A yearly maximum sea level simulator and its applications: A Stockholm case study

A yearly maximum sea level simulator for Stockholm is presented. The simulator combines extreme sea level estimates and mean sea level rise projections into a joint probabilistic framework. The framework can be used, for example, to assess the risk that new structures placed at the current minimum allowed height above the sea level can become flooded in the future. Such assessments can be used to underpin future building free levels, which would be a great improvement over the much more arbitrary criteria in use today. Another strong point of the framework is that it can be used to quantify the influence of uncertainties in mean sea level projections, estimates of sea level extremes and future emission scenarios on the risk of flooding. For Stockholm mean sea level uncertainty is found to be much more important than extreme sea level uncertainty. The framework is also set-up to test adaptation measures. It is found that protections that are built once the mean sea level has risen above some given threshold can be very efficient. Lastly, the framework is embedded into a simple decision problem that can be used to calculate risk/reward ratios for land development as a function of height above today’s mean sea level.

Diverse Responses of Phenology in Multi-Grassland to Environmental Factors on Qinghai-Tibetan Plateau in China

Revealing grassland growing season spatial patterns and their climatic controls is crucial for the understanding of the productivity change mechanism in regional terrestrial ecosystem. However, the multi-grassland phenological factors are different, which has not been well studied. In this paper, the spatio-temporal patterns of the grassland start of the growing season (SOS) and the end of growing season (EOS) were investigated using MODIS Normalized Difference Vegetation Index (NDVI) on the Qinghai-Tibetan Plateau (QTP) during 2000 to 2019. At the same time, we analyzed the factors (including extreme and mean climate, drought, solar radiation, etc.) regulating grassland phenology under ongoing climate change. The results showed that the SOS appeared first in mountain meadow, shrub-tussock, temperature steppe and desert, then in alpine steppe and alpine meadow, showed a significant advancing tendency in all types. The EOS appeared first in temperature steppe, alpine steppe and alpine meadow, then in mountain meadow, shrub-tussock and desert. Further analysis indicated that the decrease of yearly minimum value of daily minimum temperature (TNN), yearly maximum value of daily minimum temperature (TNX), Temperature vegetation dryness index (TVDI) and the increase of yearly maximum consecutive five-day precipitation (RX5day) advance the grassland spring phenology, whereas the increase of solar radiation (SR) delay the grassland spring phenology. Meanwhile, SOS and its change rate showed the trend of significant delay and decline with the increase of altitude, respectively. We also found that the decrease of TVDI, TNN and the increase of yearly mean value of temperature (MAT_MEAN), yearly mean value of daily maximum temperature (MAT_MAX) and yearly mean value of daily minimum temperature (MAT_MIN) advanced the autumn phenology. The EOS and its change rate advance and increase with increasing altitude, respectively.

The Sensitivity of Vegetation Phenology to Extreme Climate Indices in the Loess Plateau, China

Climate changes, especially increased temperatures, and precipitation changes, have significant impacts on vegetation phenology. However, the response of vegetation phenology to the extreme climate in the Loess Plateau in Northwest China remains poorly quantified. The research described here analyzed the spatial change in vegetation phenology and the response of vegetation phenology to climate change in the Loess Plateau from 2001 to 2018, using data from seven extreme climate indices based on the ridge regression method. The results showed that extreme climate indexes, TNn (yearly minimum value of the daily minimum temperature), TXx (yearly maximum value of the daily maximum temperature), and RX5day (yearly maximum consecutive five-day precipitation) progressively increased from 2001 to 2018 in the Loess Plateau region, but decrease trend was found in DRT (diurnal temperature range). The start of the growing season (SOS) of vegetation gradually advanced with precipitation from northwest to southeast, and the rate was +0.38 d/a. The overall vegetation end of the growing season (EOS) was delayed, and the trend was −2.83 d/a. The sensitivity of the different vegetation phenology to different extreme weather indices showed obvious spatial differences, the sensitivity coefficient of SOS being mainly positive in the region, whereas the sensitivity coefficient of EOS was negative generally. More sensitivity was found in the EOS to extreme climate indexes than in the SOS. Forest, shrubland and grassland have similar responses to DRT and TNn; namely, both SOS and EOS are advanced with the increase in DRT and delayed with the increase in TNn (the sensitivity coefficient is quite different) but have different responses to RX5day and TXx. These results reveal that extreme climate events have a greater impact on vegetation EOS than on vegetation SOS, with these effects varying with vegetation types. This research can provide a scientific basis for formulating a scientific basis for regional vegetation restoration strategies and disaster prediction on the Loess Plateau.

