Impact of climate change on water resources in the Yarmouk River Basin of Jordan

It is generally assumed that rainfall intensity will increase with temperature increase, irrespective of the underlying changes to the average rainfall. This study documents and investigates long-term trends in rainfall intensities, annual rainfall, and mean maximum and minimum temperatures using the Mann–Kendall trend test for nine sites in eastern Australia. Relationships between rainfall intensities at various durations and 1) annual rainfall and 2) the mean maximum and minimum temperatures were investigated. The results showed that the mean minimum temperature has increased significantly at eight out of the nine sites in eastern Australia. Changes in annual rainfall are likely to be associated with changes in rainfall intensity at the long duration of 48 h. Overall, changes in rainfall intensity at short durations (<1 h) positively correlate with changes in the mean maximum temperature, but there is no significant correlation with the mean minimum temperature and annual rainfall. Additionally, changes in rainfall intensity at longer durations (≥1 h) positively correlate with changes in the mean annual rainfall, but not with either mean maximum or minimum temperatures for the nine sites investigated.

Introduction: Global warming is the most important challenge facing man in the 21st century. Warmer weather will increase evapotranspiration, which will exacerbate droughts. One of the main causes of global warming is man himself. Humans have accelerated the Earth's climate change by producing large amounts of greenhouse gases. For this reason, information about changes in the earth's temperature in the next decades has always been considered. The results of the researchers show that climate change has obvious and significant effects on temperature and rainfall in different parts of Iran in the next decades. By predicting and estimating the extent of these effects, climate change impacts can be mitigated with adequate preparedness, low cost, and greater speed.Material and methods: In this study, the effect of climate change on the mean maximum and minimum annual and seasonal temperatures in Arak under the emission scenarios of RCP2.6, RCP4.5, RCP8.5 for the 2040s was investigated. To use the output of General Circulation Models at regional and local scales is that they are using downscaling models, are downscaled. In this study, Statistical Downscaling Model (SDSM) to downscale output of General Circulation Models CanESM2 were used. This model had an acceptable ability to simulate the average maximum and minimum seasonal and annual temperatures in the study area. Results and discussion: According to the obtained results, the mean maximum temperature in winter and spring will decrease under all three scenarios RCP2.6, RCP4.5, RCP8.5, which can indicate that the daily temperature will be cooler in these seasons. However, the mean maximum temperature will increase in summer and autumn, which may indicate that the daily temperature will be warmer in these seasons. The mean minimum temperature in winter and spring under all three scenarios RCP2.6, RCP4.5, RCP8.5 will decrease and increase in summer seasons. These results show that in the 2040s, the city of Arak has colder night temperatures in winter and spring and warmer temperatures in summer and spring. Due to the fact that warmer weather increases the demand for water and electricity, and because Arak is an industrial city with a dry climate, it can face serious challenges of water and electricity shortage in the future.Conclusion: According to the results obtained in this study, in the 2040s, Arak will have colder winters and springs, and warmer summers and autumns. The highest effect of climate change on the temperature of Arak is related to the average minimum temperature in autumn, which under the scenarios of RCP2.6, RCP4.5, RCP8.5, the average minimum temperature in autumn increased by 206.88, 196.37 and 192.27 percent, respectively. The mean annual maximum and minimum temperature under all three scenarios will increase in the 2040s. The highest increase in the mean annual maximum and minimum temperature is related to RCP8.5 and RCP2.6 scenarios, respectively, which they are equal to 4.14 and 4.38.

