Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and the overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961–2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into periods 1961–1995 (normal lake level) and 1996–2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

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Effects of Water Level Decline in Lake Urmia, Iran, on Local Climate Conditions

10.20944/preprints202007.0165.v1 ◽

2020 ◽

Author(s):

Amir Hossein Dehghanipour ◽

Davood Moshir Panahi ◽

Hossein Mousavi ◽

Zahra Kalantari ◽

Massoud Tajrishy

Keyword(s):

Water Level ◽

Lake Level ◽

Climate Variables ◽

Climate Data ◽

Lake Urmia ◽

Local Climate ◽

Climate Conditions ◽

Dewpoint Temperature ◽

Lake Center ◽

Increasing Temperature

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

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Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

10.5194/essd-2016-67 ◽

2017 ◽

Cited By ~ 1

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

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Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

Earth System Science Data ◽

10.5194/essd-9-471-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 471-495 ◽

Cited By ~ 19

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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Multi-step-ahead forecasting of hourly potential evapotranspiration for irrigation triggering in horticultural nurseries under oceanic climate

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/958/1/012025 ◽

2021 ◽

Vol 958 (1) ◽

pp. 012025

Author(s):

R Tawegoum

Keyword(s):

Potential Evapotranspiration ◽

Moving Average ◽

Recursive Algorithm ◽

Weather Conditions ◽

Irrigation Scheduling ◽

Model Parameters ◽

Sensor Failure ◽

Climate Data ◽

Local Climate ◽

Climate Conditions

Abstract Predicting hourly potential evapotranspiration is particularly important in constrained horticultural nurseries. This paper presents a three-step-ahead predictor of potential evapotranspiration for horticultural nurseries under unsettled weather conditions or climate sensor failure. The Seasonal AutoRegressive Integrated Moving Average model based on climate data was used to derive a predictor using data generated according to prior knowledge of the system behavior; the aim of the predictor was to compensate for missing data that are usually not considered in standard forecasting approaches. The generated data also offer the opportunity to capture variations of the model parameters due to abrupt changes in local climate conditions. A recursive algorithm was used to estimate parameter variation, and the Kalman filter to model the state of the system. The simulations for steady-state weather and unsettled weather conditions showed that the predictor could forecast potential evapotranspiration more accurately than the standard approach did. These results are encouraging within the context of predictive irrigation scheduling in nurseries.

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Climate Explorer: Improved Access to Local Climate Projections

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-18-0298.1 ◽

2020 ◽

Vol 101 (3) ◽

pp. E265-E273

Author(s):

Fredric Lipschultz ◽

David D. Herring ◽

Andrea J. Ray ◽

Jay R. Alder ◽

LuAnn Dahlman ◽

...

Keyword(s):

Spatial Scales ◽

Easy Access ◽

Climate Projections ◽

Climate Data ◽

Local Climate ◽

Climate Conditions ◽

Spatial And Temporal Scales ◽

Climate Resilience ◽

Projected Changes ◽

The U.S

Abstract The goal of the U.S. Climate Resilience Toolkit’s (CRT) Climate Explorer (CE) is to provide information at appropriate spatial and temporal scales to help practitioners gain insights into the risks posed by climate change. Ultimately, these insights can lead to groups of local stakeholders taking action to build their resilience to a changing climate. Using CE, decision-makers can visualize decade-by-decade changes in climate conditions in their county and the magnitude of changes projected for the end of this century under two plausible emissions pathways. They can also check how projected changes relate to user-defined thresholds that represent points at which valued assets may become stressed, damaged, or destroyed. By providing easy access to authoritative information in an elegant interface, the Climate Explorer can help communities recognize—and prepare to avoid or respond to—emerging climate hazards. Another important step in the evolution of CE builds on the purposeful alignment of the CRT with the U.S. Global Change Research Program’s (USGCRP) National Climate Assessment (NCA). By closely linking these two authoritative resources, we envision that users can easily transition from static maps and graphs within NCA reports to dynamic, interactive versions of the same data within CE and other resources within the CRT, which they can explore at higher spatial scales or customize for their own purposes. The provision of consistent climate data and information—a result of collaboration among USGCRP’s federal agencies—will assist decision-making by other governmental entities, nongovernmental organizations, businesses, and individuals.

