Analysis of the behavior of three digital elevation model correction methods on critical natural scenarios

Abstract Study region The methods explored in this study were tested in two study areas: Italy and Cuba. Study focus Virtually all Digital Elevation Models (DEM) contain flat areas or depression pixels that may be artifacts or actual landscape representations. These features must be removed before any further hydrological application can proceed. Diverse algorithms have been developed for the purpose of correcting these aspects, differing in how they handle the nature of the depressions, as well as the adopted mathematical procedures. In the present work, the behavior of a standard ( Fill ) and two advanced ( TOPAZ and PEM4PIT ) DEM correction methods on three critical natural scenarios is analyzed. Extensive flat areas, abrupt slope changes and large depressions − expressed in terms of: (1) geomorphological changes (elevation, affected area and slope); (2) flow velocity; (3) river network and width functions (WF) − are affected. New hydrological insights for the region Results confirm improved performance of the advanced methods over the standard method for each case study in Italy and Cuba. The analyzed parameters also show that correction processes are strongly influenced by the relief, the size of the predominating depressions and the neighbouring depressions. There is no one method among those compared which works optimally for every type of correction, and given that the majority of basins have diverse topographical conditions, a different approach to the corrections process and its computational procedures is likely needed

Intelligent Soft Computing Models in Water Demand Forecasting

Given the increasing trend in water scarcity, which threatens a number of regions worldwide, governments and water distribution system (WDS) operators have sought accurate methods of estimating water demands. While investigators have proposed stochastic and deterministic techniques to model water demands in urban WDS, the performance of soft computing techniques [e.g., Genetic Expression Programming (GEP)] and machine learning methods [e.g., Support Vector Machines (SVM)] in this endeavour remains to be evaluated. The present study proposed a new rationale and a novel technique in forecasting water demand. Phase space reconstruction was used to feed the determinants of water demand with proper lag times, followed by development of GEP and SVM models. The relative accuracy of the three best models was evaluated on the basis of performance indices: coefficient of determination (R2), mean absolute error (MAE), root mean square of error (RMSE), and Nash-Sutcliff coefficient (E). Results showed GEP models were highly sensitive to data classification, genetic operators, and optimum lag time. The SVM model that implemented a Polynomial kernel function slightly outperformed the GEP models. This study showed how phase space reconstruction could potentially improve water demand forecasts using soft computing techniques

Water Vulnerability in Arctic Households: A Literature-based Analysis

There is an urgent need to understand the contextual factors that influence water vulnerability of households in the Arctic. To evaluate the existing knowledge of Arctic household water vulnerability, this paper presents the results of a narrative review with a systematic search. The review identified 112 documents, including peer-reviewed articles, reviews, book chapters, proceedings papers, and meeting abstracts. The documents were analyzed for the main factors affecting water vulnerability in Arctic households, which fell into two categories: biophysical factors and anthropogenic factors. Within the biophysical category, the majority of documents noted climate change impacts on freshwater supplies and water systems, followed by attention to extreme weather and the seasonality of water supplies. Within the anthropogenic category, the vast majority highlighted infrastructure as the primary issue affecting water access, followed by economic, governance, socio-cultural, and demographic factors. Through these diverse influencing factors, this review situates the discussion of household water vulnerability in the Arctic in a more nuanced light. The categories illuminate patterns between factors, which can worsen, assuage, or mitigate water vulnerability. The complex relationships between these factors influence the degree and nature of water vulnerability in Arctic households. In order to successfully address household water vulnerability in the Arctic, these factors and their dynamic relationships must be considered in freshwater policy and management frameworks.Il existe un besoin urgent de comprendre les facteurs contextuels qui exercent une influence sur la vulnérabilité de l’eau des ménages de l’Arctique. Afin d’évaluer les connaissances déjà acquises en matière de vulnérabilité de l’eau des ménages de l’Arctique, nous présentons les résultats d’un dépouillement de textes effectué par le biais d’une recherche systématique. Ce dépouillement a permis de repérer 112 documents, comprenant des articles révisés par des pairs, des comptes rendus, des chapitres de livres, des actes de conférences et des résumés de réunions. L’analyse des documents s’est concentrée sur les principaux facteurs touchant la vulnérabilité de l’eau dans les ménages de l’Arctique. Ces facteurs relèvent de deux catégories, soit les facteurs biophysiques et les facteurs anthropiques. Dans la catégorie biophysique, la majorité des documents faisaient mention des incidences du changement climatique sur les approvisionnements en eau douce de même que sur le réseau hydrographique, après quoi l’accent était mis sur les conditions météorologiques exceptionnelles et la saisonnalité des approvisionnements en eau. Pour ce qui est de la catégorie anthropique, la grande majorité des documents mettait l’accent sur l’infrastructure comme principal enjeu influençant l’accès à l’eau, suivie de facteurs économiques, socioculturels, démographiques et de gouvernance. Grâce à ces divers facteurs d’influence, l’analyse permet d’obtenir un portrait plus nuancé de la vulnérabilité de l’eau des ménages de l’Arctique. Les catégories permettent de dégager des tendances entre les facteurs, tendances qui empirent, assouvissent ou atténuent la vulnérabilité de l’eau. Les relations complexes qui existent entre ces facteurs influencent le degré et la nature de la vulnérabilité de l’eau dans les ménages de l’Arctique. Afin de réussir à régler l’enjeu de la vulnérabilité de l’eau dans les ménages de l’Arctique, il y a lieu de tenir compte de ces facteurs et de leurs liens dynamiques en matière de gestion et de formulation de politiques concernant l’eau douce