Global climate change is becoming an increasingly important issue that threatens the imperiled planet. Quantifying the impact of climate change on the streamflow has been an essential task for the proper management of water resources to mitigate this impact. This study aims to evaluate the skill of an artificial neural network (ANN) method in downscaling precipitation, maximum temperature, and minimum temperature and assess the potential impacts of climate change on the streamflow in the Wabash River Basin of the Midwestern United States (U.S.) using the Soil and Water Assessment Tool (SWAT). A statistical downscaling technique based on an ANN method was employed to estimate precipitation and temperature at a higher resolution. The downscaled climate projections from five general circulation models (GCMs) under the three representative concentration pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for the periods of 2026–2050 and 2075–2099 as well as the historical period were incorporated into the SWAT model to assess the potential impact of climate change on the Wabash River regime. Calibration and validation of the SWAT model indicated the streamflow simulations matched the observed results very well. The ANN method successfully reproduced the observed maximum/minimum temperature and precipitation; however, bias in precipitation was observed in regard to the frequency distribution. Compared with the simulated streamflow in the historical period, the predicted streamflow based on the RCP scenarios showed an obvious decreasing trend, where the annual streamflows will be decreased by 13.00%, 17.59%, and 6.91% in the midcentury periods and 25.29%, 27.61%, and 15.04% in the late-century periods under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Climate warming dominated the streamflow decrease under the RCP2.6 and RCP4.5 scenarios. By contrast, under RCP8.5, the streamflow was affected by the joint actions of changes in temperature and precipitation.

This study assessed the impact of climate change on flood frequency and flood source area at basin scale considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated and validated for the Talar River Basin in northern Iran. Four empirical approaches including the Sangal, Fill–Steiner, Fuller, and Slope-based methods were used to estimate the Instantaneous Peak Flow (IPF) on a daily basis. The calibrated SWAT model was run under the two RCP scenarios using a combination of twenty GCMs from CMIP5 for the near future (2020–2040). To assess the impact of climate change on flood frequency pattern and to quantify the contribution of each subbasin on the total discharge from the Talar River Basin, Flood Frequency Index (FFI) and Subbasin Flood Source Area Index (SFSAI) were used. Results revealed that the projected climate change will likely lead to an average discharge decrease in January, February, and March for both RCPs and an increase in September and October for RCP 8.5. The maximum and minimum temperature will likely increase for all months in the near future. The annual precipitation could increase by more than 20% in the near future. This is likely to lead to an increase of IPF. The results can help managers and policy makers to better define mitigation and adaptation strategies for basins in similar climates.

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers.

Water Supply issues a formal withdrawal in relation to the above article by Cen Li, Xin Guo, Liping Chen, Majid Khayatnezhad and Fatemeh Gholinia. This decision has been made due to concerns that were raised regarding potential citation manipulation due to inappropriate references, and significant authorship changes during peer review. The journal did not receive a satisfactory response to these concerns and as such the Editors-in-Chief no longer have confidence in the integrity of the article.

It is anticipated that climate change will impact sediment yield in watersheds. The purpose of this study was to investigate the impacts of climate change on sediment yield from the Logiya watershed in the lower Awash Basin, Ethiopia. Here, we used the coordinated regional climate downscaling experiment (CORDEX)-Africa data outputs of Hadley Global Environment Model 2-Earth System (HadGEM2-ES) under representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5). Future scenarios of climate change were analyzed in two-time frames: 2020–2049 (2030s) and 2050–2079 (2060s). Both time frames were analyzed using both RCP scenarios from the baseline period (1971–2000). A Soil and Water Assessment Tool (SWAT) model was constructed to simulate the hydrological and the sedimentological responses to climate change. The model performance was calibrated and validated using the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The results of the calibration and the validation of the sediment yield R2, NSE, and PBIAS were 0.83, 0.79, and −23.4 and 0.85, 0.76, and −25.0, respectively. The results of downscaled precipitation, temperature, and estimated evapotranspiration increased in both emission scenarios. These climate variable increments were expected to result in intensifications in the mean annual sediment yield of 4.42% and 8.08% for RCP4.5 and 7.19% and 10.79% for RCP8.5 by the 2030s and the 2060s, respectively.

Motivated by the Millennium Drought and the current drought over much of southern and eastern Australia, this detailed statistical study compares trends in annual wet season precipitation and temperature between a coastal site (Newcastle) and an inland site (Scone). Bootstrap permutation tests reveal Scone precipitation has decreased significantly over the past 40 years (p-value=0.070) whereas Newcastle has recorded little to no change (p-value=0.800). Mean maximum and minimum temperatures for Newcastle have increased over the past 40 years (p-values of 0.002 and 0.015, respectively) while the mean maximum temperature for Scone has increased (p-value = 0.058) and the mean minimum temperature has remained stable. This suggests mean temperatures during the wet season for both locations are increasing. Considering these trends along with those for precipitation, water resources in the Hunter region will be increasingly strained as a result of increased evaporation with either similar or less precipitation falling in the region. Wavelet analysis reveals that both sites have similar power spectra for precipitation and mean maximum temperature with a statistically significant signal in the two to seven year period, typically indicative of the El-Nino Southern Oscillation climate driver. The El-Nino Southern Oscillation also drives the Newcastle mean minimum temperature, whereas the Scone power spectra has no indication of a definitive driver for mean minimum temperature. 
 