An Attempt to Utilize a Regional Dew Formation Model in Kenya

Model evaluation against experimental data is an important step towards accurate model predictions and simulations. Here, we evaluated an energy-balance model to predict dew formation occurrence and estimate its amount for East-African arid-climate conditions against 13 months of experimental dew harvesting data in Maktau, Kenya. The model was capable of predicting the dew formation occurrence effectively. However, it overestimated the harvestable dew amount by about a ratio of 1.7. As such, a factor of 0.6 was applied for a long-term period (1979–2018) to investigate the spatial and temporal variation of the dew formation in Kenya. The annual average of dew occurrence in Kenya was ~130 days with dew yield > 0.1 L/m2/day. The dew formation showed a seasonal cycle with the maximum yield in winter and minimum in summer. Three major dew formation zones were identified after cluster analysis: arid and semi-arid regions; mountain regions; and coastal regions. The average daily and yearly maximum dew yield were 0.05 and 18; 0.9 and 25; and 0.15 and 40 L/m2/day; respectively. A precise prediction of dew occurrence and dew yield is very challenging due to inherent limitations in numerical models and meteorological input parameters.

Hypothesis on impact of winter conditions on annual organic production in the northern Adriatic

<p>This study evaluates existing hypothesis according to which intensity of local winter primary production (may be high, influencing annual means), controlled by the degree of the spreading of Po River waters across the northern Adriatic (NAd), reflects on secondary annual production (microzooplankton and anchovy) of the ongoing year.</p><p>The analysis extends over a four-year period 2017-2020.</p><p>In 2017, in the open western NAd, close to the Po River delta, nutrients and phytoplankton abundances reached their yearly maximum in winter (February and March, respectively). By the end of winter, an anticyclonic gyre formed in the eastern part of the NAd, capturing waters advected from western NAd region. In the gyre area, microzooplankton abundance reached the yearly maximum in spring (June). A month later, at the same position, the abundance of the allochthonous Ctenophora Mnemiopsis leidyi that feeds on microzooplankton, along with the concentration of  Dissolved Organic matter and its Carbon (DOC) fraction, reached yearly peaks. In the western NAd, within another gyre (cyclonic), maxima in the microzooplankton abundances and DOC were recorded in spring. Results point to importance of winter conditions in yearly production cycle. In line with the existing hypothesis, phytoplankton abundance in winter 2017 was above the long-term average and coupled with extremely high zooplankton abundances and DOC concentrations in some of the following, spring or summer, months. Later, during summer, phytoplankton abundances were rather low.</p><p> </p><p>In 2018 and 2019, the data collected in the NAd were rather scarce. In 2018 no winter data were available to test the hypothesis. In 2019, high abundances of microzooplankton was observed in March, and later in September an increase in M. leidyi, which might indicate that 2019 was again a year rich in organic production.</p><p>In 2020, the above average concentrations of nutrients and chlorophyll a in winter occurred along with very high concentrations of DOC and an abundance of M. leidyi in summer.</p><p>Data collected in 2017, 2019 and 2020 support the hypothesis, pointing to large organic outputs after winters rich in production.  Numerical models show that the NAd was mostly “separated” from the rest of the Adriatic Sea during 2017-2020 by a northern branch of a large cyclonic gyre with high salinity water (from central Adriatic and/or Kvarner Bay) entering the NAd along the eastern (Istrian) coast. Such circulation system could favour the Po River waters spreading across the NAd, inducing high primary production in winter, at the beginning of the yearly pelagic cycle, with the retention/accumulation of organic matter produced in the following months.</p><p>The NAd basin has been exposed to very high salinity water intrusions since 2015 (CMR data). These occurrences, together with the formations of specific circulation patterns described above, result from regional atmospheric and/or oceanographic processes which are not yet fully understood. However, using projections of temperature and salinity from a numerical approach, and following the observed biological relations, a prediction of the organic matter production in the NAd can be obtained.</p><p>This work has been supported in part by Croatian Science Foundation under the projects EcoRENA (IP-06-2016), MARRES (IP-2018-01-1717) and ADIOS (IP-2016-06-1955).</p>

