Effects Of Extreme Events Research Articles

Retracted: Enriching the value‐at‐risk framework to ensemble empirical mode decomposition with an application to the European carbon market

AbstractUnlike common financial markets, the European carbon market is a typically heterogeneous market, characterised by multiple timescales and affected by extreme events. The traditional value‐at‐risk (VaR) with single‐timescale fails to deal with the multi‐timescale characteristics and the effects of extreme events, which can result in the VaR overestimation for carbon market risk. To measure accurately the risk on the European carbon market, we propose ensemble empirical mode decomposition (EEMD) based multiscale VaR approach. First, the EEMD algorithm is utilised to decompose the carbon price return into several intrinsic mode functions (IMFs) with different timescales and a residue, which are modelled respectively using the ARMA‐GARCH model to obtain their conditional variances at different timescales. Furthermore, the Iterated Cumulative Sums of Squares algorithm is employed to determine the windows of an extreme event, so as to identify the IMFs influenced by an extreme event and conduct an exponentially weighted moving average on their conditional variations. Finally, the VaRs of various IMFs and the residue are estimated to reconstruct the overall VaR, the validity of which is verified later. Then, we illustrate the results by considering several European carbon futures contracts. Compared with the traditional VaR framework with single timescale, the proposed multiscale VaR‐EEMD model can effectively reduce the influences of the heterogeneous environments (such as the influences of extreme events), and obtain a more accurate overall risk measure on the European carbon market. By acquiring the distributions of carbon market risks at different timescales, the proposed multiscale VaR‐EEMD estimation is capable of understanding the fluctuation characteristics more comprehensively, which can provide new perspectives for exploring the evolution law of the risks on the European carbon market.

Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China

Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR). The predicting skill of the state-of-the-art dynamic models on the CSR is still limited, leaving challenges in developing objective statistical predicting methods. A method for searching potential predicting skill of predictors (i.e., potential skill map, PSM) is proposed, which can be used to select predictors automatically based on the PSM, and a new automatic statistical prediction model of the CSR is established.Compared with traditional linear correlation analysis, the PSM using the cross-validation concept not only reflects the potential predicting skills of predictors on predictands, but is free from effects of extreme events. It is completely based on real-time statistical predicting procedure, which aims to find sufficient conditions for predictands in logical. The PSM is an important supplement to the traditional correlation coefficient map. They work together to provide potential predictors with necessary and sufficient conditions. The predictor automatic selector takes advantage of the idea of ensemble forecasting. It selects predictors with the most significant potential forecasting skill from the PSM, and then generates final forecast products by averaging a large number of predicting members. The year-by-year automatic selection of the predicators is thus realized. This solution doesn't rely on subjective experiences of foreasters, and also provides a new way to further investigate the predictability of the interannual variability of the East Asian summer monsoon. This new automatic statistical prediction model of the CSR based on the PSM and the predictor automatic selector shows a high reforecast skill for the CSR. In the 21-year reforecasting experiment from 1999 to 2019, predictors in the previous autumn and winter seasons are used to predict the CSR. Results show an average symbol agreement rate of 60% and the mean anomaly correlation coefficient of 0.436 between the reforecast and the observed CSR. As to the predicting skill (PS) score in the National Climate Center, the reforecast CSR reaches 71.00 in average. After variance correcting, the PS score further increases to 82.10, which is much higher than predicting skills of current dynamical models. It is noteworthy that the reforecast experiment in the present uses the first 12 multiple regression coefficients and EOF modes of the CSM, of which the first 4 multiple regression coefficients and EOF modes play a dominant role in the overall distribution of the CSM. By contrast, higher-order modes could further improve the reforecast skill by increasing the diversity of the reforecasting CSM, which represent their potential physical implications.

No perfect storm for crop yield failure in Germany

Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.

Stochastic modelling of sandstorms affecting the optimal operation and cleaning scheduling of air coolers in concentrated solar power plants

The operation performance of air-coolers in concentrated solar power plants decays due to particulate deposition on heat transfer surfaces. The deposition process can be seen as a stochastic phenomenon. A modelling approach is proposed to capture the uncertainty and the effect of extreme events, such as sandstorms, affecting the performance of plants located in dry places through dust or sand deposition on the air coolers. A case study of a concentrated solar power plant located in Dubai is analysed. Sandstorms generate acute and drastic fouling of the air coolers, and this is modelled as a stochastic process using historical aerosol dispersion data. Ten scenarios are generated by sampling the probability distribution of sandstorms occurrence and intensity. The optimal operation (cleaning schedule and airflow profiles) of the air coolers is established using Benders decomposition to solve the resulting large-scale mixed integer non-linear programming problem. The results of the stochastic scenarios demonstrate that substantial savings of $ 0.6 M − $ 2.7 M per year are achieved by the optimal operation. Cost is minimized by a combined reactive and proactive cleaning policy which accounts for the frequency, intensity and seasonal variability of sandstorms, in addition to the variability on local radiation and weather conditions.

