Challenges for Diadromous Fishes in a Dynamic Global Environment

2009 ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Habitat Characteristics ◽  
Explanatory Variables ◽  
Temperature And Precipitation ◽  
Management Plans ◽  
The Face ◽  
Uncertain Future ◽  
Diadromous Species

<em>Abstract</em>.-Climate change can have an effect on species distributions. The 1900 distribution and potential future distribution of diadromous fish in Europe, North Africa, and the Middle East were explored using generalized additive models (GAMs) and selected habitat characteristics of 196 basins. Robust presence-absence models were built for 20 of the 28 diadromous species in the study area using longitude, annual temperature, drainage surface area, annual precipitation, and source elevation as explanatory variables. Inspection of the relationship between each variable and species presence-absence revealed that the GAMs were generally interpretable and plausible. Given the predicted rise in annual temperature in climate models ranging between 1°C and 7°C by 2100, the fish species were classified according to those losing suitable basins, those gaining suitable basins, and those showing little or no change. It was found that the climate envelopes based on temperature and precipitation for diadromous species would, in general, be shifted farther northeastwards by 2100, and these shifting ranges were comparable with those assessed in other studies. The uncertain future of some species was highlighted, and it was concluded that conservation policy and management plans will need to be revised in the face of climate change.

scholarly journals A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

2021 ◽  
Author(s):  
Thomas Noël ◽  
Harilaos Loukos ◽  
Dimitri Defrance
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Models ◽  
Mapping Method ◽  
Cumulative Distribution ◽  
Climate Projections ◽  
Climate Modelling ◽  
Temperature And Precipitation ◽  
Surface Climate ◽  
Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

scholarly journals THE IMPACTS OF CLIMATE CHANGE ON ENERGY-WATER SYSTEMS AND ECONOMIC ANALYSIS

2020 ◽  
Vol 53 (2F) ◽  
pp. 1-17
Author(s):  
Safieh Javadinejad
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Electricity Consumption ◽  
Coupled Model ◽  
Planning System ◽  
Temperature And Precipitation ◽  
Future Time Period ◽  
History Of ◽  
Climate Change Model ◽  
Policy And Planning

In order to develop a valued decision-support system for climate alteration policy and planning, recognizing the regionally-specific features of the climate change, energy-water nexus, and the history of the current and possible future climate, water and energy supply systems is necessary. This paper presents an integrated climate change, water/energy modeling platform which allows tailored climate alteration and water-energy assessments. This modeling platform is established and described in details based on particular regional circumstances. The modeling platform involves linking three different models, including the climate change model from Coupled Model Intercomparison Project Phase 5 under the most severe scenario (Representative Concentration Pathways, Water Evaluation, and Planning system and the Long-range Energy Alternatives Planning system). This is to understand the impacts of climate variability (changes in temperature and precipitation) on water and electricity consumption in Zayandeh Rud River Basin (Central Iran) for the current (1971–2005) and future time period (2006–2040). Climate models have projected that the temperature will increase by 7 °C and precipitation will decrease by 44%, it is also proposed that electricity imports will rise during a severe dry scenario in the basin, while power generation will decrease around 8%.

scholarly journals Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data

2015 ◽  
Vol 12 (3) ◽  
pp. 2657-2706 ◽  
Author(s):  
T. Olsson ◽  
J. Jakkila ◽  
N. Veijalainen ◽  
L. Backman ◽  
J. Kaurola ◽  
...  
Keyword(s):  
Climate Change ◽  
Seasonal Changes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Catchment Scale ◽  
Temperature And Precipitation

