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- ItemMultiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India(Basel : MDPI, 2021) Kalyan, AVS; Ghose, Dillip Kumar; Thalagapu, Rahul; Guntu, Ravi Kumar; Agarwal, Ankit; Kurths, Jürgen; Rathinasamy, MaheswaranAccelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to ‐understand these events’ propagation in the near fu-ture. We have considered sixteen extreme climate indices defined by the World Meteorological Or-ganization’s Expert Team on Climate Change Detection and Indices from a long‐term dataset (1951– 2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non‐stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain com-binations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indi-cating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16‐year bands, and the two‐year period was dominant in the global power spectrum around 1970–1990. One interest-ing finding is that a dominant two‐year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes. © 2021 by the author. Licensee MDPI, Basel, Switzerland.
- ItemNear Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India(Basel : MDPI, 2020) Arumugam, Ponraj; Chemura, Abel; Schauberger, Bernhard; Gornott, ChristophImmediate yield loss information is required to trigger crop insurance payouts, which are important to secure agricultural income stability for millions of smallholder farmers. Techniques for monitoring crop growth in real-time and at 5 km spatial resolution may also aid in designing price interventions or storage strategies for domestic production. In India, the current government-backed PMFBY (Pradhan Mantri Fasal Bima Yojana) insurance scheme is seeking such technologies to enable cost-efficient insurance premiums for Indian farmers. In this study, we used the Decision Support System for Agrotechnology Transfer (DSSAT) to estimate yield and yield anomalies at 5 km spatial resolution for Kharif rice (Oryza sativa L.) over India between 2001 and 2017. We calibrated the model using publicly available data: namely, gridded weather data, nutrient applications, sowing dates, crop mask, irrigation information, and genetic coefficients of staple varieties. The model performance over the model calibration years (2001–2015) was exceptionally good, with 13 of 15 years achieving more than 0.7 correlation coefficient (r), and more than half of the years with above 0.75 correlation with observed yields. Around 52% (67%) of the districts obtained a relative Root Mean Square Error (rRMSE) of less than 20% (25%) after calibration in the major rice-growing districts (>25% area under cultivation). An out-of-sample validation of the calibrated model in Kharif seasons 2016 and 2017 resulted in differences between state-wise observed and simulated yield anomalies from –16% to 20%. Overall, the good ability of the model in the simulations of rice yield indicates that the model is applicable in selected states of India, and its outputs are useful as a yield loss assessment index for the crop insurance scheme PMFBY.
- ItemIncorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap(Basel : MDPI, 2020) Van Oijen, Marcel; Barcza, Zoltán; Confalonieri, Roberto; Korhonen, Panu; Kröel-Dulay, György; Lellei-Kovács, Eszter; Louarn, Gaëtan; Louault, Frédérique; Martin, Raphaël; Moulin, Thibault; Movedi, Ermes; Picon-Cochard, Catherine; Rolinski, Susanne; Viovy, Nicolas; Wirth, Stephen Björn; Bellocchi, GianniMulti-species grasslands are reservoirs of biodiversity and provide multiple ecosystem services, including fodder production and carbon sequestration. The provision of these services depends on the control exerted on the biogeochemistry and plant diversity of the system by the interplay of biotic and abiotic factors, e.g., grazing or mowing intensity. Biogeochemical models incorporate a mechanistic view of the functioning of grasslands and provide a sound basis for studying the underlying processes. However, in these models, the simulation of biogeochemical cycles is generally not coupled to simulation of plant species dynamics, which leads to considerable uncertainty about the quality of predictions. Ecological models, on the other hand, do account for biodiversity with approaches adopted from plant demography, but without linking the dynamics of plant species to the biogeochemical processes occurring at the community level, and this hampers the models’ capacity to assess resilience against abiotic stresses such as drought and nutrient limitation. While setting out the state-of-the-art developments of biogeochemical and ecological modelling, we explore and highlight the role of plant diversity in the regulation of the ecosystem processes underlying the ecosystems services provided by multi-species grasslands. An extensive literature and model survey was carried out with an emphasis on technically advanced models reconciling biogeochemistry and biodiversity, which are readily applicable to managed grasslands in temperate latitudes. We propose a roadmap of promising developments in modelling.
- ItemSimultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method(Basel : MDPI, 2021-8-20) Yang, Chenyao; Menz, Christoph; Fraga, Helder; Reis, Samuel; Machado, Nelson; Malheiro, Aureliano C.; Santos, João A.Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.
- ItemManaging power demand from air conditioning benefits solar pv in India scenarios for 2040(Basel : MDPI, 2020) Ershad, Ahmad Murtaza; Pietzcker, Robert; Ueckerdt, Falko; Luderer, GunnarAn Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33-45% (without DSM) to ∼45-60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply. © 2020 by the authors.
- ItemBalancing Health, Economy and Climate Risk in a Multi-Crisis(Basel : MDPI, 2021) Nathwani, Jatin; Lind, Niels; Renn, Ortwin; Schellnhuber, Hans JoachimIn the presence of a global pandemic (COVID-19), the relentless pressure on global decision-makers is to ensure a balancing of health (reduce mortality impacts), economic goals (income for livelihood sustenance), and environmental sustainability (stabilize GHG emissions long term). The global energy supply system is a dominant contributor to the GHG burden and deeply embedded in the economy with its current share of 85%, use of fossil fuels has remained unchanged over 3 decades. A unique approach is presented to harmonizing the goals of human safety, economic development, and climate risk, respectively, through an operational tool that provides clear guidance to decision-makers in support of policy interventions for decarbonization. Improving climate change performance as an integral part of meeting human development goals allows the achievement of a country’s environmental, social, and economic well-being to be tracked and monitored. A primary contribution of this paper is to allow a transparent accounting of national performance highlighting the goals of enhancing human safety in concert with mitigation of climate risks. A measure of a country’s overall performance, combined as the Development and Climate Change Performance Index (DCI), is derived from two standardized indexes, the development index H and the Climate Change Performance Index CCPI. Data are analyzed for 55 countries comprising 65 percent of the world’s population. Through active management and monitoring, the proposed DCI can illustrate national performance to highlight a country’s current standing, rates of improvement over time, and a historical profile of progress of nations by bringing climate risk mitigation and economic well-being into better alignment.