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Two-decade of monitoring spatio-temporal variation of surface albedo over a large saline lake

10.5194/egusphere-egu2020-1172 ◽

2020 ◽

Author(s):

Somayeh Sima ◽

َAmir Darzi

Keyword(s):

Temporal Variation ◽

Water Level ◽

Saline Lakes ◽

Lake Level ◽

Surface Albedo ◽

Economic Benefits ◽

Lake Urmia ◽

Diverse Range ◽

Lake Evaporation ◽

Spatio Temporal

<p>Saline lakes play a crucial role in regulating the regional climate, supporting unique biodiversity, and providing a diverse range of economic benefits. However, as a result of growing water withdrawals for human use, most of the large saline lakes worldwide are desiccating at a substantial rate. Water level decline and salinity rise affect physico-chemical characteristics of saline lakes including surface albedo. Water surface albedo impacts lake color and evaporation. Here, we investigate spatio-temporal variation of surface albedo over Lake Urmia, in northwest Iran, using the MODerate Resolution Imaging Spectroradiometer (MODIS) albedo product (MCD43D) from 2000 to 2019. Satellite-derived shortwave albedos were validated against in-situ surface albedo data measured at an online net-radiometer station on the lake. We identified two spatial patterns through Lake Urmia: 1) a decreasing trend from the outer shallow zones toward the deep inner parts, and 2) a higher mean albedo of the south arm compared to the north arm in summer. Moreover, the lake albedo varies seasonally with lake level and reaches its peak between September and October. This is mainly due to an increased concentration of total suspended solids (TSS) and phytoplankton (Duanalliea spp.) growth, which accounts for the lake red color between mid-spring and early autumn. Results also revealed that concurrent with the lake level drop since 2000, both lake-averaged surface albedo and its seasonal variation have constantly increased. The increased lake albedo affects net absorbed radiation by the lake and limits lake evaporation. Consequently, we emphasize that for large saline lakes which experience significant areal fluctuations seasonally, the use of a constant albedo to estimate lake evaporation and heat budget is inadequate. Instead, satellite-derived albedo maps encompassing the effect of lake depth, TSS, and phytoplankton growth can be used with confidence. Our findings can contribute to enhanced water, energy, and salt balance models for saline lakes by better estimation of their surface albedo.&#160;</p><p>Keywords: Surface albedo, Lake Urmia, MODIS, Water level, Phytoplankton</p>

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Incidence of the Acute Symptom of Chronic Periodontal Disease in Patients Undergoing Supportive Periodontal Therapy: A 5-Year Study Evaluating Climate Variables

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16173070 ◽

2019 ◽

Vol 16 (17) ◽

pp. 3070

Author(s):

Hikari Saho ◽

Noriko Takeuchi ◽

Daisuke Ekuni ◽

Manabu Morita

Keyword(s):

Periodontal Disease ◽

Barometric Pressure ◽

Periodontal Therapy ◽

Climate Variables ◽

Climate Data ◽

Local Climate ◽

Periodontal Condition ◽

Acute Symptom ◽

Chronic Periodontal Disease ◽

Supportive Periodontal Therapy

Although patients under supportive periodontal therapy (SPT) have a stable periodontal condition, the acute symptom of chronic periodontal disease occasionally occurs without a clear reason. Therefore, in the present study, to obtain a better understanding of this relationship in patients undergoing SPT, we hypothesized that the acute symptom of chronic periodontal disease might be affected by climate factors. We conducted a questionnaire study and carried out oral examinations on patients undergoing SPT who had been diagnosed as having the acute symptom of chronic periodontal disease. We collected climate data from the local climate office in Okayama city, Japan. We predicted parameters that affect the acute symptom of chronic periodontal disease with unidentified cause and divided patients into high and low groups in terms of climate predictors. Then we defined the cut-off values of parameters showing significant differences in the incidence of the acute symptom of chronic periodontal disease. The incidence of the acute symptom of chronic periodontal disease with unidentified cause was significantly different when the cases were classified according to the maximum hourly decrease in barometric pressure (1.5 and 1.9 hPa) (p = 0.04 and p = 0.03, respectively). This suggests that climate variables could be predictors of the acute symptom of chronic periodontal disease. Therefore, gaining a better understanding of these factors could help periodontal patients undergoing SPT prepare to avoid the acute symptom of chronic periodontal disease.