Abstract. Assessment of climate change impacts on climate and hydrology on catchment scale requires reliable information about the average values and climate fluctuations of the past, present and future. Regional Climate Models (RCMs) used in impact studies often produce biased time series of meteorological variables. In this study bias correction of RCM temperature and precipitation for Finland is carried out using different versions of distribution based scaling (DBS) method. The DBS adjusted RCM data is used as input of a hydrological model to simulate changes in discharges in four study catchments in different parts of Finland. The annual mean discharges and seasonal variation simulated with the DBS adjusted temperature and precipitation data are sufficiently close to observed discharges in the control period (1961–2000) and produce more realistic projections for mean annual and seasonal changes in discharges than the uncorrected RCM data. Furthermore, with most scenarios the DBS method used preserves the temperature and precipitation trends of the uncorrected RCM data during 1961–2100. However, if the biases in the mean or the SD of the uncorrected temperatures are large, significant biases after DBS adjustment may remain or temperature trends may change, increasing the uncertainty of climate change projections. The DBS method influences especially the projected seasonal changes in discharges and the use of uncorrected data can produce unrealistic seasonal discharges and changes. The projected changes in annual mean discharges are moderate or small, but seasonal distribution of discharges will change significantly.

scholarly journals Are the Transient and Equilibrium Climate Change Patterns Similar in Response to Increased CO2?

2020 ◽  
Vol 33 (18) ◽  
pp. 8003-8023
Author(s):  
Danqing Huang ◽  
Aiguo Dai ◽  
Jian Zhu
Keyword(s):  
Climate Change ◽  
North Atlantic ◽  
Climate Models ◽  
Surface Air Temperature ◽  
The Arctic ◽  
The North ◽  
Temperature And Precipitation ◽  
Time Periods ◽  
The North Atlantic ◽  
Subtropical Oceans

AbstractAfter a CO2 increase, whether the early transient and final equilibrium climate change patterns are similar has major implications. Here, we analyze long-term simulations from multiple climate models under increased CO2, together with the extended simulations from CMIP5, to compare the transient and equilibrium climate change patterns under different forcing scenarios. Results show that the normalized warming patterns (per 1 K of global warming) are broadly similar among different forcing scenarios (including abrupt 2 × CO2, 4 × CO2, and 1% CO2 increase per year) and during different time periods, except for the first 50 years or so when warming is weaker over the North Atlantic and Southern Ocean but stronger over most continents. During the first 200 years, this consistency is stronger over land than over ocean, but is lower in midlatitudes than other regions. Normalized precipitation change patterns are also similar, albeit to a lesser degree, among different forcing scenarios and across different time periods, although noticeable differences exist during the first few hundred years with smaller increases over the tropical Pacific. Precipitation over many subtropical oceans and land areas decreases consistently under different forcing scenarios and over all time periods. In particular, the transient and near-equilibrium change patterns for both surface air temperature and precipitation are similar over most of the globe, except for the North Atlantic warming hole, which is mainly a transient feature. The Arctic amplification and land–ocean warming contrast are largest during the first 100–200 years after CO2 quadrupling but they still exist in the equilibrium response.

Adaptive Strategies of Small Family Farms in the Face of Climate Change

2019 ◽  
pp. 316-330
Author(s):  
Mohamed El Amrani
Keyword(s):  
Climate Change ◽  
Negative Impact ◽  
Global Changes ◽  
Family Farms ◽  
Precipitation Regime ◽  
Innovative Strategies ◽  
Mitigation And Adaptation ◽  
Temperature And Precipitation ◽  
Significant Negative Impact ◽  
The Face

Climate change is now an accepted reality. It is felt globally through increased temperature and precipitation regime disruption and increased frequency of extreme events such as floods and droughts. In the absence of effective mitigation and adaptation actions, these changes could have significant negative impact on the sustainability of agriculture and the resilience of populations especially in areas with fragile ecology. However, these changes remain an issue that is difficult to grasp and still not well integrated into management strategies at the farm, sector, and territory levels. The objectives of this research are to describe the production systems, and to study the resilience strategies, perception, and adaptive practices of farms in the Tleta watershed in Northwest Morocco in the face of climate change and landscape dynamics. It describes farming systems and activities, attempts to analyze how farmers perceive global changes in their landscape, and adopts innovative strategies and practices to adapt to them. It also shows that the actions of institutional actors in the area that can contribute to the resilience of the populations are numerous but remain fragmentary and lack integration.