 References R. A., R. L. Kitching, F. Chiew, L. Hughes, P. C. D. Newton, S. S. Schuster, A. Tait, and P. Whetton. Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment of the Intergovernmental Panel on Climate Change. Technical report, Intergovernmental Panel on Climate Change, 2014. URL https://www.ipcc.ch/report/ar5/wg2/. Bureau of Meteorology. Climate Glossary-Drought. URL http://www.bom.gov.au/climate/glossary/drought.shtml. K. M. Lau and H. Weng. Climate signal detection using wavelet transform: How to make a time series sing. B. Am. Meteorol. Soc., 76:23912402, 1995. doi:10.1175/1520-0477(1995)0762391:CSDUWT>2.0.CO;2. M. B. Richman and L. M. Leslie. Uniqueness and causes of the California drought. Procedia Comput. Sci., 61:428435, 2015. doi:10.1016/j.procs.2015.09.181. M. B. Richman and L. M. Leslie. The 20152017 Cape Town drought: Attribution and prediction using machine learning. Procedia Comput. Sci., 140:248257, 2018. doi:10.1016/j.procs.2018.10.323.

Malaria is a serious global health issue, resulting in an estimated 219 million cases and 660,000 deaths in 2010, many of them in Africa.1 Malaria transmission is tied closely to environmental variables such as rainfall and temperature—even when there’s plenty of rainfall to produce breeding pools for the Anopheles mosquitoes that spread malaria, hot temperatures can hamper mosquito development.2 Some early projections predicted that climate change would cause an increase in malaria cases,3 but more recent reports suggest it’s more likely that cases will shift in their distribution rather than rise overall.4 In this issue of EHP investigators at the Massachusetts Institute of Technology (MIT) report their projections, using a new modeling tool, that there probably will not be a significant increase in malaria prevalence in West Africa, even during a worst-case scenario of increased rainfall in the region.5 The authors used the Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS) to estimate the impact of climate change on malaria transmission in West Africa. HYDREMATS is a combined hydrology and entomology model of malaria transmission developed at MIT by coauthor Elfatih A.B. Eltahir, a professor in the Department of Civil and Environmental Engineering, and former graduate student Arne Bomblies, now an assistant professor at the University of Vermont. The model uses high-resolution data on environmental variables including rainfall, temperature, topography, and soil conditions to model ephemeral breeding pools that form during intense rains. The model also tracks the simulated behavior of individual mosquitoes as they interact with their environment. The researchers used current climate data to model vectorial capacity, a measure of how efficiently mosquitoes spread malaria. They then looked at climate predictions for the time period 2080–2099 and determined which combination of temperature and rainfall changes corresponded to best- and worst-case scenarios in terms of malaria transmission. They conducted simulations using the best- and worst-case climate projections to predict vectorial capacity under each new scenario. The model did not include changes in malaria transmission due to interventions such as spraying, mosquito netting, and preventive medications. Figure 1 A child with malaria receives care in Sierra Leone. This country lies in a part of West Africa that is already saturated with malaria, and prevalence is not projected to increase with climate change. Figure 1 An ephemeral pool in Niger provides a perfect breeding site for Anopheles mosquitoes. This and other northern parts of West Africa could become too hot to sustain malaria. The northernmost areas studied are currently too dry and warm for effective malaria transmission. According to the model, they could become more suitable only if the climate becomes substantially wetter, but even then high temperatures likely would prohibit sustained transmission. The middle areas are expected to see a decrease in suitability for malaria transmission even under the wettest predictions of future climate. Southern areas could become even more suitable for transmission, but the persistent prevalence of malaria in these areas means a rise in cases is unlikely unless many people immigrate. Therefore, the investigators conclude, it appears unlikely, on the basis of this model, that climate change will increase malaria transmission in West Africa.5 “The main advantage of our malaria transmission model is that it provides a more detailed and direct relationship among environmental variables and malaria transmission than previous models,” says coauthor Teresa K. Yamana, a PhD student. “This is especially true for rainfall, because the timing of rain is just as important as the amount of rain. For example, more puddles form if there’s a big storm compared to if the same amount of rain falls over the course of several days.” Another strength of the study is its consideration of a wide range of climate predictions. Yamana explains that climate impact studies may be based on the climate predictions of a single model without knowing whether that model accurately represents the region of interest. Others average the predictions made by multiple models, but this is not a good strategy in the case of West Africa: “Half of the predictions say the climate will be wetter, half say it will be drier,6” she says, “so the average is something close to no change in rainfall—this could end up being very far from the truth.” Jonathan Patz, director of the Global Health Institute at the University of Wisconsin–Madison, is impressed by the researchers’ modeling because it “included a range of best- and worst-case scenarios to avoid bias. They also considered both temperature and rainfall, essential for malaria estimates.” He says, “Their findings are consistent with expectations that temperature projections alone explain only a part of malaria risk, and disease risk will considerably depend on rainfall and other environmental factors, particularly hydrological dynamics that vary by location.”