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Feasibility of Utilizing Photovoltaics for Irrigation Purposes in Moamba, Mozambique

Sustainability ◽

10.3390/su131910998 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10998

Author(s):

Arsénio José Mindú ◽

Jó António Capece ◽

Rui Esteves Araújo ◽

Armando C. Oliveira

Keyword(s):

Energy Systems ◽

Environmental Benefits ◽

Photovoltaic System ◽

Pump Efficiency ◽

Total System ◽

Climate Data ◽

Local Data ◽

Pv System ◽

Local Climate ◽

Climate Conditions

Agriculture plays a significant role in the labor force and GDP of Mozambique. Nonetheless, the energy source massively used for water pumping in irrigation purposes is based on fossil fuels (diesel oil). Despite the water availability and fertile soils in Moamba, Mozambique, farmers struggle with the high cost of fuels used in the pumping systems. This study was sought to analyze the feasibility of utilizing a solar photovoltaic system as a means to reduce the environmental impact caused by the diesel pumps and simultaneously alleviate the expenses regarding the use of non-environmentally friendly technologies. Site observations and interviews were undertaken in order to obtain local data regarding the water demand, current energy systems costs and distances from the source to the irrigated fields. CLIMWAT 2.0 was used for climate data acquisition and analysis. The environmental benefits, the cost effectiveness and local climate conditions show that the PV system is feasible in Moamba. Furthermore, parameters such as hydraulic energy, incident solar energy, pump efficiency and total system efficiency were used to predict the performance of the system. The results obtained are important to analyze the implementation of such energy systems.

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ClimateEU, scale-free climate normals, historical time series, and future projections for Europe

Scientific Data ◽

10.1038/s41597-020-00763-0 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Maurizio Marchi ◽

Dante Castellanos-Acuña ◽

Andreas Hamann ◽

Tongli Wang ◽

Duncan Ray ◽

...

Keyword(s):

Climate Change ◽

Lapse Rate ◽

Climate Variables ◽

Gridded Data ◽

Climate Data ◽

Local Climate ◽

Scale Free ◽

Representative Subset ◽

Local Climate Change ◽

Change Impact

AbstractInterpolated climate data have become essential for regional or local climate change impact assessments and the development of climate change adaptation strategies. Here, we contribute an accessible, comprehensive database of interpolated climate data for Europe that includes monthly, annual, decadal, and 30-year normal climate data for the last 119 years (1901 to 2019) as well as multi-model CMIP5 climate change projections for the 21st century. The database also includes variables relevant for ecological research and infrastructure planning, comprising more than 20,000 climate grids that can be queried with a provided ClimateEU software package. In addition, 1 km and 2.5 km resolution gridded data generated by the software are available for download. The quality of ClimateEU estimates was evaluated against weather station data for a representative subset of climate variables. Dynamic environmental lapse rate algorithms employed by the software to generate scale-free climate variables for specific locations lead to improvements of 10 to 50% in accuracy compared to gridded data. We conclude with a discussion of applications and limitations of this database.

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Cities of the future, visualizing climate change to inspire actions

10.1101/458018 ◽

2018 ◽

Author(s):

Jean-Francois Bastin ◽

Emily Clark ◽

Thomas Elliott ◽

Simon Hart ◽

Johan van den Hoogen ◽

...

Keyword(s):

Climate Change ◽

San Francisco ◽

Climate Impacts ◽

Climate Projections ◽

Climate Variables ◽

Climate Data ◽

Policy Makers ◽

Climate Conditions ◽

The World ◽

The Future

AbstractCombating against climate change requires unified action across all sectors of society. However, this collective action is precluded by the ‘consensus gap’ between scientific knowledge and public opinion. A growing body of evidence suggests that facts do not persuade people to act. Instead, it is visualization - the ability to simulate relatable scenarios - is the most effective approach for motivating behavior change. Here, we exemplify this approach, using current climate projections to enable people to visualize cities of the future, rather than describing intangible climate variables. Analyzing city pairs for 520 major cities of the world, we characterize which cities will most closely resemble the climate conditions of which other major cities by 2050. On average, most cities will resemble cities that are over 1000km south, and 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. We predict that London’s climate in 2050 will resemble Barcelona’s climate today, Madrid will resemble to Marrakesh, Moscow to Sofia, Seattle to San Francisco, Stockholm to Budapest, Tokyo to Changsha, etc. Our approach illustrates how complex climate data can be packaged to provide tangible information. By allowing people to visualize their own climate futures, we hope to empower citizens, policy makers and scientists to visualize expected climate impacts and adapt decision making accordingly.

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