scholarly journals Assessing Climate Change and Land-Use Impacts on Drinking Water Resources in Karstic Catchments (Southern Croatia)

2021 ◽  
Vol 13 (9) ◽  
pp. 5239
Author(s):  
Matko Patekar ◽  
Ivona Baniček ◽  
Josip Rubinić ◽  
Jasmina Lukač Reberski ◽  
Ivana Boljat ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Air Temperature ◽  
Climate Models ◽  
Anthropogenic Factors ◽  
Anthropogenic Activity ◽  
Land Use Impacts ◽  
Water Losses ◽  
Freshwater Resources ◽  
Southern Croatia

The Mediterranean freshwater resources, mostly represented by groundwater, are under increasing pressure due to natural and anthropogenic factors. In this study, we investigated possible negative effects of climate change and land-use practices on water quality and availability from five springs in the karstic catchments in southern Croatia. The investigated springs are used in the regional public water supply system. Firstly, we employed hydrogeochemical field and laboratory analyses to detect possible traces of anthropogenic activity originating from specific land use. Additionally, we performed hydrological and climate modeling to detect changes in the air temperature, precipitation, and runoff. In particular, we used three regional climate models (Aladin, RegCM3, and Promes). The results estimated an increase in the mean annual air temperature, changes in the precipitation patterns, and reductions in runoff in the study area. Hydrochemical analyses showed standard ion concentrations for karst groundwaters, elevated sulfates due to evaporite deposits in the hinterland, surprisingly low nitrate levels which disproved expected agricultural pollution, and high microbiological activity. Significant water losses are expected in the near future which require immediate attention in order to develop adaptation strategies that focus on sustainable utilization and resilience of freshwater resources. This paper was based on the Interreg Central Europe PROLINE-CE project research in the South Dalmatia.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

Assessment of future climate change impacts on the hydrological regime of selected Greek areas with different climate conditions

2016 ◽  
Vol 48 (5) ◽  
pp. 1327-1342 ◽  
Author(s):  
Spyridon Paparrizos ◽  
Andreas Matzarakis
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Hydrological Regime ◽  
Future Climate ◽  
Future Climate Change ◽  
Water Losses ◽  
Climate Conditions ◽  
The Future

Assessment of future variations of streamflow is essential for research regarding climate and climate change. This study is focused on three agricultural areas widespread in Greece and aims to assess the future response of annual and seasonal streamflow and its impacts on the hydrological regime, in combination with other fundamental aspects of the hydrological cycle in areas with different climate classification. ArcSWAT ArcGIS extension was used to simulate the future responses of streamflow. Future meteorological data were obtained from various regional climate models, and analysed for the periods 2021–2050 and 2071–2100. In all the examined areas, streamflow is expected to be reduced. Areas characterized by continental climate will face minor reductions by the mid-century that will become very intense by the end and thus these areas will become more resistant to future changes. Autumn season will face the strongest reductions. Areas characterized by Mediterranean conditions will be very vulnerable in terms of future climate change and winter runoff will face the most significant decreases. Reduced precipitation is the main reason for decreased streamflow. High values of actual evapotranspiration by the end of the century will act as an inhibitor towards reduced runoff and partly counterbalance the water losses.

scholarly journals Sources of uncertainty in climate change impacts on river discharge and groundwater in a headwater catchment of the Upper Nile Basin, Uganda

2010 ◽  
Vol 14 (7) ◽  
pp. 1297-1308 ◽  
Author(s):  
D. G. Kingston ◽  
R. G. Taylor
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Sensitivity ◽  
River Discharge ◽  
Hydrological Model ◽  
Nile Basin ◽  
Freshwater Resources ◽  
Depth Analysis ◽  
Global Mean ◽  
Impacts Of Climate Change

Abstract. The changing availability of freshwater resources is likely to be one of the most important consequences of projected 21st century climate change for both human and natural systems. However, substantial uncertainty remains regarding the precise impacts of climate change on water resources, due in part due to uncertainty in GCM projections of climate change. Here we explore the potential impacts of climate change on freshwater resources in a humid, tropical catchment (the River Mitano) in the Upper Nile Basin of Uganda. Uncertainty associated with GCM structure and climate sensitivity is explored, as well as parameter specification within hydrological models. These aims are achieved by running pattern-scaled output from seven GCMs through a semi-distributed hydrological model of the catchment (developed using SWAT). Importantly, use of pattern-scaled GCM output allows investigation of specific thresholds of global climate change including the purported 2 °C threshold of "dangerous" climate change. In-depth analysis of results based on the HadCM3 GCM climate scenarios shows that annual river discharge first increases, then declines with rising global mean air temperature. A coincidental shift from a bimodal to unimodal discharge regime also results from a projected reduction in baseflow (groundwater discharge). Both of these changes occur after a 4 °C rise in global mean air temperature. These results are, however, highly GCM dependent, in both the magnitude and direction of change. This dependence stems primarily from projected differences in GCM scenario precipitation rather than temperature. GCM-related uncertainty is far greater than that associated with climate sensitivity or hydrological model parameterisation.

