Measuring operational and quality-adjusted efficiency of Chilean water companies

The path to a sustainable management of the urban water cycle requires the assessment of both operational and quality-adjusted efficiency in a unified manner. This can be done by the use of non-radial Data Envelopment Analysis models. This study used Range Adjusted Measure models to evaluate the operational, quality-adjusted, and operational & quality-adjusted efficiency (O&QAE) scores of the Chilean water industry including water leakage and unplanned interruptions as undesirable outputs. It was found that on average water utilities presented large O&QAE scores over time. The mean O&QAE score was 0.964 which means that water utilities could further reduce costs and undesirable outputs by 3.6% on average, while trying to expand the scale of operation. This finding suggests that excellent quality-adjusted efficiency at an efficient expenditure could be feasible. It was also evidenced that customer density, mixed water resources, and ownership influenced the O&QAE of Chilean water companies.

Amazon Water Cycle Observed from Space

Satellite observations offer invaluable insights into hydrological processes and environmental change in the Amazon.

Changes in Terrestrial Evaporation across Poland over the Past Four Decades Dominated by Increases in Summer Months

Given the importance of terrestrial evaporation (ET) for the water cycle, a fundamental understanding of the water quantity involved in this process is required. As recent observations reveal a widespread ET intensification across the world, it is important to evaluate regional ET variability. The specific objectives of this study are the following: (1) to assess annual and monthly ET trends across Poland, and (2) to reveal seasons and regions with significant ET changes. This study uses the ET estimates acquired from the Global Land Evaporation Amsterdam Model (GLEAM) dataset allowing for multi-year analysis (1980–2020). The Mann–Kendall test and the Sen’s slope were applied to estimate the significance and magnitude of the trends. The results show that a rising temperature, along with small precipitation increase, led to the accelerated ET of 1.36 mm/y. This was revealed by increased transpiration and interception loss not compensated by a decrease in bare soil evaporation and sublimation. The wide-spread higher water consumption especially occurred during the summer months of June, July, and August. Comparing the two subperiods of 1980–2020, it was found that in 2007–2020, the annual ET increased by 7% compared to the reference period of 1980–2006. These results can serve as an important reference for formulating a water resources management strategy in Poland.

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

In network science and big data, the concept of finding meaningful infrastructures in networks has emerged as a method of finding groups of entities with similar properties within very complex systems. The whole concept is generally based on finding subnetworks which have more properties (links) amongst nodes belonging to the same cluster than nodes in other groups (A concept presented by Girvan and Newman, 2002). Today meaningful infrastructure identification is applied in all types of networks from computer networks, to social networks to biological networks. In this article we will look at how meaningful infrastructure identification is applied in biological networks. This concept is important in biological networks as it helps scientist discover patterns in proteins or drugs which helps in solving many medical mysteries. This article will encompass the different algorithms that are used for meaningful infrastructure identification in biological networks. These include Genetic Algorithm, Differential Evolution, Water Cycle Algorithm (WCA), Walktrap Algorithm, Connect Intensity Iteration Algorithm (CIIA), Firefly algorithms and Overlapping Multiple Label Propagation Algorithm. These al-gorithms are compared with using performance measurement parameters such as the Mod-ularity, Normalized Mutual Information, Functional Enrichment, Recall and Precision, Re-dundancy, Purity and Surprise, which we will also discuss here.

Impact of distributed meteorological forcing on snow dynamic and induced water fluxes over a mid-elevation alpine micro-scale catchment

From the micro to mesoscale, water and energy budgets of mountainous catchments are largely driven by topographic features such as terrain orientation, slope, steepness, elevation together with associated meteorological forcings such as precipitation, solar radiation and wind. This impact the snow deposition, melting and transport, which further impact the overall water cycle. However, this microscale variability is not well represented in Earth System Models due to coarse resolutions, and impacts of such resolution assumptions on simulated water and energy budget lack quantification. This study aims at exploring these effects on a 15.28 ha small mid-elevation (2000–2200 m) alpine catchment at Col du Lautaret (France). This grass-dominated catchment remains covered with snow for 5 to 6 months per year. The surface-subsurface coupled hyper-resolution (10 m) distributed hydrological model ParFLOW-CLM is used to simulate the impacts of meteorological variability at spatio-temporal micro-scale on the water cycle. These include 3D simulations with spatially distributed forcing of precipitation, solar radiation and wind compared to 3D simulations with non-distributed forcing simulation. Our precipitation distribution method encapsulates the spatial snow distribution along with snow transport. The model simulates the snow cover dynamics and spatial variability through the CLM energy balance module and under the different combinations of distributed forcing. The resulting subsurface and surface water transfers are solved by the ParFLOW module. Distributed forcing induce a snowpack with a more spatially heterogeneous thickness, which becomes patchy during the melt season and shows a good agreement with the remote sensing images. This asynchronous melting results in a longer melting period and smoother hydrological response than the non-distributed forcing, which does not generate any patchiness. Amongst the tested distributed meteorological forcing that impacts the hydrology, precipitation distribution, including snow transportation, is the most important. Solar insolation distribution has an important impact in reducing evapotranspiration depending on the slope orientation. For the studied catchment mainly facing east, it adds small differential melting effect. Wind distribution in the energy budget calculation has a more complicated impact on our catchment as it participate to accelerate the melting when meteorological conditions are favourable but does not generate patchiness at the end in our test case.

Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

Stress-Testing Framework for Urban Water Systems: A Source to Tap Approach for Stochastic Resilience Assessment

Optimizing the design and operation of an Urban Water System (UWS) faces significant challenges over its lifespan to account for the uncertainties of important stressors that arise from population growth rates, climate change factors, or shifting demand patterns. The analysis of a UWS’s performance across interdependent subsystems benefits from a multi-model approach where different designs are tested against a variety of metrics and in different times scales for each subsystem. In this work, we present a stress-testing framework for UWSs that assesses the system’s resilience, i.e., the degree to which a UWS continues to perform under progressively increasing disturbance (deviation from normal operating conditions). The framework is underpinned by a modeling chain that covers the entire water cycle, in a source-to-tap manner, coupling a water resources management model, a hydraulic water distribution model, and a water demand generation model. An additional stochastic simulation module enables the representation and modeling of uncertainty throughout the water cycle. We demonstrate the framework by “stress-testing” a synthetic UWS case study with an ensemble of scenarios whose parameters are stochastically changing within the UWS simulation timeframe and quantify the uncertainty in the estimation of the system’s resilience.