The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice

Scholars have highlighted the role of Digital Technologies (DT) in enhancing productivity and performance in Small and Medium Enterprises (SMEs). However, there is limited evidence on the use of DT for dealing with the consequences of extreme events, such as COVID-19. We discuss this gap by (i) outlining potential research avenues and (ii) reflecting on the managerial implications of using DT within SMEs to deal with the repercussions of COVID-19 and securing business continuity.

The Huelva (Spain) Tsunami-Ready Station and Its Interaction with Storm Emma (March 2018)

The sea level station operating since 1996 at Mazagón (Huelva, Spain) has been progressively upgraded to fit tsunami warning requirements, due to its location in one of the main regions at risk. Its radar water level sensor was complemented in 2017, with the addition of a pressure sensor. The performance of both sea level sensors and their response to sea level oscillations, at different frequencies, is assessed. Particular emphasis is put on the effect of extreme events, such as Storm Emma, when alternative methods to obtain 1-min data are tested, in contrast to the one based on arithmetic means. The overall differences are small, for the whole period of study (centered-root-mean-square-error below 1 cm, for 5-min, and hourly data; similar tidal parameters and sea level oscillations with periods between 30 s and 5 min). However, during Storm Emma, the pressure sensor presents sensibly lower readings than the radar, with the centered-root-mean-square-error rising to 80 mm on the March 2nd 2018. A new method to compute 1-min data, based on medians, reduced this value to 10 mm for the same day.

Impact of extreme events on pollinator assemblages.

Insect pollinators face a number of well-documented threats that challenge their survival at an individual and community level. The effect of extreme events on pollinator assemblages has received little attention to date, partly due to a lack of consensus on what constitutes extreme, but also because robust pre-event data is often lacking. Here, the term SHOCK (Sudden, High-magnitude Opportunity for a Catastrophic 'Kick') is used to encompass attributes of extreme events that carry the potential to add additional challenges to insect communities already facing environmental stressors. Selected events from two SHOCK categories are explored (those with natural origins and those that are human-mediated). The value of studying single events is considered in the context of a third category; human-enhanced SHOCKs.

Predicting Extreme-Precipitation Effects on the Geomorphology of Small Mountain Catchments: Towards an Improved Understanding of the Consequences for Freshwater Biodiversity and Ecosystems

In 2015 an intense rainfall event hit the Valleys of the Trebbia, Nure, and Aveto watercourses in the Northern Apennines. In about 6 h a mesoscale convective system deployed a stunning amount of precipitation of 340 mm, with an extreme hourly rainfall intensity of >100 mm/h. It triggered debris flows along slopes and stream channels, landslides and floods, which caused serious damages. Through the optimal combination of rainfall data and radar volumes, in this work we present a detailed rainfall analysis, which will serve as a basis to create a quantitative correlation with debris flows over elementary hydrological units. We aim at providing an objective basis for future predictions, starting from the recognition of the forcing meteorological events, and then arriving at the prediction of triggering phenomena and to the debris-flow type. We further provide seven observations/case studies on the effects of extreme-precipitation events on freshwater environments in small mountain catchments. Extreme-precipitation events are becoming more frequent and widespread globally but their ecological effects are still insufficiently understood. In general, the effects of extreme events on inland-waters’ ecosystems are highly context-dependent, ranging from deleterious to beneficial. We therefore highlight the necessity of further studies to characterize these effects in more depth to be able to include appropriate mitigation measures in environmental planning and stewardship.

A Framework to Advance the Understanding of the Ecological Effects of Extreme Climate Events

Climate change is modifying disturbance regimes, affecting the severity and occurrence of extreme events. Current experiments investigating extreme events have a large diversity of experimental approaches and key aspects such as the interaction with other disturbances, the timing, and long-term effects are not usually incorporated in a standardized way. This lack of comparability among studies limits advances in this field of research. This study presents a framework that is comprised of two experimental approaches designed to test expected changes on disturbance regime due to climate change. These approaches test the effects of disturbances becoming more clustered and more extreme. They use common descriptor variables regardless of the type of disturbance and ecosystem. This framework is completed with a compilation of procedures that increase the realism of experiments in the aforementioned key aspects. The proposed framework favours comparability among studies and increases our understanding of extreme events. Examples to implement this framework are given using rocky shores as a case study. Far from being perfect, the purpose of this framework is to act as a starting point that triggers the comparability and refinement of these types of experiments needed to advance our understanding of the ecological effects of extreme events.