Core Ideas The CERES‐Maize model was applied to estimate the impacts of climate change under RCP scenarios and the effectiveness of three typical adaptation measures for maize production in Northeast China. Maize yield would decline under the future climatic conditions if no adaptation measures were adopted. Changing planting dates, switching to later‐maturing cultivars and breeding new cultivars could mitigate the negative impacts of climate change to varying degrees. Northeast China (NEC) is an important region for maize (Zea mays L.) production in China, and is the country's most significant commercial food base. However, NEC is also one of the areas that are most significantly affected by climate change in this country. Maize is sensitive to climatic changes, and to develop effective strategies for guaranteeing regional food security, it is essential to understand the mechanisms of the impacts of climate change and the effectiveness of adaptation measures in NEC. In this study, the Crop‐Environment Resource Synthesis (CERES)‐Maize v4.5 model, coupled with newly released data for Representative Concentration Pathway (RCP) scenarios, RCP4.5 and RCP8.5, was applied to simulate maize yields for future periods (2020s, 2050s, and 2080s) and to estimate the effect of CO2 fertilization and the effectiveness of three typical adaptation measures for maize production in NEC. The results indicated a trend of a continuing decline in maize yield for both RCP scenarios, and the decrease in maize yield under RCP8.5 was predicted to be greater than that under RCP4.5. The effect of CO2 fertilization was forecast to be too small to offset the negative impacts of climate change. Three adaptation measures—changing planting dates, switching to later‐maturing cultivars, and breeding new cultivars with high thermal time requirements—could mitigate negative climate change impacts to varying degrees; switching cultivars may exert the most significant effect on increasing maize yields.

The planning of river basin management should utilize a high-resolution, process-based hydrological model to tackle issues such as diffuse pollution, drought, flood forecasting, and the impact of climate change. The studies available to date only encompass five meso-scale and one large-scale river basins in Ukraine. The objective of this study is to calibrate the Soil and Water Assessment Tool (SWAT) for all Ukrainian river basins, including upstream transboundary parts. The model could potentially assist in land management and assessing the impact of agriculture on water resources; hence, considerable attention is paid to agricultural practices and crop rotations. The Soil and Water Assessment Tool (SWAT) is a process-based semi-distributed hydrological model developed by the United States Department of Agriculture's Agricultural Research Service (USDA-ARS) in collaboration with numerous institutions. SWAT is widely used for simulating the impact of land management practices on water resources, including water quantity and quality, as well as assessing the overall environmental impact of land use and climate changes. The watershed, encompassing transboundary areas, covers an area of 873,600 km2, with Ukraine accounting for 68.7% of it. The inputs for the model consist of topography, river network, merged national soil maps with the properties for each soil polygon and underlying horizons, land cover, and agricultural practices such as crop rotations, fertilization, and operation schedules. In calibrating the model, we arranged daily discharge data from 56 gauges, snow cover from 61 locations, and crop yields of primary crops. The modeling period spans 41 years from 1980 to 2020. The modeling results are evaluated based on three criteria: the Nash-Sutcliffe coefficient (NS), the coefficient of determination (R2), and the percent bias (PBIAS). The model is available via a user-friendly web platform that features an interactive map of Ukrainian subbasins. Users can inspect the model inputs for each subbasin and monitor the daily dynamics of key outputs: river discharge, water flow components, evapotranspiration, soil water, and snow cover. The results can be downloaded as an image or a CSV file for further research. The hydrological model of Ukraine has the potential to address a wide range of issues related to water and agriculture: water supply, flood forecasting, soil water availability, water quality, the impact of climate change, and so on. The model will be expanded in the future to include sediment and nutrient transport.

