses were carried out based on monthly discharge data derived by applying the HEC‐Hydrological Modeling System at the basin scale using the daily rain data measurements obtained by the regional rainfall gauge stations and calibrated through the observed inlet water discharge registered at the Lago del Pertusillo reservoir station

The SAVEMEDCOASTS-2 webGIS: The Online Platform for Relative Sea Level Rise and Storm Surge Scenarios up to 2100 for the Mediterranean Coasts

Here we show the SAVEMEDCOASTS-2 web-based geographic information system (webGIS) that supports land planners and decision makers in considering the ongoing impacts of Relative Sea Level Rise (RSLR) when formulating and prioritizing climate-resilient adaptive pathways for the Mediterranean coasts. The webGIS was developed within the framework of the SAVEMEDCOASTS and SAVEMEDCOASTS-2 projects, funded by the European Union, which respond to the need to protect people and assets from natural disasters along the Mediterranean coasts that are vulnerable to the combined effects of Sea Level Rise (SLR) and Vertical Land Movements (VLM). The geospatial data include available or new high-resolution Digital Terrain Models (DTM), bathymetric data, rates of VLM, and multi-temporal coastal flooding scenarios for 2030, 2050, and 2100 with respect to 2021, as a consequence of RSLR. The scenarios are derived from the 5th Assessment Report (AR5) provided by the Intergovernmental Panel on Climate Change (IPCC) and encompass different Representative Concentration Pathways (RCP2.6 and RCP8.5) for climate projections. The webGIS reports RSLR scenarios that incorporate the temporary contribution of both the highest astronomical tides (HAT) and storm surges (SS), which intensify risks to the coastal infrastructure, local community, and environment

Soft Image Segmentation: On the Clustering of Irregular, Weighted, Multivariate Marked Networks

The contribution exposes and illustrates a general, flexible formalism, together with an associated iterative procedure, aimed at determining soft memberships of marked nodes in a weighted network. Gathering together spatial entities which are both spatially close and similar regarding their features is an issue relevant in image segmentation, spatial clustering, and data analysis in general. Unoriented weighted networks are specified by an ``exchange matrix", determining the probability to select a pair of neighbors. We present a family of membership-dependent free energies, whose local minimization specifies soft clusterings. The free energy additively combines a mutual information, as well as various energy terms, concave or convex in the memberships: within-group inertia, generalized cuts (extending weighted Ncut and modularity), and membership discontinuities (generalizing Dirichlet forms). The framework is closely related to discrete Markov models, random walks, label propagation and spatial autocorrelation (Moran's I), and can express the Mumford-Shah approach. Four small datasets illustrate the theory

Location patterns of urban industry in Shanghai and implications for sustainability

China’s economy has undergone rapid transition and industrial restructuring. The term “urban industry” describes a particular type of industry within Chinese cities experiencing restructuring. Given the high percentage of industrial firms that have either closed or relocated from city centres to the urban fringe and beyond, emergent global cities such as Shanghai, are implementing strategies for local economic and urban development, which involve urban industrial upgrading numerous firms in the city centre and urban fringe. This study aims to analyze the location patterns of seven urban industrial sectors within the Shanghai urban region using 2008 micro-geography data. To avoid Modifiable Areal Unit Problem (MAUP) issue, four distance-based measures including nearest neighbourhood analysis, Kernel density estimation, K-function and co-location quotient have been extensively applied to analyze and compare the concentration and co-location between the seven sectors. The results reveal disparate patterns varying with distance and interesting co-location as well. The results are as follows: the city centre and the urban fringe have the highest intensity of urban industrial firms, but the zones with 20–30 km from the city centre is a watershed for most categories; the degree of concentration varies with distance, weaker at shorter distance, increasing up to the maximum distance of 30 km and then decreasing until 50 km; for all urban industries, there are three types of patterns, mixture of clustered, random and dispersed distribution at a varied range of distances. Consequently, this paper argues that the location pattern of urban industry reflects the stage-specific industrial restructuring and spatial transformation, conditioned by sustainability objectives

The effect of distance on maternal institutional delivery choice : Evidence from Malawi

In many low- and middle-income countries, geographical accessibility continues to be a barrier to health care utilization. In this paper, we aim to better understand the full relationship between distance to providers and utilization of maternal delivery services. We address three methodological challenges: non-linear effects between distance and utilization; unobserved heterogeneity through non-random distance “assignment”; and heterogeneous effects of distance. Linking Malawi Demographic Health Survey household data to Service Provision Assessment facility data, we consider distance as a continuous treatment variable, estimating a Dose-Response Function based on generalized propensity scores, allowing exploration of non-linearities in the effect of an increment in distance at different distance exposures. Using an instrumental variables approach, we examine the potential for unobserved differences between women residing at different distances to health facilities. Our results suggest distance significantly reduces the probability of having a facility delivery, with evidence of non-linearities in the effect. The negative relationship is shown to be particularly strong for women with poor health knowledge and lower socio-economic status, with important implications for equity. We also find evidence of potential unobserved confounding, suggesting that methods that ignore such confounding may underestimate the effect of distance on the utilization of health services

Modeling local and global spatial correlation in field‐scale experiments

Precision agriculture has renewed the interest of farmers and researchers to conduct on‐farm planned comparisons and researchers with respect to field‐scale research. Cotton yield monitor data collected on‐the‐go from planned field‐scale on‐farm experiments can be used to make improved decisions if analyzed appropriately. When farmers and researchers compare treatments implemented at larger block designs, treatment edge effects and spatial externalities need to be considered so that results are not biased. Spatial analysis methods are compared for field‐scale research using site‐specific data, paying due attention to local and global patterns of spatial correlation. Local spatial spillovers are explicitly modeled by spatial statistical techniques that led to improved farm management decisions in combination with the limited replication strip trial data farmers currently collect