Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

Abstract. Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies – Marina catchment (Singapore) and Canning River (Western Australia) – representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation

Radio and X-ray study of two multi-shell Supernova Remnants: Kes79 and G352.7-0.1

We investigate two multi-shell galactic supernova remnants (SNRs), Kes79 and G352.7-0.1, to understand the causes of such morphology. The research was carried out based on new and reprocessed archival VLA observations and XMM-Newton archival data. The surrounding was investigated based on data extracted from the HI Canadian Galactic Plane Survey, the 13^CO Galactic Ring Survey and the HI Southern Galactic Plane Survey. The present study revealed that the overall morphology of both SNRs is the result of the mass-loss history of their respective progenitor stars. Kes79 would be the product of the gravitational collapse of a massive O9 star evolving near a molecular cloud and within the precursor's wind-driven bubble, while G352.7-0.1 would be the result of interactions of the SNR with an asymmetric wind from the progenitor together with projection effects. No radio point source or pulsar wind nebula was found associated with the X-ray pulsar CXOU J185238.6+004020 in Kes79. The X-ray study of G352.7-0.1, on its hand, revealed that most of the thermal X-ray radiation completely fills in the interior of the remnant and originates in heated ejecta. Characteristic parameters, like radio flux, radio spectral index, age, distance, shock velocity, initial energy and luminosity, were estimated for both SNRs.Comment: 14 pages, 13 figures. Accepted to be published in Astronomy and Astrophysic

Contrasting farmers' perception of climate change and climatic data: How (in)consistency supports risk reduction and resilience?

Farmers’ adaptation to climate change is a two-step process that involves perceiving stressors and identifying impacts to respond to variability and changes through specific actions and strategies. Hence, successful adaptation depends on how well changing climate is perceived, either from a ‘bottom-up’ pathway –where farmers observe and identify changes through past experience–, or by using a ‘top-down’ pathway –where changes are identified through climate records. A gap between both pathways tends to be related to farmers’ misperception. For example, as life experiences influence perception, farmers who have been directly affected by extreme climatic events tend to report that the probability of such event happening again is relatively high. Furthermore, as perception is in part a subjective phenomenon, therefore, different farmers in the same locality might construct different perceptions of climate change impacts even though they experience the same weather patterns. Consequently, increased attention has been put on combining the ‘civic science’ of farmers’ perceptions with the ‘formal science’ from meteorological reports to identify the (in)consistency between perceived and observed data and how this affect farmers’ resilience when facing climate change impacts. This contribution provides a review comparing farmers’ perception and climate observations to address a twofold research question: 1) Which extreme events and compound risks are perceived by farmers in contrast with observed data? And 2) How do past experiences and social-learning influence farmers’ resilience and their adaptive capacity? We analyze a portfolio of 147 articles collected from Scopus library catalogue since 2000. The bibliometrics analysis was coupled with the systematic review to 103 articles selected from the original portfolio. Comparison between perceived and observed changes were focus on what was changing (onset, duration or cessation regarding temperature and rainfall patterns) and how it was changing (amount, frequency, intensity or inter-annual variability). Results will be useful for managers, developers, and policymakers of climate adaptation strategies to be more in tune with farmers’ understandings of when and how weather is changing. Furthermore, the review could generate recommendations for the design, formulation, and implementation of adaptation policies that are better tailored to farmers’ perception at local conditions, being more efficient and conducive to risk analysis when facing climate change

Feature-tailored spectroscopic analysis of the SNR Puppis A in X-rays

We introduce a distinct method to perform spatially-resolved spectral analysis of astronomical sources with highly structured X-ray emission. The method measures the surface brightness of neighbouring pixels to adaptively size and shape each region, thus the spectra from the bright and faint filamentary structures evident in the broadband images can be extracted. As a test case, we present the spectral analysis of the complete X-ray emitting plasma in the supernova remnant Puppis A observed with XMM-Newton and Chandra. Given the angular size of Puppis A, many pointings with different observational configurations have to be combined, presenting a challenge to any method of spatially-resolved spectroscopy. From the fit of a plane-parallel shocked plasma model we find that temperature, absorption column, ionization time scale, emission measure and elemental abundances of O, Ne, Mg, Si, S and Fe, are smoothly distributed in the remnant. Some regions with overabundances of O-Ne-Mg, previously characterized as ejecta material, were automatically selected by our method, proving the excellent response of the technique. This method is an advantageous tool for the exploitation of archival X-ray data.Comment: Accepted in Astronomy & Astrophysic