In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9–16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 °C/10 a to 0.70 °C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5–40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3–20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

Planning agricultural procedures needs to take into account meteorological conditions. However, because of high associated costs, the density of meteorological stations is often not enough to cover all the cultivated or potentially cultivated areas. In this article we present a methodology to estimate seasonal maximum and minimum mean temperature in cultivated area using data registered in a sole or a few meteorological stations. The procedure is based on mesoscale modeling, which allows meteorological variables to be spatially distributed considering synoptic data and local characteristics. Simulated daily cycle of temperature was compared with data registered at six meteorological stations located in the cultivated floor of the semiarid Limari Valley (Chile, 31°S). Although in some cases the simulated temperature differs in about 2°C with the observed one, a good fit between model results and experimental data was observed. Using the simulated seasonal minimum and maximum mean temperature fields, maps of temperature differences with respect to a reference station were drawn. In order to observe the influence of the orography on the lapse rate around a station, the methodology was applied for two reference stations located in places with different orographic characteristics. Results for winter and summer seasons are shown. These generated maps can be used by farmers and agricultural planners to obtain information of seasonal minimum and maximum mean temperature from a station in any site of the cultivated area. This technique is a good alternative to obtain meteorological information at low costs, contributing to territorial planning for climate resilient agriculture sustainability.

Assessing the impact of climate change on streamflow is crucial for depicting the vulnerability of water resources and for identifying proper adaptation measures. This study used the Soil and Water Assessment Tool (SWAT) to simulate the impact of climate change on the streamflow of El Kalb river, a major perennial river in Lebanon. The model performance was tested for monthly flow at two stations under a nine-year calibration period (2003–2011) and a four-year validation period (2012–2015). The model results indicated satisfactory precision in fitting observed and simulated flow using various acceptable statistical indices. Future projections of climate change were obtained for three Representative Concentration Pathways (RCPs) (2.6, 4.5, and 8.5). The model indicated that the average annual discharge of El Kalb River in the near future (2021–2040) will decrease by around 28–29% under the three RCP scenarios. End-of-century projections (2081–2100) indicated that the flow will decrease by 23%, 28%, and 45% under RCP 2.6, RCP 4.5, and RCP 8.5, respectively.

Assessment of the potential impact of climate change on hydrology and water resources of rivers is important for future planning and management of water resources. The objective of this paper is to predict the impact of climate change on stream flow of Kulfo River. This study used Soil and Water Assessment Tool (SWAT) model and hypothetical climate change scenarios based on the fifth assessment report of Intergovernmental Panel on Climate Change (IPCC) and by review different research papers on climate change to investigate the current and two future scenarios 2050s and 2080s stream flow magnitude in the River. The SWAT mode was calibrated and Validated against stream flow and attained coefficient of determination 0.81 and 0.92, and Nash Sutcliffe Efficient of 0.68 and 0.78 during calibration and validation respectively. The hypothetical climate scenarios were compared to the observed baseline period (1987-2014) and the potential impact of climate change on stream flow quantified as, the average annual stream flow of Kulfo River is projected to increase by 5.42%, in 2050s. In contrast it was found to give the maximum decrease in discharge by -8.2% in 2080s. Increasing temperature by 0.5°C decreased stream flow rates by 2.99% in 2050s while 10% drops in rainfall resulted in a stream flow reduction by 5.28% in 2080s. Overall, the results show that stream flow in the Kulfo River will be more sensitive to change in precipitation than change in temperature.