scholarly journals Statistical assessment of the impact of climate change on social and demographic processes (on the example of the Yaroslavl region)

2019 ◽  
Vol 16 (6) ◽  
pp. 57-66
Author(s):  
V. V. Zholudeva
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Crop Yields ◽  
Anthropogenic Factors ◽  
Federal State ◽  
Negative Consequences ◽  
Statistical Assessment ◽  
Impact Of Climate Change ◽  
Demographic Processes ◽  
The Impact

The purpose of this study is to analyze current global and regional climate changes, as well as a statistical assessment of the factors that cause climate change, on the one hand, and an assessment of the impact of climate parameters on the economy, agriculture and demographic processes using the example of the Yaroslavl region, on the other hand. The study was conducted on the example of the Yaroslavl region and covers the period from 1922 to the present. First of all, the article analyzes the regulatory documents on ecology and climate change. The insufficient attention of federal and local authorities to solving the above problems, the lack of regional strategies to prevent climate change and reduce its negative consequences, which leads to the increased socio-economic risks, is noted. In order to identify factors causing climate change, a correlation and regression analysis was performed. Regression models of the dependence of crop yields on the average annual air temperature and the average annual precipitation were constructed. The statistical base of the study was compiled by the data of the Federal State Statistics Service and the territorial body of the Federal State Statistics Service for the Yaroslavl Region, as well as GISMETEO data. Processing of the research results was carried out in Microsoft Excel and SPSS.During the study, it was found that in the Yaroslavl region there is an increase in average annual and average monthly air temperatures, as well as a slight increase in precipitation, which mainly occurs due to an increase in rainfall in spring and early summer.The anthropogenic factors that cause climate change, namely the burning of fossil fuels, an increase in industrial production, an increase in the number of vehicles, as well as a change in land use and deforestation, are identified and statistically substantiated.As a result of the study, it was found that changes in climatic parameters have an impact on the economy, agriculture and demographic processes, namely: – climate change has a positive effect on agricultural production. According to studies, an increase in average air temperature is a positive factor for the agricultural sector of the Yaroslavl region, as crop yields will increase with increasing air temperature. These trends need to be considered when choosing certain varieties of crops and selecting fertilizers. Increasing the level of management and the transition to more modern technologies will have a greater effect. The efficiency and productivity of agriculture, as well as the food security of the region, will depend on these decisions; – it was found that hydro meteorological factors have a negligible effect on the growth rate of gross regional product and food production; – a statistical study showed that in the Yaroslavl region the effects of climate change on demographic processes and human health are currently insignificant.The findings can be used to develop mechanisms for adaptation to climate change and can serve as a basis for further research in the field of studying the impact of climate change on socio-economic and demographic processes in the Yaroslavl region.

scholarly journals Land Use Land Cover Changes and Their Effects on Surface Air Temperature in Myanmar and Thailand

2021 ◽  
Vol 13 (19) ◽  
pp. 10942
Author(s):  
Khun La Yaung ◽  
Amnat Chidthaisong ◽  
Atsamon Limsakul ◽  
Pariwate Varnakovida ◽  
Can Trong Nguyen
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Forest Conversion ◽  
Land Use Land Cover ◽  
Forest Land ◽  
Lulc Changes ◽  
Average Maximum

Land use land cover (LULC) change is one of the main drivers contributing to global climate change. It alters surface hydrology and energy balance between the land surface and atmosphere. However, its impacts on surface air temperature have not been well understood in a dynamic region of LULC changes like Southeast Asia (SEA). This study quantitatively examined the contribution of LULC changes to temperature trends in Myanmar and Thailand as the typical parts of SEA during 1990–2019 using the “observation minus reanalysis” (OMR) method. Overall, the average maximum, mean, and minimum temperatures obtained from OMR trends indicate significant warming trends of 0.17 °C/10a, 0.20 °C/10a, and 0.42 °C/10a, respectively. The rates of minimum temperature increase were larger than maximum and mean temperatures. The decreases of forest land and cropland, and the expansions of settlements land fractions were strongly correlated with the observed warming trends. It was found that the effects of forest land converted to settlement land on warming were higher than forest conversion to cropland. A comprehensive discussion on this study could provide scientific information for the future development of more sustainable land use planning to mitigate and adapt to climate change at the local and national levels.