Effect of Solar Tilt Angles on Photovoltaic Module Performance: A Behavioral Optimization Approach

In the present study, performance analyses of a solar module are made through the optimal variation of solar tilt angle, pertaining to the maximum generation of solar energy. The work has been carried out for a particular location at Tripura, in India, considering three different cases on an annual basis. An intelligent behavioural based algorithm, known as artificial bee algorithm (ABC), has been implemented for finding the optimal orientation of solar angle in analysing the performance. The result shows marginal differences are obtained in producing yearly maximum solar energy for different orientations of the PV module. It has been observed that the maximum average solar energy is obtained for the case where continuous adjustment is made by rotating the plane about the horizontal east-west axis within 20° to 30° tilt angle. The computed maximum and minimum of the monthly average efficiency is 10.9% and 8.7%, respectively. Further, a comparative study has been performed in generating average solar energy through optimal tilt angle by the implementation of Perturb & Observe method (P&O). The monthly average solar power computed by P&O method resulted better in a range of 2% to 15% in comparison to that obtained by ABC. While on the other hand, the efficiency computed by ABC algorithm was 15% to 19% better than that evaluated by P&O method for all the cases studied in the present work.

Measuring PV Module Performance at Different Tilt Angles in Southern Iraq Based Simulation

This paper presented that how to calculate the tilt angles and solar irradiance on photovoltaic (PV) module in southern Iraq (latitude 30° N). The latitude and day number of the city is taken into account to calculate the tilt angle and solar irradiance by using a mathematical equation. The optimum tilt angles of PV module in southern Iraq are range from 38° to 84°. The yearly maximum total and average solar irradiance is needed to determine the optimum tilt angle of PV module. The result shows that 50° of tilt angle is the best performance of PV module in southern Iraq.

Monitoring and simulation of hydrothermal conditions indicating the deteriorating stability of a perennially frozen moraine dam in the Himalayas

ABSTRACTThermal and hydrological dynamics and their impacts on the stability of a moraine dam were analyzed and simulated for the Longbasaba Lake in the Himalaya, based on soil temperature, moisture and heat flux data observed at different depths in the dam from 2012 to 2016. Annual average heat income is greater than heat expenditure on the dam surface. The mean annual temperature at observed the depths of 0–150 cm is >0°C, although the average annual air temperature was −3.6°C over the dam, indicating a relatively larger temperature difference between moraine dam and air. The volumetric soil moisture content is relatively low with an annual average of 5%, peaking after the snow cover melting and active layer thawing. Simulation results indicate that the average yearly maximum thawing depth has been ~0.3 m deeper than the average yearly maximum freezing depth during the observation period. In the past 55 years, the yearly maximum thawing depth has increased, while yearly maximum freezing depth has decreased, implying that the permafrost in the dam has been deteriorating. The annual surplus heat and increasing permafrost thawing depth will result in further deterioration of permafrost and melting of buried ice in the dam, thereby decreasing its stability.

Dynamic mean variance asset allocation: Tests for robustness

We consider a portfolio consisting of a risk-free bond and an equity index which follows a jump diffusion process. Parameters for the inflation-adjusted return of the stock index and the risk-free bond are determined by examining 89 years of data. The optimal dynamic asset allocation strategy for a long-term pre-commitment mean variance (MV) investor is determined by numerically solving a Hamilton–Jacobi–Bellman partial integro-differential equation. The MV strategy is mathematically equivalent to minimizing the quadratic shortfall of the target terminal wealth. We incorporate realistic constraints on the strategy: discrete rebalancing (yearly), maximum leverage, and no trading if insolvent. Extensive synthetic market tests and resampled backtests of historical data indicate that the multi-period MV strategy achieves approximately the same expected terminal wealth as a constant weight strategy, but with much smaller variance and probability of shortfall.