Adaptive Strategies of Small Family Farms in the Face of Climate Change

2022 ◽  
pp. 1917-1931
Author(s):  
Mohamed El Amrani
Keyword(s):  
Climate Change ◽  
Negative Impact ◽  
Global Changes ◽  
Family Farms ◽  
Precipitation Regime ◽  
Innovative Strategies ◽  
Mitigation And Adaptation ◽  
Temperature And Precipitation ◽  
Significant Negative Impact ◽  
The Face

Climate change is now an accepted reality. It is felt globally through increased temperature and precipitation regime disruption and increased frequency of extreme events such as floods and droughts. In the absence of effective mitigation and adaptation actions, these changes could have significant negative impact on the sustainability of agriculture and the resilience of populations especially in areas with fragile ecology. However, these changes remain an issue that is difficult to grasp and still not well integrated into management strategies at the farm, sector, and territory levels. The objectives of this research are to describe the production systems, and to study the resilience strategies, perception, and adaptive practices of farms in the Tleta watershed in Northwest Morocco in the face of climate change and landscape dynamics. It describes farming systems and activities, attempts to analyze how farmers perceive global changes in their landscape, and adopts innovative strategies and practices to adapt to them. It also shows that the actions of institutional actors in the area that can contribute to the resilience of the populations are numerous but remain fragmentary and lack integration.

Exploring the role of observational uncertainty and resolution mismatch in the application of bias adjustment methods

2020 ◽  
Author(s):  
Ana Casanueva ◽  
Sixto Herrera ◽  
Maialen Iturbide ◽  
Stefan Lange ◽  
Martin Jury ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Change Signal ◽  
Climate Indices ◽  
Bias Adjustment ◽  
Temperature And Precipitation ◽  
Observational Uncertainty ◽  
Distribution Mapping ◽  
Systematic Biases ◽  
The Impact

&lt;p&gt;Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many bias adjustment methods, which merely correct for deficiencies in the distribution, have been developed. Despite adjusting the desired features under historical simulations, their application in a climate change context is subject to additional uncertainties and modifications of the change signals, especially for climate indices which have not been tackled by the methods. In this sense, some of the commonly-used bias adjustment methods allow changes of the signals, which appear by construction in case of intensity-dependent biases; some others ensure the trends in some statistics of the original, raw models. Two relevant sources of uncertainty, often overlooked, which bring further uncertainties are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect).&lt;/p&gt;&lt;p&gt;In the present work, we assess the impact of these factors on the climate change signal of a set of climate indices of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature -empirical or parametric-, fitted parameters and preservation of the signals) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping -QDM-, scaled distribution mapping -SDM- and the method from the third phase of ISIMIP -ISIMIP3) preserve better the raw signals for the different indices and variables (not all preserved by construction). However they rely largely on the reference dataset used for calibration, thus present a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20km) and low (approximately 120km) spatial resolutions.&lt;/p&gt;

scholarly journals Snowfall in the Himalayas: an uncertain future from a little-known past

2015 ◽  
Vol 9 (3) ◽  
pp. 1147-1167 ◽  
Author(s):  
E. Viste ◽  
A. Sorteberg
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Reanalysis Data ◽  
Indus Basin ◽  
Data Sets ◽  
Anthropogenic Forcing ◽  
Temperature And Precipitation ◽  
The Mean ◽  
Uncertain Future ◽  
Relative Change