scholarly journals The Effects of Gridding Algorithms on the Statistical Moments and Their Trends of Daily Surface Air Temperature*

2015 ◽  
Vol 28 (23) ◽  
pp. 9188-9205 ◽  
Author(s):  
Nicholas R. Cavanaugh ◽  
Samuel S. P. Shen
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Climate Models ◽  
Decadal Variability ◽  
Surface Air Temperature ◽  
Higher Order ◽  
Statistical Moments ◽  
Weighted Sums ◽  
Higher Order Moments ◽  
Station Data

Abstract This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network–Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT’s higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.

Analysis of heat wave features and urban heat island effect under climate change

2020 ◽  
Author(s):  
Ye Tian ◽  
Klaus Fraedrich ◽  
Feng Ma
Keyword(s):  
Climate Change ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Extreme Events ◽  
Climate Models ◽  
Heat Waves ◽  
Urban Heat Island Effect ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat

<p>Extreme events such as heat waves occurred in urban have a large influence on human life due to population density. For urban areas, the urban heat island effect could further exacerbate the heat stress of heat waves. Meanwhile, the global climate change over the last few decades has changed the pattern and spatial distribution of local-scale extreme events. Commonly used climate models could capture broad-scale spatial changes in climate phenomena, but representing extreme events on local scales requires data with finer resolution. Here we present a deep learning based downscaling method to capture the localized near surface temperature features from climate models in the Coupled Model Intercomparison Project 6 (CMIP6) framework. The downscaling is based on super-resolution image processing methods which could build relationships between coarse and fine resolution. This downscaling framework will then be applied to future emission scenarios over the period 2030 to 2100. The influence of future climate change on the occurrence of heat waves in urban and its interaction with urban heat island effect for ten most densely populated cities in China are studied. The heat waves are defined based on air temperature and the urban heat island is measured by the urban-rural difference in 2m-height air temperature. Improvements in data resolution enhanced the utility for assessing the surface air temperature record. Comparisons of urban heat waves from multiple climate models suggest that near-surface temperature trends and heat island effects are greatly affected by global warming. High resolution climate data offer the potential for further assessment of worldwide urban warming influences.</p>

Twentieth-Century Surface Air Temperature over China and the Globe Simulated by Coupled Climate Models

2006 ◽  
Vol 19 (22) ◽  
pp. 5843-5858 ◽  
Author(s):  
Tianjun Zhou ◽  
Rucong Yu
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Air Temperature ◽  
Climate Models ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Future Climate Change ◽  
Intergovernmental Panel ◽  
Coupled Climate Models ◽  
Anthropogenic Forcings

Abstract This paper examines variations of the surface air temperature (SAT) over China and the globe in the twentieth century simulated by 19 coupled climate models driven by historical natural and anthropogenic forcings. Most models perform well in simulating both the global and the Northern Hemispheric mean SAT evolutions of the twentieth century. The inclusion of natural forcings improves the simulation, in particular for the first half of the century. The reproducibility of the SAT averaged over China is lower than that of the global and hemispheric averages, but it is still acceptable. The contribution of natural forcings to the SAT over China in the first half of the century is not as robust as that to the global and hemispheric averages. No model could successfully produce the reconstructed warming over China in the 1920s. The prescribed natural and anthropogenic forcings in the coupled climate models mainly produce the warming trends and the decadal- to interdecadal-scale SAT variations with poor performances at shorter time scales. The prominent warming trend in the last half of the century over China and its acceleration in recent decades are weakly simulated. There are discrepancies between the simulated and observed regional features of the SAT trend over China. Few models could produce the summertime cooling over the middle part of eastern China (27°–36°N), while two models acceptably produce the meridional gradients of the wintertime warming trends, with north China experiencing larger warming. Limitations of the current state-of-the-art coupled climate models in simulating spatial patterns of the twentieth-century SAT over China cast a shadow upon their capability toward projecting credible geographical distributions of future climate change through Intergovernmental Panel on Climate Change (IPCC) scenario simulations.

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