Extreme Events design and Mitigation Methods: A Review

Recently, extreme events have highlighted their potentially tragic effects on structural and infrastructure systems. Resilience of the Community to these extreme vents is an important issue of increasing more concern for developing design methods. Such extreme events scenarios involve many uncertainties, such as the intensity, location, and period. The extreme events may include those caused by various natural or manmade hazards, such as earthquake, strong winds, fire, blast, etc. Compared to other events, earthquake and wind are particularly critical due to their significant threats to the global structure performance and more challenges for design. Researchers have recognized that proper evaluation, modeling, and assessment of the effects of extreme events are fundamental to ensure the desired performance of structures. Therefore, the concern for developing appropriate methodologies to evaluate and design structures that can withstand the effects of extreme events has become a very active field of research in recent years. Improvement of building codes and development of new strategies are needed to mitigate the disastrous effects of extreme events. This paper presents a comprehensive review of literature surrounding designing building structures for extreme events. First, a general overview of the extreme events design and different objectives of approaches is conducted. Furthermore, a review related literature surrounding designing for earthquake resistance guidelines is presented, also highlights Performance-Based Seismic Design objectives. The available literature includes many studies for the provisions included in different design codes (China, United States and Europe). A review of literature related to wind resistance design with an overview of Performance Based Wind Design of building design method for the control of winds impacting on building structures is also presented.

Coastal oceanographic signatures of heat waves and extreme events of dense water formation during the period 2002-2012 (Barcelona, NW Mediterranean)

In a global context of climate change affecting the marine environment, it is important to consider the effect of extreme events in driving ecological change and to gain a better understanding of conditions to be expected under future scenarios. In this study we focus on monthly oceanographic data collected off Barcelona city during the period 2002-2012, in which extreme air temperatures and exceptional oceanographic events were reported in the western Mediterranean basin. These included two extreme heat waves and major episodes of dense water formation that produced unusually large deep-water contributions, induced oceanographic changes in the coastal zone and caused significant alterations to the marine ecosystem. To determine whether routine monitoring of oceanographic variables in a coastal zone can provide information for recognizing such large-scale events, temperature, salinity, turbidity and fluorescence were analysed to identify their signatures. The results provide an additional tool for monitoring oceanographic events and improving forecasts and future projections.

Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach

Unlike common financial markets, the European carbon market is a typically heterogeneous market, characterized by multiple timescales and affected by extreme events. The traditional Value-at-Risk (VaR) with single-timescale fails to deal with the multi-timescale characteristics and the effects of extreme events, which can result in the VaR overestimation for carbon market risk. To measure accurately the risk on the European carbon market, we propose an empirical mode decomposition (EMD) based multiscale VaR approach. Firstly, the EMD algorithm is utilized to decompose the carbon price return into several intrinsic mode functions (IMFs) with different timescales and a residue, which are modeled respectively using the ARMA–EGARCH model to obtain their conditional variances at different timescales. Furthermore, the Iterated Cumulative Sums of Squares algorithm is employed to determine the windows of an extreme event, so as to identify the IMFs influenced by an extreme event and conduct an exponentially weighted moving average on their conditional variations. Finally, the VaRs of various IMFs and the residue are estimated to reconstruct the overall VaR, the validity of which is verified later. Then, we illustrate the results by considering several European carbon futures contracts. Compared with the traditional VaR framework with single timescale, the proposed multiscale VaR–EMD model can effectively reduce the influences of the heterogeneous environments (such as the influences of extreme events), and obtain a more accurate overall risk measure on the European carbon market. By acquiring the distributions of carbon market risks at different timescales, the proposed multiscale VaR–EMD estimation is capable of understanding the fluctuation characteristics more comprehensively, which can provide new perspectives for exploring the evolution law of the risks on the European carbon market.

FLOODS AND SOCIAL VULNERABILITY: STUDY ON THE XINGU RIVER IN ALTAMIRA / PA

Abstract The objective of this research was to classify the social vulnerability in Altamira-PA, considering the occurrence of seasonal floods and the future scenario of stabilization of the water level in the flood quota. The Social Vulnerability Index was determined by using fluviometric station data provided by ANA and socioeconomic variables from IBGE. The results indicate a moderate to low vulnerability that does not reflect the socio-spatial environment of the area, where the alert level of 6 m is recurrently exceeded during the floods of the Xingu river. The Belo Monte hydroelectric power plant will ensure the maintenance of the level of floods, which implies in the demand of urban planning that can reduce the liabilities generated by both the maintenance of wetlands, and for the extensive relocation of people, in addition by studies that assess the influence and consequence of extreme events in the region.