Abstract. Snow and ice provide large amounts of meltwater to the Indus, Ganges and Brahmaputra rivers. This study combines present-day observations and reanalysis data with climate model projections to estimate the amount of snow falling over the basins today and in the last decades of the 21st century. Estimates of present-day snowfall based on a combination of temperature and precipitation from reanalysis data and observations vary by factors of 2–4. The spread is large, not just between the reanalysis and the observations but also between the different observational data sets. With the strongest anthropogenic forcing scenario (RCP8.5), the climate models project reductions in annual snowfall by 30–50% in the Indus Basin, 50–60% in the Ganges Basin and 50–70% in the Brahmaputra Basin by 2071–2100. The reduction is due to increasing temperatures, as the mean of the models show constant or increasing precipitation throughout the year in most of the region. With the strongest anthropogenic forcing scenario, the mean elevation where rain changes to snow – the rain/snow line – creeps upward by 400–900 m, in most of the region by 700–900 meters. The largest relative change in snowfall is seen in the upper westernmost sub-basins of the Brahmaputra. With the strongest forcing scenario, most of this region will have temperatures above freezing, especially in the summer. The projected reduction in annual snowfall is 65–75%. In the upper Indus, the effect of a warmer climate on snowfall is less extreme, as most of the terrain is high enough to have temperatures sufficiently far below freezing today. A 20–40% reduction in annual snowfall is projected.

Stability of River Flow Regimes

1992 ◽  
Vol 23 (3) ◽  
pp. 137-154 ◽  
Author(s):  
I. Krasovskaia ◽  
L. Gottschalk
Keyword(s):  
Climate Change ◽  
Flow Regime ◽  
Climate Models ◽  
River Flow ◽  
Flow Regimes ◽  
Low Flow ◽  
Future Climate Change ◽  
Mean Annual Temperature ◽  
Economic Policies ◽  
Temperature And Precipitation

One of the most important consequences of future climate change may be an alteration of the surface hydrological balance, including changes in flow regimes, i.e. seasonal distribution of flow and especially the time of occurrence of high/low flow, which is of vital importance for environmental and economic policies. Classification of flow regimes still has an important role for the analyses of hydrological response to climate change as well as for validating climate models on present climatic and hydrologic data, however, with some modifications in the methodology. In this paper an approach for flow regime classification is developed in this context. Different ways of flow regime classification are discussed. The stability of flow regimes is studied in relation to changes in mean annual temperature and precipitation. The analyses have shown that even rather small changes in these variables can cause changes in river flow regimes. Different patterns of response have been traced for different regions of the Nordic countries.

Analyzing the potential impacts of climate change on rainfed wheat production in Hamedan Province, Iran, via generalized additive models

2015 ◽  
Vol 7 (1) ◽  
pp. 212-223 ◽  
Author(s):  
Hassan Mohammadian Mosammam ◽  
Ali M. Mosammam ◽  
Mozaffar Sarrafi ◽  
Jamileh Tavakoli Nia ◽  
Hassan Esmaeilzadeh
Keyword(s):  
Climate Change ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Climatic Variables ◽  
Climate Change Scenarios ◽  
Wheat Production ◽  
Temperature And Precipitation ◽  
Hamedan Province ◽  
Rainfed Wheat ◽  
Ipcc Sres

Climate change is one of the greatest challenges in the 21st century and the agriculture sector is very vulnerable to this phenomenon. Since wheat is the most important cereal crop in Iran, we aim to analyze the potential impact of climatic variables (temperature and precipitation) on rainfed wheat productivity in Hamedan Province, Iran. For this purpose, generalized additive models have been used to model yields of rainfed wheat based on climatic variables during 2004–2012. Then, based on sensitivity of rainfed wheat to temperature and precipitation in this period, we predict the potential effects of climate change on rainfed wheat yield under the IPCC SRES A1FI and B1 climate change scenarios. Results suggest that yields of rainfed wheat would decrease in all Hamedan's counties primarily because of decreasing October to June precipitation and higher temperature. As a result, it is predicted that the yield of rainfed wheat in Hamedan under the A1F1 and B1 scenarios will fall by 41.3% and 20.6%, respectively, in the 2080s. In other words, according to the A1F1 scenario, in the 2080s, Hamedan Province's rainfed wheat production will decline from 1090 kg/ha to 639 kg/ha and under the B1 scenario to 865 kg/ha.

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