The most complete and detailed X-ray view of the SNR Puppis A

With the purpose of producing the first detailed full view of Puppis A in X-rays, we carried out new XMM-Newton observations covering the missing regions in the southern half of the supernova remnant (SNR) and combined them with existing XMM-Newton and Chandra data. The new images were produced in the 0.3-0.7, 0.7-1.0 and 1.0-8.0 energy bands. We investigated the SNR morphology in detail, carried out a multi-wavelength analysis and estimated the flux density and luminosity of the whole SNR. The complex structure observed across the remnant confirms that Puppis A evolves in an inhomogeneous, probably knotty interstellar medium. The southwestern corner includes filaments that perfectly correlate with radio features suggested to be associated with shock/cloud interaction. In the northern half of Puppis A the comparison with Spitzer infrared images shows an excellent correspondence between X-rays and 24 and 70 microns emission features, while to the south there are some matched and other unmatched features. X-ray flux densities of 12.6 X 10^-9, 6.2 X 10^-9, and 2.8 X 10^-9 erg cm^-2 s^-1 were derived for the 0.3-0.7, 0.7-1.0 and 1.0-8.0 keV bands, respectively. At the assumed distance of 2.2 kpc, the total X-ray luminosity between 0.3 and 8.0 keV is 1.2 X 10^37 erg s^-1. We also collected and updated the broad-band data of Puppis A between radio and GeV gamma-ray range, producing its spectral energy distribution. To provide constraints to the high-energy emission models, we re-analyzed radio data, estimating the energy content in accelerated particles to be Umin=4.8 X 10^49 erg and the magnetic field strength B=26 muG.Comment: Article accepted to be published in the Astronomy and Astrophysics Main Journa

Balancing Sediment Connectivity and Energy Production via Optimized Reservoir Sediment Management Strategies

Sediment connectivity plays a fundamental role in sustaining ecosystem goods and services in fluvial systems, including hydropower production. Dams alter the natural processes of sediment transport by trapping sediment and reshaping downstream hydrology and geomorphology. Due to these processes' interconnected nature, dams' impacts extend in time and space beyond the dam site to the entire river system. System-scale approaches to reduce dam impacts commonly only consider dam siting, overlooking the potential of sediment management strategies integrated into the dam operations to offer more flexible solutions for mitigation. Herein, we contribute a sediment routing model (D-CASCADE) to assess the impacts of reservoirs and their management strategies on river sediment connectivity. D-CASCADE is applied to the 3S river system, a tributary of the Mekong River, a hotspot of potential dams in the Lower Mekong. We analyze three dam development portfolios. The effect of reservoir management is examined by assessing daily sediment delivery with specific dam release strategies. Model results predict sediment yield to the Mekong to reduce by 31%-60%. Finally, we explore trade-offs between hydropower generation and sediment connectivity across cascades of multiple reservoirs. Results show that repeated flushing operations during the early wet season could significantly increase sediment delivery with minimal (max 6%) hydropower losses. While poor trade-offs between sediment and hydropower have been locked-in in the Mekong, our results highlight the potential of including sediment connectivity models in multi-objective decision-making frameworks to devise integrated water and sediment management strategies that mitigate connectivity disruptions while minimizing losses in other sectors

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

A multiwavelength study of the star forming region IRAS 18544+0112

This work aims at investigating the molecular and infrared components in the massive young stellar object (MYSO) candidate IRAS 18544+0112. The purpose is to determine the nature and the origin of this infrared source. To analyze the molecular gas towards IRAS 18544+0112, we have carried out observations in a 90" x 90" region around l = 34.69, b = -0.65, using the Atacama Submillimeter Telescope Experiment (ASTE) in the 12CO J=3-2, 13CO J=3-2, HCO+ J=4-3 and CS J=7-6 lines with an angular resolution of 22". The infrared emission in the area has been analyzed using 2MASS and Spitzer public data. From the molecular analysis, we find self-absorbed 12CO J=3-2 profiles, which are typical in star forming regions, but we do not find any evidence of outflow activity. Moreover, we do not detect either HCO+ J=4-3 or CS J=7-6 in the region, which are species normally enhanced in molecular outflows and high density envelopes. The 12CO J=3-2 emission profile suggests the presence of expanding gas in the region. The Spitzer images reveal that the infrared source has a conspicuous extended emission bright at 8 um with an evident shell-like morphology of ~ 1.5 arcmin in size (~ 1.4 pc at the proposed distance of 3 kpc) that encircles the 24 um emission. The non-detection of ionized gas related to IRAS 18544+0112, together with the fact that it is still embedded in a molecular clump suggest that IRAS 18544+0112, has not reached the UCHII region stage yet. Based on near infrared photometry we search for YSO candidates in the region and propos that 2MASS 18565878+0116233 is the infrared point source associated with IRAS 18544+0112. Finally, we suggest that the expansion of a larger nearby HII region, G034.8-0.7, might be related to the formation of IRAS 18544+0112.Comment: 14 pages, accepted for publication in A&A. Figures degraded to reduce file siz