Enriching the VaR framework to EEMD with an application to the European carbon market

AbstractUnlike common financial markets, the European carbon market is a typically heterogeneous market, characterized by multiple timescales, and affected by extreme events. The traditional value‐at‐risk (VaR) with single‐timescale fails to deal with the multi‐timescale characteristics and the effects of extreme events, which can result in the VaR overestimation for carbon market risk. To measure accurately the risk on the European carbon market, we propose an ensemble empirical mode decomposition (EEMD)‐based multiscale VaR approach. First, the EEMD algorithm is utilized to decompose the carbon price return into several intrinsic mode functions (IMFs) with different timescales and a residue, which are modelled, respectively, using the ARMA‐Generalized Autoregressive Conditional Heteroscedasticity model to obtain their conditional variances at different timescales. Furthermore, the Iterated Cumulative Sums of Squares algorithm is employed to determine the windows of an extreme event, so as to identify the IMFs influenced by an extreme event and conduct an exponentially weighted moving average on their conditional variations. Finally, the VaRs of various IMFs and the residue are estimated to reconstruct the overall VaR, the validity of which is verified later. Then, we illustrate the results by considering several European carbon futures contracts. Compared with the traditional VaR framework with single timescale, the proposed multiscale VaR‐EEMD model can effectively reduce the influences of the heterogeneous environments (such as the influences of extreme events) and obtain a more accurate overall risk measure on the European carbon market. By acquiring the distributions of carbon market risks at different timescales, the proposed multiscale VaR‐EEMD estimation is capable of understanding the fluctuation characteristics more comprehensively, which can provide new perspectives for exploring the evolution law of the risks on the European carbon market.

Vulnerability assessment including tangible and intangible components in the index composition: An Amazon case study of flooding and flash flooding

The vulnerability of cities and communities in the Amazon to flooding and flash flooding is increasing. The effects of extreme events on populations vary across landscapes, causing vulnerability to differ spatially. Traditional vulnerability studies in Brazil and across the world have used the vulnerability index for the country and, more recently, municipality scales. The vulnerability dimensions are exposure, sensitivity, and adaptive capacity. For each of these dimensions, there is a group of indicators that constitutes a vulnerability index using quantitative data. Several vulnerability assessments have used sensitivity and exposure analyses and, recently, adaptive capacity has been considered. The Geographical Information Systems (GIS) analysis allows spatial regional modeling using quantitative vulnerability indicators. This paper presents a local-scale vulnerability assessment in an urban Amazonian area, Santarém City, using interdisciplinary methods. Data for exposure and sensitivity were gathered by remote sensing and census data, respectively. However, adaptive capacity refers to local capacities, whether infrastructural or not, and the latter were gathered by qualitative participatory methods. For the mixed data used to study adaptive capacity, we consider tangible components for countable infrastructure that can cope with hazards, and intangible components that reflect social activities based on risk perceptions and collective action. The results indicate that over 80% of the area is highly or moderately vulnerable to flooding and flash flooding. Exposure and adaptive capacity were determinants of the results. Lower values of adaptive capacity play a significant role in vulnerability enhancement.

Effects of Extreme Events on Arsenic Cycling in Salt Marshes

AbstractExtreme events such as storm surges, intense precipitation, and supermoons cause anomalous and large fluctuations in water level in tidal salt marshes, which impacts the sediment biogeochemistry that dictates arsenic (As) cycling. In addition to changes in water level, which impacts soil redox potential, these extreme events may also change salinity due to freshwater inputs from precipitation or saltwater inputs due to surge. It is currently unknown how As mobility in tidal salt marshes will be impacted by extreme events, as fluctuations in salinity and redox potential may act synergistically to mobilize As. To investigate impacts of extreme events on As cycling in tidal salt marshes, we conducted a combined laboratory and field investigation. We monitored pore water and soil samples before, during, and after two extreme events: a supermoon lunar eclipse followed by a storm surge and precipitation induced by Hurricane Joaquin in fall 2015 at the St. Jones Reserve in Dover, Delaware, a representative tidal salt marsh in the Mid‐Atlantic United States. We also conducted soil incubations of marsh sediments in batch and in flow‐through experiments in which redox potential and/or salinity were manipulated. Field investigations showed that pore water As was inversely proportional to redox potential. During the extreme events, a distinct pulse of As was observed in the pore water with maximum salinity. Combined field and laboratory investigations revealed that this As pulse is likely due to rapid changes in salinity. These results have implications for As mobility in the face of extreme weather variability.