- ItemPhotodynamic Opening of the Blood–Brain Barrier and the Meningeal Lymphatic System: The New Niche in Immunotherapy for Brain Tumors(Basel : MDPI, 2022) Semyachkina-Glushkovskaya, Oxana; Terskov, Andrey; Khorovodov, Alexander; Telnova, Valeria; Blokhina, Inna; Saranceva, Elena; Kurths, JürgenPhotodynamic therapy (PDT) is a promising add-on therapy to the current standard of care for patients with glioblastoma (GBM). The traditional explanation of the anti-cancer PDT effects involves the PDT-induced generation of a singlet oxygen in the GBM cells, which causes tumor cell death and microvasculature collapse. Recently, new vascular mechanisms of PDT associated with opening of the blood–brain barrier (OBBB) and the activation of functions of the meningeal lymphatic vessels have been discovered. In this review, we highlight the emerging trends and future promises of immunotherapy for brain tumors and discuss PDT-OBBB as a new niche and an important informative platform for the development of innovative pharmacological strategies for the modulation of brain tumor immunity and the improvement of immunotherapy for GBM.
- ItemIntranasal Delivery of Liposomes to Glioblastoma by Photostimulation of the Lymphatic System(Basel : MDPI, 2022) Semyachkina-Glushkovskaya, Oxana; Shirokov, Alexander; Blokhina, Inna; Telnova, Valeria; Vodovozova, Elena; Alekseeva, Anna; Boldyrev, Ivan; Fedosov, Ivan; Dubrovsky, Alexander; Khorovodov, Alexandr; Terskov, Andrey; Evsukova, Arina; Elovenko, Daria; Adushkina, Viktoria; Tzoy, Maria; Agranovich, Ilana; Kurths, Jürgen; Rafailov, EdikThe blood–brain barrier (BBB) limits the delivery of majority of cancer drugs and thereby complicates brain tumor treatment. The nasal-brain-lymphatic system is discussed as a pathway for brain drug delivery overcoming the BBB. However, in most cases, this method is not sufficient to achieve a therapeutic effect due to brain drug delivery in a short distance. Therefore, it is necessary to develop technologies to overcome the obstacles facing nose-to-brain delivery of promising pharmaceuticals. In this study, we clearly demonstrate intranasal delivery of liposomes to the mouse brain reaching glioblastoma (GBM). In the experiments with ablation of the meningeal lymphatic network, we report an important role of meningeal pathway for intranasal delivery of liposomes to the brain. Our data revealed that GBM is characterized by a dramatic reduction of intranasal delivery of liposomes to the brain that was significantly improved by near-infrared (1267 nm) photostimulation of the lymphatic vessels in the area of the cribriform plate and the meninges. These results open new perspectives for non-invasive improvement of efficiency of intranasal delivery of cancer drugs to the brain tissues using nanocarriers and near-infrared laser-based therapeutic devices, which are commercially available and widely used in clinical practice.
- ItemOverview of compressed sensing: Sensing model, reconstruction algorithm, and its applications(Basel : MDPI, 2020) Li, Lixiang; Fang, Yuan; Liu, Liwei; Peng, Haipeng; Kurths, Jürgen; Yang, YixianWith the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors.
- ItemPhenological model intercomparison for estimating grapevine budbreak date (Vitis vinifera L.) in Europe(Basel : MDPI, 2020) Leolini, Luisa; Costafreda-Aumedes, Sergi; Santos, João A.; Menz, Christoph; Fraga, Helder; Molitor, Daniel; Merante, Paolo; Junk, Jürgen; Kartschall, Thomas; Destrac-Irvine, Agnès; van Leeuwen, Cornelis; Malheiro, Aureliano C.; Eiras-Dias, José; Silvestre, José; Dibari, Camilla; Bindi, Marco; Moriondo, MarcoBudbreak date in grapevine is strictly dependent on temperature, and the correct simulation of its occurrence is of great interest since it may have major consequences on the final yield and quality. In this study, we evaluated the reliability for budbreak simulation of two modeling approaches, the chilling-forcing (CF), which describes the entire dormancy period (endo-and eco-dormancy) and the forcing approach (F), which only describes the eco-dormancy. For this, we selected six phenological models that apply CF and F in dierent ways, which were tested on budbreak simulation of eight grapevine varieties cultivated at dierent latitudes in Europe. Although none of the compared models showed a clear supremacy over the others, models based on CF showed a generally higher estimation accuracy than F where fixed starting dates were adopted. In the latter models, the accurate simulation of budbreak was dependent on the selection of the starting date for forcing accumulation that changes according to the latitude, whereas CF models were independent. Indeed, distinct thermal requirements were found for the grapevine varieties cultivated in Northern and Southern Europe. This implies the need to improve modeling of the dormancy period to avoid under-or over-estimations of budbreak date under dierent environmental conditions. © 2020 by the authors.
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