Correcting satellite precipitation data and assimilating satellite-derived soil moisture data to generate ensemble hydrological forecasts within the HBV rainfall-runoff model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models

Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 Wcenterdotm−2, mean bias error (MBE) = 4.466 Wcenterdotm−2, coefficient of determination (R2) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 Wcenterdotm−2, MBE = −6.039 Wcenterdotm−2, R2 = 0.821] and random forest (RF) [RMSE = 95.262 Wcenterdotm−2, MBE = −11.576 Wcenterdotm−2, R2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems

A Century of Observations Reveals Increasing Likelihood of Continental-Scale Compound Dry-Hot Extremes

Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes

Controlling factors of plant community composition with respect to the slope aspect gradient in the Qilian Mountains

Slope aspect can affect soil temperature and soil type distribution, which, in turn, is likely to influence plant community composition. Three Qilian mountains, located in the northeastern part of the Qinghai–Tibetan Plateau, China, with four distinct slope aspects including south‐facing (SF), southwest‐facing (SW), northwest‐facing (NW), and north‐facing (NF) slope aspects, were studied to investigate the impact of slope aspect on plant assemblages. The results indicated that the environmental conditions were favorable under the NF and NW slope aspects as the soil water, soil organic carbon (SOC), and soil total nitrogen (STN) contents were significantly higher, and soil temperature (ST) and soil bulk density (SBD) were significantly lower than under the SF and SW aspects. Under all slope aspects, however, SOC, STN, and soil total phosphate in the top 0.2 m of topsoil accounted for about 60% of its total quantity, to a soil depth of 0.6 m. The plant communities on the SF and SW slopes were found to be primarily composed of Poa pratensis, Potentilla anrisena, and Carex aridula. In contrast, the plant community on the NW slope was mainly composed of Kobresia humilis, Carex crebra, and Potentilla bifurca, while on the NF slope it was mainly composed of Picea crassifolia, Carex scabrirostris, and Polygonum macrophyllum. The order of the influence of environmental factors on species distributions was ST > SBD > sand > STN. Results suggest that the slope aspect has an important role in the regulation of the soil environment and plant assemblages and that ST and SBD were the main factors influencing plant community composition. Furthermore, evidence from this study suggests that these mountains will become increasingly vulnerable to global warming. Thus, the plant community composition on these mountains must be monitored continuously in order to allow for strategic adaptive management

Plastics can be used more sustainably in agriculture

Plastics have become an integral component in agricultural production as mulch films, nets, storage bins and in many other applications, but their widespread use has led to the accumulation of large quantities in soils. Rational use and reduction, collection, reuse, and innovative recycling are key measures to curb plastic pollution from agriculture. Plastics that cannot be collected after use must be biodegradable in an environmentally benign manner. Harmful plastic additives must be replaced with safer alternatives to reduce toxicity burdens and included in the ongoing negotiations surrounding the United Nations Plastics Treaty. Although full substitution of plastics is currently not possible without increasing the overall environmental footprint and jeopardizing food security, alternatives with smaller environmental impacts should be used and endorsed within a clear socio-economic framework. Better monitoring and reporting, technical innovation, education and training, and social and economic incentives are imperative to promote more sustainable use of plastics in agriculture

Grassland degradation on the Qinghai-Tibetan Plateau: reevaluation of causative factors

In light of Harris (2010) finding insufficient evidence to assert a causal linkage between any of the seven previously proposed causative factors and grassland degradation on the Qinghai-Tibetan Plateau (QTP), more recent empirical studies on QTP grassland degradation were explored to ascertain whether, in fact, these factors are casually linked to grassland degradation. The mischaracterization of the underlying causes of grassland degradation among policymakers has and continues to be an obstacle to sustainable regional grassland management practices. Accumulating evidence suggests that privatization and sedentarization, small mammals, climate change, harsh environments, fragile soils, and overgrazing contribute to grassland degradation. However, neither obsolete livestock husbandry methods nor the recent conversion of rangelands to agriculture had a meaningful influence. Estimates of the total area of degraded grasslands and the establishment of grassland degradation criteria have not been properly addressed in the literature. Both omissions constitute the basis for investigating the causes of grassland degradation across the QTP and the adoption of measures to manage these grasslands sustainably