International stock market contagion: A CEEMDAN wavelet analysis

This paper investigates the contagion effect among stock markets (Asia, European and America) under time varying frequencies by use of a CEEMDAN wavelet (complete ensemble empirical mode decomposition with adaptive noise) model. Firstly, we decompose stock index return into different independent intrinsic mode functions and wavelet decomposition functions. Secondly, we reconstruct the independent IMFs and wavelet decomposition functions into three components: a high-frequency component (effects of irregular events), a low-frequency component (effects of extreme events) and the long-term trend. Thirdly, we assess the accumulated impulse response and analyze the stock markets contagion under time- varying frequencies. Results show that shocks caused by irregular events and extreme events can be transmitted between different stock markets. Moreover, shocks caused by irregular events can pose sudden and short-term risk contagion to stock returns. And shocks caused by extreme events can pose positive and sustained risk contagion to stock returns. Also, we compare our results with those from the discrete wavelet transform model and findings in the literature.

Breaks in MODIS time series portend vegetation change: verification using long‐term data in an arid grassland ecosystem

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of extreme events will be manifest in an ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations in rainfall from long-term directional increases or decreases in precipitation. A tool that successfully distinguishes seasonal from directional biomass responses would allow land managers to make informed decisions about prioritizing mitigation strategies, allocating human resource monitoring efforts, and mobilizing resources to withstand extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semiarid landscape in southern New Mexico to verify the use of Normalized Difference Vegetation Index (NDVI) time series derived from 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data as a proxy for changes in aboveground productivity. This period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks for Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites that differed in species richness, vegetation cover, and soil properties. We found that 34 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall.

Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines.

1. Climate change and extreme events, such as drought, threaten ecosystems worldwide and in particular mountain ecosystems, where species often live at their environmental tolerance limits. In the European Alps, plant communities are also influenced by land-use abandonment leading to woody encroachment of subalpine and alpine grasslands. 2. In this study, we explored how the forest-grassland ecotone of Alpine treelines will respond to gradual climate warming, drought events and land-use change in terms of forest expansion rates, taxonomic diversity and functional composition. We used a previously validated dynamic vegetation model, FATE-HD, parameterised for plant communities in the Ecrins National Park in the French Alps. 3. Our results showed that intense drought counteracted the forest expansion at higher elevations driven by land-use abandonment and climate change, especially when combined with high drought frequency (occurring every 2 or less than 2 years). 4. Furthermore, intense and frequent drought accelerated the rates of taxonomic change and resulted in overall higher taxonomic spatial heterogeneity of the ecotone than would be expected under gradual climate and land-use changes only. 5. Synthesis and applications. The results from our model show that intense and frequent drought counteracts forest expansion driven by climate and land-use changes in the forest-grassland ecotone of Alpine treelines. We argue that land-use planning must consider the effects of extreme events, such as drought, as well as climate and land-use changes, since extreme events might interfere with trends predicted under gradual climate warming and agricultural abandonment.

Is the future blue-green or brown? The effects of extreme events on phytoplankton dynamics in a semi-arid man-made lake

In arid and semi-arid regions, a hydrological regime characterized by an annual cycle of drought and rainy seasons changes the volume and retention time of reservoirs. Such changes affect the limnological characteristics and lead to changes on phytoplankton community. Phytoplankton seasonal succession was studied in a Brazilian eutrophic semi-arid water supply reservoir (Cruzeta). In this study, the changes in the biomass and species composition of phytoplankton during two annual hydrological cycles were analyzed, and the driving factors were evaluated. The composition of phytoplankton alternated between filamentous cyanobacteria, such as Cylindrospermopsis raciborskii, under conditions of mixing, and colonial species, such as Microcystis panniformis and Sphaerocavum brasiliensis, under conditions of high water column stability. The lower water level during a severe drought favored adaptive phytoplankton species with low requirement for resources, such as diatoms and cryptomonads. Extreme events, i.e., torrential rains and severe droughts, governed by the hydrological regime of the semi-arid region led to strong altered availability of resources in the watershed, directing the spatial and temporal dynamics of the phytoplankton in the Cruzeta man-made lake. The results showed an unusual behavior of the phytoplankton community contradicting the expectations about the climatic change scenario. Instead of an expected increase in cyanobacteria, the severe drought led to low biomass and resources, favoring diatoms and cryptomonads.